HS6.1 | Remote Sensing of Soil Moisture
Remote Sensing of Soil Moisture
Convener: Klaus Scipal | Co-conveners: Clément Albergel, Alexander Gruber, Nemesio Rodriguez-Fernandez, Jian Peng, Luca Brocca, David Fairbairn
Orals
| Tue, 16 Apr, 08:30–10:15 (CEST), 14:00–15:36 (CEST), 16:15–17:59 (CEST)
 
Room 3.29/30
Posters on site
| Attendance Mon, 15 Apr, 10:45–12:30 (CEST) | Display Mon, 15 Apr, 08:30–12:30
 
Hall A
Posters virtual
| Attendance Mon, 15 Apr, 14:00–15:45 (CEST) | Display Mon, 15 Apr, 08:30–18:00
 
vHall A
Orals |
Tue, 08:30
Mon, 10:45
Mon, 14:00
This session celebrates 20 years of soil moisture remote sensing at the EGU General Assembly. We invite presentations concerning the past, present and future of soil moisture estimation, including remote sensing, field experiments, land surface modelling and data assimilation and soil moisture reference networks and fiducial reference measurements (FRMs).
Over the past two decades, the technique of microwave remote sensing has made tremendous progress to provide robust estimates of surface soil moisture at different scales. From local to landscape scales, several field or aircraft experiments have been organised to improve our understanding of active and passive microwave soil moisture sensing, including the effects of soil roughness, vegetation, spatial heterogeneities, and topography. At continental scales, a series of several passive and active microwave space sensors, including SMMR (1978-1987), AMSR (2002-), ERS/SCAT (1992-2000) provided information on surface soil moisture. Current investigations in L-band passive microwave with SMOS (2009-) and SMAP (2015-), and in active microwave with MetOp/ASCAT series (2006-) and Sentinel-1, enable an accurate quantification of the soil moisture at regional and global scales. Building on the legacy of these mission operational programmes like Copernicus but also novel developments will further enhance our capabilities to monitor soil moisture, and they will ensure continuity of multi-scale soil moisture measurements on climate scales.

We encourage submissions related to soil moisture remote sensing, including:
- Global soil moisture estimation from coarse resolution active and passive sensors.
- High spatial resolution soil moisture estimation based on e.g. Sentinel observations, GNSS reflections, or using novel downscaling methods.
- Future mission concepts.
- Field experiment, theoretical advances in microwave modelling and calibration/validation activities.
- Root zone soil moisture retrieval and soil moisture data assimilation in land surface models, hydrological models and in Numerical Weather Prediction models.
- Evaluation and trend analysis of soil moisture climate data records such as the ESA CCI soil moisture product as well as soil moisture from re-analysis.
- Inter-comparison and inter-validation between land surface models, remote sensing approaches and in-situ validation networks.
- Progress towards the estimation of SI-traceable uncertainty budgets including uncertainty characterization across scales.
- Soil moisture reference networks.
- Application of satellite soil moisture products in scientific and operational disciplines.

Orals: Tue, 16 Apr | Room 3.29/30

The oral presentations are given in a hybrid format supported by a Zoom meeting featuring on-site and virtual presentations. The button to access the Zoom meeting appears just before the time block starts.
Chairpersons: Klaus Scipal, Nemesio Rodriguez-Fernandez
08:30–08:35
Global Soil Moisture
08:35–08:55
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EGU24-11338
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HS6.1
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solicited
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Highlight
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On-site presentation
Yann Kerr

The ESA (European Space Agency) led SMOS (Soil Moisture and Ocean Salinity) mission, operating since November 2009, is the first satellite dedicated to measuring surface soil moisture and ocean salinity. It has been now in operation continuously for more than 14 years, delivering a wealth of new measurements including the first time ever global, frequent, quantitative and absolute measurements of soil moisture and ocean salinity. From these measurements a large number of science and applied products have emerged ranging from strong wing or thin sea ice thickness to root zone soil moisture or biomass but also fire or flood risks prediction, snow density or freeze thaw to name but a few. Operational users (such as numerical weather prediction) have also emerged. To obtain such results several challenges had to be addressed and overcome but results show the uniqueness of L band radiometry for some crucial water cycle measurements.

Currently, using the long term data set and developing approaches to extend it in time, climate trends can start to be considered as well as teleconnections and SMOS is contributing to a large number of ECV (Essential Climate Variables) / CCI (Climate Change Initiative). At the time of writing SMOS is in very good condition and can last for a few more years, extending the length of the data sets but will not last forever. Consequently the team is both investing time in new or improved science and application products but also on potential follow on mission which would be very much similar to SMOS (or SMAP) but with a significantly improved spatial resolution.

The presentation will give an overview of the most striking new results as well as future plans.

How to cite: Kerr, Y.: SMOS after 14 years in orbit:Status, Achievements, and future plans, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11338, https://doi.org/10.5194/egusphere-egu24-11338, 2024.

08:55–09:15
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EGU24-7555
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HS6.1
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solicited
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Highlight
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On-site presentation
Wolfgang Wagner

Scatterometer soil moisture research started at the Vienna University of Technology (TU Wien) 30 years ago when attempting to use the first European C-band scatterometer flown on board of the ERS-1 satellite for wet snow mapping over the Canadian Prairies. While it quickly turned out that the detection of wet snow is impossible when the snowpack is shallow, the strong link between C-band backscatter and soil moisture under snow-free conditions became evident [1]. This motivated research on how to disentangle the backscatter contributions from soil moisture and vegetation, which cumulated in the public release of the first global satellite derived soil moisture data set in 2002 [2]. Despite the strong criticism that the scatterometer derived soil moisture data depict in reality only vegetation signals that happen to be correlated with soil moisture dynamics, the positive outcome of independent validation studies led to the decision by EUMETSAT to develop a near-real-time soil moisture service for the Advanced Scatterometer (ASCAT) flown on board of the METOP satellites. This service, being the first of its kind, became operational in 2008, and was later integrated into the Satellite Application Facility for Support to Operational Hydrology and Water Management (H SAF). For continuously improving this ASCAT service, TU Wien has carried out extensive research to quantify the soil moisture retrieval errors and improve the retrieval algorithm and workflows. In this presentation, I will provide an overview of the main developments over the past years, discuss open research challenges, and provide an outlook to the next ASCAT product releases and the upcoming, next-generation scatterometer instrument called SCA, to be flown on the Metop-SG B-satellites.

References

[1] Wagner et al. (1995) Application of Low-Resolution Active Microwave Remote Sensing (C-Band) over the Canadian Prairies, in Proc. of the 17th Canadian Symposium on Remote Sensing, Saskatoon, Saskatchewan, Canada 13-15 June 1995, 21-28.

[2] Scipal et al. (2002) The Global Soil Moisture Archive 1992-2000 from ERS Scatterometer Data: First Results, In: Proceedings IEEE Geoscience and Remote Sensing Symposium (IGARRS2002), Toronto, Canada, 24-28 June 2002, 1399-1401.

How to cite: Wagner, W.: 30 years of scatterometer soil moisture research at TU Wien: What’s next?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7555, https://doi.org/10.5194/egusphere-egu24-7555, 2024.

09:15–09:25
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EGU24-6502
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HS6.1
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ECS
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On-site presentation
Victor Pellet, Filipe Aires, and Eulalie Boucher

Land surfaces are characterised by strong heterogeneities of, among other variables, soil texture, orography, land cover, snow, or Soil Moisture (SM). SM is of broad scientific interest due to its role in the Earth system and its capital practical value for a wide range of applications from flood forecasting to agriculture. The scientific community has made significant progress in estimating SM from satellite-based passive MicroWave (MW). Most of SM estimates relie on a physical-based inversion to retrieve SM from passive MW. As an alternative to physical-based inversions, Neural Network (NN) retrieval algorithms have been successfully implemented for several sensors in recent years (Aires et al., 2005; Kolassa et al., 2016). The Soil Moisture and Ocean Salinity (SMOS) L3BT product (Al Bitar et al. 2017) uses an angle-binning scheme to organize the measured Brightness Temperature (BT) data. Three points that could improve SM retrieval will be considered in this presentation. (1) For coarse resolution MW instruments such as SMOS, NN algorithms are currently defined at the pixel level. Using the strong spatial patterns at the surface should help the SM retrieval, and we intend here to use an image-processing-based retrieval to investigate its potential. (2) Despite the important scanning angle information available on SMOS, not all angles are available for every pixel: The need to specify a limited angle configuration can drastically reduce the number of retrieved pixels, and the potential use of some large angle information is lost (Rodriguez-Fernandez et al. 2015). These missing data (both pixels and some angle configurations) could impede the use of image-based retrieval approaches. To tackle this issue, innovative machine learning techniques, such as “partial convolutional layers”, have been suggested very recently (Boucher et al. 2023), where missing data can be managed for both the spatial and the angle dimensions. This expands significantly the spatial coverage of the SMOS retrieval, especially for pixels with incomplete angle information. (3) A concept called “Localization” is also exploited, helping the ML retrieval to adapt its behaviour to specific local conditions to reduce local retrieval biases. By specializing its behaviour to local conditions, the relation between passive MW and SM is “simplified” over a particular pixel, this allows to reduce the impact of missing local information needed for a truly global model. We propose several NN and ML architectures to incorporate localization information into the networks, reducing significantly local biases.

Experiments are conducted over the CONUS using several years of SMOS data. Impacts of the image- versus the pixel-scale processing is measured, as well the spatial extension of the SM retrieval due to better missing-data handling, and the effect of the localization is analysed too. The best configuration for a global-scale retrieval is yet to be found because the spatial domain to consider is strategic for image-processing schemes, but original and important technical solutions are proposed here that could pave the way for the next generation of SM retrievals.

How to cite: Pellet, V., Aires, F., and Boucher, E.: Improved Soil Moisture SMOS Retrieval using the next generation of AI inversion schemes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6502, https://doi.org/10.5194/egusphere-egu24-6502, 2024.

09:25–09:35
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EGU24-18930
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HS6.1
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On-site presentation
Sebastian Hahn, Wolfgang Wagner, Thomas Melzer, and Mariette Vreugdenhil

The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) has been operationally distributing a global Surface Soil Moisture (SSM) product derived from the Advanced Scatterometer (ASCAT) on board the series of Metop satellites since December 2008. The first Metop mission (Metop-A launched in October 2006) has been successfully concluded in November 2021, while two Metop satellites are still operational at the moment (Metop-B launched in September 2012 and Metop-C launched in November 2018), both providing an ASCAT SSM product in near real-time (NRT) sampled at 12.5 km and 25 km. EUMETSAT directly manages the ASCAT SSM NRT service, with the soil moisture processing chain implemented as part of the EUMETSAT Polar System (EPS) Core Ground Segment (CGS). In September 2015 the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) formally took over the ASCAT SSM NRT product evolution and maintenance, while product generation stayed at the EUMETSAT EPS CGS. At that time, H SAF has already been responsible for the development and generation of the ASCAT SSM Data Record (DR) products, which consistently advanced through multiple algorithmic iterations in recent years. Beyond minor updates and occasional model parameter substitutions, the ASCAT SSM NRT products do not incorporate the latest algorithmic developments. Hence, a next-generation ASCAT SSM NRT service is currently being set up, aiming to enhance various aspects including spatial resolution and vegetation correction. The new service will run alongside the current NRT system, ensuring that users will experience a seamless transition.

In this study, we introduce the scientific innovations and algorithmic updates of the upcoming H SAF ASCAT SSM NRT products sampled at both 6.25 km and 12.5 km. A validation was performed by comparing the old and new ASCAT SSM with other satellite soil moisture products, in-situ data, and soil moisture information derived from land surface models. The results show an improved performance particularly with respect to the capability of the data to characterise extremes. Furthermore, we will discuss product format changes, such as a new Discrete Global Grid (DGG) defining a more homogeneous sampling distribution of grid points on the Earth's surface.

How to cite: Hahn, S., Wagner, W., Melzer, T., and Vreugdenhil, M.: Next-generation ASCAT surface soil moisture near real-time service, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18930, https://doi.org/10.5194/egusphere-egu24-18930, 2024.

09:35–09:45
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EGU24-19243
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HS6.1
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ECS
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On-site presentation
Giorgio Savastano, Vahid Freeman, and Philip Jales
 

Soil moisture, a critical parameter influencing various environmental processes, is a key focus in Earth observation. This study evaluates and validates Spire's GNSS-R-based soil moisture products, presenting an innovative retrieval methodology capturing spatiotemporal dynamics with enhanced precision.  

Leveraging extensive observations from Spire's GNSS-R and NASA’s CYGNSS (Cyclone Global Navigation Satellite System) satellites, our assessment involves a comprehensive comparison and validation of Spire's soil moisture data. Ground truth measurements from ISMN (International Soil Moisture Network) provide a crucial benchmark, while concurrent validation with established satellite-derived soil moisture products such as SMAP (Soil Moisture Active Passive) and ESA-CCI (Climate Change Initiative for Soil Moisture) ensures a robust understanding of Spire's GNSS-R products' performance across diverse regions. 

The insights gained from this comparative study not only contribute to the validation of Spire's products but also provides perspectives on the strengths and limitations of distinct soil moisture measurement techniques. 

How to cite: Savastano, G., Freeman, V., and Jales, P.: Assessment of Spire GNSS-R Based Soil Moisture Products: A Comparative Analysis with In-Situ and Other Satellite-Based Datasets , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19243, https://doi.org/10.5194/egusphere-egu24-19243, 2024.

09:45–09:55
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EGU24-5538
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HS6.1
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On-site presentation
Marcel M. El Hajj, Susan C. Steele-Dunne, Samer K. Almashharawi, Xuemeng Tian, Kasper Johansen, Omar A. López Camargo, Adria Amezaga-Sarries, Andreu Mas-Viñolas, and Matthew F. McCabe

Soil moisture is a key variable routinely used to understand and predict the behavior of Earth’s climate and water cycle. Over the past decade, Global Navigation Satellite Systems Reflectometry (GNSS-R) techniques have emerged as a “signal of opportunity” for continuous and near real-time soil moisture estimation. Since 2007, multipath signals have been used to estimate soil moisture primarily through three ground-based GPS receiver setup configurations. The first configuration uses two antennas, one looking toward the zenith to acquire the direct signal, and the other looking toward the ground to acquire the reflected (multipath) signal. With this ground-based GPS receiver configuration, soil moisture can be estimated from the reflection coefficient computed by dividing the averaged waveforms from direct and reflected GNSS signals. The second configuration employs an interferometric GNSS-R ground-based receiver with a single antenna, and it estimates soil moisture by analyzing the phase, amplitude, and frequency of the interference pattern between the direct and reflected signals. The third ground-based receiver configuration is known as the interference pattern technique (IPT). It employs a dual-polarized antenna oriented horizontally to measure the power fluctuations of the interference of direct and reflected signals at horizontal polarization (H-pol) and vertical polarization (V-pol). With the IPT, soil moisture is currently estimated by determining the so-called notch position, θB: the angular elevation value (θ) of the smallest interference power (IP) oscillation at V-pol. Accurate determination of θB is challenging in real GNSS-R acquisitions, especially when the IP waveform exhibits low-frequency oscillations or maintains constant amplitude over a wide range of θ. Here, we investigate the potential of a ground-based GNSS-R receiver with two linearly polarized antennas that measure the IP of direct and reflected signals in H-pol and V-pol to estimate soil moisture in a patch of very smooth bare soil and an irrigated grassland field harvested every three months. This study provides a practical method to estimate the soil moisture, through the use of the coefficient of determination between the IP waveforms at H-pol and V-pol (R²v/h). A coherent specular reflection model was employed to first explore the relationship between  and soil moisture for different values of soil roughness and vegetation water content. That relationship was applied to estimate soil moisture from  determined from GPS signals acquired continuously between May and December 2022 of bare soil (vegetation water content equal to zero). The results show that the proposed method can estimate the soil moisture of the upper 10 cm layer of bare soil with high accuracy (RMSE of 1.5 vol.%). The use of  in the irrigated grassland field produced inaccurate estimates of soil moisture, likely due to the presence of vegetation causing V-pol and H-pol to consistently oscillate out-of-phase (R²v/h= 0). The work is currently focusing on the use of amplitude and frequency of V-pol and H-pol to improve soil moisture estimation in the irrigated grassland field.

How to cite: El Hajj, M. M., Steele-Dunne, S. C., Almashharawi, S. K., Tian, X., Johansen, K., López Camargo, O. A., Amezaga-Sarries, A., Mas-Viñolas, A., and McCabe, M. F.: Soil Moisture Estimation Using the Correlation between Dual-Polarization GNSS-R Interference Patterns, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5538, https://doi.org/10.5194/egusphere-egu24-5538, 2024.

09:55–10:05
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EGU24-8930
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HS6.1
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ECS
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On-site presentation
Hamed Izadgoshasb, Emanuele Santi, Leila Guerriero, Veronica Ambrogioni, and Nazzareno Pierdicca

Various remote sensing satellites can be used for extracting soil moisture (SM), each characterized by unique spatial and temporal resolutions. Missions such as Soil Moisture Active Passive (SMAP) have provided fresh insights into the storage of near-surface soil moisture through L-band radiometry, achieving a spatial resolution of 30–50 km and the full Earth coverage in 2-3 days. The demonstrated sensitivity of the L-band electromagnetic signal to the water content of observed targets and its significant penetration depth underscores the potential of Global Navigation Satellite System-Reflectometry (GNSS-R) techniques in diverse land applications. An illustrative example of this advancing application is evident in missions like the NASA's Cyclone GNSS (CyGNSS), originally designed to detect wind speed at sea in tropical cyclones measuring the Earth surface reflections of GNSS signals of opportunity.

Within this context, the capability to retrieve soil moisture through the exploitation of GNSS-R reflections by Artificial Neural Networks has been confirmed in the literature (e.g., see [1] and [2]). In this paper, a sophisticated Artificial Neural Network (ANN) algorithm is used to explore the impact of additional auxiliary data able to account for other factors affecting the GNSS-R signal. They include topography, Above Ground Biomass (AGB), land use, roughness, soil texture, soil porosity, and dynamic variables like Vegetation Water Content (VWC) and Vegetation Optical Depth (VOD) from Soil Moisture Active Passive (SMAP). It also considers data such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Solar Induced Fluorescence (SIF) from Moderate Resolution Imaging Spectroradiometer (MODIS). Moreover, the effect of using latitude/longitude as input on the performances of the algorithm is assessed. The study also aims at evaluating the impact of different stratification approaches, setting up different ANN’s in different geographical and landcover-based stratifications. To assess how these variables contribute to improving the accuracy of soil moisture retrieval, the datasets are collocated in space and time and resampled onto the EASE grid v2.0 projection at 25km resolution. The algorithm is subsequently trained and validated using target soil moisture values derived from SMAP L3 global daily products and in-situ measurements from the International Soil Moisture Network (ISMN). The work has been carried out in the framework of the ESA Scout 2 HydroGNSS mission development, expected to be launched at the end of 2024.

 

Reference

[1]       E. Santi et al., “Combining Cygnss and Machine Learning for Soil Moisture and Forest Biomass Retrieval in View of the ESA Scout Hydrognss Mission,” Sep. 2022, doi: 10.1109/IGARSS46834.2022.9884738.

[2]       O. Eroglu, M. Kurum, D. Boyd, and A. C. Gurbuz, “High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks,” Remote Sensing 2019, Sep. 2019, doi: 10.3390/RS11192272.

How to cite: Izadgoshasb, H., Santi, E., Guerriero, L., Ambrogioni, V., and Pierdicca, N.: Exploring the importance of auxiliary datasets for soil moisture retrieval based on GNSS Reflectometry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8930, https://doi.org/10.5194/egusphere-egu24-8930, 2024.

10:05–10:15
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EGU24-16418
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HS6.1
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ECS
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On-site presentation
Benedetta Brunelli, Davide Festa, Francesco Mancini, and Wolfgang Wagner

Synthetic Aperture Radar (SAR) has a high potential for measuring superficial soil moisture (SSM) dynamics over regional and global scales. Taking advantage of the continuous supply of Sentinel-1 C-band acquisitions, soil moisture is operationally mapped at kilometer-scale resolution using a change detection method (https://land.copernicus.eu/global/). However, the superimposed effect of the vegetation layer causes significant biases in the retrieval over densely vegetated areas or crop fields characterized by seasonal variations. L-band SAR data, due to their penetration capabilities through the canopy, are sensitive to SSM even where higher-frequency signal gets strongly attenuated. However, data availability has remained limited to a few space missions, e.g. ALOS and ALOS-2. Accordingly, limited applications have investigated the use of change detection models using L-band SAR satellite data.

In the context of the current development of new active L-band satellites, such as SAOCOM (2018), ALOS-4 (2023), NISAR (2024), Tandem-L (2024), and Rose-L (2028) this work aims to explore the potential of SAOCOM data, which has become available since July 2022 over the European territory, to track soil moisture variations underneath crops and natural vegetation. L-band backscattering responses have been jointly evaluated in respect of Sentinel-1 data.

A preprocessing workflow for SAOCOM Single Look Complex (SLC) acquisitions is developed to produce a 1 km co-polarized backscattering time series. The topics addressed are i) the improvement of coregistration between the different SAR sensors; ii) the use of radiometric terrain corrected gamma nought compared to the standard ground range detected (sigma nought) data; iii) the effect of SAOCOM acquisition strategies, such as incidence angle variation and inhomogeneous coverage, on the backscattering trends; iv) the optimization of the dynamic masking procedure to exclude low sensitivity pixel. Subsequently, the preprocessed scenes are ingested into an EO data cube (TUW-GEO/yeoda) and the well-established change detection method is implemented. The methodology is tested over the Po Valley (Italy), where the constellation achieves the highest revisit frequency. The resulting SSM product is compared to Sentinel-1 gamma nought retrievals and to modeled SSM from ERA5-Land reanalysis.

Preliminary results show the potential of SAOCOM data for soil moisture mapping below the vegetation layer, which is essential for studying the effect of SSM climate-linked variations on vegetation growth, and could serve as a foundation for the development of multifrequency approaches.

How to cite: Brunelli, B., Festa, D., Mancini, F., and Wagner, W.: Surface Soil Moisture retrieval via change detection using SAOCOM L-band data over the Po Valley (Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16418, https://doi.org/10.5194/egusphere-egu24-16418, 2024.

Coffee break
Chairpersons: Clément Albergel, Luca Brocca
14:00–14:03
14:03–14:13
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EGU24-16611
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HS6.1
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ECS
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On-site presentation
Wolfgang Preimesberger, Pietro Stradiotti, Thomas Frederikse, Martin Hirschi, Nemesio Rodriguez-Fernandez, Alexander Gruber, and Wouter Dorigo

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming at present from 19 satellites operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.
However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.
Since the requirement of a gap-filled ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available.
In this study we address this requirement and introduce the ESA CCI SM GAP-FILLED product. We present the framework around a widely used discrete cosine transform based method (DCT-PLS), and discuss the interpolation of soil moisture in the case of frozen soils and dense vegetation cover. We demonstrate a method to model the expected uncertainty introduced by the interpolation process. We evaluate the impact of gap-filling on the data set and thereof derived statistics such as anomalies and long-term trends. 

How to cite: Preimesberger, W., Stradiotti, P., Frederikse, T., Hirschi, M., Rodriguez-Fernandez, N., Gruber, A., and Dorigo, W.: A gap-filled global long-term satellite soil moisture climate data record from ESA CCI SM, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16611, https://doi.org/10.5194/egusphere-egu24-16611, 2024.

14:13–14:23
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EGU24-13282
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HS6.1
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ECS
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On-site presentation
Elena Leonarduzzi and Reed M. Maxwell

High-resolution soil moisture is key for a wide range of applications such as water resources management, agriculture, and natural hazard monitoring and prediction. In the last few decades, the prominent approach for deriving high resolution soil moisture fields over large areas has been downscaling of satellite observations. These approaches, which are mainly machine learning based, use the coarse resolution estimate from the satellite, combine it with other variables which impact soil moisture distribution (e.g., landcover, topography, soil characteristics) and/or other remote sensed products with higher spatial resolution, and use the in-situ soil moisture observations for training/testing.

Here, we follow a different approach, which takes advantage of physics-based hydrological simulations. First, we create a downscaling playground by using historical simulations of the hydrological model ParFlow-CLM over the continental USA (CONUS). We use the 1 km2 run as our high-resolution estimate, and an upscaled version (averages over 10x10 km2 gridcells) as representative of the coarse resolution estimate. By doing this, we remove two of the biggest issues when downscaling soil moisture: first, we know there’s a perfect match between high- and low-resolution soil moisture and second, we can train and test the model freely over the entire domain, as information is available for every gridcell, without being constrained by the number/locations of in-situ stations. In terms of downscaling approach, we use a random forest model, trained on coarse resolution soil moisture, drainage area, slope, elevation, hydraulic conductivity, porosity, and landcover. We carry out several experiments changing the locations and timing of both the training and testing sets. These experiments allow us, for example, to test whether the in-situ stations available are adequate in number and representative of the entire domain for reliable downscaled products.

Finally, we take advantage of this playground to develop a new downscaling product. We train the same random forest model, but over the CONUS domain, using all gridcells. This results into a model that has learned the spatial scaling of soil moisture between the two resolutions and can predict the 1 km2 over CONUS, fed by a 10x10 km2 estimate in addition to static predictors. We then use the model in a predictive mode, feeding it the coarse resolution estimate from Soil Moisture Active Passive (SMAP) satellite, creating a high-resolution (1 km2) version of SMAP soil moisture.

How to cite: Leonarduzzi, E. and Maxwell, R. M.: Soil moisture downscaling based on physics-based hydrological simulations: a downscaling playground and a novel high-resolution downscaling product for the continental USA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13282, https://doi.org/10.5194/egusphere-egu24-13282, 2024.

14:23–14:33
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EGU24-5442
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HS6.1
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On-site presentation
Raffaele Albano, Arianna Mazzariello, Tedosio Lacava, Raphael Quast, Wolfgang Wagner, and Aurelia Sole

Climate change is already causing suffering and damage, representing the greatest current challenge and threat to our planet. As global temperatures increase, widespread shifts in weather systems occur, making events such as droughts and floods more intense and unpredictable. Both have a direct connection to the variability of Soil Moisture (SM), which therefore needs to be provided at adequate spatiotemporal resolutions and with good accuracy along the soil profile. Currently, there are no satellite SM products that can offer information at high temporal and spatial resolutions, particularly when investigating root zone and large spatial scales. Blending satellite products with similar characteristics but different features in terms of resolution may allow us to face such a gap.  In this light, the 25 km Metop ASCAT Surface Soil Moisture (SSM) product, with a sub-daily temporal resolution (2-6 measurements per day), and the weekly improved SSM S-1 data at 1 km spatial resolution are based on satellite acquisitions in the same microwave spectral region (i.e., the C-band) processed with the RT1 algorithm (Quast et al., 2023)

In this work, we fused, firstly, these products to obtain a daily 1 km soil moisture product, named SCAT- SAR SWI, following the method of Bauer-Marschallinger et al. (2018). As inputs, we used the ASCAT H119 - H120 (Climate Data Record v7 Extension 12.5 km sampling) and an optimized version of SENTINEL 1 SM products made available by the Technological University of Wien for the January 2017 - July 2022 period. Subsequently, we applied the Soil Moisture Analytical Relationship (SMAR) model (Manfreda et al., 2014) to the SCAT-SAR SWI surface product to obtain RZSM information. This made it possible to depict the Basilicata region (southern Italy) test case in four dimensions (time t plus x, y, and z) at high spatiotemporal resolutions. The performance of the developed SCAT- SAR SWI SMAR product, as well as that of the SCAT- SAR SWI, was evaluated for comparison with the 1 km ERA5-Land downscaled SM data (i.e., volumetric_soil_water_layer_1; volumetric_soil_water_layer_2).

The results are encouraging, demonstrating the capability of the product to discriminate the behaviour of areas characterized by different SM contents based on their orography and precipitation regimes. The western part of the region, more affected by precipitation and more mountainous than the other sections of the region, shows indeed a positive correlation (R0.8) with the ERA 5 LAND 1 km product, higher than that obtained for the flatter western subset (R0.6-0.7). This is likely due to the more consistent precipitation patterns in the western part.

Reference

  • Bauer-Marschallinger, B. et al., 2018. Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering. Remote Sensing 10, 1030.
  • Manfreda, S., et al. 2014. A physically based approach for the estimation of root-zone soil moisture from surface measurements. HESS 18, 1199–1212.
  • Quast, R., et al., 2023. Soil moisture retrieval from Sentinel-1 using a first-order radiative transfer model—A case-study over the Po-Valley. Rem. Sens. Of Env., 295, 113651

How to cite: Albano, R., Mazzariello, A., Lacava, T., Quast, R., Wagner, W., and Sole, A.: On the assesment of a new 4D soil moisture product over Basilicata Region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5442, https://doi.org/10.5194/egusphere-egu24-5442, 2024.

14:33–14:43
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EGU24-9289
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HS6.1
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On-site presentation
A multi-task-oriented spatial and temporal convolutional neural network for high-resolution soil moisture estimation
(withdrawn)
Yinghong Jing, Liupeng Lin, and Yao Li
14:43–14:46
14:46–14:56
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EGU24-1769
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HS6.1
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Highlight
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On-site presentation
Peter Weston, Patricia de Rosnay, and David Fairbairn

Bias-correction (BC) is typically needed prior to the assimilation of satellite-derived soil moisture (SM) observations in land surface models. Active ASCAT and passive SMOS satellite-derived SM observations are assimilated in the ECMWF integrated forecasting system (IFS). Prior to assimilation, the ASCAT SM observations are bias-corrected using a seasonal rescaling technique. SMOS level 1 observations are converted to level 2 SM via a neural network, which is trained on the global ECMWF operational SM analysis. However, neither of these techniques allow for non-stationary biases and the globally trained SMOS neural network is affected by local biases. In this presentation a two-stage filter is employed in the ECMWF IFS to dynamically correct biases in the SM observations, whilst allowing the assimilation to correct sub-seasonal scale errors. Over a 3-year test period this adaptive BC approach leads to (i) reduced observation-model biases, (ii) slight improvements in SM analysis performance against in situ data and (iii) reduced mean errors in relative humidity forecasts near the surface in the northern hemisphere midlatitudes. This will benefit the development of a unified coupled land-atmosphere data assimilation system in the context of the CERISE European project (Copernicus Climate Change Service Evolution). Furthermore, it is expected that the assimilation of non-biased ASCAT SM observations will improve the root-zone SM products for the hydrological satellite applications facility (H SAF).  

How to cite: Weston, P., de Rosnay, P., and Fairbairn, D.: Adaptive soil moisture bias correction in the ECMWF land data assimilation system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1769, https://doi.org/10.5194/egusphere-egu24-1769, 2024.

14:56–15:06
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EGU24-3537
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HS6.1
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ECS
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On-site presentation
Jacopo Dari, Paolo Filippucci, Luca Brocca, Renato Morbidelli, Carla Saltalippi, and Alessia Flammini

Groundwater represents a massive portion of the total freshwater available. It is the primary source of water for more than two billion people worldwide and an essential source for agriculture in many areas of the world. It is well known that water resources are expected to face an ever-increasing stress during the upcoming decades because of the combined effects of human exploitation and climate changes, and groundwater is not an exception. Recently, the monitoring of hydrological fluxes through approaches based on the closure of the water cycle budget has been boosted by the availability of multi-source satellite data sets. Under this perspective, this study aims at presenting a novel approach for estimating groundwater recharge rates from satellite surface soil moisture observations through a water balance approach. In order to do this, data from groundwater monitoring networks over selected pilot areas in central Italy have been collected for validation purposes. Several remotely sensed soil moisture products have been evaluated, limiting the selection to latest high-resolution (1 km) data sets, namely an experimental product derived by SMAP (Soil Moisture Active Passive) and developed by Planet Labs, the operational Sentinel-1 soil moisture data delivered by the Copernicus Global Land Service (CGLS) and a newer Sentinel-1 retrieval based on a first-order radiative transfer model (RT1). Preliminary results show a general good agreement between observed and satellite-derived recharge periods, with highest quantitative agreements found for stations monitoring shallower aquifers. Even though further investigation is required, the proposed framework opens the interesting perspective of an innovative hydrological application of satellite soil moisture data and, if successful, it can be potentially upscaled to different targets (i.e., from the regional to the country scale).

How to cite: Dari, J., Filippucci, P., Brocca, L., Morbidelli, R., Saltalippi, C., and Flammini, A.: A water balance approach for estimating groundwater recharge rates through high-resolution satellite soil moisture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3537, https://doi.org/10.5194/egusphere-egu24-3537, 2024.

15:06–15:16
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EGU24-168
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HS6.1
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ECS
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On-site presentation
Ayoob Karami, Laurent Longuevergne, Amen Al-Yaari, Didier Michot, and Youssef Fouad

This study aims to develop and evaluate a new simple unsaturated zone model in frequency domain interconnecting different types of ground or satellite-based observations (effective rainfall, surface soil moisture, root zone soil moisture, groundwater recharge) of a single dynamic system. The proposed formulation based on 5 coherent transfer functions (Tfs) linking observations 2 by 2, with a limited number of parameters, is rooted in the Nash linear reservoir model. The curve shape of the TFs can be adjusted by key parameters such as flow complexity, response time and the share of surface runoff  each of which carries distinct and well-defined physical interpretations.  The model's efficacy was assessed using surface soil moisture, root zone soil moisture and and recharge data in a fractured crystalline-rock aquifer situated in Ploemeur, South Brittany, France. Each TF's was initially independently optimized, with parameters assigned through a detailed review of the literature and consideration of the physical characteristics of the site. The outcomes highlighted the methodology's potential, offering a comprehensive depiction of root-zone soil moisture and aquifer recharge dynamics. In the subsequent phase, the model parameterized via a joint optimization of TFs. we find out that the joint approach has the ability to elevate the accuracy and reliability of the model, ensuring its stable and robust behavior. The simplicity of the procedure, with a small number of easily interpretable parameters, makes it suitable for broader applications in different regions.

How to cite: Karami, A., Longuevergne, L., Al-Yaari, A., Michot, D., and Fouad, Y.: A frequency-domain model to predict surface soil moisture, root zone soil moisture and aquifer recharge  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-168, https://doi.org/10.5194/egusphere-egu24-168, 2024.

15:16–15:26
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EGU24-12837
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HS6.1
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ECS
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On-site presentation
Toni Schmidt, Martin Schrön, Steffen Zacharias, Till Francke, and Jian Peng

Soil moisture products play a pivotal role in monitoring and predicting droughts that affect crop yield, water supply, and land–atmosphere interactions. The availability of various satellite-based soil moisture products allows for a comprehensive investigation of droughts on a large scale. However, limitations in their spatial sampling impact their suitability for regional applications. Accurately inferring sub-pixel heterogeneity is crucial for a representative understanding of regional dynamics and their implications for drought assessment across diverse landscapes. Furthermore, the shallow vertical support of satellite-based soil moisture products hinder the detection and quantification of droughts within the sub-surface. This study leverages multi-scale data fusion, aiming to replicate soil moisture extremes both regionally and in the sub-surface. We integrate ground-based Cosmic-Ray Neutron Sensing (CRNS) data, representing soil moisture within an extensive soil volume, with high-resolution Sentinel-1 data. Employing machine learning models that account for spatiotemporal autocorrelations, our objective is to generate gridded soil moisture data representing regional sub-surface dynamics. Tested in a German catchment, our approach tackles challenges associated with the scarcity of CRNS stations and the complexities of integrating multi-scale data. Our findings establish a foundation for monitoring regional droughts in the sub-surface across extensive areas.

How to cite: Schmidt, T., Schrön, M., Zacharias, S., Francke, T., and Peng, J.: Mapping Regional Sub-Surface Soil Moisture Dynamics and Extremes on the Large Scale Through Data Fusion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12837, https://doi.org/10.5194/egusphere-egu24-12837, 2024.

15:26–15:36
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EGU24-16429
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HS6.1
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On-site presentation
Hui Lu, Jiaxin Tian, and Ruijie Jiang

Land data assimilation systems, by assimilating land surface remote sensing observations, such as soil moisture (SM) products from SMAP, SMOS, and AMSR2, and combining the advantages of the land surface model, are able to produce spatiotemporally seamless data on the state of the land surface, including soil moisture and temperature in the surface layer and rooting zone, as well as energy fluxes at the surface. However, because of the coarse resolution of prevailing passive microwave soil moisture remote sensing products as well as the lesser accuracy of high-resolution soil moisture products, there is no high-resolution land data assimilation system available.

In this study, we developed a high-resolution land data assimilation system by using a machine learning algorithm in combination with a dual-cycle assimilation system. We first used random forests to generate high-resolution soil moisture products from passive microwave soil moisture, and then used the dual-cycle assimilation system to correct the bias of the soil moisture products and assimilated them into the land surface model, and finally produced high-resolution land surface state datasets.  The high-resolution assimilation system was validated on observations from three soil moisture observation networks on the Tibetan Plateau. The results show that the system is capable of producing reliable soil moisture products at different resolutions, such as 5 km, 10 km, etc., with ubRMSE less than 0.06m3/m3.

How to cite: Lu, H., Tian, J., and Jiang, R.: Development of a high-resolution land data assimilation system with integrated machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16429, https://doi.org/10.5194/egusphere-egu24-16429, 2024.

Coffee break
Chairpersons: Alexander Gruber, David Fairbairn
16:15–16:25
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EGU24-8721
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HS6.1
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ECS
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On-site presentation
Jaime Gaona, Davide Bavera, Guido Fioravanti, Luca Ciabatta, Paolo Filippucci, Stefania Camici, Hamidreza Mosaffa, Silvia Puca, Nicoletta Roberto, Pietro Stradiotti, and Luca Brocca

Soil moisture is a crucial state variable for understanding the water cycle. The increasingly available soil moisture data from remote sensing and models is rapidly facilitating improved hydrological analysis and evaluation of climate change impacts. To discern the degree of alteration of soil moisture, the patterns of spatiotemporal anomalies must be considered, but often product-specific uncertainties are overlooked. Such limitations are of particular concern for the operational monitoring and long-term evaluation of soil moisture.

Among the sources of uncertainty jeopardizing remotely sensed and modeled soil moisture, this study evaluates over Europe (1) the heterogeneous spatial patches of validity, (2) the residual trends in the series, and (3) the sensitivity of anomaly detection to the baseline period of popular soil moisture products such as the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF), the passive subset of the Climate Change initiative on SM (CCIp) and the European Drought Observatory (EDO) datasets.

The inter-comparison of these remotely sensed and modeled soil moisture products by triple collocation analysis and against data of the international soil moisture network (ISMN) provides insightful results regarding (1) the contrasting patches of accurate soil moisture estimates, (2) the existence of residual temporal trends in the series, and (3) the differing sensitivity of the products to the baseline period for anomaly analysis. The factors impacting products are subject to debate, particularly concerning spatial and temporal consistency.

Merged products combining H SAF, EDO and CCIp are also assessed to elucidate their potential and limitations for operational monitoring in comparison to individual products. Overall, the combined products equal or exceed the performance of individual products while incorporating specific benefits and drawbacks. Outcomes also inform about the best-performing product by area and period.

All in all, the study illustrates the notable degree of consistency of commonly available soil moisture databases for multiple applications, despite some constraints, while highlighting the potential of merged soil moisture products for the operational monitoring of droughts within the European Drought Observatory (EDO) system.

How to cite: Gaona, J., Bavera, D., Fioravanti, G., Ciabatta, L., Filippucci, P., Camici, S., Mosaffa, H., Puca, S., Roberto, N., Stradiotti, P., and Brocca, L.: Strenghts and limitations of common soil moisture products for operational drought monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8721, https://doi.org/10.5194/egusphere-egu24-8721, 2024.

16:25–16:35
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EGU24-6782
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HS6.1
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ECS
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On-site presentation
Pavan Muguda Sanjeevamurthy, Mariette Vreugdenhil, Sebastian Hahn, Samuel Massart, Carina Villegas-Lituma, Roland Lindorfer, and Wolfgang Wagner

Agriculture faces increased challenges due to intense and frequent droughts caused by climate change. Accurate and timely monitoring of drought conditions, therefore, becomes paramount to taking quick and decisive actions towards its impact mitigation. Agricultural droughts occur due to prolonged periods of low rainfall and high temperatures, which lead to soil moisture deficits, increasing plant water stress and adversely affecting crops. This study explores the potential use of a new demonstrational ASCAT surface soil moisture (SSM) product sampled at 6.25 km provided by EUMETSAT's Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) to monitor agricultural droughts. The study focuses on East Africa, a region severely affected by consecutive years of droughts, resulting in acute food and water shortages that have put the livelihood of millions at risk. 

This study offers a preliminary quality assurance of the ASCAT SSM 6.25 km product with ERA5-Land and ESA CCI (passive) SSM, respectively, focusing on the effects of subsurface scattering and changes in land cover. We first conducted a correlation analysis to gain insights into the general quality and subsurface scattering effects in arid regions of East Africa. Furthermore, to address significant wetting trends caused by land cover changes, which were previously observed in the H SAF ASCAT SSM 12.5 km climate data record product (H119), the dry and wet backscatter reference parameters are estimated on a yearly basis as part of the TU Wien change detection algorithm. The effectiveness of this novel approach is then quantified by comparing trends of ASCAT SSM 6.25 km product with trends of the other SSM datasets.

To assess the potential for agricultural drought monitoring, a convergence of evidence approach was used. Here, the ASCAT SSM 6.25 km anomalies are compared to anomalies in CHIRPS precipitation, LSA SAF (Land Surface Analysis of EUMETSAT) land surface temperature, and CGLS (Copernicus Global Land Service) vegetation datasets for previously recorded drought events. We also generated two drought indicators based on anomalies using the ASCAT SSM 6.25 km product: SMAPI (Soil Moisture Anomaly Percentage Index) and Z-scores, which were evaluated with SPEI (Standardised Precipitation and Evapotranspiration Index) to assess the similarity in spatial patterns of droughts.

Our assessments show that the demonstrational ASCAT SSM 6.25 km product corresponds well with other SSM datasets. Moreover, the drought indicators derived from it effectively capture precipitation deficits and increased land surface temperature compared to SPEI (drought conditions), indicating its potential for agricultural drought monitoring.

How to cite: Muguda Sanjeevamurthy, P., Vreugdenhil, M., Hahn, S., Massart, S., Villegas-Lituma, C., Lindorfer, R., and Wagner, W.: A Case Study on Agricultural Drought Monitoring using ASCAT Surface Soil Moisture at 6.25 km sampling over Eastern Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6782, https://doi.org/10.5194/egusphere-egu24-6782, 2024.

16:35–16:45
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EGU24-10374
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HS6.1
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ECS
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On-site presentation
Robert Carles-Marqueño, Martí Perpinyà-Vallès, and Maria José Escorihuela

Accurate estimation of soil hydraulic properties, specifically field capacity (FC) and wilting point (WP), collectively known as Water Holding Capacity (WHC), is crucial for effective water resource management in agriculture and the environment. Traditionally, WHC is obtained through soil sampling and laboratory analysis. Pedo Transfer Functions (PTFs) have been developed to estimate WHC from soil composition data, simplifying the process but still relying on accurate soil measurements.

In response, we propose a novel algorithm for dynamic FC and WP estimation based on continuous soil moisture time series from remote sensing. This study includes a preliminary accuracy assessment of the downscaled 100-m soil-moisture time-series obtained from a combination of SMAP and Landsat data against in-situ stations from the International Soil Moisture Network (ISMN) which also include WP and FC measurements. Leveraging these long time series of soil moisture data enables a more nuanced and adaptive characterization of soil hydraulic properties over time. This approach recognizes the influence of factors such as precipitation, evapotranspiration, and land management practices on soil moisture variability.

Furthermore, we perform a comparative analysis with SoilHydroGrids’ WP and FC as a benchmark, to underscore the advancements, enhancements and potential limitations of our approach. Our results demonstrate a noteworthy enhancement in the estimation of Field Capacity, reducing the Root Mean Square Error (RMSE) from 0.15m³/m³ to 0.09m³/m³. Moreover, our algorithm exhibits slightly superior predictions for the wilting point when compared against laboratory measurements. Generally, our approach is capable of identifying a larger range of WP and FC values, which is also seen in the in-situ data.

How to cite: Carles-Marqueño, R., Perpinyà-Vallès, M., and Escorihuela, M. J.: High Resolution Soil Hydraulic Properties estimation from remotely sensed soil moisture time series., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10374, https://doi.org/10.5194/egusphere-egu24-10374, 2024.

16:45–16:49
16:49–16:59
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EGU24-11186
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HS6.1
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ECS
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Highlight
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On-site presentation
Irene Himmelbauer, Daniel Aberer, Nicolas Bader, Wolfgang Preimesberger, Wouter Dorigo, François Gibon, Arnaud Mialon, Philipp Richaume, Monika Tercjak, Alexander Boresch, Raffaele Crapolicchio, and Alexander Gruber

In situ soil moisture data is used as the main reference for the validation of satellite soil moisture products.  Although in situ measurements are often referred to as the “ground truth” and we have an understanding of the error sources, the magnitudes of the uncertainties associated with in situ measurements and methods to eliminate these uncertainties are hardly known. However, in order to achieve the best possible Return Of Investment (ROI) for a satellite mission, reliable and fully characterized in situ reference datatsets are crucial.

ESA`s Fiducial Reference Measurement for Soil Moisture project (FRM4SM) was launched in 2021 to tackle the establishment of comprehensive and fully characterized, traceable uncertainty budgets for in situ soil moisture observations at the satellite footprint scale. The project aims to address the following scientific questions to facilitate the creation and exploitation of such Fiducial Reference Measurements (FRMs), using the International Soil Moisture Network (ISMN) as the in situ source and ESA’s Soil Moisture and Ocean Salinity (SMOS) mission as an example satellite product:

(1) understand the status quo and means to establish an (SI-)traceable uncertainty budget for in situ soil moisture measurements

(2) identify error sources that impact the in situ measurement

(3) create quality indicators that allow to identify the most reliable “soil moisture FRMs” from the ISMN

(4) verify and demonstrate the merit of these select soil moisture FRMs within validation case studies,

(5) create protocols and procedures for the creation and use of such an FRM subset,  which are built upon community=agreed standards and practices

(6) integrate the established FRM dataset and all developed validation methods into the freely-accessible Quality Assurance for Soil Moisture (QA4SM) online validation service

In this presentation, we will introduce the FRM4SM project and highlight our latest achievements and ongoing developments. Furthermore, we will discuss future directions, and give insights into challenges that need to be overcome in order to achieve a traceable uncertainty budget calculation for in situ soil moisture data at the satellite footprint scale.

How to cite: Himmelbauer, I., Aberer, D., Bader, N., Preimesberger, W., Dorigo, W., Gibon, F., Mialon, A., Richaume, P., Tercjak, M., Boresch, A., Crapolicchio, R., and Gruber, A.: Fiducial Reference Measurements for Soil Moisture (FRM4SM): recent progress in error source identification and traceable uncertainty budget calculation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11186, https://doi.org/10.5194/egusphere-egu24-11186, 2024.

16:59–17:09
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EGU24-19160
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HS6.1
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Virtual presentation
Emil Andreasen Klahn, Peter Friis Østergaard, Henrik Kjeldsen, and Jan Nielsen

Soil moisture is one of the essential climate variables, and it is interesting from both meteorological and agricultural perspectives. At the same time, soil moisture measurements span several length scales, from in-field studies with point scale sensors, to field scale with CRNS-probes to even larger study areas investigated with remote sensing. These different techniques make use of fundamentally different physical principles for obtaining soil moisture measurements, and harmonizing these measurements requires that SI-traceability can be demonstrated. For the common metric of volumetric water content, this requires demonstrating traceability of both water and soil volume.

The Danish Technological Institute (DTI) has implemented a dedicated setup for determination of water content. The setup takes samples ranging from 100 g up to 2 kg and a volume up to 2 liters, which makes measurements on representative soil samples feasible. In contrast to the to the traditional loss-on-drying method, the DTI reference setup provides SI-traceability of the water content, through measurements of air flow and dew point temperature.

In this presentation, the design principles of the setup and the utilization of the setup for SI-traceable calibration of point scale soil moisture sensors are described.

How to cite: Andreasen Klahn, E., Friis Østergaard, P., Kjeldsen, H., and Nielsen, J.: Towards SI-traceable calibration of soil moisture sensors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19160, https://doi.org/10.5194/egusphere-egu24-19160, 2024.

17:09–17:19
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EGU24-14987
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HS6.1
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ECS
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On-site presentation
Maud Formanek, Alexander Gruber, and Pietro Stradiotti

This study aims to improve the ESA CCI soil moisture dataset uncertainty estimates by including the sampling uncertainty of the triple collocation analysis in the uncertainty propagation of the data merging scheme. The ESA CCI soil moisture product merges data from multiple sensors through a weighted average. This strategy aims to increase both the temporal and spatial sampling density while reducing random retrieval errors. Optimal error reduction is obtained by assigning the weights according to each sensor’s specific uncertainty characteristics, expressed as σi-2/(∑σj-2) . The uncertainties σi are determined via triple collocation analysis (TCA) applied to soil moisture estimates from a land surface model, and from a passive and an active microwave satellite instrument.

However, the uncertainty estimates obtained from TCA are themselves uncertain as a result of finite sample size. Notably, this ‘uncertainty of the uncertainty’ (UU) can be derived analytically for simple error models, but lacks a similar analytical solution for the affine error model (which includes both additive and multiplicative biases) employed in the CCI SM algorithm.

The magnitude of the UU has serious implications for the weighted averaging and the resulting uncertainty of the merged products: 1) it introduces an additional term in the uncertainty of the merged product stemming from the uncertainty of the weights themselves, and 2) the UU can reach a threshold, where the weighted average yields worse results than an unweighted average. In this study, we calculate the UU via bootstrapping of the TCA results for three sensors (ASCAT, SMAP and SMOS) and investigate its impact on the uncertainty of the merged dataset.

How to cite: Formanek, M., Gruber, A., and Stradiotti, P.: What is the uncertainty of the uncertainty and (why) does it matter?  Propagating uncertainties of weight estimates through soil moisture data merging, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14987, https://doi.org/10.5194/egusphere-egu24-14987, 2024.

17:19–17:29
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EGU24-2102
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HS6.1
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ECS
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Highlight
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On-site presentation
Nicolas Bader, Wolfgang Preimesberger, Monika Tercjak, Alexander Boresch, Daniel Aberer, Irene Himmelbauer, François Gibon, Arnaud Mialon, Raffaele Crapolicchio, and Alexander Gruber

The purpose of the Quality Assurance for Soil Moisture (QA4SM) service is to provide a central, cloud-based platform for soil moisture data validation. QA4SM is an easy-to-use graphical web interface that caters to both producers of satellite soil moisture data as well as users of such products. It provides the means to assess quality requirements for satellite products, as defined by the Global Climate Observing System (GCOS) for example, all the way to the validation and (inter)comparison of satellite data against (fiducial) reference measurements and land surface model data.

QA4SM delivers reproducible validation results based on a consistent methodology and community-agreed best practices. Numerous well-known data products are readily available and periodically kept up to date. This includes satellite products of different levels from SMOS, SMAP, ASCAT, and Sentinel-1 missions. Further, data products from both the Copernicus Climate Change Services (C3S) and the ESA Climate Change Initiative (CCI) are provided, too. Also included is data from the International Soil Moisture Network (ISMN) and reanalysis model data such as NASA’s GLDAS-Noah or ECMWF’s  ERA5(-Land). Beyond that, users can upload custom datasets to the platform in different formats.

QA4SM offers a broad palette of processing tools such as: the filtering of datasets according to flags or versions; spatial and temporal scaling options; the selection of spatial and temporal subsets; temporal matching methods; and different metric and anomaly calculations for up to six datasets simultaneously. Means for a subsequent publication of the results, including the generation of a digital object identifier (DOI), are implemented as well.

In this talk, we will present the functionalities and tools provided by QA4SM, and report on recent updates, the latest features, and planned future developments of the platform. Both scientific and technical aspects will be discussed.

How to cite: Bader, N., Preimesberger, W., Tercjak, M., Boresch, A., Aberer, D., Himmelbauer, I., Gibon, F., Mialon, A., Crapolicchio, R., and Gruber, A.: Quality Assurance for Soil Moisture (QA4SM): A Platform for Validating Satellite Soil Moisture Data Against Fiducial Reference Measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2102, https://doi.org/10.5194/egusphere-egu24-2102, 2024.

17:29–17:39
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EGU24-9030
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HS6.1
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On-site presentation
Miroslav Zboril and María de los Ángeles Millán Callado and the SoMMet Consortium

Soil moisture is one of the Essential Climate Variables as defined by the WMO Global Climate Observing System. Several soil moisture observation systems exist on multiple scales, however, poorly harmonized due to the lack of interlinks. There is a need to establish the chain of traceability, the metrological assessment of uncertainties and the harmonisation of soil moisture measurements within the hydrological cycle, on multiple scales ranging from point-scale sensors to satellite remote sensing techniques. In addition, there is an urgent need for real-time, continuous, high-quality, high-resolution and metrologically traceable and harmonised data on soil moisture.

To address these needs, the project SoMMet (Soil Moisture Metrology) has been set up in the framework of the European Partnership on Metrology of EURAMET. The aim of the project is to develop sound metrological tools and establish a metrological foundation for soil moisture measurement methods on multiple scales, supporting the traceability and harmonisation initiatives.

On the point scale (10-1 m – 101 m), novel primary and secondary standards of humidity measurement will be developed specifically for soil samples. On the intermediate range (102 m – 103 m), the metrological basis of the cosmic-ray neutron sensing (CRNS) method will be established. On the large scale (103 m – 104 m), satellite-based remote sensing techniques will be utilized to derive the soil moisture products. Based on dedicated comparison measurement campaigns, tools for cross-disciplinary harmonisation of the individual methods will be developed. Furthermore, soil moisture data fusion approaches will be researched, aiming at integrating the multi-scale soil moisture measurements to provide new schemes and recommendations to facilitate the generation of high-quality, temporally and spatially consistent soil moisture information useful for land surface sciences and applications.

The project consortium consists of nine National and Designated Metrology Institutes and nine research institutions. It cooperates with other projects and networks currently dealing with soil moisture monitoring and open issues of missing soil moisture harmonisation. The project 21GRD08 SoMMet has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States.

How to cite: Zboril, M. and Millán Callado, M. D. L. Á. and the SoMMet Consortium: Project SoMMet - Metrology for multi-scale monitoring of soil moisture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9030, https://doi.org/10.5194/egusphere-egu24-9030, 2024.

17:39–17:49
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EGU24-11565
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HS6.1
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ECS
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Highlight
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On-site presentation
Sadra Emamalizadeh, Alessandro Pirola, Cinzia Alessandrini, Anna Balenzano, and Gabriele Baroni

The accurate estimation of soil moisture is fundamental for understanding hydrological processes and optimizing water resource management, particularly in agricultural areas. The SoMMet project, a joint research project within the Programme 'European Partnership on Metrology' of EURAMET, contributes significantly to this field by developing and establishing a metrological framework for soil moisture measurements covering lateral scales ranging from the decimetre to kilometre. Among the different research activities, the project aims to compare and harmonize various soil moisture observation methods, addressing their uncertainty, sensing volume, and systematic effects. This involves a systematic review of methods, comparison of their spatial and temporal characteristics, and the development of a harmonization approach.

In line with these activities, in this contribution we present the comparison performed between a remote sensing product, Soil Water Index (SWI) by Copernicus Global Land Service (CGLS), and soil moisture estimated by ground-based Cosmic-Ray Neutron Sensors (CRNS). The study, conducted in 4 sites in Northern Italy, spans the entire growing season of 2021 and incorporates SWI data at multiple depths. We explore the correlation between vegetation vigor (NDVI) and soil moisture trends to understand the spatial mismatch among soil moisture products. The results show a general good correlation between remote sensing and ground measurements. The agreement between the two soil moisture observations, however, is not consistent in time. The differences are mainly attributed to the role of the vegetation.

This research is pivotal for identifying representative spots for ground measurements, enhancing the utility of soil moisture products across applications. In conclusion, our abstract showcases the importance of advancing soil moisture estimation methods, addressing uncertainties and representativeness. The integration of metrological principles, harmonization approaches, and comparisons between different observation methods demonstrates the holistic approach in enhancing our understanding of soil-water dynamics.

How to cite: Emamalizadeh, S., Pirola, A., Alessandrini, C., Balenzano, A., and Baroni, G.: Advancing soil moisture estimation across scales: insights from the SoMMet Project , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11565, https://doi.org/10.5194/egusphere-egu24-11565, 2024.

17:49–17:59
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EGU24-16181
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HS6.1
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ECS
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On-site presentation
Katoria Lesaalon Lekarkar, Jonas Lembrechts, Stefaan Dondeyne, and Ann van Griensven

Soil moisture plays a crucial role in the earth's system cycle by connecting the water, energy, and carbon cycles. It actively influences hydrological processes and affects the occurrence of climate-related hazards, such as droughts, heatwaves, and wildfires.

This places soil moisture at the centre of agro- and biometeorological monitoring and forecasting. Despite its crucial role, the global network of soil moisture observations remains the least developed among the major land-based components of the hydrological cycle, with strong regional imbalances in coverage. Remote sensing products provide spatially and time-continuous alternatives to in-situ soil moisture data. However, these products remain coarse in spatial resolution and the lack of in-situ validation data in data-scarce regions such as Africa undermines the application of such products.

The emergence of low-cost technologies has enabled the deployment of monitoring networks that provide large spatial coverage at a fraction of the cost of traditional monitoring networks, offering unique opportunities to upscale coverage and address data scarcity. One such device is the TMS-4 logger from TOMST which has found wide application globally due to its robustness, high temporal resolution, large data storage, and long battery life, guaranteeing independent data collection over an extended period. TMS loggers also offer the advantage that a large amount of georeferenced timeseries data from these devices has already been compiled into the global SoilTemp-database (a database currently largely focused on temperature data). The available timeseries of observations from these devices already covers all the continents (including Antarctica) and comprise observations from close to 10,000 locations that are currently hosted within SoilTemp, with ongoing deployment in at least 9 African countries. Our mission is thus firstly, to consolidate this data as submitted in its raw form to the SoilTemp database, adequately calibrate it, and ultimately avail it as an open-access resource. This global coordination and standardization offer the opportunity to integrate this data into the existing global database of soil moisture monitoring (the International Soil Moisture Network). Secondly, we aim to increase global coverage of soil moisture monitoring using the low-cost TOMST TMS-4, and further facilitate its use by non-scientists in citizen science projects across the globe. The result of this initiative should be a drastic increase in the global coverage of soil moisture data, especially in data-scarce areas such as Africa, which would provide an indispensable resource for validating satellite products, improving drought monitoring, and countless other environmental applications.

How to cite: Lekarkar, K. L., Lembrechts, J., Dondeyne, S., and van Griensven, A.: SoilMoist: A global network of soil moisture observations based on emerging low-cost technologies., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16181, https://doi.org/10.5194/egusphere-egu24-16181, 2024.

Posters on site: Mon, 15 Apr, 10:45–12:30 | Hall A

The posters scheduled for on-site presentation are only visible in the poster hall in Vienna. If authors uploaded their presentation files, these files are linked from the abstracts below, but only on the day of the poster session.
Display time: Mon, 15 Apr 08:30–Mon, 15 Apr 12:30
Chairpersons: Klaus Scipal, Nemesio Rodriguez-Fernandez, Luca Brocca
A.52
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EGU24-1119
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HS6.1
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ECS
Yeswanth Naidu Adigarla and Sarmistha Singh

Soil moisture stands as a pivotal element in land-atmospheric interplay, crucially affecting water, energy, and biogeochemical cycles. At a larger spatial scale, meteorological forces and land cover patterns significantly influence soil moisture dynamics. Prior studies primarily focused on investigating soil
moisture variability with soil texture and topography which have prominent influence factors at small spatial scales. Previous studies have employed various correlation techniques to explore these interactions however, correlation does not necessarily imply causation. Our study employs a causal discovery approach, specifically, Peter Clark Momentary Conditional Independence (PCMCI) using 8-day satellite-based gridded data from NASA's SMAP soil moisture, precipitation, leaf area index (LAI), evapotranspiration (ET), land surface temperature (LST), and vapor pressure deficit (VPD). The data was collected across diverse climate classes in India, and seasonal effects were removed to get valuable causal insights. Our results reveal that precipitation plays a predominant role in arid regions compared to humid ones, while LST and VPD significantly impact sub-arid regions. Notably, ET demonstrates substantial influence in the sub-humid regions. The maximum lag in causation is 2.5 to 3 months between the soil moisture and other variables. These findings significantly enhance our comprehension of soil moisture dynamics across various agro-climatic classes, thereby help in enhancing soil moisture predictions and improving agricultural drought forecasting.

How to cite: Adigarla, Y. N. and Singh, S.: Causal Insights on the dynamics of Soil Moisture across Agro-climatic regions of India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1119, https://doi.org/10.5194/egusphere-egu24-1119, 2024.

A.53
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EGU24-4919
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HS6.1
Jaehwan Jeong, Minha Choi, Kiyoung Kim, and Hyungsuk Kimm

Soil moisture is a key factor controlling the hydrological phenomena at the land surface, i.e., the state and movement of water on the ground. Microwave remote sensing from space greatly contributes to reducing the lack of information on surface soil moisture. However, there are still challenges in the practical use of satellite-based soil moisture products for difficult situations such as dense vegetation, surface roughness, coarse spatial resolution, complex topography, etc. This is particularly true in South Korea, where more than 70% of the country is mountainous. Here, ground reference data from an observation network is essential for rigorous validation and retrieval of soil moisture. The Cosmic-Ray Neutron Probe (CRNP) is a promising approach for building a ground soil moisture observation network in areas with dense forests and mountainous terrain. CRNP requires no invasive sensor installation (i.e., no damage to vegetation and no interruption to soils) and also has intermediate spatial coverage that minimizes the scale mismatch between traditional ground data and satellite remote sensing products. This study assesses the applicability of CRNP for monitoring soil moisture in Korea. Hongcheon CRNP site, one of the prototype sites for the Korean cOsmic-ray Soil Moisture Observing System (KOSMOS), was established in August 2022, and this study used the Hongcheon site’s soil moisture datasets (one from CRNP and the others from multiple frequency-domain sensors) to calibrate CRNP-derived soil moisture and to evaluate satellite-based surface soil moisture product. Through this study, we discuss the potential of CRNP for enhancing the ground soil moisture observation network and the possibility of expanding the CRNP network throughout South Korea.

 

Acknowledgment: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A6A3A01087645), the National Research Foundation of Korea (NRF) grant funded by Ministry of Science and ICT (202300209986), and BK21 FOUR Program of Agriculture-forestry Bioresource Convergence Center (ABC), Seoul National University, Seoul, Korea.

How to cite: Jeong, J., Choi, M., Kim, K., and Kimm, H.: Assessment of soil moisture estimation using cosmic-ray neutron probe: Towards building Korean cOsmic-ray Soil Moisture Observing System (KOSMOS), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4919, https://doi.org/10.5194/egusphere-egu24-4919, 2024.

A.54
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EGU24-11026
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HS6.1
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ECS
Thomas Himmer, Carolina Damm, Luca Zappa, and Wouter Dorigo

Soil moisture (SM) is a key component of the Earth system and a key factor in climatological and hydrological processes as it regulates water, carbon and energy fluxes between land and atmosphere. For various applications – including monitoring and forecasting of hydro-climatic extremes (floods, droughts), forest fires, and crop yield estimation – high-resolution SM information is required. Currently, the ESA CCI (Climate Change Initiative) product provides a long-term, global record of SM with daily temporal resolution and a spatial resolution of 25km (0.25°). This coarse resolution can limit its usefulness in some of the mentioned fields of application.

This study aims to improve the spatial resolution of the ESA CCI SM product to 1km (0.01°) through machine learning, incorporating dynamic and static ancillary variables influencing the spatial organization of SM at this finer scale. This procedure consists of two steps, in which the coarse resolution data is first downscaled to 0.05° and then further to 0.01°. Currently, the ancillary variables used in the downscaling process consist of land cover information from the Copernicus Global Land Service (CGLS) including soil properties, land cover types, the Normalized Difference Vegetation Index (NDVI) and a digital elevation model. Recent assessments against in-situ measurements from the International Soil Moisture Network (ISMN) across Europe reveal that the downscaled SM offers a more detailed portrayal of the spatial distribution of SM compared to the original ESA CCI product while retaining the high temporal accuracy. However, these investigations also show that the impact of the NDVI on the model prediction is small.

In future iterations of the downscaling model, the goal is to explore possibilities for incorporating more influential variables that achieve greater information gain (e.g. Land surface temperature) and to examine other machine learning approaches in addition to the currently used random forest regressor to further improve the downscaling accuracy.

How to cite: Himmer, T., Damm, C., Zappa, L., and Dorigo, W.: Downscaling ESA CCI Soil Moisture: From 0.25° to 0.01° using a two-step machine learning approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11026, https://doi.org/10.5194/egusphere-egu24-11026, 2024.

A.55
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EGU24-11072
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HS6.1
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ECS
Shirin Moradi, Fang Li, David Mengen, Harry Vereecken, and Carsten Montzka

Sustainable irrigation practices are crucial for efficient water management, particularly as over 70% of the Earth's freshwater is dedicated to agricultural production. This study delves into the importance of optimizing modeling resolution to achieve reliable soil moisture assessments.

Here, we investigate the spatio-temporal soil moisture simulation at the root zone on the Rur catchment in western Germany (covering approximately 2300 km2). Employing high spatial resolutions of 500 and 250 meters, our investigation utilizes CosmoRea6 atmospheric data and World Soil Information (ISRIC) soil grid and texture data to comprehensively characterize soil properties. The coupled land surface-subsurface model (CLM-ParFlow) is applied, considering intricate hydrological processes within the soil-plant-atmosphere system. Validation is conducted through a multi-faceted approach, incorporating data from the Soil Moisture Active Passive (SMAP) satellite, Cosmic-ray Neutron Sensor (CRNS) stations, and Synthetic Aperture Radar (SAR)-Sentinel-1, with a specific focus on the soil moisture assimilation using high-resolution Sentinel-1 data.

The study explores the belief that increasing the resolution of input data and employing data assimilation techniques with high-resolution remote sensing data enhance the reliability of simulated soil moisture, particularly in areas with diverse soil textures and land uses. The outcomes bear significant implications for optimized modeling resolution, considering computational costs and sustainable irrigation practices. This understanding of soil moisture dynamics empowers stakeholders in agriculture to optimize water usage, improve crop productivity, and minimize environmental impacts.

How to cite: Moradi, S., Li, F., Mengen, D., Vereecken, H., and Montzka, C.: Examining the Impact of Modeling Resolution on Soil Moisture Simulation Using Multi-Faceted Remote Sensing Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11072, https://doi.org/10.5194/egusphere-egu24-11072, 2024.

A.57
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EGU24-16013
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HS6.1
Wolfgang Korres, Tunde Olarinoye, Fay Boehmer, Kasjen Kramer, Stephan Dietrich, Marcel Reinhardt, and Matthias Zink

Soil moisture and its measurements are important in various fields and applications, from agriculture and hydrology to climate modelling, ecology and ecosystem health management. The monitoring of soil moisture gained widespread recognition in the early 2000s as an integral component of the hydrological and meteorological observation systems. This momentum was accelerated with the establishment of several soil moisture monitoring networks. The collection of data from different networks with a diversity of sensors and data formats, harmonization, quality control, archiving of in situ soil moisture data and ensuring the free accessibility of this data for end-users underline the motivation behind the foundation of the International Soil Moisture Network (ISMN) in 2009.

All in situ data sourced from the different providers undergo two quality checks. First, a visual inspection of the data excluding near real-time data and second, a rule-based automatic quality control procedure before inclusion in the ISMN database to ensure high quality research-ready soil moisture data for end-user. Thirteen different plausibility checks are applied to every singular hourly observation, which is flagged then as dubious if one of these checks fail, otherwise as “good”. These plausibility checks can be categorized into: i) a geophysical range verification, detecting the exceedance of certain thresholds (e.g., soil moisture values below 0 Vol.-%), ii) geophysical consistency methods, taking either ancillary in situ data if available or NASA’s GLDAS Noah data into account. An example is the flagging of soil moisture where soil temperature is negative). And iii) spectrum-based approaches, using the first and second derivatives of the entire soil moisture timeseries to detect dubious soil moisture patterns (i.e., spikes, breaks, and plateaus).

Publications in recent years point to the great potential of Deep Learning (DL) based methods for identifying anomalies in time series data. In this study, the potential of Long Short-Term Memory (LSTM) and Transformer models for anomaly detection in soil moisture time series is being investigated. Therefore, randomly selected time series from the ISMN are manually (visually) quality flagged (labelled). In order to be able to label these data we developed a guidance how to visually quality control in situ soil moisture data. Different Deep Learning methods in combination with varying external data sets (e.g. precipitation time series) are validated against the manually labelled data and compared to the previously implemented flagging method. The method will be further developed and evaluated for its use in ISMN operations. The incorporation of additional flagging information, especially when enhanced by Deep Learning methods, is anticipated to lead to a better usability of soil moisture data, as well as promoting a more robust quality control by the ISMN for its users in the future.

How to cite: Korres, W., Olarinoye, T., Boehmer, F., Kramer, K., Dietrich, S., Reinhardt, M., and Zink, M.: Potential of Deep Learning based quality control methods for soil moisture time series in an operational data service, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16013, https://doi.org/10.5194/egusphere-egu24-16013, 2024.

A.58
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EGU24-16136
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HS6.1
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ECS
Daniel Aberer, Nicolas Bader, Irene Himmelbauer, Wolfgang Preimesberger, Alexander Boresch, Monika Tercjak, François Gibon, Arnaud Mialon, Raffaele Crapolicchio, Wouter Dorigo, and Alexander Gruber

The Global Climate Observing System Essential Climate Variables (GCOS ECVs) requirements define a target threshold of 0.005 m³/m³ per decade for satellite soil moisture product stability. As admitted by GCOS, this threshold lacks robust justification in the scientific literature, prompting critical assessment. Moreover, no commonly-accepted method exists to assess satellite soil moisture product stability to begin with.

In this study, we investigate the suitability of existing in situ soil moisture monitoring networks contained in the International Soil Moisture Network (ISMN) for stability assessment. The selection of such stable reference sites is based on two criteria: (i) sites that are considered “fiducial reference sites” as defined by the Fiducial Reference Measurements for Soil Moisture (FRM4SM) project; and (ii) sites that provide suitable temporal coverage for the time spans over which satellite product stability is required (i.e., 10 years or more).  Using these select reference sites, we assess the stability of various common satellite soil moisture products (e.g., ASCAT, SMOS) using Theil-Sen slopes that are calculated for various validation metrics (e.g., median annual Pearson correlations or unbiased Root Mean Square Differences). In addition, we investigate the impact of data gaps and scarcity on the calculated stability metrics.

Analyses were carried out using the the python toolbox for evaluating soil moisture observations (pytesmo; https://github.com/TUW-GEO/pytesmo). The goal is to include stability metrics as part of the QA4SM online validation service in the future (https://qa4sm.eu/).

How to cite: Aberer, D., Bader, N., Himmelbauer, I., Preimesberger, W., Boresch, A., Tercjak, M., Gibon, F., Mialon, A., Crapolicchio, R., Dorigo, W., and Gruber, A.: Using fiducial reference measurements for assessing soil moisture product stability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16136, https://doi.org/10.5194/egusphere-egu24-16136, 2024.

A.59
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EGU24-18051
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HS6.1
Verena Jagersberger, Valentina Pelzmann, Johannes Ehrendorfer, Jutta Eybl, Korbinian Breinl, Peter Strauss, Gernot Klammler, and Thomas Weninger

The Austrian Hydrographic Service operates an extensive network of hydrographic measuring stations all over the country. The soil water measuring network comprises 62 stations where, among others, water content, matric potential, and soil temperature are measured. Only through the implementation of a consistent quality control system and data harmonization is it possible to provide expedient datasets that can be published or used for further purposes. Although automated quality control procedures for soil moisture are integrated in some national and international networks, there is currently no uniform quality control and harmonization of measurement data implemented in the Austrian network. Thus, the goal of this study was to evaluate existing options for the implementation of a system for the control of data quality with the highest possible degree of automatization.

Technical implementation and structure of the stations vary greatly in the monitoring network in terms of measuring depths and installed sensors. Of the 62 measuring stations, 15 were set up as so-called type-2 measuring stations with a lower number of sensors and an IOT data transmitter. The aim of this type of station being reduced installation and maintenance effort as well as lower costs. On the other end of the complexity range, weighing lysimeters with field reference are maintained since decades.

To establish the most appropriate procedure, established systems from international literature, like ISMN and SaQC or NASMD, were compiled and tested for their applicability in the Austrian monitoring network. For evaluation, the results of the automated quality check routines were compared to those of a visual expert check via an error matrix. The aim was to determine which quality control procedures are most effective and lead to the best results in terms of flagging different quality levels or likely errors. Leaning on the findings of the quality control procedures for soil moisture, a procedure for the matric potential shall be developed and implemented in the network, since this state variable of soils is crucial for understanding soil water processes.

How to cite: Jagersberger, V., Pelzmann, V., Ehrendorfer, J., Eybl, J., Breinl, K., Strauss, P., Klammler, G., and Weninger, T.: Implementation of quality control in a national soil moisture monitoring system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18051, https://doi.org/10.5194/egusphere-egu24-18051, 2024.

A.60
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EGU24-15155
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HS6.1
Suhyb Salama

Satellite products of hydrological variables are essential to understanding the spatial and temporal variability of the earth's system of systems, for example, the hydrosphere and its intricate interactions with the biosphere and the atmosphere. Hydrological products from multiple satellites are regularly harmonised to produce long-term synoptic climate data records. The fidelity of these satellite-based climate records forms the backbone to quantify and analyse the variability of the water cycle and the effect of climate change over extended temporal and spatial scales. This fidelity is assessed with statistical metrics that measure the goodness of fit (GoF) between satellite products and in-situ measurements. Commonly used GoF metrics include: the slope and intercept of type-II regression, determination coefficient R2, and difference metrics like bias, root means squared differences, mean absolute differences (MAD) and their relative measures. These metrics do not need to be in harmony, for example, a high R2 value is not necessarily associated with a close-to-unity slope, or a low bias is not ineludibly translated to a low MAD. Presenting these metrics in a table for comparing various retrieval models makes it even more challenging to draw clear conclusions. In part, this confusion could be mitigated by using statistical charts, for example, Taylor or radar diagrams. These diagrams offer the capability to graphically summarise how closely satellite products match the measurements. Nonetheless, there is no unique measure that can be used to describe the GoF. In this essay, we develop a universal methodology with the capability of providing the scientific community with a quantitative and holistic measure of GoF of satellite products. The method ingests statistical validation metrics commonly employed in hydrology or any specific discipline of geoscience, transforms them into unity scalars with the same direction (0 is low and one is high accuracy) and projects them into a unity circle.  The resulting area is then calculated and normalised to the maximum expected area for a percentage GoF measure. As the projection is equiangular, each unique sequence of the employed metrics will give a different answer. With permutation, the GoF values are calculated from all possible sequences. The maximum possible accuracy and the largest probable uncertainty are subsequently derived from the resulting population. This procedure results in a unique GoF that integrates all used validation metrics and provides a collective measure of accuracy. Although it was developed for satellite-derived hydrological products, the proposed method can be applied to any statistical metrics used to measure the goodness of fit between modelled and measured biophysical variables.

How to cite: Salama, S.: Validation of satellite hydrological products: which goodness-of-fit to use?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15155, https://doi.org/10.5194/egusphere-egu24-15155, 2024.

A.61
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EGU24-9990
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HS6.1
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ECS
Yingtao Wei, Liupeng Lin, Jie Li, Ruyi Feng, and Huifang Li

Soil moisture is a key state variable in the ecosystem. However, existing soil moisture products often cannot have both high spatial resolution and long time series. Therefore, it is important to downscale the soil moisture products for fine application. So far,the soil moisture downscaling methods can be summarized as satellite-based methods, model-based methods, and learning-based methods. Satellite-based methods can integrate the advantageous features of different satellite, but face challenging in practical applications due to spatiotemporal differences and cloud coverage. Model-based methods offer superior interpretability of physical processes, but the model parameters are difficult to obtain and cannot fully characterize the non-linear mapping relationship in real physical processes. Learning-based methods have the prominent ability to fit nonlinear relationship, while existing learning-based methods do not take the correlation and redundancy between various covariates into consideration, and the extraction of key features is insufficient. Hence, we proposed Self-learning Weight calibration based soil moisture Downscaling Network (SWDN) to couple the learning-based model with the weight calibration strategy for improving the products accuracy, as shown in Fig 1.

The proposed SWDN constructs a complex mapping relationship model from multi-factor geoscience parameters to soil moisture. Under this framework, the residual dense connection network is adopted as the backbone for feature extraction and guides the reconstruction of soil moisture spatial information. The spatial weight and multi-factor weight self-learning modules are designed to adaptively calibrate feature weights of spatial direction and multi-factors, respectively. By updating parameters of above two modules, the weights of key features are enhanced and the weights of redundant features are weakened to achieve efficient extraction of discriminative features. Subsequently, under the assumption of scale invariance, model from geoscience parameters to soil moisture is fully trained on low spatial resolution data and applied on high spatial resolution geoscience parameters to generate high-precision soil moisture products. Experiments on the Western Continental United States dataset show that the proposed SWDN method exhibits superior performance with higher consistency with in-situ measurements and richer spatial texture information over the comparison methods. Compared to the state of the art downscaling methods, results demonstrate that the R and RMSE reach 0.44 and 0.077 , which improve 16% and 5% respectively. The maps of soil moisture distribution before and after downscaling are shown in Fig 2.

Fig.1. The structure of the SWDN method for soil moisture downscaling.

Fig.2. Mapping of SM distribution before and after downscaling on 2017.7.17. (a) Original SMAP; (b) BPNN downscaled SM; (c) DBN downscaled SM; (d) RDN downscaled SM;(e) SWDN downscaled SM.

How to cite: Wei, Y., Lin, L., Li, J., Feng, R., and Li, H.: A Self-learning Weight Calibration Based Residual Dense Network for Soil Moisture Downscaling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9990, https://doi.org/10.5194/egusphere-egu24-9990, 2024.

A.62
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EGU24-13050
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HS6.1
Alan Farsad and Keith Bellingham

Soil moisture is a significant factor in Earth’s hydrological cycles that influences weather, drought, climate, and water resources on land and in water bodies. However, throughout most of the 20th century, soil moisture received less attention and was not included in many hydrological studies. In the 1978, J. W. Deardorff with the United States’ National Center for Atmospheric Research started to demonstrate the relationship between soil moisture and meteorologic conditions. Just two years later in 1980, G. C. Topp at the University of Toronto developed the Topp Equation - the first empirical calibration for soil moisture using time domain reflectometry (TDR). Additionally, that same year, M. T. van Genuchten published the van Genuchten Equation, which established a numerical relationship between soil moisture an unsaturated hydrologic head. Starting in the 1990s, the United States Department of Agriculture began using impedance-based soil moisture sensor technology to equip SNOTEL sites for water shed scale water supply forecasts. Since then, numerous large-scale regional meteorological networks incorporate soil moisture sensors, often referred to as ‘mesonets’, have emerged worldwide.

 

Soil water dynamics is complex often not well understood. Analytical methods using electromagnetic principles rely on the behavior and distribution electromagnetic energy in soil, making the operational theory of commercial sensors unclear at times. Soil moisture exhibits significant variabilities in space and time, as well as being influenced by hydrological and mineralogical properties of the soil. These factors give rise to several misconceptions about soil moisture monitoring.

 

This presentation discusses the growing importance of soil moisture as a critical parameter of the Earth’s hydrological cycle. This discussion also focuses on the objective and goals of North American soil moisture monitoring networks. Furthermore, the availability and emerging electromagnetic sensor technologies are reviewed. Lastly, calibration and validation soil sensors are also examined.

How to cite: Farsad, A. and Bellingham, K.: A Review of Regional and National Meteorological Networks offering soil moisture sensors and a review of the analytical methodology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13050, https://doi.org/10.5194/egusphere-egu24-13050, 2024.

A.63
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EGU24-18167
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HS6.1
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ECS
Cecile M.M. Kittel, Radoslaw M. Guzinski, and Mikkel H. Bojesen

At the interface of the surface energy balance and land surface hydrology, soil moisture is a key descriptor for hydrology, ecosystem dynamics and climate variables. Monitoring of soil moisture is of high value in multiple contexts, including water resources management through irrigation detection and quantification, and in land management and climate initiatives. In Denmark, drained organic low-lying soils represent 7% of agricultural land but are responsible for around 50% of the greenhouse gas emissions from agriculture. Soil moisture monitoring is key to prioritizing the decommissioning of relevant farmland. Soil moisture can be observed from space with radar or radiometer instruments; however, applications are often limited by the sensor penetration depth, which restricts the vertical spatial resolution to the top few centimeters of the soil. Spatial sampling is often in the order of kilometers, and too coarse for field-scale screening.

In this study, we propose to use the widely applied FAO-56 soil water balance model for crop evapotranspiration (ET) estimates and use it to estimate soil moisture in the root zone through reverse modelling. Using EO estimates of ET at high resolution derived from Sentinel-2 optical images, downscaled Sentinel-3 thermal images, and the TSEB (Two-Source Energy Balance) ET model, we derive a map of soil moisture in Denmark at 20 m daily resolution. Precipitation is obtained from ECMWF Era-5 and the OpenLand soil texture and characteristics dataset is used to parameterize the soil column. Landuse information at parcel scale from the Danish Agricultural Agency is used to estimate the root zone depth along with time series of Leaf Area Index. The approach is based on optical observations which have limited applicability in cloudy regions and winter months. We therefore apply gap filling using temporal interpolation of the ET time series or assumptions on soil moisture conditions to obtain the final decadal and monthly soil moisture time series.

The map is validated against probe measurements of soil moisture at 21 different sites across Denmark. Overall, the RMSE is around 5% m3/m3 and spatio-temporal patterns are well captured. The main limitations can be attributed to the soil parameterization as well as uncertainties in the coarser climate forcing. This study presents a unique dataset at national scale using publicly available datasets and combining Earth Observations with physical and conceptual modelling to obtain a key hydrological and biophysical parameter. The approach can be extended to most farm and grasslands and can be adjusted, where more precise local parameterization is available. The dataset as presented here, is used to inform a screening and management tool for the Danish Environmental Protection Agency to evaluate the impact of decommissioning low-lying farmlands, and to support research efforts in quantifying nitrogen dioxide emissions from poorly drained soils.

How to cite: Kittel, C. M. M., Guzinski, R. M., and Bojesen, M. H.: A 20-meter root-zone soil moisture dataset using Earth Observations and water balance modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18167, https://doi.org/10.5194/egusphere-egu24-18167, 2024.

A.64
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EGU24-5023
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HS6.1
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ECS
François Gibon, Arnaud Mialon, Philippe Richaume, Nemesio Rodriguez-Fernandez, Yann Kerr, Daniel Aberer, Nicolas Bader, Alexander Boresch, Raffaele Crapolicchio, Wouter Dorigo, Alexander Gruber, Irene Himmelbauer, Wolfgang Preimesberger, and Monika Tercjak

Evaluating the uncertainties of satellite soil moisture (as SMOS or SMAP) is crucial for enhancing our comprehension of climate mechanisms, such as the water cycle or the energy balance. The commonly used method is to evaluate the agreement between the satellite data and a reference, which are often ground measurements. However, the measurand in the present case is soil moisture at the satellite footprint scale, which means a much larger spatial and temporal scale than the in situ one. Various methods are employed to address these scale mismatches, such as multiple spatial sampling with in situ measurements within the satellite footprint (dense networks with strategic location installation) or the probes’ classification with representativeness indicators (based on triple collocation analysis, for example). Within ESA's Fiducial Reference Measurement for Soil Moisture project (FRM4SM), we propose to investigate the level of heterogeneity within the SMOS satellite footprint due to its influence on the complexity of the retrieval model and also its influence on the scale mismatch with the reference. To do so, various indices are developed to i) quantify the footprint heterogeneity in terms of the spatial distribution of hydro-geophysical parameters, and ii) analyse the impact on the retrieval quality. We present the analysis using indices of diversity of surface conditions (Shannon and Gini indices), and indices based on the level of similarities of hydro-geophysical conditions between the probes’ environment and the satellite footprint. Results show that even though the Shannon index is not significantly related to the soil moisture retrieval performances, the index based on the similarities of surface conditions better correlates with the retrieval performances.

How to cite: Gibon, F., Mialon, A., Richaume, P., Rodriguez-Fernandez, N., Kerr, Y., Aberer, D., Bader, N., Boresch, A., Crapolicchio, R., Dorigo, W., Gruber, A., Himmelbauer, I., Preimesberger, W., and Tercjak, M.: Assessment of SMOS soil moisture considering the heterogeneity of geophysical parameters within the footprint, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5023, https://doi.org/10.5194/egusphere-egu24-5023, 2024.

Posters virtual: Mon, 15 Apr, 14:00–15:45 | vHall A

The posters scheduled for virtual presentation are visible in Gather.Town. Attendees are asked to meet the authors during the scheduled attendance time for live video chats. If authors uploaded their presentation files, these files are also linked from the abstracts below, but only on the day of the poster session. The button to access Gather.Town appears just before the time block starts.
Display time: Mon, 15 Apr 08:30–Mon, 15 Apr 18:00
Chairpersons: Jian Peng, Alexander Gruber, Clément Albergel
vA.14
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EGU24-1160
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HS6.1
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ECS
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Arijit Chakraborty, Manabendra Saharia, Sumedha Chakma, Sujay V. Kumar, and Augusto Getirana

Soil moisture is a significant environmental factor that influences both the water and energy balance at the land-atmosphere interface. Therefore, proper assessment of the spatial and temporal distribution of soil moisture is crucial for many hydrological applications such as weather forecasting, agricultural water resource management and drought monitoring. This study involves the assimilation of Soil Moisture Active Passive (SMAP) soil moisture dataset within a land surface model and the evaluation of its performance in precise estimation of soil moisture by comparing the statistics with respect to standard European Space Agency’s Climate Change Initiative (ESA-CCI) soil moisture dataset. The Ensemble Kalman Filter technique has been used for assimilating SMAP soil moisture data using Noah-MP land surface model within NASA Land Information System (LIS) framework. The data assimilation (DA) framework includes Cumulative Distribution Function (CDF) matching for bias correction and twenty ensembles per tile. Meteorological forcings for the simulations have been taken from MERRA2 and IMD. Improvement or degradation due to DA has been analyzed in terms of the difference in anomaly correlation between open loop (OL) and DA soil moisture outputs with respect to the ESA-CCI soil moisture dataset over the entire Indian domain. The DA result shows improvement over larger areas in the case of MERRA2 forced simulations than IMD+MERRA2. The seasonal impact of DA in terms of the differences in DA and OL simulated soil moisture shows less variability in summer than winter. The results are validated with in-situ soil moisture datasets. Overall, the study shows that data assimilation is giving better results than open loop LSM simulation, which can be used for improved estimation of other water balance components.

How to cite: Chakraborty, A., Saharia, M., Chakma, S., Kumar, S. V., and Getirana, A.: Incorporating Data Assimilation into Land Surface Model simulation for better estimation of Surface Soil Moisture over India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1160, https://doi.org/10.5194/egusphere-egu24-1160, 2024.

vA.15
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EGU24-12005
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HS6.1
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ECS
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Jeenu John, Laxmi Sushama, and Shinto Roose

Continuous and long-term surface soil moisture (SSM) data is essential for advancing the understanding of land-atmospheric interactions and climate change studies. Despite the contributions of different satellite missions in acquiring SSM measurements, the presence of data gaps poses a significant challenge. In this study, a machine learning (ML) framework is developed to expand the Soil Moisture Ocean Salinity (SMOS) SSM observations in both spatial and temporal domains over Canada. In the initial phase of the proposed framework, ML models, including random forest (RF) and convolutional neural network (CNN), are trained and validated using SSM-relevant climatic and geophysical variables extracted from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5) for the 2011 to 2020 period and SMOS SSM for the same period as the target variable. While evaluating the developed models with unseen data from the years 2021 and 2022, the RF model shows slightly better performance when compared to that of CNN. The average root mean square error (RMSE) for RF is 0.0369 m3/m3 (Pearson correlation coefficient, R=0.94), while for CNN, the RMSE is 0.0494 m3/m3 (R= 0.89), with prediction biases mostly noted for regions with large inter-annual variability. Similarly, RF and CNN yield average RMSE values of 0.014 m3/m3 and 0.0635 m3/m3, respectively, when evaluated for spatial filling for the case of grid cells excluded during the training process. Hence, in the second phase of the proposed framework, the RF model is selected to extend the SMOS dataset for the 2008-2010 period. The temporal correlation analysis between the extended SMOS and Advanced Scatterometer (ASCAT) indicates reasonable correlations with values above 0.6, while the spatial correlation analysis reveals similar patterns between the two datasets, with smaller values for the summer season owing to the importance of local processes on SSM during this period. However, spatiotemporal extension of SSM to encompass surface types excluded during training remains a challenge and needs further studies. The developed framework holds the potential to address the spatio-temporal data gaps in other regions since both datasets are globally available.

How to cite: John, J., Sushama, L., and Roose, S.: A Machine Learning Framework for Extending SMOS Surface Soil Moisture Observations over Canada, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12005, https://doi.org/10.5194/egusphere-egu24-12005, 2024.

vA.16
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EGU24-14333
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HS6.1
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ECS
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Kirthana Somaskandan, Ravi prakash Kumar, and Balaji Devaraju

Soil moisture plays a predominant role in driving the hydrological cycle, being the initial terrestrial variable to interact with precipitation. Its inherent temporal and spatial variability leads to complexity in estimation. Numerous hydrological and climate models adopt a simplified approach by treating soil moisture as a constant parameter in certain regions. While this simplification is intended to streamline complexity, it often introduces inaccuracies and uncertainties into these models. Conventional methods of measuring soil moisture are point-based, requiring labor-intensive, time-consuming, and often destructive procedures. The Soil Moisture Active Passive (SMAP) satellite, designed for soil moisture estimation, operates with a temporal resolution of 3 days and fails to capture the occurrences of extreme events. This study tries to overcome those limitations by estimating soil moisture daily using GNSS-Reflectrometry mission CYGNSS, which has a temporal resolution of 7 hours. The primary observable is the surface reflectance which depends on the surface property of the ground. Ulaby developed a water cloud model to estimate soil moisture using surface reflectance irrespective of LULC. An extended water cloud model was proposed that includes the Leaf Area Index of the land cover in the Chambal sub-basin of Ganga Basin, India. Using SMAP as a reference, The extended water cloud model achieved a correlation of 76.16 % in barren land (RMSE of 0.014) which is higher than in vegetated land with a correlation of 74.42 %. 

How to cite: Somaskandan, K., Kumar, R. P., and Devaraju, B.: Soil Moisture estimation using GNSS- A spatiotemporal analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14333, https://doi.org/10.5194/egusphere-egu24-14333, 2024.

vA.17
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EGU24-18841
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HS6.1
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ECS
Ananya Sharma

Development of multi-sensor algorithm for enhancing the spatial and temporal resolution of Surface Soil Moisture
Ananya Sharma1, Manika Gupta1, Vikrant Maurya1, Juby Thomas1, Prashant K Srivastava2
1) Department of Geology, University of Delhi, Delhi, India
2) Institute of Environment and Sustainable Development, Banaras Hindu University, Banaras, India

Abstract

Surface soil moisture (SSM) is a crucial antecedent parameter for determination of various hydro-geomorphological conditions in the field of atmospheric and agricultural science. The available remotely sensed SSM datasets (AMSR-2, SMAP, SMOS) present with significantly degraded accuracy when compared to the in-situ measurements in heterogenous regions of India, as soil moisture retrievals through earth observation satellites are considerably sensitive to varying vegetation cover, biomass and surface roughness. A notable trade-off exists between the enhancement of spatial and temporal resolution. Advancements in methodological innovations must continually be sought to mitigate this trade-off, pushing the boundaries of what is achievable in both spatial and temporal dimensions. In the present study, we have utilized two distinct methodologies for the derivation of SSM product at a spatial resolution of 20 meters. The first approach involves the utilization of an enhanced Land Surface Temperature Product (LST) at a spatial resolution of 20 meters, in conjunction with Landsat-8 Normalized Difference Vegetation Index (NDVI) data to derive SM using the Soil Evaporative Efficiency Model. The second 
approach employs Sentinel-1 backscatter coefficients, specifically at VV polarization, coupled with MODIS Leaf Area Index (LAI). These datasets areintegrated within a modified water cloud model, facilitating the derivation of the SSM product. This methodology exploits the sensitivity of Sentinel-1 radar backscatter to surface moisture variations and complements this information with LAI, ensuring a robust characterization of soil moisture content. A single algorithm has been devised to harmoniously integrate the two approaches, thereby yielding the temporal resolution within the range of 2 to 5 days. In the algorithm, on instances where the data modeling from the former approach encounters limitations by virtue of the scarcity of input datasets, recourse is sought through the latter approach. Such a sequential approach ensures a comprehensive and adaptable analytical framework, allowing for an increased spatial as well as temporal resolution of SSM datasets.

How to cite: Sharma, A.: Development of multi-sensor algorithm for enhancing the spatial and temporal resolution of Surface Soil Moisture , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18841, https://doi.org/10.5194/egusphere-egu24-18841, 2024.

vA.18
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EGU24-4383
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HS6.1
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ECS
|
Alok Kumar Maurya and Amey Pathak

Potato belongs to the Solanaceae family, which is known for being highly susceptible to abiotic stress, particularly related to water. Understanding how and to what extent potato plants respond to different wavelengths of light is essential for gaining insights into soil water and plant water status, chlorophyll levels, and optimal timing for watering. Studying the spectral response of potato plants before and after irrigation, particularly focusing on agricultural management, resource optimization, and enhancing crop output. This study aimed to investigate the spectral characteristics of potato plants before and after irrigation, while plants are under varying soil-water conditions. This was achieved by implementing different irrigation schedules that maintained soil moisture levels at 25%, 50%, 65%, and 85% of the Maximum Allowable Depletion (MAD) of Available Soil Moisture (ASW). The plants' spectral responses were measured using a portable spectroradiometer. During the vegetative stage of the potato crop, plants treated with MAD50 and MAD25 experienced a slightly higher spectral reflectance in the green band spectrum before irrigation indicating healthy plants (Chlorophyll abundance). In general, MAD50-treated plants showed a higher spectral reflectance than plants treated with MAD25, MAD65, and MAD85 in the red-edge band (730-750nm) and NIR band spectrum. Furthermore, MAD85-treated plants exhibited higher spectral reflectance in all spectra than MAD65, MAD50, and MAD25-treated plants after irrigation. However, the red band (around 680nm) was almost saturated for all plants treated with MADs before and after irrigation, except MAD85-treated plants, after irrigation.  In addition, we have found the least variations in spectral reflectance of the MAD25 and MAD50-treated plants prior and post-irrigation. Whereas, MAD65 shows a spectral reflectance increase of +1.54-2.97% in the green band and +2.23-12.18% in the red-edge and NIR band after irrigation. Similarly, MAD85 exhibits a reflectance increase of +3.56-6.29% in the green band and +4.24-19.73% in the red-edge and NIR band after irrigation.  These findings highlight that optimum soil moisture is required for plants to be effective in MAD25 and MAD50 compared to the other delayed irrigation conditions. This research suggests an effective irrigation schedule to adapt in situations where adverse impacts of climate change, such as unpredictable water supply, water scarcity, and decreased irrigation expenses affects production. Assessing the baseline spectral response of crops before irrigation aids in detecting indications of water stress, while post-irrigation assessment helps determine whether the provided water has relieved stress and promoted robust plant development.

Keywords: Potato crop, Spectral response, Handheld Spectroradiometer, Water-stress, Irrigation

 

How to cite: Maurya, A. K. and Pathak, A.: Effects of Water Stress on Spectral Reflectance of Potato Crop Grown in Open Field Conditions , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4383, https://doi.org/10.5194/egusphere-egu24-4383, 2024.

vA.19
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EGU24-11751
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HS6.1
Juan Manuel Sánchez, Elisabet Walker, Álvaro Sánchez-Virosta, and Alfonso Calera

Soil moisture (SM) plays an important role in the interactions between the atmosphere and the land surface, and has been widely recognized as a key variable of the climate system. Over the last decades, several global satellite products have been generated to monitor SM at different spatial and temporal resolutions. To use these products it is important to validate them with in-situ observations. In this study, the performance of the Soil Water Index (SWI) and Surface Soil Moisture (SSM) Copernicus’s products and the Soil Moisture Active Passive (SMAP) SMAP L3_SM_P_E product was evaluated over an irrigated almond orchard located in the semiarid area of Tarazona de la Mancha, Spain (39.2660N, -1.9397W). The almond trees were planted in 2017 in a homogeneous field of about 10-ha. The Copernicus SSM and SWI products at 1-km spatial resolution provide daily SM images covering Europe since 2015. The SSM is retrieved from the Sentinel-1 radar backscattering and SWI combines Sentinel-1 and Metop ASCAT data. The Level-3 SMAP product provides SM data every 2-3 days retrieved by the SMAP radiometer. SMAP L3_SM_P_E has a spatial resolution of 9 km. Here, the moisture content in the topsoil (5 cm) estimated by the satellite products was evaluated against observed SM measurements for the 2019-2023 period. Even though the field sensor registers SM data at different depths (10-120 cm), the SM observations of the first 10 cm were used to analyze the remote sensing products. The accuracy of the products was defined using the following statistics; the determination coefficient (R2), the root mean square difference (RMSD), bias, and the unbiased root mean square difference (ubRMSD). The results obtained show that in general, the evaluated products capture the temporal variability of the SM measurements. For SSM and SMAP differences against in-situ data resulted in RMSD of about 4.34 vol% and 4.85 vol%, respectively. Also, SMAP overestimates the observed data with a considerable bias of 3.60 vol%. These deviations could be due to the coarse spatial resolution, however, it achieves the highest correlation (R2=0.64). SSM shows a good agreement with in-situ measurements, yielding the lowest bias (bias=0.10 vol%), but poorer R2 than the other evaluated datasets (R2=0.22). The SWI product (RMSD=3.79 vol%, ubRMSD=3.73 vol%, R2=0.36, and bias=0.66 vol%) performs the best compared to SMAP and SSM. These results obtained in almonds are comparable to validation results published for other regions and land covers. Therefore, it is possible to indicate that satellite SM data and, specifically the SWI product could benefit the local water resources management.

How to cite: Sánchez, J. M., Walker, E., Sánchez-Virosta, Á., and Calera, A.: Evaluation of Copernicus and SMAP soil moisture products in an almond orchard in southeastern Spain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11751, https://doi.org/10.5194/egusphere-egu24-11751, 2024.