HS6.1 | Remote Sensing of Soil Moisture
EDI

We invite presentations concerning soil moisture estimation, including remote sensing, field experiments, land surface modelling and data assimilation and the establishment of fiducial reference measurements (FRMs). The technique of microwave remote sensing has made much progress toward its high potential to retrieve surface soil moisture at different scales. From local to landscape scales, several field or aircraft experiments (e.g. SMAPvex) have been organised to improve our understanding of active and passive microwave soil moisture sensing, including the effects of soil roughness, vegetation, spatial heterogeneities, and topography. At continental scales, a series of several passive and active microwave space sensors, including SMMR (1978-1987), AMSR (2002-), ERS/SCAT (1992-2000) provided information on surface soil moisture. Current investigations in L-band passive microwave with SMOS (2009-) and SMAP (2015-), and in active microwave with Metop/Ascat series (2006-) and Sentinel-1, enable accurate quantification of the soil moisture at regional and global scales. Future missions, such as the CIMR Copernicus High Priority Candidate Mission, the EPS-SG Metop-SG/SCA and continuity of the Sentinel programme, will further enhance soil moisture remote sensing accuracy and spatial resolution, and they will ensure continuity of multi-scale soil moisture measurements on climate scales.

We encourage submissions related to soil moisture remote sensing, including:
- Field experiment, theoretical advances in microwave modelling and calibration/validation activities.
- High spatial resolution soil moisture estimation based on e.g. Sentinel observations, GNSS reflections, or using novel downscaling methods.
- Preparation of future missions including CIMR, Metop-SG/SCA, SMOS-High Resolution, Terrestrial Water Resources Satellite, etc.
- Root zone soil moisture retrieval and soil moisture data assimilation in land surface models, hydrological models and in Numerical Weather Prediction models.
- Evaluation and trend analysis of soil moisture climate data records such as the ESA CCI soil moisture product as well as soil moisture from re-analysis.
- Inter-comparison and inter-validation between land surface models, remote sensing approaches and in-situ validation networks.
- Uncertainty characterization across scales.
- Soil moisture reference networks.
- Application of satellite soil moisture products for improving hydrological applications.

Convener: Clément Albergel | Co-conveners: Irene HimmelbauerECSECS, Alexander GruberECSECS, David Fairbairn, Jian Peng, Luca Brocca, Nemesio Rodriguez-Fernandez
Orals
| Fri, 28 Apr, 10:45–12:30 (CEST), 14:00–15:40 (CEST), 16:15–17:57 (CEST)
 
Room 3.29/30
Posters on site
| Attendance Fri, 28 Apr, 08:30–10:15 (CEST)
 
Hall A
Orals |
Fri, 10:45
Fri, 08:30

Orals: Fri, 28 Apr | Room 3.29/30

Chairpersons: Clément Albergel, Nemesio Rodriguez-Fernandez, Jian Peng
10:45–10:50
Retrieval of soil moisture & soil moisture products
10:50–11:00
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EGU23-13224
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HS6.1
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Highlight
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On-site presentation
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Wolfgang Preimesberger, Pietro Stradiotti, Remi Madelon, Robin van der Schalie, Nemesio Rodriguez-Fernandez, Martin Hirschi, Adam Pasik, Alexander Gruber, Wouter Dorigo, Richard de Jeu, Richard Kidd, and Clement Albergel

ESA CCI Soil Moisture (SM) is a long-term global Climate Data Record of soil water content stored in the surface soil layer, derived from satellite observations in the microwave domain. To make it suitable for long-term analyses of the climate system, ESA CCI SM merges observations from a total of 19 satellite radiometers and scatterometers (active and passive systems) into harmonized records covering a period of more than 40 years (from 1978 onwards). ESA CCI SM is currently in its 8th development cycle. Following every development cycle, the CCI algorithm is adopted to create the Copernicus Climate Change (C3S) soil moisture data records. These operational records are extended on a regular basis to provide input data for time-critical applications such as monitoring systems. 

The data sets have been widely used (100+ scientific publications per year) in studying the water, energy and carbon cycles over land, understanding land surface-atmosphere hydrological feedbacks, assessing the impact of climate change on the occurrence of climatic extremes, assimilation into and evaluation of climate models. ESA CCI SM has been the main input for assessing global soil moisture conditions as presented in the BAMS “State of the Climate” reports for more than 10 years, while C3S has been used in the yearly “European State of the Climate” reports for several years now

In this presentation we give an overview over the algorithm underlying the ESA CCI SM product with a focus on new scientific developments included in the latest version. These comprise an improvement in the estimation of intra-annual uncertainties and two additional, experimental versions of the COMBINED product: 1) a gap-filled version in which data points between satellite overpasses are interpolated using statistical methods without the use of ancillary data; and 2) a model-independent version in which all merged sensors are scaled to L-band observations, as opposed to model values in previous versions. We show how both ESA CCI and C3S have been used in recent years to monitor droughts and floods globally and in Europe, respectively.

The development of ESA CCI and C3S SM has been supported by ESA’s Climate Change Initiative for Soil Moisture (Contract No. 4000104814/11/I-NB & 4000112226/14/I-NB) and the Copernicus Climate Change Service implemented by ECMWF through C3S 312a Lot 7 & C3S2 312a Lot 4 Soil Moisture.

How to cite: Preimesberger, W., Stradiotti, P., Madelon, R., van der Schalie, R., Rodriguez-Fernandez, N., Hirschi, M., Pasik, A., Gruber, A., Dorigo, W., de Jeu, R., Kidd, R., and Albergel, C.: ESA CCI and C3S Soil Moisture - New developments and recent applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13224, https://doi.org/10.5194/egusphere-egu23-13224, 2023.

11:00–11:10
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EGU23-7441
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HS6.1
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On-site presentation
Wolfgang Wagner, Samuel Massart, Bernhard Raml, Raphael Quast, Pavan Muguda Sanjeevamurthy, Claudio Navacchi, Felix Reuß, Bernhard Bauer-Marschallinger, and Mariette Vreugdenhil

Most scientific studies dealing with the retrieval of soil moisture data from Synthetic Aperture Radar (SAR) data focus on the formulation, training, and validation of the models used to convert the backscatter measurements into soil moisture data, while paying little attention to how the backscatter data are preprocessed. This is insofar surprising given that the topography of the Earth surface in combination with the variable SAR imaging geometry may introduce strong orbit-related geometric effects that obscure the soil moisture signal in backscatter time series. Furthermore, backscatter mechanisms are characterized by a very high spatial variability, leading to variable sensitivity to soil moisture. Differences in backscatter mechanisms and soil moisture sensitivity are hardly ever accounted for except for masking some obvious soil-moisture-insensitive areas such as water bodies, dense forest and urban areas. In this contribution we give an overview of the ongoing efforts at TU Wien to develop Sentinel-1 preprocessing workflows to produce 1 km backscatter time series that are optimized to the task of retrieving soil moisture data at the same spatial resolution. The following topics are addressed: (i) the use of radiometric terrain corrected backscatter data instead of the standard ground range detected products, (ii) the masking of subsurface scattering areas, dense forest and other soil-moisture-insensitive areas, and (iii) the standardization of the backscatter data to a reference incidence angle using machine learning techniques. Our preliminary results over Europe and the Mediterranean region show a substantial improvement of the Sentinel-1 soil moisture retrievals that would be impossible to achieve by a sole focus on the scientific retrieval algorithm.

Acknowledgements

We acknowledge funding by the European Space Agency (DTE Hydrology and 4DMED), the Copernicus Land Monitoring Service, and the Austrian Space Applications Programme (ROSSHINI and GHG-KIT). The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC).

How to cite: Wagner, W., Massart, S., Raml, B., Quast, R., Muguda Sanjeevamurthy, P., Navacchi, C., Reuß, F., Bauer-Marschallinger, B., and Vreugdenhil, M.: Improving 1km Sentinel-1 Soil Moisture Retrievals by Optimizing Backscatter Preprocessing Workflows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7441, https://doi.org/10.5194/egusphere-egu23-7441, 2023.

11:10–11:20
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EGU23-8302
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HS6.1
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Virtual presentation
Ebtehaj Ardeshir and Divya Kumawat

Soil and its water content can remain unfrozen below an insulative snow cover and modulate snowmelt infiltration and runoff. In this article, an emission model is proposed to account for L-band microwave emission of wet soils below a dry snowpack covered with an emerging moderately dense vegetation canopy. The model links the well-known Tau–Omega emission model with the snowpack dense media radiative transfer (DMRT) theory and a multilayer composite reflection model to account for the impacts of a snow layer on the upwelling soil and the downwelling vegetation emission, respectively. It is demonstrated that even though dry snow is a low-loss medium at the L-band, omission of its presence leads to underestimation of soil moisture (SM), especially when soil (snow) becomes wetter (denser). Constrained inversion of the proposed emission model, using brightness temperatures from the Soil Moisture Active and Passive (SMAP) satellite, shows that the retrievals of SM and vegetation optical depth (VOD) are achievable with unbiased root-mean-squared errors of 0.060 m3m3 and 0.124 [–], when compared with the in situ data from the International Soil Moisture Network (ISMN) and VOD-derived values from the normalized difference vegetation index (NDVI) obtained from the moderate resolution imaging spectroradiometer (MODIS) observations.

How to cite: Ardeshir, E. and Kumawat, D.: Passive Microwave Retrieval of Soil Moisture Below Snowpack at L-Band Using SMAP Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8302, https://doi.org/10.5194/egusphere-egu23-8302, 2023.

11:20–11:30
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EGU23-2739
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HS6.1
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Virtual presentation
Mehrez Zribi, Karin Dassas, Pascal Fanise, Vincent Dehaye, Michel Le Page, and Aaron Boone

Soil moisture plays an essential role in understanding the soil-vegetation-atmosphere interface and in managing water resources for irrigation. In recent years, the Global Navigation Satellite System Reflectometry (GNSS-R) technique has shown great potential in estimating and monitoring this parameter. In this context, various global operational products are already offered based on data from the CYGNSS satellites. In this study, we propose an analysis of an airborne campaign with measurements from the GLORI instrument at the Urgell agricultural site, in Spain. It is a polarimetric instrument allowing acquisitions in both LHCP (Left Hand Circular Polarized) and RHCP (Right Hand Circular Polarized) polarizations and L1 frequency band.

In parallel with three flights carried out on the study site in July 2021, various in situ measurements are carried out on twenty reference plots (soil moisture, Leaf area index, soil roughness). An analysis of the incidence angle effect on the GNSS-R reflectivity measurements is proposed. It illustrates larger effects for RHCP polarization. A normalization of data for one incidence angle is proposed. A sensitivity analysis of GLORI measurements to soil moisture is then discussed. The effect of vegetation cover on the degradation of this sensitivity is highlighted. The LHCP polarization displays a higher sensitivity to soil moisture.

An inversion model based on the two-omega approach is calibrated and validated with the in situ data acquired on the reference plots. Reflectivity is simulated as a function of soil moisture and the optical Normalized Difference Vegetation Index (NDVI) index which describes the dynamics of the plant cover. An RMSE close to 0.07m3/m3 is retrieved for soil moisture validation.

Soil moisture maps, based on the application of the inversion model, are proposed at a spatial resolution of 100 m for three realized flights. A correlation with precipitation events, as well as the presence or absence of irrigation is clearly observed.

How to cite: Zribi, M., Dassas, K., Fanise, P., Dehaye, V., Le Page, M., and Boone, A.: Analysis of GLORI GNSS-R airborne measurements for soil moisture estimation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2739, https://doi.org/10.5194/egusphere-egu23-2739, 2023.

11:30–11:40
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EGU23-17157
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HS6.1
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On-site presentation
Andre Daccache, Derek Houtz, Mohammad Emani, and Armand Ahmadi

Spaceborne microwave radiometers are historically used to estimate and analyze global soil moisture and ocean salinity. Despite providing valuable inputs to global hydrological models, the use of satellite-based L-band radiometry in agriculture was limited by the coarse spatial resolution not pertinent to field level observations or by the cost and size of the ground-based system.  TerraRad has recently developed a portable dual polarization passive L-band radiometer (PoLRa) for meter-scale retrievals of soil moisture (SM) and vegetation optical depth (VOD) suitable for field level application. PoLRa is designed to be mounted on UAV, fitted on ATV’s, or fixed on a tripod for continuous measurement. To examine the potential of L-band microwave radiometry in precision irrigation, the retrieved VOD and SM from PoLRa were compared against high resolution vegetation indices (i.e NDVI), plant parameters (i.e. LAI), surface soil moisture and  soil apparent electrical conductivity (ECa) from multi-frequency EMI soil scanner. We will summarize the findings from measurements conducted over bare soil, alfalfa, tomatoes, almonds and olive fields in California. We will also discuss the performance of the L-band radiometer in detecting spatial soil variability, surface soil moisture content, plant water status and vigor. We will also identify research gaps and limitations for L-band use for precision irrigation.

How to cite: Daccache, A., Houtz, D., Emani, M., and Ahmadi, A.: Assessment of of high-resolution L-band radiometry application in precision irrigation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17157, https://doi.org/10.5194/egusphere-egu23-17157, 2023.

11:40–11:50
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EGU23-8533
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HS6.1
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ECS
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On-site presentation
Giulia Graldi and Alfonso Vitti

Superficial soil moisture is a key hydrological variable playing a main role in the fluxes of water and heat between land and atmosphere. Its spatial and temporal variations are indeed crucial for applications such as environmental modelling and agricultural management. Soil moisture direct measurements lack spatial representativeness, while soil moisture spatialized information could be derived from satellite data acquired by active microwave sensors. In recent years, missions such as ESA’s (European Space Agency) Sentinel-1 SAR (Synthetic Aperture Radar) mission has provided open data at high resolution (up to 20 m) and temporal frequency (6 days at the equator latitudes before December 2021).

In the proposed work, high resolution data of Sentinel-1 are analyzed on an agricultural area at a resolution of 20 m over a 4 year period. In particular, it is proposed a hierarchical approach for detecting time and space domains where coherently apply a Change Detection method for retrieving soil moisture from the co-polarized band of Sentinel-1 data. The study is conducted at the field scale. Given the agricultural land use of the study area, the total SAR backscattered signal is modelled as the sum of vegetation and attenuated soil contributions.

At first, a classification for masking out the sub-areas dominated by a volumetric response due to vegetation is performed. For doing this, the adaptive thresholding method proposed by Satalino et al., 2014 [1] is performed on proper SAR parameters, such as the VH band, the RVI (Radar Vegetation Index) adapted to Sentinel-1 data [2], and the cross-polarized Interferometric Coherence. The resulting classifications derived from the different parameters are then compared. When working on an agricultural area at the resolution of 20 m, the effects of the soil roughness changes on the backscattering coefficient could not be neglected. For considering them, since no soil roughness data are available on the study area, a time series analysis for detecting steep changes in the co-polarized band is performed. By doing this, it is expected to detect temporal clusters in which no soil roughness variations occur, and thus where a CD method can be applied. The results of the latter classification may be compared with an optical roughness index.

REFERENCES

[1] G. Satalino, A. Balenzano, F. Mattia and M. W. J. Davidson, "C-Band SAR Data for Mapping Crops Dominated by Surface or Volume Scattering," in IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 2, pp. 384-388, Feb. 2014, doi: 10.1109/LGRS.2013.2263034.

[2] M. Trudel, F. Charbonneau, R. Leconte, “Using RADARSAT-2 polarimetric and ENVISAT-ASAR dual-polarization data for estimating soil moisture over agricultural fields”. Canadian Journal of Remote Sensing 2012, 38, 514–527.

How to cite: Graldi, G. and Vitti, A.: Hierarchical clustering of Sentinel-1 SAR data for soil moisture estimation at the field scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8533, https://doi.org/10.5194/egusphere-egu23-8533, 2023.

11:50–12:00
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EGU23-7837
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HS6.1
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ECS
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On-site presentation
Hongtao Shi, Kai Qin, Fengkai Lang, Lingli Zhao, Yaqin Sun, Jinqi Zhao, and Jie Qin

High spatial resolution soil moisture (SM) mapping is essential for a wide range of applications, especially for precision irrigation and crop management. This work proposes an SM estimation method combined with time series of L-band fully polarimetric synthetic aperture radar (PolSAR) and passive SM products over crop areas. Regarding the challenge of eliminating vegetation canopy scattering on SM estimation, model-based polarimetric decomposition is implemented as a pretreatment step in which the surface scattering component in both HH and VV channels are extracted. Afterward, dual-pol surface scattering information normalization is dealt with the cosine-squared incidence angle normalization method, which makes it possible for SM inversion with multiple tracks and multi-incidence SAR observations. With the time series of normalized surface scattering information, the alpha approximation-based change detection algorithm (AACD) is used for SM estimation. Since the AACD algorithm is reported with an underdetermined problem of parameter solution and the underestimation issue of soil moisture inversion, an extended AACD which incorporates dual-pol (HH and VV) SAR observations, namely the Dual-pol AACD algorithm, is proposed in this study. Besides, the minimum and maximum values of passive microwave soil moisture data of the whole study area and the entire study period are introduced as constraints in Dual-pol AACD when solving the unknown parameters of the real part of the soil dielectric constant. Finally, the obtained time series of soil dielectric constants are converted to volumetric soil water content using dielectric mixing model. 56 sets of collected UAVSAR L-band data with 4 different flight lines (#31603, #31604, #31605, #31606) of Winnipeg, Manitoba, Canada in 2012 (SMAPVEX12) are used to validate the Dual-pol AACD algorithm. Passive microwave SM constraints are collected from Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR-2) products. The performance of the proposed method is evaluated by comparing the in-situ measurements against the soil moisture estimates of wheat, corn, soybeans, bean, and canola fields at different phenological stages. Results show that the proposed method provides an accuracy of RMSE ≤ 6.5 cm3•cm-3 over all the selected crop fields, which is better than that without the introduction of constraints from passive microwave SM products. This work also compares the SM estimation performance using constraints from SMOS and AMSR-2. In addition, SM estimates in different crop fields and growth stages are also provided regarding the variation of crop morphological characteristics and biophysical properties. It concludes that the proposed SM estimation method has great potential for local and global SM mapping in a high resolution with existing and upcoming L-band SAR data, such as ALOS-2 (Japan), LT-1 (China), NISAR (America and India) and Tandem-L (Germany), etc.

How to cite: Shi, H., Qin, K., Lang, F., Zhao, L., Sun, Y., Zhao, J., and Qin, J.: Soil Moisture Estimation over Crop Fields Combined with Fully Polarimetric SAR and Passive Microwave Products Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7837, https://doi.org/10.5194/egusphere-egu23-7837, 2023.

12:00–12:10
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EGU23-16908
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HS6.1
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ECS
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Virtual presentation
Jiangtao Liu, Chaopeng Shen, Farshid Rahmani, and Kathryn Lawson

Detailed and accurate soil moisture is critical for many applications, such as forecasting agricultural drought and pests and mapping landslides. Deep learning can perform extraordinarily well in soil moisture, streamflow, and model uncertainty estimation. However, these models may inherit disadvantages of training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. Here, we propose a novel multiscale DL scheme that learns from satellite and in situ data to predict daily soil moisture at 9 km. The model outperforms land surface models, the SMAP satellite product, and a candidate machine learning model. Based on spatial cross-validation, it achieved a median correlation of 0.901 and a root-mean-square error of 0.034 m3/m3 over sites in the conterminous United States. Our scheme generally applies to topics in the geosciences with multiscale data, breaking the limitations of a single dataset.

How to cite: Liu, J., Shen, C., Rahmani, F., and Lawson, K.: A multiscale deep learning model integrating satellite-based and in-situ data for high-resolution soil moisture predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16908, https://doi.org/10.5194/egusphere-egu23-16908, 2023.

12:10–12:20
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EGU23-6589
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HS6.1
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On-site presentation
Rajat Bindlish

The NISAR mission will provide global data sets of Earth land surface dynamics that are critical to multiple Earth Science disciplines including observations of ecosystem carbon and water cycles. Soil moisture is a surface hydrosphere state variable and plays a key role in global terrestrial hydrology.  It controls the partitioning of water and energy fluxes at the land surface.  NISAR's L-band SAR backscatter measurements are similar to those planned for the L-band radar of the Soil Moisture Active/Passive (SMAP) mission, although at much finer spatial resolution.

NISAR soil moisture using a time-series ratio algotrithm is currently being developed. The final NISAR soil moisture product will have 200m spatial resolution with 12-day exact revisit time. A time-series ratio algorithm was implemented using UAVSAR (SMAPVEX12 field experiment) and SMAP radar observations. In this paper, the performance of the time series ratio algorithm was assessed using in situ observations. Performance of the soil moisture retrieval algorithm was also assessed for dual polarization and quad-polarization observations modes.

How to cite: Bindlish, R.: NISAR Soil Moisture retrievals using the Time-Series Ratio Algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6589, https://doi.org/10.5194/egusphere-egu23-6589, 2023.

12:20–12:30
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EGU23-6773
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HS6.1
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On-site presentation
Francesco De Zan

High-resolutions soil moisture products from remote sensing are very valuable but are not free from limitations. Products based on back-scatter change can give conflicting results over dry areas, when the penetration in dry soils becomes significant.[1]

The interferometric phase of Synthetic Aperture Radar acquisitions (in short, the InSAR phase) contains information about the soil moisture variations of the observed target.

This work shows the characteristics of a novel In-SAR-based soil moisture product derived from Sentinel-1 radar observations. The algorithm is based on phase closure inversion, an observable which is immune from atmosphere and deformation contributions to the phase.[2]

The proposed soil moisture product has a resolution of about 200 m and a good coverage in arid and semi-arid regions. It has the potential of filling the gaps of existing high-resolution products based on backscatter change.

An example of the InSAR-based soil moisture product is given in the following figure, which shows a moisture pattern over a rare rain event in the Namibian gravel plain in 2021. Notice the fine structure of channels present in the product. The colorscale units are m3/m3.

 

Validation

The figure below presents a comparison with the ERA5 weather model and a radiometer-based product (C3S passive), which, despite the low resolution, seem to be reliable also over dry areas. The InSAR soil moisture time series corresponds to one year of Sentinel-1 acquisitions over eastern Spain from the ascending orbit direction. The standard deviation of the difference between the InSAR soil moisture and ERA5 surface soil moisture is just under 3% (m3/m3). Notably, the errors are concentrated on a few dates. As expected, products derived from scatterometry (C3S active) are rather unreliable over this site.

Comparisons with weather radar precipitation data validate the high resolution patterns seen in the InSAR product. The match between the two is typically very good.

So far, all the results indicate that the InSAR-based soil moisture can be a reliable product on arid regions and can complement back-scatter change methods in areas with significant penetration. It is expected that InSAR-based soil moisture product will be able to cover larger portions of the land areas with sensors operating at longer wavelengths.

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References

[1] F. De Zan and G. Gomba, “Vegetation and soil moisture inversion from SAR closure phases: First experiments and results,” Remote Sensing of Environment, vol. 217, pp. 562–572, 2018

[2 ] W. Wagner, R. Lindorfer, T. Melzer, S. Hahn, B. Bauer-Marschallinger, K. Morrison, J.-C. Calvet, S. Hobbs, R. Quast, I. Greimeister-Pfeil, and M. Vreugdenhil, “Widespread occurrence of anomalous C-band backscatter signals in arid environments caused by subsurface scattering,” Remote Sensing of Environment, vol. 276, 2022

How to cite: De Zan, F.: An InSAR-based Soil Moisture Product for Arid Regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6773, https://doi.org/10.5194/egusphere-egu23-6773, 2023.

Lunch break
Chairpersons: Nemesio Rodriguez-Fernandez, David Fairbairn, Luca Brocca
Data assimilation and impact assessment
14:00–14:10
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EGU23-6976
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HS6.1
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ECS
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On-site presentation
Timothée Corchia, Bertrand Bonan, Nemesio Rodriguez-Fernandez, Gabriel Colas, and Jean-Christophe Calvet

In the context of global warming, the frequency and intensity of extreme events such as droughts are increasing, and a better modeling of vegetation response to climate is needed. Monitoring the impact of extreme events on land surfaces involves a number of soil-plant system variables such as soil water content, soil temperature and vegetation leaf area index (LAI). These variables control the carbon, water, and energy land surface fluxes. They can be monitored either by using the unprecedented amount of data from the fleet of Earth observation satellites or by using land surface models. Alternatively, all available sources of information can be combined by assimilating satellite observations into models.

 

The LDAS-Monde Land Data Assimilation System is a tool developed by the Centre National de Recherches Météorologiques (CNRM). It allows the joint assimilation of Advanced SCATterometer (ASCAT) surface soil moisture and Copernicus Global Land service (CGLS) LAI retrievals into the ISBA (Interaction Sol-Biosphère-Atmosphère) land surface model of Meteo-France, with the objective of better representing leaf biomass and root-zone soil moisture. The ASCAT C-band radar backscatter coefficients (σ0) contain information on both surface soil moisture and vegetation and its assimilation could prove beneficial. For this, an observation operator that links σ0 to the ISBA land surface variables is needed.

 

In this work, a method for the assimilation of ASCAT σ0 into ISBA using LDAS-Monde is presented. In a first step, observation operators are built using machine learning. Neural networks (NN) are trained using as inputs modeled soil surface moisture, soil temperature, rainwater interception by leaves and CGLS satellite observations of LAI. Then the observation operators are implemented into LDAS-Monde, making it capable of assimilating the satellite product. The method is implemented over southwestern France, where in situ soil moisture observations are available. It is shown that the assimilation of σ0 alone markedly improves the simulation of LAI and soil moisture in agricultural areas. Results vary from one land cover type to another.

How to cite: Corchia, T., Bonan, B., Rodriguez-Fernandez, N., Colas, G., and Calvet, J.-C.: Added value of machine learning in the assimilation of ASCAT observations into the ISBA land surface model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6976, https://doi.org/10.5194/egusphere-egu23-6976, 2023.

14:10–14:20
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EGU23-7562
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HS6.1
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ECS
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Virtual presentation
Richa Prajapati, Indu Jayaluxmi, Jeffrey Walker, and Jean-François Mahfouf

The L-band (1.4 GHz) microwave radiometer provides soil moisture (SM) information limited to around 5 cm soil depth. Deeper information (up to 10 cm) can be obtained using low frequency sensors (P-band: 0.3-1 GHz) with reduced effects of surface roughness and vegetation. The present study explored the capability of P-band and/or L-band singly or in combination, via direct assimilation of brightness temperature (Tb) into the Joint UK Land Environment Simulator (JULES) land surface model. JULES was driven by ERA-5 (ECMWF Reanalysis v5) meteorological forcing data and calibrated model parameters for bare soil. The assimilation framework consists of a radiative transfer model to convert simulated SM to Tb and an Ensemble Kalman Filter to generate an observation corrected SM trajectory. This framework was first validated with an open loop experiment in a synthetic environment over Cora Lynn, Victoria, Australia for the period of 9th May to 14th June, 2019. Assimilation experiments with synthetic observations were then set-up to investigate the sensitivity of i) number of ensembles, ii) observation error, iii) incidence angle, iv) assimilation interval, and iv) frequency bands. The diagnostics (Kalman gain and Jacobians) showed that P band was more sensitive to the deeper layers as compared to L-band. The results also showed substantial improvement in the soil moisture analysis state in both the dry and wet period of the study when both L- and P-band Tbs were assimilated. Further study will include investigating improvement in soil moisture estimates when using real field observations and assimilating Tb with multiple incidence angles.

Keywords: Ensemble Kalman filter, Tb assimilation, P-band, JULES Land surface model, Radiative Transfer Model

How to cite: Prajapati, R., Jayaluxmi, I., Walker, J., and Mahfouf, J.-F.: Towards development of a P- and L-band Tb assimilation framework in the JULES Land Surface Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7562, https://doi.org/10.5194/egusphere-egu23-7562, 2023.

14:20–14:30
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EGU23-8489
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HS6.1
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ECS
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On-site presentation
Xu Shan, Susan Steele-Dunne, Sebastian Hahn, Wolfgang Wagner, Bertrand Bonan, Clement Albergel, Jean-Christophe Calvet, and Ou Ku

ASCAT normalized backscatter and slope are jointly assimilating into the ISBA-A-gs land surface model (LSM) to constrain plant water dynamics processes. An Extended Kalman filter is used as the data assimilation (DA) algorithm with a trained Deep Neural Network as the observation operator to link the states and the observations (Shan et al., 2022). DA and model open loop (OL) runs are performed on ASCAT grid points (GPIs) containing ISMN stations in Europe and validated using data from 2017 to 2019. Performances of DA and OL are evaluated against ISMN in-situ soil moisture observations in different layers and satellite-based LAI observations from the 1km v2 Copernicus Global Land Service project (CGLS) product.

Analysis of DA diagnostics suggests that our DA system is free of bias. Domain median values of innovations, residuals and increments are all around zero. The reduction of standard deviation of residuals compared to innovations shows that DA is effective in reducing uncertainties. Median values of O-A/O-F are close to unity, suggesting the weight given to the ASCAT observables ensures that they provide valuable information to constrain the model. Time series standard deviation of normalized innovations are shown to be around 1 which means our DA system satisfies the Gaussian hypothesis. Regional variations in the mean standard deviation suggests that the performance of the assimilation framework varies somewhat across different land covers.

Aggregated across space and time, the improvement in domain median values of ubRMSE and KGE are not statistically significant. However, improvement is observed in some land cover types, and at specific times of year. For example, analysis of the monthly performances in Agricultural grid points shows that DA corrects deeper soil moisture in spring. Results from our previous studies suggest that this may be due to the indirect link between deeper soil water availability and vegetation water status revealed by ASCAT slope. There are also improvements in LAI in fall and winter, suggesting potential values of ASCAT observables in crop senescence. This is consistent with results from Bonan et al. (2014), who found that assimilation of LAI with SSM  could shift the delayed plant phenological cycle simulated by ISBA compared towards real observations (Bonan et al., 2014). In addition, it is important to note that assimilation is performed at the ASCAT resolution scale, while the ISMN provides point-scale soil moisture.

Analysis of DA diagnostics as well as performance statistics suggest that the efficacy of ASCAT assimilation is sensitive to the prescribed model and observation errors. Ongoing research is focused on providing realistic quantitative values of both to ensure that the information contained in the ASCAT backscatter and slope can be optimally used.

 

How to cite: Shan, X., Steele-Dunne, S., Hahn, S., Wagner, W., Bonan, B., Albergel, C., Calvet, J.-C., and Ku, O.: ­­Joint assimilation of ASCAT backscatter and slope into the ISBA land surface model at ISMN stations over Western Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8489, https://doi.org/10.5194/egusphere-egu23-8489, 2023.

14:30–14:40
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EGU23-1116
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HS6.1
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On-site presentation
Oscar Javier Rojas Muñoz, Jean-Christophe Calvet, Bertrand Bonan, Nicolas Baghdadi, Jean-Pierre Wigneron, Mehrez Zribi, and Catherine Meurey

In a warming climate where the frequency and intensity of extreme events (such as droughts and floods) are increasing, a better representation and estimation of land surface variables remains a crucial step to study their response to climate change. Soil moisture is a key variable of the water cycle. Monitoring soil moisture, either by in situ measurements or by satellite observations allows better prediction and anticipation of droughts and floods, especially in agricultural regions. In order to fully exploit the growing number of satellite observations data, assimilation techniques can be used to integrate these data into land surface models.

In this work, surface soil moisture (SSM) observations from Sentinel-1 (S1) satellite are assimilated into the ISBA model at the kilometer scale. The main objective is to evaluate the added value of the SSM assimilation and its impact on the ISBA model simulations, driven by atmospheric variables from the AROME weather forecast model. The Land Data Assimilation System tool (LDAS-Monde) of Météo-France is used. The SSM S1 product covers the period 2017-2019, over two regions in south of France and one in Spain. The native resolution of the S1 product is 10 m, and the aggregated 1 km product only covers areas where radar signal interpretation is possible. The two areas of interest in France are the Toulouse and the Montpellier regions. In these two areas, in situ soil moisture measurements are available (SMOSMANIA network and Meteopole-Flux stations of Meteo-France). The area of interest in Spain is located between Salamanca and Valladolid, where the REMEDHUS network of in-situ soil moisture measurements is located. In situ SSM observations at a depth of 5 cm were gathered from all stations at an hourly temporal resolution. The S1 SSM shows a good agreement with the in situ observations, including over the Météopole-Flux site which is located in a semi-urban area.

The impact of assimilating SSM products is evaluated over three surface variables: SSM at the 1 – 4 cm soil deph layer (WG2), at the root zone at 30 cm soil depth (WG5) and on the Leaf Area Index (LAI). Three experiments are then carried out over the three regions: assimilation of the S1 SSM product alone, assimilation of the LAI retrieved from the Copernicus Global land Service (CGLS), and one last experience where S1 SSM is jointly assimilated with LAI.

The results of these experiments on one hand show that when SSM alone is assimilated, almost no improvement is observed on WG2 between the ISBA model outputs and the assimilation outputs when compared to in situ measurements. On the other hand, when SSM is jointly assimilated with LAI, there is a stronger impact on WG2 and thus the outputs are closer to the in situ observations. Concerning WG5, the impact of assimilating SSM and LAI is found to be even stronger.

 

How to cite: Rojas Muñoz, O. J., Calvet, J.-C., Bonan, B., Baghdadi, N., Wigneron, J.-P., Zribi, M., and Meurey, C.: Soil moisture monitoring at kilometre scale: assimilation of Sentinel-1 products in ISBA, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1116, https://doi.org/10.5194/egusphere-egu23-1116, 2023.

14:40–14:50
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EGU23-8638
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HS6.1
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ECS
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On-site presentation
Zdenko Heyvaert, Samuel Scherrer, Wouter Dorigo, Michel Bechtold, and Gabriëlle De Lannoy

As soil moisture and vegetation water content both affect the emissivity from the land surface, each of them can be derived from satellite-based passive microwave measurements. In this study, we use soil moisture retrievals from the 36 km SMAP L2 product and X-band vegetation optical depth (VOD) from AMSR2 LPRM version 6. VOD is a proxy for vegetation water content, linked to the leaf area index (LAI). We developed a machine learning-based observation operator to map LAI to VOD.

We assimilate the SMAP and AMSR2 products into the Noah-MP land surface model (LSM) with dynamic vegetation. This is done by means of a one-dimensional Ensemble Kalman Filter (EnKF) within the NASA Land Information System (LIS). SMAP soil moisture retrievals update soil moisture in each of the four soil layers of the LSM, while AMSR2 VOD retrievals update the LAI. A cumulative distribution function (CDF) matching approach rescales the soil moisture retrievals to the model climatology. Model LAI is mapped to VOD by means of the above-mentioned observation operator. The resulting data assimilation (DA) system produces consistent estimates of all land surface variables on a quarter-degree regular grid over the European continent from 1 April 2015 through 31 March 2022.

This joint SMAP and AMSR2 DA system is validated by assessing a number of geophysical variables. The surface and root-zone soil moisture estimates are evaluated using in situ observations from the ISMN. Gross primary production (GPP) and evapotranspiration are evaluated using FLUXNET data. Estimates for LAI are compared with optical satellite data from MODIS. The results are compared with open loop (model-only), and SMAP- and AMSR2-only DA experiments.

SMAP-only DA primarily improves soil moisture estimates, while AMSR2-only DA mainly improves estimates of GPP and ET. Preliminary results indicate that the joint DA has the potential to combine the improvements of both individual assimilation systems.

How to cite: Heyvaert, Z., Scherrer, S., Dorigo, W., Bechtold, M., and De Lannoy, G.: Joint assimilation of SMAP soil moisture and AMSR2 vegetation optical depth retrievals into the Noah-MP land surface model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8638, https://doi.org/10.5194/egusphere-egu23-8638, 2023.

14:50–15:00
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EGU23-15691
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HS6.1
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Virtual presentation
Nicola Montaldo, Roberto Corona, and Andrea Gaspa

Data assimilation techniques allow for optimally merging remote sensing observations in ecohydrological models, guiding them for improving land surface flux predictions. Nowadays freely available remote sensing products, like those of Sentinel 1 radar, Landsat 8, and Sentinel 2 sensors, allow for monitoring land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index, NDVI, for leaf area index, LAI) at unprecedented high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semi-arid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. An assimilation approach that assimilates radar backscatter and grass and tree NDVI in a coupled vegetation dynamic-land surface model is proposed. It is based on the Ensemble Kalman filter (EnKF), and it is not limited to assimilate remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), the saturated hydraulic conductivity, and the grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture in an heterogeneous ecosystem in Sardinia, for 5 years period with contrasting hydrometeorological (dry vs wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared to the calibrated (“true”) values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors less than 4% and 15%, respectively, although the initial model conditions were extremely biased.

How to cite: Montaldo, N., Corona, R., and Gaspa, A.: On the Assimilation of Remote Sensing Data for Soil Moisture Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in an Heterogeneous Ecosystem under water-limited conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15691, https://doi.org/10.5194/egusphere-egu23-15691, 2023.

Applications & case studies Part I
15:00–15:10
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EGU23-4916
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HS6.1
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ECS
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On-site presentation
Luca Zappa, Stefan Schlaffer, and Wouter Dorigo

The European Space Agency (ESA) Climate Change Initiative (CCI) provides long-term surface soil moisture (SM) records with daily temporal resolution. However, the coarse spatial resolution of approximately 25 km limits their use in many hydrological applications, such as agricultural water management, drought monitoring, and rainfall-runoff response.  

To address this constraint, we downscaled the CCI SM product to 0.01° (~ 1 km) using machine learning and a set of static and dynamic variables affecting the spatial organization of SM. In particular, datasets describing the vegetation status throughout time, as well as land cover class and soil and topographic attributes were fed into a Random Forest model. 

Here, we will first present in detail the methodological framework that allowed us to generate the high-resolution dataset. Then, we will thouroughly evaluate its accuracy against in-situ measurements from across Europe, and further compare it to other SM products (e.g., from Sentinel-1). Finally, we will highlight the strengths and limitations of the downscaled SM dataset and discuss possible improvements.

How to cite: Zappa, L., Schlaffer, S., and Dorigo, W.: Downscaling the ESA CCI Soil Moisture: a new European dataset at 1 km for the period 2008-2020, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4916, https://doi.org/10.5194/egusphere-egu23-4916, 2023.

15:10–15:20
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EGU23-14842
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HS6.1
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ECS
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On-site presentation
Toni Schmidt, Martin Schrön, Zhan Li, Till Francke, Steffen Zacharias, Anke Hildebrandt, and Jian Peng

In the last decades, a variety of soil moisture products from remote sensing and process-based modeling have been created to study the terrestrial water cycle on a large scale. While satellite-based products are only representative of the water content at the topmost soil surface, model-based products aim at overcoming this limitation by estimating the water content in the deeper soil. In order to map the water content in the course of droughts and to analyze plant water absorption and transpiration, the water content in their rooting depths is of particular interest but out of scope for satellite-based products. Ground-based cosmic-ray neutron sensors are able to estimate soil water content at depths from 0 to 20 or 70 cm, depending on the soil water content. Their data offer a promising reference for the vertical extrapolation of satellite-based soil moisture products. In most soil moisture product assessment studies, assessment metrics are usually provided as single values over a certain period of time. However, this disregards the temporal dynamics of the metrics and the underlying processes. Here, we analyze the temporal dynamics of biases of cutting-edge soil moisture products from remote sensing and process-based modeling, in order to assess their potential to monitor plant-available soil water content. As a reference, we use soil moisture estimations from different sites of the Cosmic-Ray Soil Moisture Observation System (COSMOS) in Germany, covering a six-year time span (2015–2020) that includes the drought of 2018. We found that the biases have an annual frequency with a peak in summer for all selected products. Distinct peaks in 2018 and 2019 are outstanding and show the underestimation of the dry-down in subsurface soil layers caused by the drought. Additionally, there is a positive trend of the biases, even across different depths of multi-layer model-based products. The results suggest that the biases during the 2018 drought and subsequent years are due to soil drying at depths that are both below the coverage of the satellite sensors and not captured by the models. This demonstrates that the dry-down during droughts cannot be replicated by the chosen satellite- and model-based soil moisture products. For the accurate estimation of plant-relevant soil water content during droughts, a careful assessment of soil moisture products along with ground-based measurements is necessary. Our findings serve as a basis for improving current soil moisture products.

How to cite: Schmidt, T., Schrön, M., Li, Z., Francke, T., Zacharias, S., Hildebrandt, A., and Peng, J.: Soil moisture products underestimated plant-relevant dry-down during the recent drought in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14842, https://doi.org/10.5194/egusphere-egu23-14842, 2023.

15:20–15:30
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EGU23-12832
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HS6.1
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Virtual presentation
Martin Hirschi, Bas Crezee, Wouter Dorigo, and Sonia I. Seneviratne

Drought events have multiple adverse impacts on environment, society, and economy. It is thus crucial to monitor and characterise such events. Here, we compare the ability of selected current reanalysis and merged remote-sensing products to represent major seasonal and multi-year drought events of the last two decades globally. We consider the ERA5, and the related ERA5-Land, as well as the MERRA-2 reanalysis products, and the ESA CCI, and the corresponding near-real time C3S remote-sensing soil moisture products (both encompassing an ACTIVE, a PASSIVE and a COMBINED product). In addition, an ESA CCI-based root-zone soil moisture estimate derived from the COMBINED product is used. The considered products offer opportunities for drought monitoring since they are available in near-real time.

We focus on soil moisture (or agroecological) drought and analyse documented events within pre-defined spatial and temporal bounds derived from the scientific literature. Based on standardised daily anomalies of surface and root-zone soil moisture, the drought events are characterised by their magnitude, duration, spatial extent, and severity (i.e., the combination of duration and standardised anomalies below -1.5).

All investigated products capture the investigated drought events. Overall, responses of surface soil moisture are weakest for the ACTIVE remote-sensing products in all metrics. The magnitudes (i.e., the minimum of the standardised anomalies over time) are also reduced in MERRA-2. This is also the case for the spatial extents of most of the remote-sensing products. These differences in drought severity and magnitude for single events are also consistent with inter-product differences in dry-season trends in soil moisture, which are diverse and party contradictory. In the case of MERRA-2, the reanalysis shows regional biases in surface air temperature trends compared to a ground observational product, which suggests that this reanalysis product underestimate drought trends. In the case of the microwave remote sensing products, their lower penetration depth compared to that of the top layer of the involved land surface models, as well as sensing issues of active microwave remote sensing under very dry conditions are likely to explain their partly weaker drought responses. In the root zone (based on the reanalysis products and the ESA CCI root-zone soil moisture estimate), the drought events often show prolonged durations, but weaker magnitudes and smaller spatial extents. Based on the overall observational evidence and the consideration of the respective performance and limitations of the included products, the present analyses suggest a consistent global tendency towards drying during the last two decades in several regions.

How to cite: Hirschi, M., Crezee, B., Dorigo, W., and Seneviratne, S. I.: Characterising recent drought events in the context of dry-season trends using current reanalysis and remote-sensing soil moisture products, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12832, https://doi.org/10.5194/egusphere-egu23-12832, 2023.

15:30–15:40
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EGU23-937
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HS6.1
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ECS
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On-site presentation
Ramesh Visweshwaran, Raaj Ramsankaran, and Eldho T i

During Data Assimilation (DA) in a hydrological model, observations of soil moisture (SM) and streamflow (Q) at interior locations are often assimilated together during the multivariate case to improve streamflow estimates at the catchment outlet. In addition to model states, model parameters need to be updated periodically to account for the variations caused by climatic and human factors during the assimilation period. Therefore, in this study, time-varying multivariate assimilation of ASCAT SM observations and streamflow gauge data from interior sites are ingested into a conceptual two-parameter model, which simulates streamflow using a water budget equation. The Bharathapuzha river basin, lying in the Western Ghats of Southern India is chosen as the study area. In this study, the Ensemble Kalman filter (EnKF), a sequential assimilation approach, is utilized to update the model’s states and parameters at a daily time step. Meanwhile, the computational burden of assimilating such a massive observation needs to be dealt with. A plausible solution is to perform assimilation only at those timesteps when the model is sensitive to the assimilating variable. Consequently, two assimilation scenarios were performed apart from the open-loop (OL) simulations. In the first scenario, all the available SM observations are assimilated irrespective of their sensitivity (DA1). Whereas, in the second scenario, only sensitive SM observations are assimilated into the model (DA2). Results revealed that during both the assimilation scenarios, the model showed improved performance as compared to the open-loop simulations. KGE value improved from 0.68 (during OL) to 0.85 (during DA1) and 0.81 (during DA2). An intriguing fact is that during the second scenario (DA2) when only a subset of sensitive observations was assimilated, the model still showed similar results as DA1. Results highlight that assimilating only spatiotemporally sensitive observations would not affect the model’s performance substantially. Instead, the assimilation efficiency can be enhanced by abbreviating the computational burden.

How to cite: Visweshwaran, R., Ramsankaran, R., and T i, E.: Improving Streamflow Estimates using an Efficient Time Variant Multivariate Assimilation of Soil Moisture and Streamflow Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-937, https://doi.org/10.5194/egusphere-egu23-937, 2023.

Coffee break
Chairpersons: Alexander Gruber, Irene Himmelbauer, Jian Peng
16:15–16:17
16:17–16:27
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EGU23-15089
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HS6.1
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ECS
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Virtual presentation
Shaik Salma and Basavanand Dodamani

For the purpose of defining soil moisture in vegetated areas, abstraction from vegetation-soil interactions must be taken into account, and it must be quantified based on multiple scattering effects, due to the various phases of agricultural crop and its effect on vegetation growth due to the wave signal effect. Entropy-alpha cluster-based (entropy and alpha band clusters are obtained by using K-means unsupervised classification) decomposition approach has been utilised using the Sentinel-1 SAR data to determine the principal scattering contributions from the soil and the vegetation in order to take the effects of plant-soil interactions into consideration. The variation of entropy and alpha is plotted in the target decomposition because the first and second eigenvalues of the covariance matrix for dual-pol data, which indicates a controversial second scattering mechanism. However, anisotropy must be taken into consideration in order to account the impact of vegetation-soil multiple scattering interactions.

The entropy, alpha, and anisotropy bands of considered crop pixels were extracted, and examined the correlation of determination (R2) of crop pixels with each band of decomposition. The R2 for entropy-alpha was achieved less compared to alpha-anisotropy and entropy-anisotropy bands combination. Even though the R2 is high with anisotropy element, anisotropy indicates the presence of a second scattering mechanism and is particularly useful where entropy is high to improve scattering mechanisms. The coefficient of determination between the multiple scattering effects and the backscattering coefficient varies with the crop growth stage. During the initial stages of paddy crop, the R2 is very less whereas as the stage of crop changes, the R2 showed significant varaition at the late vegetative stage of paddy crop due to the vegetation-soil multiple interactions of wave signal. Hence, from the analysis, it is concluded that, the crops can contribute the multiple-scattering effect irrespective of the dominance of either vegetation or soil contribution, which needs to be properly accounted for retrieving soil moisture.

How to cite: Salma, S. and Dodamani, B.: Monitoring the effect of multiple scattering using Sentinel-1 SAR data: A case study of paddy fields, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15089, https://doi.org/10.5194/egusphere-egu23-15089, 2023.

16:27–16:37
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EGU23-481
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HS6.1
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ECS
|
Virtual presentation
Lucía María Cappelletti, Anna Sörensson, Mercedes Salvia, Romina Ruscica, Pablo Spennemann, Maria Elena Fernández-Long, and Esteban Jobbágy

Important progress has been made in recent years in characterizing surface soil moisture (SSM) dynamics at regional scales, both through remote sensing estimates and new in situ networks. Each of these databases has intrinsic features, such as the dynamic range of SSM, the temporal frequency of acquisition and the occurrence of data gaps periods. Improving the understanding of the limitations and the biases that these features can introduce in the characterization of the SSM dynamics is crucial to increase the potential and the consistency of the data sources validations. As a case study, we consider an area of the Argentinean Pampas dedicated to rainfed agro-industrial production. The region is extremely flat and has a sub-humid climate with a high seasonality of both rainfall and cropping. It is also subject to flooding and waterlogging that can last from days to months. The combination of their characteristics makes the region a natural laboratory that is distinguished by a wide dynamic range of SSM conditions. In this context, we study two types of bias. First, considering that data gaps in SSM registries are not usually taken into account in the calculation of representative statistics, we explore if these data gaps are given by spurious behaviors and their impact on SSM statistical metrics. Secondly, and taking into account the characteristics of the region, we assess the bias introduced by the placing of in situ devices on a land cover that is not representative, but which are contained in the remote sensing estimation area. As SSM satellite data we employed estimates from the SMOS and SMAP missions, in conjunction with SSM in situ data preceding from a network belonging to the Argentina National Commission for Space Activities. During the study period (2015-2019), we found a month-long gap resulting from the filtering of high SSM values in the SMAP data. These values are not spurious but typical for this flood-prone region, according to reports from national institutions and comparison with other data sources that identify high soil water content at the same period. In the case of the SMOS data, it presents a period of more than a year with very low data frequency due to radio-frequency interference. We found that ignoring the lack of SMOS data for periods on the seasonal scale, biases in simple statistics are introduced, which might cause erroneous conclusions to be drawn. We also identified that using the in situ data is not possible to represent the transition between growing and fallow seasons. Furthermore, the in situ data fail to capture waterlogging situations, which only became evident with the extensive integration of the satellite data. In this context, our study shows the importance of using multiple sources of information, avoiding taking any one source as absolute truth, with caution about the temporal and spatial biases introduced by both in situ and remote data.

 

How to cite: Cappelletti, L. M., Sörensson, A., Salvia, M., Ruscica, R., Spennemann, P., Fernández-Long, M. E., and Jobbágy, E.: Multiple information sources to characterize surface soil moisture dynamics in flood-prone rainfed agricultural areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-481, https://doi.org/10.5194/egusphere-egu23-481, 2023.

16:37–16:47
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EGU23-5888
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HS6.1
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ECS
|
On-site presentation
Daniel Blank, Annette Eicker, and Andreas Güntner

Information on water storage changes in the soil can be obtained on a global scale from different types of satellite observations. While active or passive microwave remote sensing is limited to investigating the upper few centimeters of the soil, satellite gravimetry can detect changes in the full column of terrestrial water storage (TWS), but cannot distinguish between storage variations occurring in different soil depths. Jointly analyzing both data types promises interesting insights into the underlying hydrological dynamics and may enable a better process understanding of water storage change in the subsurface.

In this study, we investigate the global relationship of (1) several satellite soil moisture (SM) products and (2) non-standard daily TWS data from the GRACE and GRACE-FO satellite gravimetry missions on a sub-monthly time scale. The analysis of these GRACE data on a daily basis could be beneficial for identifying hydro-climatic extreme events such as heavy precipitation or flood events that occur on a sub-monthly basis.

We sample all TWS and SM data sets to a common 1 degree spatial resolution and decompose each signal to sub-monthly frequencies by high-pass filtering. We find increasingly large correlations between the TWS and SM for deeper SM integration depths (root zone vs. surface layer). Even for high-pass-filtered (sub-monthly) variations, significant correlations of up to 0.6 can be found in regions with large high-frequency variability. Time spans with particularly large signal variability, that might hint at extreme events, are identified and compared in both in the TWS and the SM time series. Precipitation data were added to the analysis to provide further evidence for the causes/generation of SM and TWS variations.

How to cite: Blank, D., Eicker, A., and Güntner, A.: Common high-frequency variations of water storage in remotely sensed soil moisture and daily satellite gravimetry, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5888, https://doi.org/10.5194/egusphere-egu23-5888, 2023.

Error characterization
16:47–16:57
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EGU23-7437
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HS6.1
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On-site presentation
Wade Crow, Andreas Colliander, and Fan Chen

While great strides have been made in their accuracy and availability, the overall utility of satellite-derived surface soil moisture (SM) datasets derived from passive microwave radiometry is still reduced by their relatively coarse spatial resolution (typically >30 km). In response to this shortcoming, many independent satellite-based SM downscaling approaches have been introduced recently. However, owing to limitations in the spatial sampling characteristics of existing SM ground-monitoring networks, it has proven difficult to obtain reliable reference SM observations at the target downscaling resolution for these approaches (typically 1 to 10 km). As a result, the objective evaluation of SM downscaling approaches is often challenging and/or limited to very localized conditions. In this talk, we introduce and evaluate a point-scale downscaling (PSD) benchmarking strategy whereby spatially sparse, long-term, point-scale SM observations available from existing ground-based SM networks are utilized for the objective benchmarking of downscaled satellite-based SM products. First, we will define criteria that must be met for a given SM downscaling strategy to add either temporal accuracy or spatial skill relative to its coarse-resolution SM baseline. Next, we will illustrate, both analytically and numerically, that such criteria can be accurately evaluated using sparse, point-scale SM observations available from existing ground-based SM networks. Finally, we apply our new PSD benchmarking approach to evaluate existing fine-scale SM products. Results demonstrate that the PSD approach, in concert with existing ground-based network data, can be leveraged to robustly evaluate SM downscaling approaches.

How to cite: Crow, W., Colliander, A., and Chen, F.: Benchmarking downscaled satellite-based soil moisture products using sparse, point-scale ground observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7437, https://doi.org/10.5194/egusphere-egu23-7437, 2023.

16:57–17:07
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EGU23-1419
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HS6.1
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On-site presentation
Rolf Reichle, Qing Liu, Joseph Ardizzone, Michel Bechtold, Wade Crow, Gabrielle De Lannoy, John Kimball, and Randal Koster

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation.  The product is based on the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observations-based precipitation forcing. 

In this presentation, we describe three recent improvements in the L4_SM algorithm.  First, satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) are used in two ways: (i) The climatology to which all L4_SM precipitation forcing inputs are rescaled is based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project v2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions.  Second, the Catchment model now includes the recently developed PEATCLSM hydrology module for peatlands and uses an updated global map of peatlands.  Third, revised parameters are used in the L-band radiative transfer model that converts the simulated soil moisture and temperature estimates into Tb predictions for use in the radiance-based L4_SM analysis.  Specifically, climatological parameters for the scattering albedo, soil roughness, and (seasonally-varying) vegetation opacity were derived from the SMAP Level-2 radiometer soil moisture retrieval product.   

The revised precipitation inputs result in considerably improved anomaly time series correlation skill of L4_SM surface soil moisture in South America, Africa, Australia, and parts of East Asia.  Particularly large improvements are seen in central Australia and Myanmar, where the quality of the gauge-only precipitation product used in earlier L4_SM versions was particularly poor.  In peatlands, the dynamics of water table depth, surface soil moisture and evapotranspiration are considerably improved when evaluated against in situ measurements.  Moreover, the time series correlation of surface and root-zone soil moisture vs. in situ measurements is slightly improved, owing to the improved annual cycle phasing of the Level-2 derived vegetation opacity parameters.  Collectively, these improvements are also manifested in smaller Tb observation-minus-forecast residuals.

How to cite: Reichle, R., Liu, Q., Ardizzone, J., Bechtold, M., Crow, W., De Lannoy, G., Kimball, J., and Koster, R.: Recent Improvements in the SMAP Level-4 Soil Moisture Product, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1419, https://doi.org/10.5194/egusphere-egu23-1419, 2023.

17:07–17:17
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EGU23-8102
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HS6.1
|
On-site presentation
François Gibon, Alexander Boresch, Irene Himmelbauer, Daniel Aberer, Raffaele Crapolicchio, Raúl Díez-García, Wouter Dorigo, Philippe Goryl, Alexander Gruber, Yann Kerr, Arnaud Mialon, Wolfgang Preimesberger, Philippe Richaume, Nemesio Rodriguez-Fernandez, Roberto Sabia, Klaus Scipal, Pietro Stradiotti, and Monika Tercjak

The aim of this presentation is to report on recent advances concerning the satellite based soil moisture validation done through the ESA project “Fiducial Reference Measurement for Soil Moisture (FRM4SM)”. The main objective of this two years project (May 2021 - May 2023) is to study the means to inform on the confidence in soil moisture data products for the whole duration of a satellite mission. Composed of three international partners (AWST, CESBIO and TU WIEN), it aims at the identification and creation of standards for independent, fully characterized, accurate and traceable (i.e., fiducial) in situ soil moisture reference measurements with corresponding independent validation methods and uncertainty estimations for a satellite mission. The ground reference data is drawn from the International Soil Moisture Network (ISMN). New quality indicators are created to better characterize the aptness of ISMN measurements for satellite soil moisture validation, and protocols provided to identify a select set of fiducial reference data. The satellite part, in charge of independent validation methods, focuses efforts towards the Soil Moisture Ocean Salinity (SMOS) mission from ESA. Finally, the easy-to-use interface for the comparison of satellite soil moisture data against land surface models and in situ data, the Quality Assurance for Soil Moisture (QA4SM), targets to implement all created FRM protocols from ground measurement to validation methods created within the FRM4SM project.

 

How to cite: Gibon, F., Boresch, A., Himmelbauer, I., Aberer, D., Crapolicchio, R., Díez-García, R., Dorigo, W., Goryl, P., Gruber, A., Kerr, Y., Mialon, A., Preimesberger, W., Richaume, P., Rodriguez-Fernandez, N., Sabia, R., Scipal, K., Stradiotti, P., and Tercjak, M.: Fiducial Reference Measurements for Soil Moisture (FRM4SM): Toward a better understanding of (satellite) soil moisture uncertainties, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8102, https://doi.org/10.5194/egusphere-egu23-8102, 2023.

17:17–17:27
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EGU23-14267
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HS6.1
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ECS
|
On-site presentation
Daniel Aberer, Wolfgang Preimesberger, Pietro Stradiotti, Samuel Scherrer, Monika Tercjak, Alexander Gruber, Wouter Dorigo, Alexander Boresch, Irene Himmelbauer, François Gibon, Philippe Richaume, Arnaud Mialon, Yann Kerr, Ali Mahmoodia, Raffaele Crapolicchio, Roberto Sabia, Raul Garcia, Philippe Goryl, and Klaus Scipal

Quality assessment is an integral part of creating climate data records. Producers of satellite based records want to evaluate whether their products fulfill certain quality requirements, such as the ones set by the Global Climate Observing System (GCOS) of the World Meteorological Organization (WMO) or by the Committee on Earth Observation Satellites (CEOS). Users of these data, on the other hand, are usually interested in their fitness-for-purpose in terms of specific applications, temporal/spatial subsets, and how different data sets of the same variable compare to each other.
Quality Assurance for Soil Moisture (QA4SM) is an online validation service for (inter)comparing soil moisture records and assessing their quality, incorporating best practices, in a standardized, traceable way via an easy-to-use graphical user interface. The processing chain includes automatic preprocessing (filtering, temporal/spatial matching, scaling) of input data and computation of a set of quality metrics (e.g., correlation, bias, signal-to-noise-ratio). It provides an open and flexible framework in which users can upload their own data for comparison to state-of-the-art records that are already integrated in the service. These include reference data from the International Soil Moisture Network (ISMN), reanalysis data from ERA5 and GLDAS Noah, and various satellite based records such as SMOS, SMAP, Sentinel-1, ESA CCI, and C3S. 
In this presentation we give insight into the scientific and technical background of developing a cloud-based validation service and its current capabilities. We explain the advantages a service like this has, and how it can benefit users of climate data records with minimal effort.

The service was launched as part of the Quality Assurance for High Spatial and Temporal Resolution Soil Moisture Data (QA4SM-HR) project through the Austrian Research Promotion Agency (FFG) and is currently developed within the framework of the European Space Agency’s Fiducial Reference Measurement for Soil Moisture (FRM4SM) project. It can be accessed at: https://qa4sm.eu

How to cite: Aberer, D., Preimesberger, W., Stradiotti, P., Scherrer, S., Tercjak, M., Gruber, A., Dorigo, W., Boresch, A., Himmelbauer, I., Gibon, F., Richaume, P., Mialon, A., Kerr, Y., Mahmoodia, A., Crapolicchio, R., Sabia, R., Garcia, R., Goryl, P., and Scipal, K.: QA4SM: a service for transparent and reproducible evaluation of satellite soil moisture products, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14267, https://doi.org/10.5194/egusphere-egu23-14267, 2023.

17:27–17:37
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EGU23-9685
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HS6.1
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ECS
|
On-site presentation
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo

Root zone soil moisture, as the water available for plant uptake, effects evapotranspiration and has an important role in predicting droughts and agricultural yields. While microwave remote sensing retrievals are limited to observing the topmost few centimetres of the soil, they can be used with a variety of methods to infer the water content in the root zone due to the existing link between the dynamics in both layers. Regardless of their methodologies, most root zone soil moisture datasets do not provide uncertainty estimates.
Among the techniques for approximating root zone soil moisture, the exponential filter method stands out as a relatively non-complex approach essentially smoothing and delaying surface observations which are generally characterized by greater temporal dynamics. The uncertainties of the exponential filter method are poorly analysed and typically unavailable. 
To address this gap, we extend the standard law for the propagation of uncertainties to characterize the random error variances of the exponential filter-based root zone soil moisture estimates. The proposed method considers the uncertainties of the input surface soil moisture retrievals and their availability in time as well as those of the exponential filter’s parameter and the method’s model structural error. The latter two components of the uncertainty budget are temporally-static values estimated from ground reference measurements at various depths. The resulting time-variant uncertainty estimates are realistic both in magnitude and temporal variations. 

How to cite: Pasik, A., Gruber, A., Preimesberger, W., De Santis, D., and Dorigo, W.: Improved uncertainty estimates for the exponential filter method in a long-term error characterised root-zone soil moisture dataset., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9685, https://doi.org/10.5194/egusphere-egu23-9685, 2023.

17:37–17:47
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EGU23-13940
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HS6.1
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ECS
|
On-site presentation
Pietro Stradiotti, Alexander Gruber, Wolfgang Preimesberger, Rémi Madelon, Robin van der Schalie, Nemesio Rodriguez-Fernandez, Martin Hirschi, Wouter Dorigo, Richard Kidd, and Clément Albergel

Multi-decadal climate data records of soil moisture (SM) are generated by merging distinct satellite microwave remote sensing data sets to serve countless Earth System applications. Such products generally outperform the individual sensor records as they provide a least squares solution to the merging of SM. Within the well known European Space Agency's (ESA) Climate Change Initiative (CCI) for SM product, it was demonstrated that a performance leap is achieved by informing the averaging of multiple retrievals with Triple Collocation Analysis (TCA)-based uncertainty estimates of the input data sets. However, while the approach taken to generate the ESA CCI SM product assumes a constant random error variance for an entire sensor period, it has become evident that errors in SM remote sensing retrievals fluctuate throughout the year. This has been linked to the fact that environmental parameters---foremost vegetative growth---are characterized by a seasonality, such that their impact on the SM retrieval varies with the same cycle. Therefore, taking this seasonal component into account in the least squares formulation of the merging problem is a logical next step. This study examines whether a seasonal adaptation of TCA leads to a performance improvement in the merging, using input data from the ASCAT, AMSR2, and SMAP missions and the GLDAS2.1 model as a climatology baseline. The two key findings are that i) since seasonal uncertainty variations affect all sensors in a similar way, they cause only marginal changes in their relative weighting, which leads to the merged SM estimates not changing significantly from the static to the seasonal merging; yet ii) an evaluation against in situ data suggests that the estimated uncertainties of the new merged product are more representative of their actual seasonal behavior. Such improved uncertainty representation is potentially beneficial to various applications, for instance in the weighting of SM observations for assimilation in physical models. Based on these findings, we conclude that using a dynamic TCA approach can add value to merged products such as the ESA CCI SM by providing a more realistic characterization of data set uncertainty---in particular its temporal variation.

How to cite: Stradiotti, P., Gruber, A., Preimesberger, W., Madelon, R., van der Schalie, R., Rodriguez-Fernandez, N., Hirschi, M., Dorigo, W., Kidd, R., and Albergel, C.: Accounting for Seasonal Soil Moisture Retrieval Errors in the Generation of Climate Data Records, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13940, https://doi.org/10.5194/egusphere-egu23-13940, 2023.

17:47–17:57
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EGU23-11433
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HS6.1
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Virtual presentation
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Raffaele Albano, Arianna Mazzariello, Teodosio Lacava, Salvatore Manfreda, and Aurelia Sole

Several remote sensing (RS) microwave-based SM products are available in recent years and offer an extraordinary opportunity to quantify land surface soil moisture (SSM). These products provide soil moisture (SM) estimates with different levels of accuracy which are influenced by  climate, vegetation, and soil features. For this reason, several studies aimed at assessing satellite SM data performance also for comparison with in situ measurements (e.g., the International Soil Moisture Network – ISMN),  have already tried to investigate the relationship among SM and the type of coverage or the climatic conditions.

In any case, no one of these studies (i) have considered together climate, vegetation, and soil features when characterising accuracy of SM derived from RS, or (ii) analysed separately the uncertainty due to interaction with the water-soil cycle variables and the uncertainty due to the wetting condition of the upper layer; indeed, the topsoil wetness variability affects the penetration depth of microwave radiation bringing additional errors when comparing information collected at different depths, from surface to the root zone.

In this context, the present study aims (i) to assess the accuracy of SSM measurement through the implementation of an intercomparison between satellite and the terrestrial International Soil Moisture Network data among the European ecoregions which are considered the largest homogeneous area in terms of climate, vegetation and potentially investigable soil cover. Furthermore, considering that soil characteristics add further uncertainty due to the soil saturation condition when the upper soil layer is excessively dry or excessively wet, the study explores (ii) the local dynamics of soil moisture described by the probability density function of SM. 

Five satellite SM products have been studied, considering those derived from the National Aeronautics and Space Administration (NASA) mission (SMAP), as well as those generated by the European Space Agency (ESA) mission (SMOS, ASCAT, ESA CCI, SENTINEL -1) while the ISMN data were considered as a ground truth.

The results show the best or worst performance of the above cited satellite retrievals in different climate, vegetation, and soil features by looking at their variability at ecoregion scale. Moreover, the approach of multimodality using the ASCAT product, which is provided in % of saturation, validated by the test of the excess mass of Ameijeiras-Alonso, following the removal of phenological seasonality, has proven to be an excellent tool for characterising errors in dry areas, confirming that worse performance occurs in areas with a dry phase observed.

How to cite: Albano, R., Mazzariello, A., Lacava, T., Manfreda, S., and Sole, A.: Satellite-based soil moisture product performance assessment among the EU Ecoregions , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11433, https://doi.org/10.5194/egusphere-egu23-11433, 2023.

Posters on site: Fri, 28 Apr, 08:30–10:15 | Hall A

Chairpersons: Clément Albergel, Nemesio Rodriguez-Fernandez, Luca Brocca
A.88
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EGU23-2128
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HS6.1
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ECS
Peijun Li, Yuanyuan Zha, Chak-Hau Michael Tso, Liangsheng Shi, Danyang Yu, Yonggen Zhang, Wenzhi Zeng, and Jian Peng

The international soil moisture network (ISMN) provides an important in-situ soil moisture dataset, which is widely utilized for hydrology, agriculture, environmental sciences, and remote sensing validation studies. ISMN soil moisture measurements are generally based on the relationship between soil moisture and other directly observable variables (e.g., dielectric constant) and therefore tend to be influenced by many factors at different installation sites, such as temperature, bulk density, texture, and mineralogy. Based on a previous study (Li, et al., 2020), it is found that coupling a linear bias-aware physical soil water model with data assimilation can effectively detect and calibrate the soil moisture measurement bias. The utilization of a sophisticated physical soil water model can accurately identify the bias but generally requires high costs, which makes extensive evaluation of the ISMN dataset on a large scale difficult. Therefore, a simplified model with less computational cost and satisfying simulation accuracy is needed. Herein, an efficient and bias-aware soil bucket balance model with a data assimilation scheme is developed. The newly developed model without significant accuracy loss has been used to evaluate ISMN data in the Conterminous United States (CONUS). Results show that the proposed model can effectively identify the bias direction based on soil water balance, and that there are many measurements with bias in the ISMN dataset over CONUS.

How to cite: Li, P., Zha, Y., Tso, C.-H. M., Shi, L., Yu, D., Zhang, Y., Zeng, W., and Peng, J.: Bias detection of ISMN soil moisture measurement through soil water balance model and data assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2128, https://doi.org/10.5194/egusphere-egu23-2128, 2023.

A.89
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EGU23-1850
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HS6.1
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ECS
Paul Vermunt, Rogier Van der Velde, Harm-Jan Benninga, Bas Retsios, and Mhd. Suhyb Salama

In situ soil moisture measurement networks are essential for developing, improving and validating satellite soil moisture products. In the east of the Netherlands, an area susceptible to droughts, a monitoring network for soil moisture and – temperature has been operational since 2009. Spread across an area of 45 by 40 km, twenty profile monitoring stations observe moisture and temperature in the root zone. Four field campaigns were conducted in order to calibrate the sensors and to assess the spatial representativeness of the measurements.

The network has proven to be of great value for validation of satellite products (e.g. for SMAP soil moisture). In addition, continuation of the measurements will increase its value for climate studies. Currently, the network is being redesigned to better suit operational water management in the region, while preserving the value of long time series for climatological research, and taking into account its potential value for future missions. Here, we present an overview of the network and the observations since its establishment, including the adjustments that are being made to the network and research opportunities.

Van der Velde, R., Benninga, H.J.F., Retsios, B., Vermunt, P.C., Salama, M.S. (2022) - Twelve years profile soil moisture and temperature measurements in Twente, the Netherlands. Earth System Science Data Discussions, 1-44.

Velde, dr. ir. R van der (University of Twente) (2022): Twelve years profile soil moisture and temperature measurements in Twente, the Netherlands. DANS. https://doi.org/10.17026/dans-znj-wyg5

How to cite: Vermunt, P., Van der Velde, R., Benninga, H.-J., Retsios, B., and Salama, Mhd. S.: A network of in situ soil moisture observations operational since 2009, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1850, https://doi.org/10.5194/egusphere-egu23-1850, 2023.

A.90
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EGU23-3057
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HS6.1
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ECS
|
Beilei Zan, Huiming Wang, Jiangfeng Wei, Yuanyuan Song, and Qianqian Mao

Soil moisture is a crucial component of the water cycle and plays an important role in regional weather and climate. However, owing to the lack of In Situ observations, an accurate understanding of the spatiotemporal variations of soil moisture (SM) on the Tibetan Plateau (TP) is still lacking. In this study, we used three gridded SM products to characterize the spatiotemporal features of SM on the TP during the warm season (May to August). We analyzed the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5), Global Land Data Assimilation System (GLDAS), and Soil Moisture Active Passive (SMAP) datasets and used station observation data and triple collocation to quantify product accuracy and consistency. Results of the evaluation based on observation data show that both ERA5 and GLDAS overestimate SM, while the accuracy of SMAP is high. In terms of capturing the temporal variations of SM measured at stations, the performance of ERA5 and that of SMAP are superior to that of GLDAS. According to the evaluation based on triple collocation, SMAP exhibits the smallest random error over the TP and the highest temporal correlation with the unknown true SM in eastern TP. For SMAP, SM variability is the largest in the southern TP. For ERA5 and GLDAS, variability in the western TP is substantially larger than that for SMAP. Low-frequency (30–90 days) variations are the largest contributor to TP SM intraseasonal variability. Relative to SMAP, the contribution of high-frequency variations is low in ERA5 and GLDAS. Land-atmosphere coupling is stronger (weaker) in the western (southeastern) TP, which is relatively dry (wet). Our evaluation of SM product performance over the TP may facilitate the use of these products for disaster monitoring and climate and hydrological studies.

How to cite: Zan, B., Wang, H., Wei, J., Song, Y., and Mao, Q.: Spatiotemporal characteristics of soil Moisture and land – atmosphere coupling over the Tibetan Plateau derived from three gridded datasets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3057, https://doi.org/10.5194/egusphere-egu23-3057, 2023.

A.91
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EGU23-7778
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HS6.1
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ECS
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Suyog Khose and Damodhara Rao Mailapalli

Information on near-surface soil moisture content (SMC) is very important for various applications such as irrigation scheduling, precision farming, watershed management, climate change analysis, drought prediction, meteorological investigations etc. Soil moisture information acquired from remotely sensed satellite data has been widely used in the recent past. However, these remote sensing data's low spatial and temporal resolution is a limitation for agricultural applications. Unmanned aerial vehicles (UAV)-based soil moisture predictions are thriving, but the studies are limited with fewer ground truth data. This study aims to predict the surface soil moisture content using UAV-based multispectral data and machine learning techniques. The UAV-based multispectral data are acquired from an altitude of 40 m. Surface soil samples were collected at an interval of two days to estimate gravimetric soil moisture content. Four machine-learning algorithms (Linear Regression, SVR, RFR, KNN) were used to develop the relationship between near-surface SMC and multispectral data. At high surface SMC, the soil has low spectral reflectance as compared to low surface SMC. The linear regression algorithm performed best, with R2 as 0.89 among the other ML algorithms. Also, blue band reflectance was correlated well with the surface SMC as compared to green, red, NIR and red-edge bands. The findings indicated that UAV-based high-resolution multispectral image analytics could accurately predict the surface SMC. The developed approach of estimation of near SMC may be helpful for farmers and irrigation planners to schedule irrigation and crop management accordingly.

Keywords:  Surface soil moisture content; Remote sensing; UAV; Multispectral imageries; Machine learning

How to cite: Khose, S. and Mailapalli, D. R.: Prediction of Surface Soil Moisture Content using Multispectral Remote Sensing and Machine Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7778, https://doi.org/10.5194/egusphere-egu23-7778, 2023.

A.92
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EGU23-5733
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HS6.1
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ECS
Yoon-Jeong Kwon, Sumiya Urangchimeg, Minwoo Park, and Hyun-han Kwon

The drought risk in Korea has been gradually increasing, and the southern part of South Korea has experienced prolonged exposure to extremely low precipitation from the summer of 2021 until 2022, leading to the depletion of available water within two months. Droughts can be classified into meteorological, agricultural, and hydrological droughts under different definitions. The drought indices are routinely used to effectively monitor and cope with different drought conditions. In this perspective, various hydrometeorological factors (precipitation, temperature, streamflow, and soil moisture) are required to derive the drought indices according to the classification. Among the factors, the lack of soil moisture data has been an issue in effectively deriving the agricultural drought index compared to precipitation and temperature-based drought indices such as SPI and SPEI. Currently, research on satellite (i.e., C-band SAR) for water resources management is being conducted in South Korea. The agricultural drought index is commonly based on the satellite-based soil moisture and vegetation index, thus, an accurate estimation of soil moisture from the satellite information could be viewed as a main issue in terms of monitoring agricultural drought. In this study, we develop a novel hybrid stochastic simulation model for soil moisture at multiple locations (or grids) with relevant predictors, including hydrometeorological variables and satellite information.

 

Acknowledgement : This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Water Management Program for Drought, funded by Korea Ministry of Environment(MOE)(2022003610001)

How to cite: Kwon, Y.-J., Urangchimeg, S., Park, M., and Kwon, H.: Establishment of soil moisture data using satellite information and calculation of hydrological drought index using it, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5733, https://doi.org/10.5194/egusphere-egu23-5733, 2023.

A.93
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EGU23-12269
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HS6.1
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ECS
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Samuel Massart, Mariette Vreugdenhil, Bernhard Bauer-Marschallinger, Claudio Navacchi, Bernhard Raml, Alena Dostálová, and Wolfgang Wagner

The current generation of Synthetic Aperture Radars (SAR) has a high potential to retrieve surface soil moisture (SSM) at a kilometer-scale resolution. Research has shown that a change detection approach applied to the backscatter from the Sentinel-1 mission was able to yield a consistent kilometer-scale SSM product over Europe. This product is operational and available on the Copernicus Global Land Service (CGLS) website (https://land.copernicus.eu/global/). A known problem of the CGLS algorithm is its reduced performance over areas with dense vegetation. The combined influence of vegetation water content and geometry on the backscatter signal results in a lower sensitivity to SSM. This effect is especially observed over woody vegetation such as broadleaved or coniferous forests. In addition, a wet bias is detected in the CGLS SSM data during the growing season over land cover with seasonal variation of vegetation.

This study utilizes the native resolution of Sentinel-1 in its interferometric wide swath mode (20x22m), resampled to a 20m pixel spacing, to assess three dense vegetation masks over Europe. The masks are derived from forest/tree cover maps based on Sentinel-1, Sentinel-2, or a combination of both. At 20m, the backscatter pixels are selectively filtered to discard the ones flagged as non-soil moisture sensitive. The masked backscatter at 20m sampling is then resampled to a kilometer scale and used as input for the CGLS change detection model algorithm. The resulting SSM product is compared to in-situ stations from the International Soil Moisture Network (ISMN) and with modeled soil moisture from ERA5-Land. The results sug gest that masking dense vegetation consistently improves the SSM signal over regions containing both forested areas, and croplands or grasslands.

This study highlights the potential of masking non-soil moisture sensitive pixels at the native resolution of the Sentinel-1 backscatter. The results demonstrate the ability of high-resolution forest masking to mitigate the effect of dense vegetation on the CGLS SSM product.

How to cite: Massart, S., Vreugdenhil, M., Bauer-Marschallinger, B., Navacchi, C., Raml, B., Dostálová, A., and Wagner, W.: Mitigating the impact of dense vegetation on theSentinel-1 surface soil moisture over Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12269, https://doi.org/10.5194/egusphere-egu23-12269, 2023.

A.94
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EGU23-16006
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HS6.1
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ECS
Irene Himmelbauer, Alexander Gruber, Daniel Aberer, Wolfgang Preimesberger, Pietro Stradiotti, Wouter A. Dorigo, Alexander Boresch, Monika Tercjak, Francois Gibon, Arnaud Mialon, Philippe Richaume, Yann Kerr, Raul Diez Garcia, Raffaele Crapolicchio, Roberto Sabia, Klaus Scipal, and Philippe Goryl

To this day, in situ soil moisture data is viewed as ground truth by the satellite soil moisture (SSM) community. In general, little is still commonly known regarding the traceability of ground measurement uncertainty and their overall in uncertainty budget, which can impact satellite SSM product quality assessments.

Within ESA’s “Fiducial Reference Measurement for Soil Moisture (FRM4SM, May 2021 - May 2023)” project, objectives are set towards building fully characterized and traceable (i.e., fiducial) in situ measurements following community-agreed guidelines from the GEOS/CEOS Quality Assurance for Soil Moisture (QA4EO) framework. These so called “fiducial reference data” (FRM) should have associated Quality Indicators (QI) attached to evaluate their fitness for purpose building upon agreed reference standards (SI if possible). Moreover, such data should be easily and openly accessible, validation case studies should demonstrate their utility and reliability, and protocols and procedures should be established for the usage of such FRM datasets to make scientific studies intercomparable and reproducible.

As part of the FRM4SM project, the following questions were addressed using the International Soil Moisture Network (ISMN) as a ground reference database and the Soil Moisture and Ocean Salinity (SMOS) mission as an example satellite product:

(1) What makes “fiducial reference data” fiducial?

(2) Is the creation of a globally-representative FRM subset already feasible for SSM?

(3) What are the current limitations of in situ observations that limit fiduciality?

(4) What is needed to create a full traceability chain from in situ point measurements to the satellite footprint scale?

In this presentation, we will discuss these questions in detail and report on related findings of the FRM4SM project.

How to cite: Himmelbauer, I., Gruber, A., Aberer, D., Preimesberger, W., Stradiotti, P., Dorigo, W. A., Boresch, A., Tercjak, M., Gibon, F., Mialon, A., Richaume, P., Kerr, Y., Diez Garcia, R., Crapolicchio, R., Sabia, R., Scipal, K., and Goryl, P.: Analyzing the reliability of in situ soil moisture measurements for satellite product validation: What makes fiducial reference measurements fiducial?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16006, https://doi.org/10.5194/egusphere-egu23-16006, 2023.

A.95
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EGU23-16966
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HS6.1
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ECS
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Highlight
Avery Walters and Venkataraman Lakshmi

The Feather River Basin is home to California’s deadliest wildfire, the 2018 Camp Fire, and to the largest single fire in the state’s history, the 2021 Dixie Fire (CalFire, 2022). Each of these events took place in the last five years. In 2021 alone, the Dixie Fire and the Beckwourth Complex Fire combined to burn over 1 million acres of land in the Feather River region (FRLT, 2022). The Dixie Fire burned despite taking place within the burn scar of the 2012 Chips Fire (Graff, 2021). Such exceptional wildfire activity is a cause for further studies.

Our research proposes analyzing satellite data for the Feather River Basin to measure the hydrological effects of wildfire. This study aims to produce monthly observations of major hydrological conditions (i.e. precipitation, soil moisture, vegetation index and streamflow) over the past five to ten years. A one-kilometer sub-daily soil moisture dataset will be used to characterize soil moisture anomalies. Additionally, visual as well as infrared imagery will be collected from commercial high-spatial resolution satellite sensors, which have revisit times of about one hour and resolutions of about one meter. This should help characterize fire extent as well as understand the effects of fire on soil moisture. In situ measurements, when available, will be used to validate satellite-derived observations. 

The Feather River Basin is a high-profile area of the United States with 27 million people dependent upon it for water. The Feather River is the Sierra Nevada’s largest and northernmost river, and the nearby Oroville Dam is America’s tallest dam. Furthermore, the basin is home to continental America’s largest high-alpine meadow– also an important stopover site for migratory birds (American Rivers, 2022). California’s dry climate, combined with shortened snowmelt periods, steep mountain terrain and strong winds, already make it a hotbed for wildfire. A warming climate threatens this landscape with even higher likelihoods of extreme wildfire events. The results of this study will help understand how increasingly common and severe wildfires affect watershed hydrology.

How to cite: Walters, A. and Lakshmi, V.: Using Earth Observations to Measure Hydrological Effects of Wildfires in the Feather River Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16966, https://doi.org/10.5194/egusphere-egu23-16966, 2023.

A.96
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EGU23-1903
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HS6.1
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ECS
Alexander Gruber and Rolf Reichle

In this study, we assimilate Soil Moisture Active Passive (SMAP) mission brightness temperature (Tb) observations into NASA's Catchment Land Surface Model using an Ensemble Kalman filter to update surface and root-zone soil moisture simulations. Different time series components of the Tb observations are assimilated including anomalies, inter-annual variations, and high-frequency variations. To optimize the weights that the data assimilation (DA) puts on the observations, the ratio between the uncertainties of modeled and observed Tb is approximated using modeled and observed soil moisture uncertainties estimated using triple collocation analysis. Results are compared to a benchmark experiment that mimics the operational SMAP Level-4 algorithm, which assimilates Tb observations using a spatially-constant 4 Kelvin (K) observation uncertainty. 

All DA experiments exhibit notable skill improvements in most regions. Improvements are greatest for the inter-annual variations in the simulations of both surface and root-zone soil moisture (mean improvements in terms of Pearson correlation (-) are 0.08 and 0.06, respectively). Anomaly simulations improve similarly (0.07), and improvements in the high-frequency variations are only observed for surface soil moisture simulations (0.06). Strikingly, however, no notable difference in skill—neither improvement nor deterioration—is observed between the experiments that use optimized observation uncertainty parameters and the 4 K benchmark experiment. We show, analytically, that this may be explained by the presence of large observation operator errors, which have the potential to render post-update uncertainty insensitive to inaccuracies in the Kalman gain. 

How to cite: Gruber, A. and Reichle, R.: Uncertainty Estimation for SMAP Level-1 Brightness Temperature Assimilation at Different Timescales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1903, https://doi.org/10.5194/egusphere-egu23-1903, 2023.

A.97
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EGU23-3403
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HS6.1
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ECS
Alby Duarte Rocha, Stenka Vulova, Christian Schulz, Michael Förster, and Birgit Kleinschmit

Drought events and environmental disturbances related to water scarcity have become more severe and frequent, affecting food security and endangering vulnerable biomes. Reliable soil moisture content (SMC) estimations at the landscape scale are therefore essential to understand patterns in drought events and vegetation response to such occurrences. Accurate soil moisture predictions can support actions to mitigate water scarcity effects in vegetation, for instance, by precisely managing crops to avoid further depleting limited water resources. However, most available SM products derived from remote sensing (RS) or meteorological data are supplied at a coarse spatial scale and are unsuitable for heterogeneous landscapes in terms of topography and land cover. The gaps between significant changes in SMC levels at the root zone and the vegetation response during the dry and wet seasons are still unknown. Before defining whether up-scaling (or modelling) in situ data using RS or downscaling coarse images to a landscape scale would resolve this research gap, a better understanding of temporal and spatial contributions and uncertainties of different technologies to SMC products is needed. Despite the advance in sensors and processing capacity, a combination of spatial and temporal resolution required for SMC retrieval is unlikely to be available soon globally. Satellite sensors (e.g. microwave, optical, thermal) present different limitations and rely on proxies and assumptions to indirectly derive SMC at the root zone. Moreover, the relationships across time can be biased by weather conditions, masked by land cover type and clouds, or misled by spurious correlations between meteorological and plant trait variables (phenology). For instance, microwave signals can be affected over dense forests, snow cover, or steep topography. Furthermore, optical data are often unavailable due to cloud cover or have their reflectance drastically change from living vegetation to bare soil between two acquisitions in non-permanent crop fields. Therefore, multi-platform approaches, combining technologies and resolutions to derive a versatile and accurate SMC product, should be prioritized. As the model relies on indirect relationships with plant traits or moisture from the topsoil rather than the underlying hydrological processes, the spatial-temporal patterns (and autocorrelation) should not be neglected as they carry crucial information about water balance. In this study, we analyze 38 soil moisture probes installed in landscapes with different vegetation cover, topography, and soil type in Germany. The SMC measurements are provided by cosmic-ray neutron sensors (CRNSs), a non-invasive technology that provides measurements at a field scale (130 to 240m radius). The CRNS time-series measurements are compared to RS and meteorological products. Auxiliary variables such as precipitation, evapotranspiration, and vegetation parameters (e.g. LAI) are also aligned with the SMC and RS-derived products. The similarity and mismatching of the explanatory time-series patterns compared to the reference SMC for different vegetation cover (forest, grassland, and crops), season, regional characteristics (climate, soil properties, and topography), and resolutions (temporal and spatial) are presented. The results can support the development of a soil moisture retrieval approach at a medium to high spatial resolution based on a data cube combining different RS platforms and auxiliary variables.

How to cite: Duarte Rocha, A., Vulova, S., Schulz, C., Förster, M., and Kleinschmit, B.: Time series profiles of CRNS derived soil moisture content compared to remote sensing and meteorological derived products: insights for up- and downscaling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3403, https://doi.org/10.5194/egusphere-egu23-3403, 2023.

A.98
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EGU23-4279
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HS6.1
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ECS
Iuliia Burdun, Michel Bechtold, Mika Aurela, Gabrielle De Lannoy, Ankur R. Desai, Elyn Humphreys, Santtu Kareksela, Viacheslav Komisarenko, Maarit Liimatainen, Hannu Marttila, Kari Minkkinen, Mats B. Nilsson, Paavo Ojanen, Sini-Selina Salko, Eeva-Stiina Tuittila, Evelyn Uuemaa, and Miina Rautiainen

Water table constitutes a master control of the general biogeochemistry in northern peatlands. The performance of peatland simulations in global ecosystem models is strongly hampered by the accuracy of the water table predictions. We examined the applicability of the Optical TRApezoid Model (OPTRAM) to monitor the temporal fluctuations in water table over 53 intact, restored, and drained northern peatlands in Finland, Estonia, Sweden, Canada, and the USA from 2018 through 2021. Various OPTRAM were computed based on Sentinel-2 data with the Google Earth Engine cloud platform. We found that (i) the choice of vegetation index utilised in OPTRAM does not significantly affect OPTRAM performance; (ii) the tree cover density is a significant factor controlling the sensitivity of OPTRAM to water table dynamics; (iii) the relationship between water table and OPTRAM often disappears for deep water tables. Based on an anomaly analysis, we further found that OPTRAM seems to be in particular suitable to monitor long-term (i.e., interannual) water table variability while the performance for short-term changes (e.g., response to individual rain events) was lower. Overall, our results support the application of OPTRAM to monitor water table dynamics in intact and restored northern peatlands with low tree cover density when the water table is shallow to moderately deep.

How to cite: Burdun, I., Bechtold, M., Aurela, M., De Lannoy, G., Desai, A. R., Humphreys, E., Kareksela, S., Komisarenko, V., Liimatainen, M., Marttila, H., Minkkinen, K., Nilsson, M. B., Ojanen, P., Salko, S.-S., Tuittila, E.-S., Uuemaa, E., and Rautiainen, M.: Temporal water table dynamics derived from optical satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4279, https://doi.org/10.5194/egusphere-egu23-4279, 2023.

A.99
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EGU23-590
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HS6.1
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ECS
Sahil Sharma, Deepak Swami, and Amit Dubey

To compute optimal sampling locations for a lower Himalayan watershed various methods were used. The methods were compared at various scales: watershed scale, landform scale and seasonal scale. The methods compared for the evolution of optimal sampling strategy are statistical sampling, geostatistical sampling, stratified sampling, bootstrap methods and random combination method. To examine the methods, field experiments were conducted in a sampling domain of 425 km2 to study the patterns. At the watershed scale total number of 24 locations were evaluated which were distributed into 12 agricultural, 6 forest and 6 grassland landforms. To study the seasonal patterns comparison for the Rabi and Kharif seasons was done. The results indicated that the random combination method provides a simplified and efficient sampling strategy compared to the other methods. Further the random combination method has an inherent advantage of requiring very minimal input information whereas, the statistical and stratified sampling strategy requires data that has to be independent and normally distributed. The geostatistical methods requires a semi variogram model to get the necessary results. To obtain the results at the same level of error the random combination method gives lesser number of sampling locations required. Additionally the computational efficiency of the random combination method can be increased generating smaller groups of samples for the optimality estimation.  

How to cite: Sharma, S., Swami, D., and Dubey, A.: Estimating Optimal Sampling Locations of Surface Soil Moisture at Different Scales Using Various Methods and comparing them for a Lower Himalayan Watershed Region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-590, https://doi.org/10.5194/egusphere-egu23-590, 2023.

A.100
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EGU23-11066
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HS6.1
Seongkeun Cho, Jaehwan Jeong, and Minha Choi

Soil moisture is a key factor in analyzing the water cycle on the land surface. Active microwave sensor has been widely used for spatially representative soil moisture content regardless of weather conditions. Especially C-band microwave sensors (Scatterometer and Synthetic Aperture Radar) loaded on a satellite were adopted for capturing soil moisture over the vegetated area. However, in heterogeneously or thickly vegetated areas, it is difficult to get accurate soil moisture content with SAR sensor for high spatial resolution (< 1 km). In this study, high-resolution soil moisture content in mountainous areas is estimated and evaluated with in-situ soil moisture observation and a Cosmic-Ray Neutron probe (CRNP) sensor. To evaluate the satellite-based soil moisture product, the SMC Soil Moisture observation site, designed for monitoring soil moisture content, was used. The site has 16 FDR sensors for 10 cm, 20 cm, and 30 cm. At the center of the site, CRNP is operated for measuring spatial soil moisture content. Firstly, the Sentinel-1 backscattering signal, strongly affected by land surface conditions in the mountainous areas, was analyzed. Then, canopy attenuation and the relation between the backscattering signal and the local incidence angle on the mountain were evaluated. Secondly, Sentinel-1 images on the observation site were resampled to 10 m, 50 m, 100 m, and 150 m. Water Cloud Model and change detection method were applied to estimate soil moisture content for the 4 scales. Lastly, estimated soil moisture content was compared with CRNP soil moisture data and ASCAT data on observation sites. Error analysis for each pixel included in ASCAT pixels was conducted to figure out the main obstacles of soil moisture estimation on mountains. With the result of this study, high-resolution soil moisture estimation on the Korean peninsula which mainly consists of the mountainous area would be suggested.

Acknowledgment: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C2010266). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(2021R1A6A3A01087645)

How to cite: Cho, S., Jeong, J., and Choi, M.: Sentinel-1 Soil moisture validation with Multiple sources of observations in the mountainous area in Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11066, https://doi.org/10.5194/egusphere-egu23-11066, 2023.