Displays

The geosynchronous C-band SAR mission Hydroterra (initially called G-CLASS) is one of three candidate missions for ESA's upcoming Earth Explorer 10 programme (scheduled for launch in 2027-2028). While current available satellite-borne C-band radar instruments have a rather long re-visit time (ASCAT METOP A,B,C: daily, Sentinel-1 A,B: 3-6 days), the fact that the Hydroterra satellite would be in a geosynchronous orbit opens the possibility for a C-band radar dataset with much finer temporal resolution. The image-formation process and operations concept incorporated within the Hydroterra system however requires choices of spatial and temporal resolution of the final product.

The presented experiment is intended to highlight potential benefits associated with high temporal sampling of Hydroterra observations for the understanding of daily and sub-daily soil-moisture and vegetation processes. In order to generate a backscatter dataset that simulates observations at high temporal resolution, a parametric first-order radiative transfer model (RT1) [1] is first calibrated with incidence-angle dependent Sentinel-1 C-band backscatter data as well as auxiliary soil-moisture (SM) and leaf-area-index (LAI) timeseries provided by the SURFEX-ISBA [2] land-surface model over south-western France. Once the model-parameters are obtained, a simulated backscatter timeseries at high temporal resolution is generated by performing a forward-simulation using the retrieved model-parametrizations and auxiliary SM and LAI datasets at hourly intervals.

The simulated dataset is then used (in conjunction with the LAI dataset) to simulate a retrieval of SM under a set of possible observation conditions, e.g. varying soil- and vegetation properties (represented via the RT1 model parameters), different temporal resolutions (1,3,6,12 hourly), incidence-angles and noise-levels. In a final step, the obtained SM retrievals from the simulated dataset are used to assess the effects on rainfall estimates obtained via the SM2RAIN [3] algorithm.

The outcome of those simulations is intended to help quantifying the choices of spatial and temporal resolution for the Hydroterra mission concept from a soil properties applications point of view.

The work has been supported by the FFG-ASAP project "DWC-Radar" and the ESA project "Hydroterra (former G-CLASS) Phase-0 Science and Requirement".

References:

[1] Quast, R.; Albergel, C.; Calvet, J.-C.; Wagner, W. A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations. Remote Sens. 2019, 11, 285.

[2] Masson, V.; Le Moigne, P.; Martin, E.; Faroux, S.; Alias, A.; Alkama, R.; Belamari, S.; Barbu, A.; Boone, A.; Bouyssel, F.; et al. The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes. Geosci. Model Dev. 2013, 6, 929–960.

[3] Brocca, L., Massari, C., Ciabatta, L., Moramarco, T., Penna, D., Zucco, G., Pianezzola, L., Borga, M., Matgen, P., Martínez-Fernández, J. (2015). Rainfall estimation from in situ soil moisture observations at several sites in Europe: an evaluation of SM2RAIN algorithm. Journal of Hydrology and Hydromechanics, 63(3), 201-209, doi:10.1515/johh-2015-0016. .

How to cite: Quast, R., Wagner, W., Calvet, J.-C., Albergel, C., Bertrand, B., Brocca, L., Filippucci, P., and Hobbs, S.: Assessing prospects of sub-daily radar-observations to improve the understanding of soil- and vegetation dynamics., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10102, https://doi.org/10.5194/egusphere-egu2020-10102, 2020.

The validation of surface soil moisture products derived from SAR satellites data is challenged by the difficulty of reliably measuring in-situ soil moisture at shallow soil depths of a few centimetres, consistent with the penetration depth of the microwave beam. Our analysis shows that the apparent accuracy of the remote sensing products is underestimated by comparison with inconsistent probe data or measurements at greater soil depths. Our alternative approach uses in-situ meteorological measurements to determine rainfall and potential evapotranspiration, to be used with soil hydrological properties as inputs to a water balance model to estimate surface soil moisture independently of the satellite data. In-situ soil moisture measurements are used to validate and refine the model parameters. The choice of appropriate soil hydrological parameters with which to convert remotely sensed surface soil moisture indices to volumetric moisture content is shown to have a significant impact on the bias and offset in the regression analysis. To illustrate this, Copernicus SSM data is analysed by this method for a number of COSMOS-UK soil moisture monitoring sites, showing a significant improvement in the coefficient of determination, bias and offset over regression analysis using in-situ measurements from soil moisture probes or the cosmic ray soil moisture sensor itself. This will benefit users of such products in agriculture, for example, in determining actual soil moisture deficit.

How to cite: Beale, J., Waine, T., Corstanje, R., and Evans, J.: A performance assessment method for SAR satellite-derived surface soil moisture data using a soil-water balance model, meteorological observations, and soil pedotransfer functions., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3387, https://doi.org/10.5194/egusphere-egu2020-3387, 2020.

Historically, microwave radiometry has usually been used for retrieving estimates of soil moisture. As these measurements are also sensitive to vegetation, the attenuation of the microwave signal from vegetation, described by the vegetation optical depth (VOD) parameter can be used an analog of above-ground canopy biomass. This study explores the relative and joint utility of assimilating soil moisture and VOD retrievals from passive microwave radiometry within the NoahMP land surface model. The impact of assimilation on key water and carbon budget terms are quantified through comparisons against reference datasets. The results indicate that the assimilation of soil moisture retrievals has a positive impact on the simulation of surface soil moisture and little impact on evaporative fluxes. In contrast, VOD assimilation has significant impacts on the simulation of vegetation conditions, root zone soil moisture, and evapotranspiration (ET). Over water limited domains with sparse vegetation where soil moisture is the primary control on ET, the assimilation of surface soil moisture is more beneficial than VOD DA. In contrast, over regions with dense vegetation and where water availability is not limiting, transpiration has a significant influence on evapotranspiration. The assimilation of VOD is more beneficial in developing improvements in ET over such areas. The results of this study confirm that soil moisture and VOD retrievals provide independent information that can be jointly exploited through their simultaneous assimilation.

How to cite: Kumar, S., Holmes, T., bindlish, R., de Jeu, R., and Peters-Lidard, C.: Exploiting the information in soil moisture and vegetation optical depth retrievals from passive microwave radiometry. , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12143, https://doi.org/10.5194/egusphere-egu2020-12143, 2020.

Soil moisture plays a key role in land-atmosphere interactions through its influence on the energy and water cycles. Furthermore, its spatiotemporal variations can affect the development and persistence of extreme weather events. Consequently, soil moisture information is required for a wide range of research and applications, such as agricultural monitoring, flood and drought prediction, climate projection, and carbon-cycle modeling. Despite its scientific and societal importance, observations of soil moisture are sparse, in particular across time and at large spatial scales. Only models and satellite retrievals can provide global soil moisture information. While the ability of land surface models to represent the complex land-atmosphere interplay is still limited, satellite-based soil moisture data are a valuable alternative. However, these products suffer from a scaling based on models, and can only capture the top few centimeters of the soil. 

In this study, we aim to augment satellite-based soil moisture data using machine learning. For this purpose we integrate satellite soil moisture with multiple hydro-meteorological data streams to derive global gridded soil moisture using Long Short-Term Memory (LSTM) neural networks. These networks are trained using in-situ soil moisture measurements as target data. With the resulting self-learned relationships, the LSTMs can produce in-situ-like soil moisture globally. We further analyze the implications of using point-scale target data to infer large scale information. The new dataset is derived separately for the surface and the deeper soil, thereby extending beyond the range covered by the satellite-based products. The integration of many data streams and multiple soil moisture observations through a powerful synergistic technique offers the potential to yield high accuracy. This is tested through rigorous cross-validation of the derived dataset. Finally, the planned datasets will permit consistent long-term, large-scale analysis to enhance our understanding of the hydrology-biosphere-climate interplay, to better constrain models and to support hydrological hazards monitoring and climate projections.

How to cite: Orth, R. and Oh, S.: Augmenting satellite-derived soil moisture with multiple data streams using machine learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7428, https://doi.org/10.5194/egusphere-egu2020-7428, 2020.

Mapping near-surface soil moisture (θ) is of tremendous relevance for a broad range of environment-related disciplines and meteorological, ecological, hydrological and agricultural applications. Globally available products offer the opportunity to address θ in large-scale modelling with coarse spatial resolution such as at the landscape level. However, θ estimation at higher spatial resolution is of vital importance for many small-scale applications. Therefore, we focus our study on a small-scale catchment (MFC2) belonging to the “Alento” hydrological observatory, located in southern Italy (Campania Region). The goal of this study is to develop new machine-learning approaches to estimate high grid-resolution (about 17 m cell size) θ maps from mainly backscatter measurements retrieved from C-band Synthetic Aperture Radar (SAR) based on Sentinel-1 (S1) images and from gridded terrain attributes. Thus, a workflow comprising a total of 48 SAR-based θ patterns estimated for 24 satellite overpass dates (revisit time of 6 days) each with ascendant and descendent orbits will be presented. To enable for the mapping, SAR-based θ data was calibrated with in-situ measurements carried out with a portable device during eight measurement campaigns at time of satellite overpasses (four overpass days in total with each ascendant and descendent satellite overpasses per day in November 2018). After the calibration procedure, data validation was executed from November 10, 2018 till March 28, 2019 by using two stationary sensors monitoring θ at high-temporal (1-min recording time). The specific sensor locations reflected two contrasting field conditions, one bare soil plot (frequently kept clear, without disturbance of vegetation cover) and one non-bare soil plot (real-world condition). Point-scale ground observations of θ were compared to pixel-scale (17 m × 17 m), SAR-based θ estimated for those pixels corresponding to the specific positions of the stationary sensors. Mapping performance was estimated through the root mean squared error (RMSE). For a short-term time series of θ (Nov 2018) integrating 136 in situ, sensor-based θ (θ_insitu) and 74 gravimetric-based θ (θ_gravimetric) measurements during a total of eight S1 overpasses, mapping performance already proved to be satisfactory with RMSE=0.039 m³m^-³ and R²=0.92, respectively with RMSE=0.041 m³m^-³ and R²=0.91. First results further reveal that estimated satellite-based θ patterns respond to the evolution of rainfall. With our workflow developed and results, we intend to contribute to improved environmental risk assessment by assimilating the results into hydrological models (e.g., HydroGeoSphere), and to support future studies on combined ground-based and SAR-based θ retrieval for forested land (future missions operating at larger wavelengths e.g. NISARL-band, Biomass P-band sensors).

How to cite: Schönbrodt-Stitt, S., Nasta, P., Ahmadian, N., Kurtenbach, M., Conrad, C., Romano, N., Bogena, H., and Vereecken, H.: Exploring the use of machine-learning techniques to integrate ground- and remote sensing-based observations for efficient near-surface soil moisture mapping, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9092, https://doi.org/10.5194/egusphere-egu2020-9092, 2020.

Canadian Space Agency (CSA) has recently started receiving and processing the images from the recently launched C-band RADARSAT Constellation Mission (RCM). The backscatter and soil moisture retrievals products from the previously launched RADARSAT-2 agree well with both in-situ measurements and surface soil moisture modeled with land surface model Soil, Vegetation, and Snow (SVS). RCM will provide those products at an even better spatial coverage and temporal resolution. In preparation of the potential operational application of RCM products in Canadian Meteorological Center (CMC), this paper presents the scenarios of assimilating either soil moisture retrieval or outright backscatter signal in a 100-meter resolution version of the Canadian Land Data Assimilation System (CaLDAS) on field scale with time interval of three hours. The soil moisture retrieval map was synthesized by extrapolating the regression relationship between in-situ measurements and open loop model output based on soil texture lookup table. Based on this, the backscatter map was then generated with the surface roughness retrieved from RADARSAT-2 images using a modified Integral Equation Model (IEM) model. Bias correction was applied to the Ensemble Kalman filter (EnKF) to mitigate the impact of nonlinear errors introduced by multi-sourced perturbations. Initial results show that the assimilation of backscatter is as effective as assimilating soil moisture retrievals. Compared to open loop, both can improve the analysis of surface moisture, particularly in terms of reducing bias.  

How to cite: Sun, L., Belair, S., Carrera, M., and Bilodeau, B.: Comparing Assimilation of Soil Moisture and C-band Backscatter in High Resolution Land Surface Model , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10277, https://doi.org/10.5194/egusphere-egu2020-10277, 2020.

Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Microwave-based satellite remote sensing offers unique opportunities for the large-scale monitoring of soil moisture at frequent temporal intervals. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. Several downscaling techniques based on high-resolution remotely sensed data proxies have been proposed (1 km to 100 m). Although these techniques yield aesthetically pleasing maps, by neglecting how the water and energy fluxes physically interact with the landscape, these approaches often fail to provide soil moisture estimates that are hydrologically consistent.

This work introduces a state-of-the-art framework that combines a process-based hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution brightness temperature to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). We demonstrate this framework by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission and subsequently merging the HydroBlocks-RTM and the SMAP L3-enhanced brightness temperature at the HRU scale. This allows for hydrologically consistent SMAP-based soil moisture retrievals at an unprecedented 30-m spatial resolution over continental domains. 

We applied this framework to obtain 30-m SMAP-based soil moisture retrievals over the contiguous United States (2015-2018). When evaluated against sparse and dense in-situ soil moisture networks, the 30-m soil moisture retrievals showed substantial improvements in performance at field and watershed scales, outperforming both the SMAP L3-enhanced and the SMAP L4 soil moisture products. This work leads the way towards hydrologically consistent field-scale soil moisture retrievals and highlights the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications. 

How to cite: Vergopolan, N., Chaney, N. W., Beck, H. E., Pan, M., Sheffield, J., and Wood, E. F.: Hyper-resolution land surface modeling enables 30-m SMAP-based soil moisture at continental scales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12717, https://doi.org/10.5194/egusphere-egu2020-12717, 2020.

As part of the European Space Agency (ESA) Climate Change Initiative (CCI) a more than 40 year long climate data record (CDR) is produced by systematically combining Level-2 datasets from separate missions. Combining multiple level 2 datasets into a single consistent long-term product combines the advantages of individual missions and allows deriving a harmonised long-term record with optimal spatial and temporal coverage. The current version of ESA CCI Soil Moisture includes a PASSIVE (radiometer-based) dataset covering the period 1978 to 2019, an ACTIVE (scatterometer-based) product covering the period 1991-2019 and a COMBINED product (1978-2019). 

The European Commission’s Copernicus Climate Changes Service (C3S) uses the ESA CCI soil moisture algorithm to produce similar climate data records from near-real-time Level-2 data streams.  These products are continuously extended within 10 days after data acquisition and instantaneously made available through the C3S Climate Data Store. In addition to a daily product, monthly aggregates as well as a dekadal (10-days) products are produced.

In this presentation we give an overview of the latest developments of the ESA CCI and C3S Soil Moisture datasets, which include the integration of SMAP and various algorithmic updates, and use the datasets to assess the hydrological conditions of 2019 with respect to a 30-year historical baseline.

The development of the ESA CCI products has been supported by ESA’s Climate Change Initiative for Soil Moisture (Contract No. 4000104814/11/I-NB and 4000112226/14/I-NB). The Copernicus Climate Change Service (C3S) soil moisture product is funded by the Copernicus Climate Change Service implemented by ECMWF through C3S 312b Lot 7 Soil Moisture service.

How to cite: Dorigo, W., Preimesberger, W., Pasik, A., Gruber, A., Moesinger, L., and Scanlon, T.: ESA CCI and C3S Soil Moisture: latest product updates and climate assessments , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19198, https://doi.org/10.5194/egusphere-egu2020-19198, 2020.

We present our ongoing efforts to deliver surface soil moisture information at agricultural field scales using airborne or satellite synthetic aperture radar (SAR) data through the development and inversion of physical models for forward radar scattering from vegetation surfaces. While the past successful results were validated at 40-deg incidence angle for the Soil Moisture Active passive mission, the current work extends the incidence angle range from 30 to 50 degs so that the algorithm may apply to the future L-band NASA-ISRO SAR (NI-SAR) mission. NI-SAR aims at providing global soil moisture data at 200m resolution every 6 days.

The soil moisture retrievals were validated over agriculture sites in Canadian Prairies using L-band airborne SAR data, where the fields experienced entire crop growth stages and two cycles of wetting and drydowns. The forward models were developed over NI-SAR’s incidence angle range of 30 to 50 degs for individual crops.

The estimates are accurate to unbiased rmse of 0.053, 0.058 and 0.047 m3/m3 in volumetric water content for soybean, wheat, and pasture fields respectively over diverse conditions of vegetation growth and soil wetness. Surface roughness and vegetation amount were retrieved simultaneous to the soil moisture solutions. The roughness estimates are realistic.

There was no significant effect of the local incidence angle on the retrieval performance, most likely because the path length of the radar wave through the vegetation (and therefore extinction of the soil moisture signal) did not vary much with incidence angle. The results are encouraging for successful soil moisture mapping for the NI-SAR mission.

How to cite: Kim, S. and Liao, T.: Robust retrieval of surface soil moisture across wide-ranging incidence angles over short crops: for application to NI-SAR, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13213, https://doi.org/10.5194/egusphere-egu2020-13213, 2020.

Spire Global operates the world’s largest and rapidly growing constellation of CubeSats performing GNSS based science and Earth observation. The Spire constellation, performs a variety of GNSS science, including radio occultation (GNSS-RO), ionosphere and space weather measurements, and precise orbit determination. In December 2019, Spire launched two new satellites to perform GNSS reflectometry (GNSS-R). GNSS-R is a relatively new technique based on a passive bistatic radar system. The potential of space-borne GNSS-R observations for ocean and land applications has been demonstrated by other GNSS-R missions, including the NASA Cyclone Global Navigation Satellite System (CYGNSS) and the UK’s Technology Demonstration Satellite, TechDemoSat (TDS-1). 

We present initial results from these new Spire GNSS-R satellites that are primarily focused on retrieving soil moisture but also estimate other Earth surface properties such as ocean wind speeds and flood inundation/wetland mapping. Prior to the launch of Spire’s GNSS-R satellites and in preparation for Level-2 data production, we developed algorithms and processing chains for land applications. We will present Spire's Soil Moisture (SM) retrieval method using CYGNSS observations. We evaluated the implemented SM change detection algorithm by comparing the Spire’s daily SM product with NASA’s Soil Moisture Active Passive (SMAP) observations and in-situ SM measurements. The results of study indicate remarkable retrieval skills of the GNSS-R technique for soil moisture monitoring at a medium spatial resolution. Spire’s GNSS-R satellites are tuned for land applications with a series of hardware and software optimizations for better signal calibration and acquiring many more data per satellite compared to CYGNSS. A more robust GNSS-R SM retrieval at finer spatial resolution will be possible in the near future after having more Spire satellites in orbit.

Spire’s current and future GNSS-R satellites will provide unprecedented sub-daily global coverage with sub-kilometer spatial resolution. Such intensive data acquisition is of great importance for many land and ocean applications. 

How to cite: Freeman, V., Masters, D., Jales, P., Esterhuizen, S., Ebrahimi, E., Irisov, V., and Ben Khadhra, K.: Earth Surface Monitoring with Spire’s New GNSS Reflectometry (GNSS-R) CubeSats, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13766, https://doi.org/10.5194/egusphere-egu2020-13766, 2020.

Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling. 
Here we present the first long-term SWI dataset from ESA CCI Soil Moisture v04.5 COMBINED product, covering a 40-year period between 1978 and 2018. The ESA CCI dataset is unique because of its long-term global coverage based on merged observations from both active and passive sensors. The SWI is calculated for eight T-values (1, 5, 10, 15, 20, 40, 60, 100), where T-value is a temporal length ruling the infiltration; depending on the soil characteristics it translates into different soil depths.
Primary results show promise for pursuing development of an operational SWI product. Here, we present the results of SWI validation against data from the International Soil Moisture Network (ISMN) using the QA4SM framework, as well as results of the attempt to establish relationship between T-values and particular soil depths.

How to cite: Pasik, A., Bauer-Marschallinger, B., Preimesberger, W., Scanlon, T., Dorigo, W., and Hahn, S.: Towards long-term satellite root-zone soil moisture: 40-year Soil Water Index dataset from ESA CCI COMBINED Soil Moisture product., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15066, https://doi.org/10.5194/egusphere-egu2020-15066, 2020.

Abstract:

Surface soil moisture plays an important role in the exchange of water and energy between the land surface and the atmosphere, and critical to climate change study. The Tibetan Plateau (TP), known as “The third pole of the world” and “Asia’s water towers”, exerts huge influences on and sensitive to global climates. Long time series of and spatio-temporal continuum soil moisture is helpful to understand the role of TP in this situation. In this study, a dataset of 14-year (2002–2015) Spatio-temporal continuum remotely sensed soil moisture of the TP at 0.25° resolution is obtained, combining MODIS optical products and ESA (European Space Agency) ECV (Essential Climate Variable) combined soil moisture products based on General Regression Neural Network (GRNN). The validation of the dataset shows that the soil moisture is well reconstructed with R² larger than 0.65, and RMSE less than 0.08 cm³ cm^-3 and Bias less than 0.07 cm³ cm^-3 at 0.25° and 1° spatial scale, compared with the in-situ measurements in the central of TP. And then, spatial and temporal characteristics and trend of SM over TP were analyzed based on this dataset.

Keywords: Soil moisture; Remote Sensing; Dataset; GRNN; ECV; Tibetan Plateau

How to cite: Cui, Y., Zeng, C., Zhou, J., and Chen, X.: A Spatial and Temporal Continuum Remotely Sensed Soil Moisture Dataset of the Tibet Plateau From 2002 to 2015, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20846, https://doi.org/10.5194/egusphere-egu2020-20846, 2020.

The Quality Assurance for Soil Moisture (QA4SM) service is an online validation tool to evaluate and intercompare the performance of state-of-the-art open-access satellite soil moisture data records (https://qa4sm.eodc.eu). QA4SM implements routines to preprocess, intercompare, store and visualise validation results based on community best practices and requirements set by the Global Climate Observing System and the Committee on Earth Observation Satellite. The focus on traceability in terms of input data, software and validation results improves reproducibility and sets the basis for a community wide standard for future validation studies.

Within the validation framework a number of up-to-date soil moisture datasets are provided. Satellite data include multi-sensor records such as the European Space Agency’s Climate Change Initiative (ESA CCI) and the Copernicus Climate Changes Services (C3S) Soil Moisture datasets and single sensor products e.g. from SMAP, SMOS or Metop ASCAT. Reference data within the service include the full in-situ data archive of the the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) and land surface model/reanalysis products, e.g. from the European Centre for Medium-Range Weather Forecasts (ECMWF). General validation metrics between dataset pairs (such as correlation or RMSD amongst others) and triples (Triple Collocation) are part of the service. QA4SM allows users to select from a number of input parameters to specify temporal or spatial subsets of data to evaluate and provides options for data filtering, validation of anomalies and the use of different scaling methods.

Within this study we show the current status of the service, present its scope of operation and give an outlook on future developments such as the integration of high resolution data.

This work was supported by the QA4SM project, funded by the Austrian Space Applications Programme (FFG).

How to cite: Preimesberger, W., Scanlon, T., Baum, D., Bakcsa, Z., Boresch, A., and Dorigo, W.: QA4SM: Development of a traceable online satellite soil moisture validation system , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19099, https://doi.org/10.5194/egusphere-egu2020-19099, 2020.

ESA’s SMOS mission is celebrating 10 years of measurements in 2020 and is still producing soil moisture data of interest for many applications. One of the successes of this mission is its unexpected applications of soil moisture, such as thin ice sheets over the ocean, above ground biomass and carbon stocks, crop yields or rainfall estimation. We believe that knowledge of soil moisture time series contains information that are closely related to the functioning of the hydrosphere (infiltration, evaporation, groundwater recharge) and the biosphere (vegetation development, crop yield, carbon storage). These two compartments are traditionally studied using models forced by precipitation rates and atmospheric variables. However, beyond the difficulty of measuring the precipitation rate accurately from space, a non-negligible portion of rain does not infiltrate the soil either because it is intercepted by vegetation or because of the surface runoff.

In this study, we assume that SMOS retrieved soil moisture dynamics (0-5 cm) can inform us on much deeper soil horizons. Given that the water that reaches the root zone (0-200cm) and groundwater necessarily transits at some point through the surface, we can hypothesize that surface soil moisture dynamics intrinsically contains information on water dynamics in deeper layers.

To test this idea, we used Richards' 1D model and forced the first layer of the model with 5-cm in-situ soil moisture measurements from the AMMA-CATCH observatory sites in West-Africa. A variation of soil moisture at the surface generates moisture variations in the deeper layers according to the hydrodynamic parameters of the model: soil conductivity at saturation (Ks), shape parameters of the retention curve (α and m), soil porosity (θ_sat). For highly permeable soils, water rapidly infiltrates the soil column and creates a groundwater table with its seasonal dynamics. For more impermeable soils, water remains close to the surface and there is no groundwater recharge. This approach satisfyingly compares with in-situ measurements concerning both root zone soil moisture profiles and water table dynamics.

In a second step, the proposed methodology was applied to measurements derived from the SMOS satellite over the whole of Africa. To substitute in situ measurements, the GRACE satellite gravity data is used to compare with simulated soil water variations. This comparison allows to reject a lot of hydrodynamic parameters, and to select the best combination of the 4 parameters. Finally, the method makes it possible to produce maps of water table depths and their temporal dynamics at the scale of the African continent from information on surface soil moisture from SMOS (0-5cm) and soil water content from GRACE satellite.

How to cite: Pellarin, T., Oxarango, L., Cohard, J.-M., Depeyre, A., Hector, B., Kerr, Y., and Vandervaere, J.-P.: Groundwater dynamics retrievals in Africa using SMOS soil moisture measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21581, https://doi.org/10.5194/egusphere-egu2020-21581, 2020.

Numerous surface soil moisture (SSM) products are available from remote sensing, ranging different spatial and temporal resolutions. Varying techniques are employed to retrieve SSM and different spatial scales highlight different distributions. Notwithstanding this variety between the available data, all of them should be coherent with the recorded rainfall and irrigation.

In this work we have crossed recorded precipitations with a number of SSM products deriving from remote sensing: Soil Moisture Ocean Salinity (SMOS) mission, Soil Moisture Active Passive (SMAP) mission, European Space Agency Climate Change Initiative (ESA-CCI) products, Copernicus Global Land Operations product, a Neural Network SSM retrieval algorithm and AMSR-2 data.

All the dataset products have been compared with recorded precipitation from on-ground stations over two agricultural sites in Italy: one in the north, near Lake Garda (Chiese Irrigation Consortium) and the other in the south-east in the Apulia region (Capitanata Irrigation Consortium).

In both cases, a first SSM-rain comparison through well-established indexes (Pearson and Spearman correlations) has not yielded encouraging results.

Then, a methodology has been developed to determine whether the variation of SSM is consistent with the presence/absence of precipitation. An Agreement Index (AI) has been derived as a way to measure the coherency between SSM and precipitation. Any time a measure of SSM is available, a positive or negative value for the AI is recorded, according to the rainfall registered since the previous measurement. During the irrigation season (March through September), the presence of this artificial input of water into the system is also taken into account. For every year, the proportion between “coherent” SSM-rainfall pairings (positive AIs) and “non-coherent” pairings (negative AIs) has been computed.

This method is applied to all SSM products in the dataset, and results are compared. When aggregating the results for all the pixels within the irrigation consortia, all seem to align to a similar proportion between “coherent” and “non-coherent” SSM-rainfall pairings, notwithstanding the wide variety of data types, spatial resolutions and retrieval methods. However, even if the overall performances of the products are similar, each shows different spatial distributions, as each product is influenced differently by the physical features of the different areas.

How to cite: Corbari, C., paciolla, N., Al Bitar, A., Kerr, Y., and Mancini, M.: Irrigation and precipitation consistency with SMOS, SMAP, ESA-CCI, Copernicus, Neural Network SSM, AMSR-2 remotely sensed soil moisture , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4951, https://doi.org/10.5194/egusphere-egu2020-4951, 2020.

LDAS-Monde is a global Land Data Assimilation System developed in the research department of Météo-France (CNRM) to monitor Land Surface Variables (LSVs) at various scales, from regional to global. With LDAS-Monde, it is possible to assimilate satellite derived observations of Surface Soil Moisture (SSM) and Leaf Area Index (LAI) e.g. from the Copernicus Global Land Service (CGLS). It is an offline system normally driven by atmospheric reanalyses such as ECMWF ERA5.

In this study we investigate LDAS-Monde ability to use atmospheric forecasts to predict LSV states up to weeks in advance. In addition to the accuracy of the forecast predictions, the impact of the initialization on the LSVs forecast is addressed. To perform this study, LDAS-Monde is forced by a fifteen-day forecast from ECMWF for the 2017-2018 period over the Contiguous United States (CONUS) at 0.2^o x 0.2^o spatial resolution. These LSVs forecasts are initialized either by the model alone (LDAS-Monde open-loop, no assimilation, Fc_ol) or by the analysis (assimilation of SSM and LAI, Fc_an). These two sets of forecast are then assessed using satellite derived observations of SSM and LAI, evapotranspiration estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and evapotranspiration), LDAS-Monde provides reasonably accurate predictions two weeks in advance. Additionally, the initial conditions are shown to make a positive impact with respect to LAI, evapotranspiration, and deeper layers of soil moisture when using Fc_an. Moreover, this impact persists in time, particularly for vegetation related variables. Other model variables (such as runoff and drainage) are also affected by the initial conditions. Future work will focus on the transfer of this predictive information from a research to stakeholder tool.

How to cite: Mucia, A., Albergel, C., Bonan, B., Zheng, Y., and Calvet, J.-C.: From Monitoring to Forecasting the Land Surface Condition Using a Land Data Assimilation System: Application over the Contiguous United States, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4460, https://doi.org/10.5194/egusphere-egu2020-4460, 2020.

Flash drought is characterized by a rapid onset at subseasonal time scale and enormous impact on society and economics. However, only few extreme case studies assessed the impact of flash drought on vegetation, without specific definition to identify the rapidly intensification stage of flash drought. Here, we use soil moisture to identity flash drought events at in-situ and regional scales, and detect the response of vegetation photosynthetic function using eddy covariance and satellite observations of carbon fluxes and sun-induced chlorophyll fluorescence (SIF). Different vegetation types show high sensitivity to flash drought especially for savanna and grassland, and the lag time between flash drought and ecological response is usually 8-16 days. The resistance of woody plants can be attributed to the positive anomalies of inherent water use efficiency during flash drought. Vegetation over semi-arid and semi-humid is also vulnerable to flash drought. The quick response of vegetation to flash drought is a new challenge for drought monitoring.

How to cite: Zhang, M. and Yuan, X.: Sensitivity of carbon fluxes to flash drought based on long-term FLUXNET and satellite observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6319, https://doi.org/10.5194/egusphere-egu2020-6319, 2020.

FY-3(Feng Yun 3) satellites series are the China’s second-generation polar-orbiting meteorological satellites. FY-3B is the second satellite of FY3 series which was launched on November 5, 2010. One of the eleven instruments on board the FY-3B satellite is the Microwave Radiation Imager (MWRI) which is a highly sensitive microwave radiometer. It is China’s first space-borne microwave radiometer. It has 5 different frequencies from 10.65GHz to 89GHz with dual polarization. The MWRI instrument provides measurements of terrestrial, oceanic, and atmospheric parameters, including precipitation rate, sea ice concentration, snow water equivalent, soil moisture, atmospheric cloud water, and water vapor. Soil moisture, as a key parameter in the drought monitoring, becomes especially concerned. The FY-3B/MWRI soil moisture product provides global observations of land surface soil moisture. The current soil moisture retrieval algorithm of FY-3B/MWRI uses the brightness temperature with both v and h polarizations of 10.65GHz to eliminate the effects of surface roughness and vegetation simultaneously. For the bare surface soil estimation part, the algorithm is based on a parameterized surface emission model (the Qp model) which uses a physically based soil moisture inversion technique for application with passive microwave measurements. For the vegetation correction part, the algorithm uses the empirical relationship between the NDVI and the vegetation water content to estimate the vegetation optical depth. The spatial resolution of FY-3B/MWRI soil moisture product is 0.25°×0.25°. In recent years, drought occurs frequently worldwide. As the only microwave sensor which operationally provides global soil moisture products currently in china, the FY-3B/MWRI soil moisture product plays an important part in drought monitoring during the meteorological service. In the summer of 2014, Henan Province which is located in the middle area of China suffered severe drought. The soil moisture of this area remained a very low level all along until significant precipitation finally came in last September. In the year of 2018, there was a severe drought occurred in Afghan, we used a long-time data series to analyze this drought event. The result showed that the FY-3B/MWRI soil moisture can objectively reflect the spatial distribution and development process of drought. This paper will give an introduction of the applications of FY-3B/MWRI soil moisture product during these drought event.

How to cite: Sun, R., Zhang, Y., and Wu, S.: The application of FengYun-3 Microwave Radiation Imager soil moisture product in drought monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13688, https://doi.org/10.5194/egusphere-egu2020-13688, 2020.

The water-limited region frequently experiences extreme climate variability.  This region, however, has relatively little hydrological information to characterize the catchment dynamics and its feedback to the climate system. This study assesses the relative benefits of using remotely sensed soil moisture, in addition to sparsely available in-situ soil moisture and stream flow observations, to improve the hydrologic understanding and prediction.  We propose a multi-variable approach to calibrate a hydrologic model, Soil and Water Assessment Tool (SWAT), a semi-distributed, continuous catchment model, with observed streamflow and in-situ soil moisture.  The satellite soil moisture products (~ 5 cm top soil) from the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) are then used to evaluate the model estimates of soil moisture over the spatial scales through time.  The results show the model calibrated against streamflow only could provide misleading prediction for soil moisture.  Long term in-situ soil moisture observations, albeit limited availability, are crucial to constrain model parameters leading to improved soil moisture prediction at the given site.  Satellite soil moisture products provide useful information to assess simulated soil moisture results across the spatial domains, filling the gap on the soil moisture information at landscape scales. The preliminary results from this study suggest the potential to produce robust soil moisture and streamflow estimates across scales for a semi-arid region, using a distributed catchment model with in-situ soil network and remotely sensed observations and enhance the overall water budget estimations for multiple hydrologic variables across scales.  This research is conducted on Merriwa catchment, a semi-arid region located in the Upper Hunter Region of NSW, Australia.

How to cite: Yeo, I.-Y., Binesh, A., Willgoose, G., Hancock, G., and Yeteman, O.: From hillslope to catchment scale hydrologic prediction in a semi-arid region with in-situ observations, satellite soil moisture products, and a distributed catchment model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21765, https://doi.org/10.5194/egusphere-egu2020-21765, 2020.

Rainfall-induced shallow landslides are very dangerous phenomena, widespread all over the world, which could provoke significant damages to buildings, roads, facilities, cultivations and, sometimes, loss of human lives. For these reasons, it is necessary assessing the most prone zones in a territory which is particularly susceptible to these phenomena and the frequency of the triggering events, according to the return time of them, which generally correspond to intense and concentrated rainfalls. The most adopted methodologies for the determination of the susceptibility and hazard of a territory are physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall scenarios. Whereas, these methodologies could be applied in a reliable way in little catchments, where geotechnical and hydrological features of the materials affected by shallow failures are homogeneous. Data-driven models could constraints these, even if they are generally built up taking into only the predisposing factors of shallow instabilities, allowing to estimate only the susceptibility of a territory, without considering the frequency of the triggering events. It is then required to consider also triggering factors of shallow landslides to allow these methods to estimate also the probability of occurrence and, then, the hazard. This work presents the development and the implementation of data-driven model able to assses the spatio-temporal probability of occurrence of shallow landslides in large areas by means of a data-driven technique. The model is based on Multivariate Adaptive Regression Technique (MARS), that links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to soil saturation degree and rainfall amounts, which are available for entire a study area thanks to satellite measures. The methodological approach is testing in 30-40 km² wide catchments of Oltrepò Pavese hilly area (northern Italy), where detailed inventories of shallow landslides occurred during past triggering events and corresponding satellite soil moisture and rainfall maps are available. This work was made in the frame of the ANDROMEDA project, funded by Fondazione Cariplo.

How to cite: Vivaldi, V., Bordoni, M., Lucchelli, L., Corradini, B., Brocca, L., Ciabatta, L., and Meisina, C.: A data-drive model for the assessment of shallow landslides hazard with the integration of satellite soil moisture and rainfall data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22240, https://doi.org/10.5194/egusphere-egu2020-22240, 2020.

The soil is a heterogonous substance consists of three phases: solid, gas and liquid, where the latter is mainly water – the natural solvent with very high heat capacity. Due to this physical property and the fact that water is a common substance on our planet, it has a significant impact for stability of the climate on Earth. Another water property, the dielectric constant much higher than in other soil ingredients, is often used to determine soil water content. As an example, the Time Domain Reflectometry (TDR) technique for in situ soil moisture measurements may be mentioned. For soil moisture assessments at global scale, the satellite-based instruments were designed and launched into space, e.g. Soil Moisture and Ocean Salinity (SMOS) or Soil Moisture Active Passive (SMAP). Those satellites are measuring brightness temperature of soil in microwave (L-band) domain. The algorithms that retrieve soil moisture from L-band measurements by nonlinear optimisation engage several parameters such as soil temperature, its roughness and vegetation cover. In the presented work, we introduce a much simpler method that base on three facts: i) a high water heat capacity cause that, during the diurnal night/day cycle, the soil with higher water content cools down and heats up slower than dry soil. This phenomenon was quantified by thermal inertia; ii) brightness temperature is related to the effective temperature of the surface and iii) plants are generally semi-transparent for L-band microwaves, what gives a possibility for probing soil properties underneath vegetation. Due to iii) we assumed that L-band soil albedo (needed in thermal inertia computations) is constant. The proposed approach seems to be reasonable, as both variables, brightness temperature and thermal inertia, strongly depend on soil water content. The method was evaluated using ELBARA (European Space Agency L-band Radiometer) instrument operating at Bubnow test site in Poland. The ELBARA is a directional receiver at 1.4 GHz frequency (the same as received by SMOS satellite), installed on the Earth’s surface, at 6-meter tower. In the years 2016-2019, we conducted 16 field campaigns – we measured surface soil moisture in situ using TDR, and interpolate it to semi-continuous grid using geostatistics. Then, the driest and the wettest points (in space and time) were chosen and assigned to, respectively, maximum and minimum thermal inertia. Basing on that, the model retrieving soil moisture was built, and the other measurements served as validation assembly. Simple regression methods revealed good or moderately good agreement between modelled and measured data. Some outliers, probably induced by meteorological phenomena disturbing stable soil cooling and heating such as rain or wind, have been noticed.

Research was partially conducted under the project “Water in soil - satellite monitoring and improving the retention using biochar” no. BIOSTRATEG3/345940/7/NCBR/2017 which was financed by Polish National Centre for Research and Development in the framework of “Environment, agriculture and forestry” – BIOSTRATEG strategic R&D programme.

How to cite: Lukowski, M., Gluba, L., Rafalska-Przysucha, A., Szewczak, K., and Usowicz, B.: A simple method for soil moisture calculation using data from ELBARA III passive radiometer and thermal inertia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4656, https://doi.org/10.5194/egusphere-egu2020-4656, 2020.

Soil moisture is a fundamental variable in the water and energy cycle and its knowledge in many applications is crucial. In the last decade, some authors have proposed the use of satellite soil moisture for estimating and improving rainfall, doing hydrology backward. From this research idea, several studies have been published and currently preoperational satellite rainfall products exploiting satellite soil moisture products have been made available.

The assessment of such products on a global scale has revealed an important result, i.e., the soil moisture based products perform better than state of the art products exactly over regions in which the data are needed: Africa and South America. However, over these areas the assessment against rain gauge observations is problematic and independent approaches are needed to assess the quality of such products and their potential benefit in hydrological applications. On this basis, the use of the satellite rainfall products as input into rainfall-runoff models, and their indirect assessment through river discharge observations is an alternative and valuable approach for evaluating their quality.

For this study, a newly developed large scale dataset of river discharge observations over 500+ basins throughout Africa has been exploited. Based on such unique dataset, a large scale assessment of multiple near real time satellite rainfall products has been performed: (1) the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement), IMERG Early Run, (2) SM2RAIN-ASCAT (https://doi.org/10.5281/zenodo.3405563), and (3) GPM+SM2RAIN (http://doi.org/10.5281/zenodo.3345323). Additionally, gauge-based and reanalysis rainfall products have been considered, i.e., (4) the Global Precipitation Climatology Centre (GPCC), and (5) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. As rainfall-runoff model, the semi-distributed MISDc (Modello Idrologico Semi-Distribuito in continuo) model has been employed in the period 2007-2018 at daily temporal scale.

First results over a part of the dataset reveal the great value of satellite soil moisture products in improving satellite rainfall estimates for river flow prediction in Africa. Such results highlight the need to exploit such products for operational systems in Africa addressed to the mitigation of the flood risk and water resources management.

How to cite: Brocca, L., Camici, S., Massari, C., Ciabatta, L., Filippucci, P., Villarini, G., and Tramblay, Y.: Satellite soil moisture improves rainfall just where needed, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5303, https://doi.org/10.5194/egusphere-egu2020-5303, 2020.

Hydrological drought is increasing due to the collaborative influence of climate change and human activities, especially in populated river basins. Despite drought monitoring skills are improved over the last decade for the development of satellite technology and global measuring networks, there are still challenges for an accurate simulation and prediction of hydrological drought in small spatial scale. In this study, in order to improve small scale drought monitoring, soil moisture datasets with different spatial scales, including multi-satellite-retrieved soil moisture dataset released by the Europe Space Agency’s Change Initiative (ESA CCI) with a spatial resolution of 0.25° and in-situ soil moisture dataset measured in dots, are considered to assimilate into the 2-km Digital Elevation Model (DEM) based distributed rainfall-runoff model (DDRM). The 2-km soil moisture simulations coupled with outlet streamflow simulations are used to identify hydrological drought in the Yangtze River basin. Three assimilation scenarios, including (i) only assimilating satellite soil moisture; (ii) jointly assimilating satellite and in-situ soil moisture; (iii) correcting satellite soil moisture by in-situ data firstly, and assimilating the corrected satellite soil moisture into the model, are developed to identify the influence of different scenarios on drought monitoring. Results indicate that all assimilation scenarios significantly improve 2-km soil moisture drought monitoring, and slightly improve streamflow drought monitoring. The scenario of assimilating corrected satellite soil moisture dataset into the model has the best performance, and the scenario of only assimilating satellite data has the worst. This study recommends a valuable assimilation scenario of the distributed hydrological model for better improving drought monitoring in a small spatial scale.

How to cite: Yang, H., Xiong, L., and Xu, C.-Y.: Improving 2-km drought monitoring by assimilating satellite and in-situ soil moisture into a distributed hydrological model in the Yangtze River basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2908, https://doi.org/10.5194/egusphere-egu2020-2908, 2020.

In order to enhance our understanding of the hydrologic cycle, frequent, reliable and detailed information on precipitation are fundamental. In-situ measurements are the traditional source of this information, but they have limited spatial representativeness and the number of stations worldwide is declining and their access is often troublesome. Satellite products are able to overcome these issues and actually are the main, if not the only, source of information over many areas of the world. Notwithstanding this, the spatial resolution is still limited to tens or hundreds of kilometers, limiting their usefulness for hydrological applications. In the recent decade, a new approach for estimating rainfall from satellite-derived soil moisture observations has been proposed, named SM2RAIN (Brocca et al., 2014) and based on the inversion of the soil water balance equation. The application of SM2RAIN to Sentinel-1 satellites carrying a C-band Synthetic Aperture Radar (CSAR) sensor can provide rainfall data at unprecedented spatial and temporal resolution.

In this study, we combined the soil moisture data retrieved from backscatter observations of Sentinel-1 (1.5/4 days temporal frequency over Europe, 500 m sampling) with the soil moisture data obtained from ASCAT sensor, onboard of METOP satellites (8-24 h temporal frequency, 12.5 km sampling) through a data fusion algorithm. The result is an innovative soil moisture dataset with a temporal resolution of 1 day and a spatial resolution of 1 km (Bauer-Marschallinger et al., 2018). These data are used as input for SM2RAIN, obtaining as output a rainfall product with temporal and spatial sampling of 1 day and 1 km, respectively.

The approach was applied over test regions in Italy and Austria obtaining promising results. Specifically, the comparison with high density observations from raingauges and meteorological radars has allowed the assessment of the method at high spatial resolution and varying temporal resolution. Results show that good quality rainfall estimates at 1 km of spatial resolution can be obtained in reproducing 3- to 5-day rainfall accumulations. Further testing will be carried out in the next months and presented at the conference.

Acknowlodgments

The activity is funded by DWC radar project, Austrian Space Applications Programme, FFG Project 873658.

Reference

Bauer-Marschallinger, B., Paulik, C., Mistelbauer, T., Hochstöger, S., Modanesi, S., Ciabatta, L., Massari, C., Brocca, L. & Wagner, W. (2018). Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering. Remote Sensing, 10(7), 1030. doi:10.3390/rs10071030

Brocca L., Ciabatta L., Massari C., Moramarco T., Hahn S., Hasenauer S., Kidd R., Dorigo W., Wagner W., Levizzani V. – “Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data”. J. Geophys. Res. Atmos. vol. 119, pp. 5128–5141, 2014. doi: 10.1002/2014JD021489

How to cite: Filippucci, P., Brocca, L., Tarpanelli, A., Massari, C., Ciabatta, L., Wagner, W., Bauer-Marschallinger, B., and Saltalippi, C.: Remote sensing of rainfall at high spatial-temporal resolution through soil moisture , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20275, https://doi.org/10.5194/egusphere-egu2020-20275, 2020.

This study focuses on the estimation of soil moisture deficit from root zone soil moisture information derived from remotely sensed passive microwave surface soil moisture data for a period of fifteen years (2002 to 2016) for the Indo-Gangetic basin. The remote sensing datasets used to estimate soil moisture deficit are Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Advanced Microwave Scanning Radiometer - 2 (AMSR-2) by JAXA and NASA. As India is an agrarian country, it is one of the largest producers of sugarcane at the global level and hence, this is the test crop considered for this work. The Indo-Gangetic basin has numerous culturable command areas with dynamic meteorological patterns, soil type, land use and land cover, agricultural practices, water and crop management with different sources of irrigation. Rain-fed irrigation is the primary source of water for crop production in this basin. Sugarcane crop is characterised by specific root depth, crop water requirement, crop length and crop phenology. In India, meteorological parameters primarily, precipitation, temperature and evapotranspiration and the meteorological seasons define the agricultural season (irrigation to harvesting). Here, an interrelationship between soil moisture deficit (at varying depth) and meteorological parameters, precipitation based meteorological indices (Rainfall Anomaly Index, Standardized Precipitation Index and Effective Drought Index), ground-based crop indices (crop yield index, crop area index and crop production index) is analysed at the annual and seasonal scale. The study indicates the paramount effect of the aforementioned factors on soil moisture deficit variable. The temporal variation of soil moisture deficit being served as a proxy for crop water requirement and the model developed from the same provides vital information for an efficient irrigation scheduling, sustainable water resource management for increased crop production and developing crop insurance schemes and policies at the basin level.

How to cite: Sure, A. and Dikshit, O.: Spatiotemporal Evaluation of Remote Sensing Derived Soil Moisture Deficit for the Sugarcane Crop: A Case Study for the Indo-Gangetic Basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1411, https://doi.org/10.5194/egusphere-egu2020-1411, 2020.

Soil moisture plays a key role in land surface processes. Both remote sensing and model simulation have their respective limitations in the estimation of soil moisture on a large spatial scale. Data assimilation is a promising way to merge remote sensing observation and land surface model (LSM), thus having a potential to acquire more accurate soil moisture. Two mainstream assimilation algorithms (variational-based and sequential-based) both need model and observation uncertainties due to their great impact on assimilation results. Besides, as far as land surface models are concerned, model parameters have a significant implication for simulation. However, how to specify these two uncertainties and parameters has been confusing for a long time. A dual-cycle assimilation algorithm, which consists of two cycles, is proposed for addressing the above issue. In the outer cycle, a cost function is constructed and minimized to estimate model parameters and uncertainties in both model and observation. In the inner cycle, a sequentially based filtering method is implemented to estimate soil moisture with the parameters and uncertainties estimated in the outer cycle. For the illustration of the effectiveness of the proposed algorithm, the Advanced Microwave Scanning Radiometer of earth Observing System (AMSR-E) brightness temperatures are assimilated into land surface model with a radiative transfer model as the observation operator in three experimental fields, including Naqu and Ngari on the Tibetan Plateau, and Coordinate Enhanced Observing (CEOP) reference site on Mongolia. The results indicate that the assimilation algorithm can significantly improve soil moisture estimation.

How to cite: Tian, J., Qin, J., and Yang, K.: Improving soil moisture estimation through a dual-cycle assimilation strategy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6342, https://doi.org/10.5194/egusphere-egu2020-6342, 2020.

Estimates of water volumes stored in the root-zone of vegetation are a key element controlling the hydrological response of a catchment. Remotely-sensed soil moisture products are available globally. However, they are representative of the upper-most few centimeters of the soil. For reliable runoff predictions, we are interested in root-zone soil moisture estimates as they regulate the partitioning of precipitation to drainage and evaporation. The Soil Water Index approximates root-zone soil moisture from near-surface soil moisture and requires a single parameter representing the characteristic time length T of temporal soil moisture variability. Climate and soil properties are typically assumed to influence estimates of T, however, no clear quantitative link has yet been established and often a standard value of 20 days is assumed. In this study, we hypothesize that optimal T values are linked to the accumulated difference between precipitation (water supply) and evaporation (atmospheric water demand) during dry periods with return periods of 20 years, and, thus, to catchment-scale vegetation-accessible water storage capacities. We identify the optimal values of T that provide an adequate match between estimated SWI from several satellite-based near-surface soil moisture products (derived from AMSR2, SMAP and Sentinel-1) and modeled time series of root-zone soil moisture from a calibrated process-based model in 16 contrasting catchments of the Meuse river basin. We found that optimal values of T vary between 1 and 98 days with a median of 17 days across the studied catchments and soil moisture products. We furthermore show that T, which was previously known to increase with increasing depth of the soil layer, is positively and strongly related with catchment-scale root-zone water storage capacity, estimated based on long-term water balance data.  This is useful to generate estimates of root-zone soil moisture from satellite-based surface soil moisture, as they are a key control of the response of hydrological systems.

How to cite: Bouaziz, L., Steele-Dunne, S., Schellekens, J., Weerts, A., Stam, J., Sprokkereef, E., Winsemius, H., Savenije, H., and Hrachowitz, M.: Catchment-scale connection between vegetation accessible storage and satellite-derived Soil Water Index, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6917, https://doi.org/10.5194/egusphere-egu2020-6917, 2020.

Soil moisture drought is a natural, reoccurring phenomenon that can affect any part of the land. It consists one of the most challenging problems for the modern agriculture as it directly affects the water, energy and food security nexus. Remote sensed soil moisture products have been proved to be valuable tools for the study of the soil moisture droughts. The European Space Agency (ESA), through the Climate Change Initiative (CCI) is currently providing nearly 4 decades of global satellite observed, fully homogenized soil moisture (SM) data for the uppermost soil layer. This data is valuable as it consists one of the most complete in time and space observed soil moisture dataset available. One of the main limitations that ESA CCI SM exhibits is the limited depth at which the soil moisture is estimated (limited to approximately 5cm of soil). In this work we use the ESA CCI SM data to estimate the Soil Water Index (SWI) at the global scale, which can serve as a soil moisture approximation for different depths. The SWI is a simple index that simulates the infiltration process. It utilizes an infiltration parameter T, which is related to the hydraulic characteristics. In this work, the T parameter is calibrated and validated at point scale based on soil moisture measurements of the International Soil Moisture Network (ISMN) and the FluxNet2015 (Tier 1) datasets. The regionalization of the T parameter at global scale is performed by linking T to physical soil descriptors using multilinear regression. Physical soil descriptors were obtained from the Soil Grids 250m dataset, i.e. bulk density, sand/silt/clay fractions, soil organic carbon and coarse fragments. The result of this operation is an SWI dataset for a series of different depths between 0 and 1m. This dataset can be used for the systematic evaluation of global hydrological models on their ability to simulate the soil water.

How to cite: Grillakis, M. G., Koutroulis, A. G., Polykretis, C., and Alexakis, D. D.: Estimating soil moisture at various depths from near surface ESA CCI Soil Moisture, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8613, https://doi.org/10.5194/egusphere-egu2020-8613, 2020.

The rate at which land surface soils are drying following rain events is an important feature of terrestrial models since it determines, for example, the water availability for vegetation, the occurrences of droughts, and the surface heat exchanges. As such, soil moisture (SM) “drydowns”, i.e. the SM temporal dynamic following a significant rainfall event, are of particular interest when evaluating and calibrating land-surface models. By investigating drydowns, characterized by an exponential decay time scale metric τ, we aim to improve the representation of soil moisture in the ORCHIDEE global land-surface model. In this presentation, we consider τ calculated over a number of ISMN (International Soil Moisture Network) sites found within the footprint of FLUXNET towers. These in-situ sites cover a range of vegetation types and climates. Using the ORCHIDEE land-surface model, we first compare τ from the modelled SM timeseries to the same values computed from the in-situ SM measurements. We then assess the potential of using τ as a data assimilation metric to constrain some parameters of the ORCHIDEE model through a standard Bayesian optimisation procedure; we first select a number of key of water, carbon, and energy parameters through a sensitivity analysis. The optimised soil moisture timeseries are evaluated using the FLUXNET evapotranspiration and GPP data. We conclude by considering the potential of  global satellite products like SMOS or the ESA-CCI surface SM satellite data in order to scale up the experiment to a global scale optimisation.

How to cite: Raoult, N., Ottle, C., Peylin, P., and Bastrikov, V.: Characterising and assimilating surface soil moisture drydowns in the ORCHIDEE land-surface model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9448, https://doi.org/10.5194/egusphere-egu2020-9448, 2020.

The present project presents the technical implementation, testing and validation of a soil moisture retrieval algorithm in Python using C-band Sentinel-1 data at high incidence angle (∼42°). The retrieval algorithm is based on the alpha approximation, first developed by [Balenzano et al. 2011]. The alpha approximation utilizes the dense temporal coverage of the Sentinel-1 mission, assuming that changes in backscatter between subsequent acquisitions are only due to variations in soil moisture, such that vegetation and roughness can be neglected. The area used for testing the algorithm was chosen to be the region surrounding the Foulum test center for agricultural studies in Denmark, due to the availability of time series from 2018 of in situ soil moisture measurements to be used for validation. Masking of too densely vegetated areas have been performed using the cross-polarized component of the SAR backscatter, which have been validated using NDVI maps. 

Auxiliary data, including land cover maps and parcel borders enable the computation of backscatter field means, significantly reducing the impact of speckle noise and thus decreasing uncertainty of the estimated soil moisture. Consequently, the results have field scale resolution (i.e. ∼0.1 km). The permittivity to soil moisture inversion is performed using a polynomial model by [Hallikainen et al. 1985], where a soil texture map provide the information necessary to obtain precise results. 

Further work will aim toward applying a change detection algorithm in order to detect sudden temporal changes in vegetation and surface roughness, as the alpha approximation is inherently sensitive to such sudden changes.

The study has received partial funding from Innovation Fund Denmark, contract number: 7049-00004B (MOIST).

How to cite: Krebs Lange-Willman, S., Skriver, H., and Sandholt, I.: Estimation of soil moisture from Sentinel data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10581, https://doi.org/10.5194/egusphere-egu2020-10581, 2020.

The Central Andes are characterized by a steep climatic and environmental gradient with large spatial and temporal variations of associated hydrological parameters. In this region, important hydrological components are integrated water vapor (IWV) and soil moisture. Both parameters can be monitored in parallel by using Global Navigation Satellite System - Reflectometry (GNSS-R) techniques. Soil moisture can furthermore be estimated using Synthetic Aperture Radar (SAR) data.

As part of International Research Training Group-StRATEGy project, our research aims at monitoring IWV and soil moisture with new station data in the Central Andes. According to the needs of the research, four independent GNSS ground stations and in-situ soil-moisture sensors were installed in spring 2019. Each station is located at different altitude along the climatic gradient and contains various quality GNSS receivers. It has been shown that high-quality receivers provide precise measurements, while low-quality receivers have not been widely tested for these applications. A goal of this project is the direct comparison of data quality from each site and receiver type. Additionally, soil moisture sensors were installed at each site. This set-up will help to evaluate the quality of the GNSS receivers. Moreover, the GNSS-based remote sensing approaches are directly compared to traditional Time-Domain Reflectometry (TDR) techniques. Meteorological data are used for studying the relation between the magnitude of precipitation events and soil moisture, as well as the time needed to spot a significant change in soil moisture after a precipitation event.

GNSS-R soil moisture estimations and in-situ measurements were compared with estimations derived from SAR data. More specifically, we used data from Sentinel-1 and Satélite Argentino de Observación COn Microondas (SAOCOM) missions. Sentinel-1 is a fully operational mission that uses C-band wavelengths, while SAOCOM relies on L-band wavelength, but is still in a calibration phase. We analyze both wavelengths and estimate the potential for soil-moisture measurements in the Argentinean Andes.

How to cite: Antonoglou, N., Bookhagen, B., Dadamia, D., de la Torre, A., and Wickert, J.: GNSS-based remote sensing: Innovative observation of key hydrological parameters in the Central Andes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11586, https://doi.org/10.5194/egusphere-egu2020-11586, 2020.

Prediction of water-related natural disasters such as droughts, floods, wildfires, landslides, and dust outbreaks on a regional-scale can benefit from the high-spatial-resolution soil moisture (SM) data of both satellite and modeled products. The reason is that the amount of surface SM controls in the partitioning of outgoing energy fluxes into latent and sensible heat fluxes.

Recently, NASA’s SMAP mission has been implemented, in order to provide 3-km and 1-km SM data from a combination of SMAP and Sentinel-1A/B observations along with 9- and 36-km SM data retrieved from an L-band radiometer brightness temperature (TB). The 3-km and 1-km SM products were produced by combining the Sentinel-1A/B C-band radar backscatter and SMAP radiometer TB observations.

In the present study, we assimilated SMAP-enhanced (9-km) and SMAP/Sentinel-1A/B SM (3-km and 1-km) products into a land surface model (LSM): SMAP-enhanced and SMAP/Sentinel-1A/B SM data were assimilated into Noah-MP3.6 LSM. Then, these products were evaluated against ground observations in the United States. Three DA products’ error characteristics were intercompared: (1) SMAP-enhanced 9-km DA, (2) SMAP/Sentinel-1A/B 3-km DA and (3) SMAP/Sentinel-1A/B 1-km DA.

When SMAP and SMAP/Sentinel SM data sets were assimilated into LSM, the R- and ubRMSE values for 9-, 3-, and 1-km SM data were greatly improved.

How to cite: Kim, H., Lakshmi, V., Kumar, S., and Kwon, Y.: Assimilation of SMAP-enhanced and SMAP/Sentinel-1A/B soil moisture data into land surface models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12565, https://doi.org/10.5194/egusphere-egu2020-12565, 2020.

Recent observation-based studies have shown that satellite-based antecedent soil moisture can provide useful information on runoff production. The patterns uncovered can be used to benchmark the degree of coupling between antecedent soil moisture, rainfall totals and runoff production, and to determine if hydrologic models can reproduce these patterns for a particular model parameterization of their rainfall-runoff processes. The goal of our study is twofold; First, it derives the relationships between runoff ratio and its major controls, including rainfall total, antecedent soil moisture, and vegetation using remotely sensed data products. Second, it aims to determine if the model is capable to reproduce these relationships and use them to validate model parameters and streamflow predictions. For this purpose, SMAP (Soil Moisture Active Passive) satellite-based soil moisture, S-band radar rainfall, MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation index, and USGS (United States Geological Survey) daily streamflow observations are used. The study domain consists of thirty-eight basins less than 1000 km² located in an agricultural region in the United States Midwest. For each basin, daily streamflow predictions, before and after adjustments to the hydrologic model are compared with observations. The comparisons are done for four years (2015-2018) using multiple performance metrics. This study could serve as a data-driven approach for parameterization of rainfall-runoff partitioning in hydrologic models using remotely sensed observations. 

How to cite: Jadidoleslam, N., Mantilla, R., and Krajewski, W.: Exploring potential of remotely sensed data in parameterization of hydrologic model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12675, https://doi.org/10.5194/egusphere-egu2020-12675, 2020.

Soil moisture, controlling the fraction of the water between grounds and atmosphere, has been observed from various measurements to understand the hydrological cycle. Remotely sensing techniques using active and passive microwaves are regarded as an effective method for monitoring soil moisture at the regional scale. To evaluate remotely sensed soil moisture products, ground measurements including Time Domain Reflectometry (TDR) or Frequency Domain Reflectometry (FDR), and Cosmic-Ray Neutron Probe (CRNP) are widely used. In other words, field experiments considering the characteristics of sensors and soil must be preceded to retrieve soil moisture using remote sensing data. Especially, it is more even more important when applying remote sensing in complex terrain such as a mountainous region. Although there are still many challenges in the use of remote sensing technology in complex terrain, monitoring of inaccessible areas is one of the advantages of remote sensing. Therefore this study aimed to establish the soil moisture station, which employs the integration of a CRNP and FDR sensors installed within the CRNP footprint at multiple measurement depths (10, 20, 30, and 40 cm) at the mountainous region. The CRNP was firstly calibrated and subsequently combined with field average soil moisture based on a simple merging framework, to provide a field-scale soil moisture product at each corresponding layer. It was used to evaluate for large scale soil moisture validation by comparing with several model and satellite-based soil moisture products including GLDAS, SMAP, AMSR2, ASCAT, and SAR Sentinel-1. From the preliminary application of field-scale soil moisture for remotely sensed soil moisture evaluation indicated a reasonable accuracy with the highest correlation to GLDAS soil moisture product (0.87 at 40 cm), suggesting the potential of this station. An introduced protocol for estimating soil moisture in the complex mountainous region is expected to provide a better understanding of terrain impacts on soil moisture variability by assimilating field data and satellite-based products through Land Surface Model for improving soil moisture measurements.

How to cite: Jeong, J., Cho, S., Oh, S., Baik, J., and Choi, M.: A Protocol for Establishing Soil Moisture Observations at the Complex Mountainous Region., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13757, https://doi.org/10.5194/egusphere-egu2020-13757, 2020.

The resolution of satellite of satellite-derived soil moisture data products has matured, notably in recent years due to the Soil Moisture Active Passive mission (SMAP) launched in 2015. Whilst spatial resolutions still fall short of those suitable for field-scale monitoring, there are several ‘value-added’ RS soil moisture products available at the regional (e.g. SMAP: 36km) to meso-scale (e.g. SMAP: 9km) resolution. Although the intended 3km scale SMAP product did not materialise due to the failure of the L-Band radar, a potential substitute product has recently become available (Das et al. 2019). The SMAP-Sentinel1 product combines data from SMAP and C-Band Sentinel 1A/B SAR data to synthesise global soil moisture at a 3km and 1km resolution (~6 day revisit for Europe).

Evaluation of these products against ground-based measurements in the USA and elsewhere is encouraging, but only preliminary evaluation has been undertaken in the United Kingdom. Evaluation is always challenging because (i) rather than a direct measurement, satellite estimates are based on other measured properties (e.g. brightness temperature) with soil moisture algorithmically inferred, (ii) ground-based measurements are highly localised in comparison with the measurement averaged over the satellites much larger pixel resolution, and (iii) satellite sensors typically estimate only surface soil moisture (0-5cm).

The COSMOS-UK network, under development since 2013, provides high resolution soil moisture data at 51 sites in the UK, corresponding to a variety of climatic, soil and land cover settings. Sites typically contain soil moisture probes at a variety of depths (including 10cm) as well as a cosmic ray sensor. The latter integrates soil moisture over an area of ~12ha, and while not matching the spatial scale or soil depth of satellite measurements, it does avoid some of the field-scale heterogeneity issues associated with point-based measurements.

The 9km SMAP L3 product performs well against 10cm soil probe measurements at most sites (>70% at ubRMSE <0.04), and seasonal patterns in performance are evident. Satellite measurements performed less well in comparison with COSMOS-UK estimates (68% at ubRMSE <0.06). Downscaling the SMAP L3 product based on hydroclimatology improves performance in some cases but worsens it in others. The SMAP-Sentinel 1 product generally performs worse than the 9km SMAP L3 product.  Reasons for spatio-temporal variations in correlations and performance are proposed including reference to soil profile characteristics and properties at each site, as well as vegetation and climatic setting.

How to cite: Quinn, N. W., Newton, C., Boorman, D., Horswell, M., and West, H.: Progress in evaluating satellite soil moisture products in Great Britain against COSMOS-UK and in-situ soil moisture measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15831, https://doi.org/10.5194/egusphere-egu2020-15831, 2020.

Reliable measurements of soil moisture (SM) are required for many applications worldwide, e.g., for flood and drought forecasting, and for improving the agricultural water use efficiency (e.g., irrigation scheduling). For the retrieval of large-scale SM datasets with a high temporal frequency, remote sensing methods have proven to be a valuable data source. (Sub-)daily SM is derived, for example, from observations of the Advanced Scatterometer (ASCAT) since 2007. These measurements are available on spatial scales of several square kilometers and are in particular useful for applications that do not require fine spatial resolutions but long and continuous time series. Since the launch of the first Sentinel-1 satellite in 2015, the derivation of SM at a spatial scale of 1 km has become possible for every 1.5-4 days over Europe (SSM1km) [1]. Recently, efforts have been made to combine ASCAT and Sentinel-1 to a Soil Water Index (SWI) product, in order to obtain a SM dataset with daily 1 km resolution (SWI1km) [2]. Both datasets are available over Europe from the Copernicus Global Land Service (CGLS, https://land.copernicus.eu/global/). As the quality of such a dataset is typically best over grassland and agricultural areas, and degrades with increasing vegetation density, validation is of high importance for the further development of the dataset and for its subsequent use by stakeholders.

Traditionally, validation studies have been carried out using in situ SM sensors from ground networks. Those are however often not representative of the area-wide satellite footprints. In this context, cosmic-ray neutron sensors (CRNS) have been found to be valuable, as they provide integrated SM estimates over a much larger area (about 20 hectares), which comes close to the spatial support area of the satellite SM product. In a previous study, we used CRNS measurements to validate ASCAT and S1 SM over an agricultural catchment, the Hydrological Open Air Laboratory (HOAL), in Petzenkirchen, Austria. The datasets were found to agree, but uncertainties regarding the impact of vegetation were identified.

In this study, we validated the SSM1km, SWI1km and a new S1-ASCAT SM product, which is currently developed at TU Wien, using CRNS. The new S1-ASCAT-combined dataset includes an improved vegetation parameterization, trend correction and snow masking. The validation has been carried out in the HOAL and on a second site in Marchfeld, Austria’s main crop producing area. As microwaves only penetrate the upper few centimeters of the soil, we applied the soil water index concept [3] to obtain soil moisture estimates of the root zone (approximately 0-40 cm) and thus roughly corresponding to the depth of the CRNS measurements. In the HOAL, we also incorporated in-situ SM from a network of point-scale time-domain-transmissivity sensors distributed within the CRNS footprint. The datasets were compared to each other by calculating correlation metrics. Furthermore, we investigated the effect of vegetation on both the satellite and the CRNS data by analyzing detailed information on crop type distribution and crop water content.

[1] Bauer-Marschallinger et al., 2018a: https://doi.org/10.1109/TGRS.2018.2858004
[2] Bauer-Marschallinger et al., 2018b: https://doi.org/10.3390/rs10071030
[3] Wagner et al., 1999: https://doi.org/10.1016/S0034-4257(99)00036-X

How to cite: Panic, D., Pfeil, I., Salentinig, A., Vreugdenhil, M., Wagner, W., Wahbi, A., Fulajtar, E., Said, H., Franz, T., Heng, L., and Strauss, P.: Area-representative validation of remotely sensed high resolution soil moisture using a cosmic-ray neutron sensor, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16222, https://doi.org/10.5194/egusphere-egu2020-16222, 2020.

The increasing availability of remotely sensed soil moisture (SM) observations has brought great interest in their use in data assimilation (DA) frameworks in order to improve streamflow simulations. However, the added-value of assimilating satellite SM into rainfall-runoff models is still difficult to be quantified, and much more research is needed to fully understand benefits and limitations.

Here, an extensive evaluation of remotely sensed SM assimilation on hydrological model performances was carried out, involving 775 catchments across Europe. Satellite observations for over a decade from the three ESA CCI SM products (ACTIVE, PASSIVE and COMBINED) were assimilated in a lumped rainfall-runoff model which includes a thin surface layer in its soil schematization, by using the Ensemble Kalman Filter (EnKF). Observations were mapped into the space of modelled surface layer SM through a monthly CDF-matching prior to DA, while the observation error variance was calibrated in every catchment in order to maximize the assimilation efficiency.

The implemented DA procedure, aimed at reducing only random errors in SM variables, generally resulted in limited runoff improvements, although with some variability within the study domain. Factors emerging as relevant for the assessment of assimilation impact were: i) the open-loop (OL) model performance; ii) the remotely sensed SM accuracy for hydrological purposes; iii) the sensitivity of the catchment response to soil moisture dynamics; and also iv) issues in DA implementation (e.g., violations in theoretical assumptions).

The open-loop model results contributed significantly to explain differences in assimilation performances observed within the study area as well as at the seasonal scale; overall, the high OL efficiency is the main cause of the slight improvements here observed after DA. The integration of satellite SM information, showing greater skills in correspondence of poorer streamflow simulations, confirmed a potential in reducing the effects of rainfall inaccuracies.

The variability in satellite SM accuracy for hydrological purposes was also found to be relevant in DA assessment. The ACTIVE product assimilation generally provided the best streamflow results within the study catchments, followed by COMBINED and PASSIVE ones, while factors affecting the SM retrieval such as vegetation density and topographic complexity were not found to have a decisive effect on DA results.

Low assimilation performances were obtained when runoff was dominated by snow dynamics (e.g., in the northern areas of the study domain, or in winter season at medium latitudes), due to the SM conditions having a negligible effect on the hydrological response.

Finally, in basins where SM was persistently near the saturation value, deteriorations in hydrological simulations were observed, mainly attributable to violation of error normality hypothesis in EnKF due to the bounded nature of soil moisture.

In conclusion, the added-value of assimilating remotely sensed SM into rainfall-runoff models was confirmed to be linked to multiple factors: understanding their contribution and interactions deserves further research and is fundamental to take full advantage of the potential of satellite SM retrievals, in parallel with their progress in terms of accuracy and resolutions.

How to cite: De Santis, D., Massari, C., Camici, S., Modanesi, S., Brocca, L., and Biondi, D.: Added-value of satellite soil moisture assimilation in hydrological modelling: an evaluation through a large experiment over Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16239, https://doi.org/10.5194/egusphere-egu2020-16239, 2020.

The International Soil Moisture Network (ISMN, https://ismn.geo.tuwien.ac.at/) is an international cooperation to establish and maintain a unique centralized global data hosting facility, making in situ soil moisture data easily and freely accessible. This database is an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models. 

In situ measurements are crucial to calibrate and validate satellite soil moisture products. For a meaningful comparison with remotely sensed data and reliable validation results, the quality of the reference data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collecting their data in different units, at different depths and at various sampling rates. Besides, quality control is rarely applied and accessing the data is often not easy or feasible.

The ISMN has been created to address the above-mentioned issues and is building a stable base to assist EO products, services and models. Within the ISMN, in situ soil moisture measurements (surface and sub-surface) are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free.

Founded in 2009, the ISMN has grown to a widely used in situ data source including 61 networks with more than 2600 stations distributed on a global scale and a steadily growing user community > 3200 registered users strong. Time series with hourly timestamps from 1952 – up to near real time are stored in the database and are available through the ISMN web portal, including daily near-real time updates from 6 networks (> 900 stations). With continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN evolved into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS) and the online validation service Quality Assurance for Soil Moisture (QA4SM). In general, ISMN data is widely used in a variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.).

About 10’000 datasets are available through the web portal. However, the spatial coverage of in situ observations still needs to be improved. For example, in Africa and South America only sparse data are available. Innovative ideas, such as the inclusion of soil moisture data from low cost sensors (eventually) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.

In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.

How to cite: Aberer, D., Himmelbauer, I., Schremmer, L., Petrakovic, I., Dorigo, W., Goryl, P., and Sabia, R.: The International Soil Moisture Network in assistance of EO soil moisture validation products, services and models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16493, https://doi.org/10.5194/egusphere-egu2020-16493, 2020.

In this contribution, the opportunity to use Sentinel-1 radar data (S-1RD) to monitor the soil moisture (SM) of the soil surface (SS) is presented. Ground & Space monitoring event (G&SME) was carried out on 20/08/2019 at the experimental field (48º36ʹ31.86ʺ, 44º10ʹ50.65ʺ) of All-Russian Scientific Research Institute of Irrigated Agriculture (VNIIOZ, Volgograd region, Russian Federation). At the moment of G&SME the southern part of the plot was represented by fallow field and the northern part of it was covered by sparse coverage of alfalfa with NDVI varied within: a) 0.154-0.188 according to Sentinel-2 MSI; b) 0.091-0.202 according to Planet satellite constellation. The field was 190 m wide and 300 m long with presence of the 1,5 degrees slope in south to north direction. In west-eastern direction the field is flat with surface microrelief formed by plowing across to the slope. After data obtained with photogrammetric survey from UAV the SS roughness varied within 1.1-2.6 cm. Due to heavy clay content of soil it shows low permeability that caused by soil runoff generation during previous irrigation water application. Part of this runoff was stored at local depressions located near the southern border. During monitoring event a 40 not disturbed georeferenced soil samples were collected from 0-5cm layer. In vitro, these samples were used to obtain a SM for verification of the radar data.

Backscattering coefficient was acquired from Sentinel-1A in the interferometric broadband mode at the frequency of 5.4 GHz at VH and VV polarizations. Standard processing of S-1RD was carried out using ESA SNAP software (precision orbits, calibration, speckle filtering (sequential use of two Gamma map filters with a size of 3x3 pixels), geometric correction based on a DEM done after UAV photogrammetric survey data). Due to the fact that sensing angle within the field varied little, normalization of the backscattering coefficient with respect to one sensing angle was not carried out. For slightly covered and bare parts of the field a polarimetric analysis (H-a decomposition using complex images with VH and VV polarizations) revealed mainly a SS scattering mechanism (zones 6 and 9 in the H-a diagram). To retrieve SM retrieving from Sentinel-1 an algorithm based on the neural network (NN) was used. In contrast to the existing approaches, in our approach, a nadir reflectivity was used as the main output parameter of NN, as the input parameters were used backscattering coefficient measured at VH and VV polarizations. The NN was used to predict the reflectivity of the SS. Then reflectivity was inverted to SM with the use of Levenberg-Marquardt minimization algorithm and Mironov's dielectric model, taking into account a soil clay content. Based on S-1RD, the proposed NN, consisting of two hidden layers of 12 neurons in each, allows to predict SM relative to ground based SM with the determination coefficient of 0.948 and the standard deviation of 2.04%. The developed technique was tested for both bare and sparsely vegetated parts of the field.

Acknowledgments: The reported study was funded by RFBR, project number 19-29-05261 мк

How to cite: Muzalevskiy, K., Zeyliger, A., Zinchenko, E., Ermolaeva, O., Melikhov, V., and Novikov, A.: Remote Sensing of the Soil Moisture at the Agricultural Test Field in Volgograd Region with The Using Sentinel-1 Observations and Neural Network-Based Algorithm, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16529, https://doi.org/10.5194/egusphere-egu2020-16529, 2020.

The launch of series of Sentinel constellations has provided data continuity of ERS, Envisat, and SPOT-like observations, in order to meet various observational needs for spatially explicit physical, biogeophysical, and biological variables of the ocean, cryosphere, and land research activities. The synergistic use of this publicly-accessible SAR images and temporally collocated optical remote sensing datasets has provided great potential for estimating high-resolution soil moisture information. In this study, advanced integral equation model (AIEM) which simulates the backscattering coefficient of bare soil and the Water-Cloud Model (WCM) accounting for the scattering effect from vegetation, are coupled to map high-resolution soil moisture. Validation conducted in large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research (HiWATER-MUSOEXE) in northwest of China showed RMSE of 0.04~0.071 m3m3. In addition, the accuracies in describing vegetation contribution from backscatter coefficient were intercompared between different models including WCM and ratio vegetation model. Sensitivity analysis of soil moisture estimation accuracy to vegetation index also extends to different optical remote sensing data sets including Sentinel-2, Landsat 8 and MODIS.

How to cite: Qiu, J.: The synergistic use of Sentinel SAR and optical remote sensing for mapping high-resolution soil moisture, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17018, https://doi.org/10.5194/egusphere-egu2020-17018, 2020.

Soil moisture data assimilation has found increased applicability in hydrology due to easily available remotely sensed soil moisture data. Numerous studies in the past have explored the possibility of assimilating soil moisture information for improving streamflow forecasting. The general understanding is that if better soil moisture data can provide better streamflow forecast. However, to our knowledge no study has so far focused on understanding if the hydrological model itself has a role in assimilation of soil moisture data. In this regard, here we use three different conceptual hydrological models for soil moisture assimilation: (1) Dynamic Budyko (DB), (2) GR4J, and (3) PDM model. Assimilation of GLDAS observed soil moisture is carried out for four MOPEX basins using Ensemble Kalman Filter. DB model’s performance improved after soil moisture data assimilation for all the study basins. However, deterioration in performance was observed for GR4J and PDM for all the basins after the assimilation exercise. The performance of the assimilated models is evaluated in terms of Assimilation Efficiency (AE), which was found to be varying from 17.11 to 22.56%, from -20.98 to -41.29%, and from -8.4 to -38.23%, respectively, for DB, GR4J, and PDM. Overall, our results highlight the importance of the hydrological models structure in a soil moisture data assimilation exercise.

How to cite: Nayak, A. K., Biswal, B., and Sudheer, K. P.: Assimilation of soil moisture data for improving streamflow prediction: Is there a role for the hydrological model structure? , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19109, https://doi.org/10.5194/egusphere-egu2020-19109, 2020.

A comprehensive understanding of temporal variability of root-zone and surface soil moisture (SM) and the relationship with the underlying soil characteristics is of great importance in hydrological and agricultural applications. For the last ten years, global and frequent satellite SM observations have been available to investigate SM dynamics. However, validating remote sensing retrievals against in-situ observations based on the comparison of collocated SM time series is complicated. While satellite retrievals are approximated from inversion models over an area, in-situ measurements are determined at point-scale. This usually produces different SM dynamic ranges and biases in the corresponding time series. Moreover, the influence of soil properties and meteorological conditions can cause SM time series obtained from indirect remote sensing techniques and direct in-situ observations to be non-linearly related. Dynamic Time Warping (DTW) is a dynamic programming technique, capable of coping with temporal distortions by aiming for finding the optimal match between time series.

In this study, DTW was used to provide a time lag evolution as a continuous dissimilarity measure comprising the main temporal variability features of two time series. The DTW technique was applied to SM time series from the Soil Moisture and Ocean Salinity mission (SMOS) L4 product developed at Barcelona Expert Center (BEC) with in-situ measurements at top- and subsoil-representative depth levels, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW parameters were customized to the particular input time series to obtain a robust and meaningful time lag. Seasonal differences in SM dynamics were analyzed in a clustering approach by investigating the link between SM time series and SM-regime-related parameters including precipitation and categorical features such as soil type and land use. Since the technique resolves the non-linear behaviour of time series, it has the potential to generally assess major differences in SM acquisition techniques. It could also be useful to investigate spatial SM variability in heterogeneous regions and to make informed choices in future sensor deployment in SM networks.

How to cite: Herbert, C., Pablos, M., Vall-llossera, M., and Camps, A.: Dynamic time warping analysis of the evolution of SMOS surface and in-situ soil moisture time series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19211, https://doi.org/10.5194/egusphere-egu2020-19211, 2020.

We evaluate the potential of Sentinel-1A & 1B satellite images to estimate the volumetric soil moisture content over an alluvial fan of the Kosi River in the North Bihar, India. Over this region, only dual polarised images (VH and VV) are available. However, the existing backscattering models (i.e., Dubois, Oh and IEM models) uses quad polarised (VV, VH, HH and HV) images for the estimation of soil permittivity and surface roughness over the bareland. To overcome the constraint of dual polarised data, we eliminated one of the unknown (i.e. surface roughness) by developing a regression model between the in-situ measured surface roughness and the ratio of backscatter values (VH/VV) in dB.  In a field campaign in the Kosi Fan from December 10-21, 2019, we have measured surface roughness, soil temperature, soil pH and soil moisture at 78 different location using the pin-meter, soil survey instrument (soil temperature and pH), and Time Domain Reflectometer (TDR) respectively. The average surface roughness and soil moisture varies between (0.61 - 5.45) cm and (0.12-0.53) m³/m³ respectively in the study area.

Further, using the surface roughness we modify the Dubois, Oh and IEM models. This reduces the number of unknowns in the models from two to one; the soil permittivity. We compute the soil permittivity from the inversion of the existing backscattering models. Finally, we use the permittivity values in the Top’s model to estimate the volumetric soil moisture in the study area. Our initial results exhibit a good correlation (R² = 0.85) to the in-situ measured soil moisture.

How to cite: Singh, A., Gaurav, K., and Kumar, S.: Evaluating the potential of Sentinel-1 images for the estimation of soil moisture on an alluvial Fan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19614, https://doi.org/10.5194/egusphere-egu2020-19614, 2020.

Microwave remote sensing has been recognized as an effective method for monitoring soil moisture, since the dielectric properties of a soil medium are strongly connected with the water content held within the soil. As a result, a positive relationship between backscatter measurements from active microwave instruments and soil moisture can be observed. However, it has been noticed that this behavior may change unexpectedly in case of very dry soils and the presence of strong subsurface scatterer. Such anomaly has been found in backscatter measurements from the Advanced Scatterometer (ASCAT) on-board the series of Metop satellites in arid and semi-arid regions. This unusual behavior was detected initially due to strong negative correlations in validation studies of the ASCAT soil moisture product, which is derived using the TU Wien change detection method. The current formulation of the TU Wien soil moisture retrieval algorithm is not able to model the impact of strong subsurface scattering effects, which leads to a wrong (wet) interpretation of dry soils.

In this study we analyze and evaluate a new method to account for subsurface scattering effects in the TU Wien soil moisture retrieval algorithm. The new approach assumes a negative relationship between backscatter and soil moisture in areas with temporal persistent subsurface scattering effects. More challenging are regions where subsurface scattering only occurs during dry periods, which requires to identify the transition between the alternating backscatter and soil moisture relationship first. In fact, this leads to a V-shaped function between backscatter and soil moisture and requires a reference soil moisture data set to determine the exact time period dominated by subsurface scattering.

The new ASCAT soil moisture product with a better interpretation of subsurface scattering from dry soils is globally validated against other remotely sensed soil moisture products (ESA CCI Passive) and soil moisture information from land surface models (Noah GLDAS). The results indicate that in areas with persistent subsurface scattering the assumed inverse relationship between backscatter and soil moisture compares well to other soil moisture products. Better results are also achieved in areas with a temporal dependency of subsurface scattering, but future work is needed to better characterize the exact time period when scattering mechanism start to mix and shift.

How to cite: Hahn, S., Wagner, W., Quast, R., and Salentinig, A.: Subsurface scattering effects in the ASCAT soil moisture product, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20000, https://doi.org/10.5194/egusphere-egu2020-20000, 2020.

Current radar systems are generally monostatic. However, some theoretical research indicated the potential of bistatic radar measurements to improve applications. In the research presented, we explore the use of InSAR/PolInSAR mono- and bistatic measurements acquired in L-band for soil moisture monitoring. The main objective of this study is to compare the performance of soil moisture retrieval from monostatic with that obtained through bistatic observations.

The recent BelSAR campaign (in 2018) provided time series of airborne mono- and bistatic measurements at L-band, recorded during the growing season including bare soil conditions. In addition, in situ measurements of soil moisture and surface roughness were acquired concurrently with the airborne flights. Here, we provide an initial assessment of the sensitivity of the scatter observations with respect to soil moisture and surface roughness. The literature suggests that the impact of surface roughness on the retrieval of soil moisture decreases due to the simultaneous use of the mono- and bistatic measurements. However, our preliminary results show that the bistatic data do not provide substantial added value to reduce the impact of surface roughness on soil moisture retrieval. Further, we validate both mono- and bistatic scatter simulations from the Advanced Integral Equation Model (AIEM) using the airborne measurements. The AIEM allows additional investigations with respect to the sensitivity towards surface roughness and soil moisture of both mono- and bistatic scattering signals, as well as the impacts of sensor-related parameters such as the incidence angle, the bistatic configuration (e.g. the location of the second sensor), the frequency and the polarization.

How to cite: Tronquo, E., Lievens, H., and Verhoest, N. E. C.: Assessing simultaneous mono- and bistatic airborne radar observations for soil moisture retrieval, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20744, https://doi.org/10.5194/egusphere-egu2020-20744, 2020.

Soil moisture is an important factor affecting global circulation (climate, carbon, and water), disasters (drought, floods, and forest fires), and crop growth, so the production of soil moisture data is important. Currently, satellite-based soil moisture data is available from NASA’ SMAP (Soil Moisture Active Passive) and ESA’ SMOS (Soil Moisture and Ocean Salinity) data. Since these data are based on passive microwave sensor, they have low spatial resolution. Therefore, it is difficult to observe the distribution of soil moisture on a local scale. The purpose of this study is to produce high resolution soil moisture for monitoring on a local scale. For this purpose, we performed soil moisture modeling using high resolution satellite data (Sentinel-1 SAR (synthetic-aperture radar), Sentinel-2 MSI (multispectral instrument)) and deep learning. Deep learning is a method improving the problems of traditional neural networks such as overfitting, gradient vanishing, and local optimal solution through development of learning methods such as dropout, ReLU (Rectified Linear Unit), and so on. Recently, it has been used for estimation of surface hydrologic factors (soil moisture, evapotranspiration, etc.). The study area is an agricultural area located in Manitoba and Saskatoon, Canada. In-situ soil moisture data was constructed from RISMA (Real-Time In-Situ Soil Monitoring for Agriculture). In order to develop an optimal soil moisture model, various condition experiments on hyper-parameters affecting the performance of model were carried out and their performance was evaluated.

How to cite: Lee, S.-J. and Lee, Y.-W.: Estimation of Soil Moisture Content Using Deep Learning and High-Resolution Satellite Imagery (Sentinel-1 and 2), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21049, https://doi.org/10.5194/egusphere-egu2020-21049, 2020.

Soil moisture is very important in several disciplines such as agriculture, hydrology and meteorology. It can be mapped using active and passive microwave remote sensing techniques. From literature it is observed that quad polarized data acquired at L-band is sensitive to soil moisture and can map surface soil moisture at high spatial resolution. The main objective of this study is to analyze the potential use of L-band radar data for the retrieval of surface soil moisture over small scale agricultural areas under vegetation cover conditions. Study area selected for this study was Malavalli, village in Karnataka state India which falls in Tropical semi-arid region. Two radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the study area between 23/07/2018 and 17/09/2018 which has spatial resolution of 5m. Ground Soil moisture over 30 sample sites were collected in synchronization with satellite pass over the study area. Acquired ALOS PALSAR-2 images were processed using PolSARpro (Polarimetric SAR data Processing and Education Toolbox). ALOS PALSAR-2 has been processed and lee speckle filter is applied with window size of 3*3. Surface soil moisture distribution over small scale tomato fields are mapped by adding incidence angle using Oh Model. Incidence angle map which is not available with PolSARpro (Polarimetric SAR data Processing and Education Toolbox) software was derived using the polynomial given in the leader file which was required for oh model inversion. Study site clearly shown increasing trend of soil moisture from July to September. It is interesting to note that vegetation and urban areas are clearly discriminated in the PauliRGB images. The retrieval of soil moisture using Oh model is validated using Ground truth samples. The accuracy of Oh model over small scale tomato fields with RMSE of 1.83 m³/m^-3.

How to cite: Gururaj, P., Umesh, P., and Shetty, A.: Assessment of surface soil moisture distribution across small scale tomato fields using L band SAR data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-623, https://doi.org/10.5194/egusphere-egu2020-623, 2020.

The soil water retention curve (SWRC) at the wet part is important for understanding and modeling the water flow and solute transport in the vadose zone. However, direct measurements of SWRC is often laborious and time consuming processes. The Campbell function is a simple method to fit the measured data. The parameters of the Campbell function have been recently proven that can be predicted using visible-near-infrared spectroscopy. However, predicting the SWRC using image spectral data could be an inexpensive and fast method. In this study, 100-cm³ soil samples from Denmark were included and the soil water content was measured at a soil-water matric potential from pF 1 [log(10)= pF 1] up to pF 3. The anchored Campbell soil-water retention function was selected instead of the original. Specifically, in this function the equation is anchored at the soil-water content at pF 3 (θ_pF3) instead at the saturated water content. The image spectral data were correlated with the Campbell parameters [θ_pF3, and the pore size distribution index (Campbell b). The results showed the potential of remote sensing to be used as a fast and alternative method for predicting the SWRC in a large-scale.

How to cite: Pittaki-Chrysodonta, Z., Moldrup, P., V. Iversen, B., Knadel, M., and W. de Jonge, L.: Retrieving soil-water retention curve at the wet part by remote sensing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21677, https://doi.org/10.5194/egusphere-egu2020-21677, 2020.

Soil water content (SWC) is a vital factor for soil sciences. Nowadays, there are many methods for estimating SWC, including the Time-domain reflectometry (TDR) and the Gravimetric method. Nevertheless, most of them may cause damages to soil structure and require a large workforce and resources. The optical method is a non-destructive and cost-efficient; therefore, recommended for SWC estimations.

This study analyses soil samples at the field site, as well as it uses aerial photo-shooting to obtain the digital image distribution of surface soil. Both soil samples and digital images were categorized into groups; 9 in total, depending on time parameters (one group equals one day). More specifically, the gravimetric method was selected for the SWC measurements in the laboratory, while the images were modified in such a way so to match the CIE 1931 XYZ color space resolution for further calculations. Then, comparing the CIE 1931 XYZ color space data with the Soil Water Content correlation of 9 groups by validation.

According to the findings, the sensitivity of CIE 1931 XYZ color space in SWC alternations is high. Additionally, it can be observed that the SWC result data of the model are similar to the SWC measurements; therefore, the CIE 1931 XYZ color space can be applied to agriculture and disaster prevention, and it is a cost-efficient method for SMC estimations, and it can provide several benefits.

How to cite: Wang, C.-H., Lin, H.-R., Chen, J.-L., Huang, S.-Y., and Wen, J.-C.: Using Unmanned Aerial Vehicle to Obtain Digital Images and Estimating In-Situ Soil Water Content, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1826, https://doi.org/10.5194/egusphere-egu2020-1826, 2020.

Soil moisture is one of the key variables for crop modeling and scheduling farm operations. Current available soil moisture products are generated at global or regional scales and its spatial resolution (~1km²) is usually too coarse for common small farms. Within the framework of the EU Horizon2020 funded TWIGA project, we intended to provide improved soil moisture estimates at crop field scale. The advantage of focusing at the scale of a single crop is that the algorithm selection can be based more on the retrieval accuracy rather than on the computing performance.

Time series of Sentinel-1 SAR backscatter (at VH and VV polarizations) and Sentinel-2 NDVI observations, on each crop field, were assimilated with a semiempirical polarimetric backscattering model for bare soil surfaces (Oh) coupled with a Water Cloud model (WCM). Some of the model parameters are the actual variables of interest to be estimated, in our case the daily surface soil moistures. They were estimated by a Bayesian inversion approach. The key advantage of using WCM, is that the effects of vegetation on backscatter can be taken into account, and therefore soil moisture estimates are available even when vegetation is present. The empirical model parameters (surface roughness, and A and B parameters of WCM) were calibrated with in-situ data from four stations in Ghana, with observations every 30 minutes from May to October 2019 at 10 cm depth. The calibration was based on a hierarchical Bayesian regression, to take into account that model parameter distributions might vary across land cover types and across in-situ stations themselves. The validation was based on the comparison between the soil moisture observations of one in-situ station and estimates from the model couple calibrated with the data from the other three in-situ stations. That procedure was repeated for each station. Correlation coefficients were above 0.64 and root mean square error bellow 0.065 m³/m³ in two out of the four stations. Accuracy tended to be dependent on field size, due to the well known SAR speckle noise. The station with the lowest accuracy was locate on a 30x30m² field. Accuracy was additionally affected by likely sudden changes on the surface soil or vegetation during the analysis time windows. Correlation coefficients were higher (~0.85) on the time periods without such sudden changes.

Given the results of the current study, we would recommend that the location of eventual future in-situ stations should be preferably placed on larger fields, larger than 30x30m². Further research would be needed to improve the model and understand better its limitations for an eventual operational implementation.

How to cite: Padilla, M., Pérez, A., and Pinilla, M.: Combining time series of Sentinel-1 and -2 with in-situ data for estimating soil moisture at crop field scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13647, https://doi.org/10.5194/egusphere-egu2020-13647, 2020.

Soil moisture is an important component of the Earth system and plays an important role in land-atmosphere interactions. Remote sensing of soil moisture is of great scientific interest and the scientific community has made significant progress in soil moisture estimation using Earth observations. Currently, several operational coarse spatial resolution soil moisture datasets have been produced and widely used for various applications such as climate, hydrology, ecosystem and agriculture. Due to the strong demand for high spatial resolution soil moisture in regional applications, much effort has been recently devoted to the generation of high spatial resolution soil moisture from either Sentinel-1 observations or downscaling of existing coarse resolution soil moisture datasets. The aim of this study is to evaluate high spatial resolution soil moisture products derived from multisource satellite observations. First, the COSMOS-UK measured soil moisture was used to validate existing satellite-based soil moisture datasets including SMAP_9km, SMOS_1km, Sentinel-1, and Sentinel-1/SMAP combined products. Second, an approach based on triple collocation was applied to inter compare these satellite products in the absence of a reference dataset. Third, two merging schemes including a simple average and a triple collocation method were used to develop a combined satellite soil moisture product based on existing satellite soil moisture datasets. From the above analysis, it is found that merging all the above soil moisture data provides a better estimate of soil moisture than any of them alone. Therefore, we conclude that combining existing satellite-based soil moisture products has the potential to provide the best estimate of high spatial resolution soil moisture in the UK.

How to cite: Peng, J., Quaife, T., Pinnington, E., Evans, J., Harris, P., Robinson, E., Blyth, E., and Dadson, S.: High resolution soil moisture estimation and evaluation from Earth observation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18099, https://doi.org/10.5194/egusphere-egu2020-18099, 2020.

This work investigates the performance of two soil moisture retrieval methods using optical and radar satellite data. The study was conducted in areas with predominant agricultural land use since soil moisture is one of the parameters of interest in a wider study for water resource optimization in agricultural practices such as irrigation scheduling.
The two methods considered are based on the identification of changes in the investigated parameter between two acquisition dates. The implemented methods have been applied to study areas characterized by different orographic complexity and land use heterogeneity. Data from the European Space Agency (ESA) Sentinel 1 and Sentinel 2 missions were used, and results were validated with field measurements from the International Soil Moisture Network (ISMN).
At first, the methods were applied in a mountainous area of an irrigation consortium in Trentino (Italy), where the results pointed out the complexity of the study and the limitations of the current models in these contexts. Factors such as orographic complexity, type and physiological state of crops make the reduction of SAR data particularly complex to model.
The methods were then tested in a simpler orographic context such as that of the Po Valley in Bologna (Italy), also characterized by agricultural land use.
Finally, the methods were applied in a lowland with agricultural vocation located in Spain, for which an extended archive of soil moisture measurements distributed by the ISMN is available. In this context, the models were analyzed and were evaluated both functional and parametric adjustments of the models on the basis of the previous case studies.
Some of the results obtained are of high quality, while others highlight the complexity of the problem faced and the need for further investigation: increasing the number of case studies and using optical or SAR vegetation index different from the mainly used NDVI, could enhanced the models used for soil moisture retrieval.

How to cite: Graldi, G., Bignotti, S., Bezzi, M., and Vitti, A.: Combined use of Sentinel SAR and optical data for soil moisture estimation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-722, https://doi.org/10.5194/egusphere-egu2020-722, 2020.

The Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission – Water (GCOM-W) satellite provides global surface soil moisture as well as other water-related variables over the earth. With its brightness temperature observations at 10 and 36 GHz, the global soil moisture product is operationally created by the Japan Aerospace Exploration Agency (JAXA) based on the Koike’s algorithm (Koike et al., 2004) using the Polar Index (PI) and the Index of Soil Wetness (ISW). A land data assimilation system, LDAS-UT, has been also developed by Yang et al. (2007) to retrieve the optimized soil moisture estimates using both the brightness temperature observation and a land surface model.

In this study, we applied the distributed hydrological model, WEB-DHM (Wang et al., 2009), which incorporates the same land surface model with LDAS-UT, to a river basin in Cambodia and then calculated the brightness temperature at 6.9GHz from the simulated soil moisture distribution, using the same forward model as LDAS-UT. The temporal and spatial distribution of soil moisture was calibrated and validated against in-situ observation through river discharge using WEB-DHM, and the calculated brightness temperature was compared with the AMSR2 observation at 6.9 GHz. In addition to the dielectric mixing model by Dobson (Dobson et al., 1985) which is originally used in the LDAS-UT as well as in the JAXA's soil moisture retrieval algorithm, the performance of the Mironov model (Mironov et al., 2004) was examined as an alternative for the dielectric mixing model in the forward calculation and the calculated results from the two models were compared.

Along with the hydrological simulation, field measurements and laboratory experiments were conducted in Cambodia and Japan to evaluate the dielectric behavior of wet soils with different soil water content at a point scale. A ground microwave radiometer was temporally installed over a paddy field in Japan to measure the brightness temperature at 6.9GHz directly from the near surface. Soil samples were also taken from this field as well as several other locations in Japan and Cambodia to measure the permittivity with different soil moisture content with a network analyzer in the laboratory, in order to examine the dielectric behavior of wet soils for different soil textures. The measured results were then compared with the Dobson and Mironov models to evaluate their performance for Asian soils.

How to cite: Tsujimoto, K. and Ohta, T.: Examination of Dielectric Models in AMSR2 Soil Moisture Estimation Algorithm for Japanese and Cambodian Soils, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21078, https://doi.org/10.5194/egusphere-egu2020-21078, 2020.