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.

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 m3⋅m−3 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.