HS6.1

How to cite: Vreugdenhil, M., Greimeister-Pfeil, I., Preimesberger, W., Brocca, L., Camici, S., Massart, S., Enenkel, M., and Wagner, W.: Satellite soil moisture for drought assessment and early-warning in water limited regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9918, https://doi.org/10.5194/egusphere-egu22-9918, 2022.

Drought events have multiple adverse impacts on environment, society, and economy. Monitoring and characterising such events is thus crucial. Here we test 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). The considered products offer opportunities for drought monitoring since they are available in near-real time.

We focus on soil moisture (or agricultural) drought and analyse events within pre-defined spatial and temporal bounds derived from 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 are able to indicate the investigated drought events. Overall, responses of surface soil moisture are often strongest for the reanalysis products ERA5 and ERA5-Land and weakest for the remote-sensing products (in particular for the ACTIVE satellite products). The weaker drought severities in the remote-sensing products are related to shorter event durations as well as partially less pronounced negative standardised soil moisture anomalies. The magnitudes (i.e., the minimum of the standardised anomalies over time) are reduced in MERRA-2 and in the ACTIVE satellite products. Diverse global distributions of long-term trends in dry-season soil moisture may explain some differences in the drought responses of the products. Also, the lower penetration depth of microwave remote sensing compared to the top layer of the involved land surface models, as well as sensing issues of active microwave remote sensing under very dry conditions could explain the partly weaker drought responses of the remote-sensing products during the investigated events. In the root zone (based on the reanalysis products), the drought events often show prolonged durations, but weaker magnitudes and smaller spatial extents.

How to cite: Hirschi, M., Crezee, B., Dorigo, W., and Seneviratne, S. I.: Characterising recent drought events using current reanalysis and remote-sensing soil moisture products with a standardised anomalies-based framework, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11252, https://doi.org/10.5194/egusphere-egu22-11252, 2022.

In recent years, the availability of high-resolution observations (<1km, sub-daily) from remote sensing, in situ monitoring networks and new sensors/techniques (drones, citizen science), in addition to the increased computational and storage capacity, have fostered the development of modelling systems at high resolution for hydrological applications. The European Commission (EC) is promoting these developments through the EU’s digital strategy, the Green Deal, and specifically the Destination Earth initiative. The development of digital twins of the Earth System is currently in the EC agenda as one of the most pressing activities to be accomplished to build a resilient society able to cope with adverse extreme events (flood, drought, heatwaves, landslides), exacerbated by global and climate changing.

Despite the high-resolution hydrology is an important opportunity for future research and operation applications, the challenges to be addresses are quite a lot and non-trivial. First, the increased computational capabilities are far from being sufficient to develop high resolution hydrological systems because observations (e.g., precipitation, evapotranspiration, soil moisture, river discharge) have to be available not only at high resolution, but also with sufficient accuracy. A second problem is related to the representation of physical processes that, at high resolution, are significantly different from processes at coarse resolution (20km), currently modelled at large scale. Last but not least, the human impact on the water cycle (e.g., irrigation, reservoir management, river water diversion) acting at very high resolution, challenges the current attempts of reproducing a digital replica of the Earth.

The launch of Sentinel-1 satellites has opened a number of opportunities for developing high resolution satellite soil moisture and precipitation products. These products are an important element for building a Digital Twin Earth (DTE) for Hydrology, i.e., for the reconstruction of the water cycle at high resolution. In this contribution we will present the recent advances over this topic carried out under European Space Agency projects DTE Hydrology and Irrigation+. Specifically, we will present the application of high-resolution products for hydrological applications: flood simulation, landslide risk prediction and irrigation water management. Finally, the main challenges to be addressed will be discussed.

How to cite: Brocca, L., Ciabatta, L., Massari, C., Camici, S., Barbetta, S., Tarpanelli, A., Filippucci, P., Dari, J., and Mosaffa, H.: High resolution (1 km) soil moisture and precipitation for developing a Digital Twin Earth for hydrology, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1883, https://doi.org/10.5194/egusphere-egu22-1883, 2022.

Surface Soil Moisture (SM) plays a key role in the Earth water cycle and many hydrological processes (Koster 2004), it is essential for accurate weather forecasting (Rodriguez-Fernandez 2019) and agriculture management (Guerif 2000). SM was also identified as one of the 50 “Essential Climate Variables” (ECVs) by the Global Climate Observing System (GCOS). Long time series of ECVs are crucial to monitor the Earth’s climate evolution, and developing them is the goal of initiatives such as the European Space Agency’s Climate Change Initiative (ESA CCI).

The ESA SM CCI product (Gruber 2019) provides global time series for the 1979-2021 period at 25-km resolution using scatterometers and passive microwave sensors. Based on extensive feedbacks from the user communities of SM products, a strong need for higher spatial resolutions SM data was identified (Dorigo 2018, Peng 2020). This also includes climate applications such as assessment of climate change impacts at regional level.

SM can be estimated at high spatial resolution using Synthetic Aperture Radars such as Sentinel-1 (S1). Several high resolution (HR) S1 SM data sets exist such as the products from the Copernicus Global Land Service and the one using the S²MP (Sentinel-1/2 Soil Moisture Product) algorithm (El Hajj 2017). Despite the actual short temporal coverage of such data, it is worth to evaluate them in the context of the ESA CCI as potential future HR SM long time series, and also as benchmarking references for HR SM data sets that could be obtained by the downscaling of coarser resolution sensors.

In this context, the S²MP algorithm, which was originally designed to retrieve SM at a plot level, was adapted to compute SM maps at 1-km resolution over six 100-km^² regions in the Southwest and Southeast of France, Tunisia, North America, Spain and Australia. The S²MP algorithm is based on a neural network approach using backscattering coefficients and incidence angles from S1, and either NDVI from Sentinel-2 (S2) or that of Sentinel-3 (S3), as input data.

Both S1+S2 and S1+S3 1-km SM maps are compared to HR SM data from the SMAP+S1 product and the Copernicus SM and Soil Water Index (SWI) data sets. The S1+S2 and S1+S3 SM maps are in very good agreement in terms of correlation (R > 0.9), bias (< 0.05 m³.m^-3) and standard deviation of the difference (STDD < 0.025 m³.m^-3) over the 6 regions of study. They also are well correlated (R ~ 0.6-0.7) with the Copernicus products over homogeneous pixels containing croplands and herbaceous vegetation. However, the results are more mitigated over Tunisia and mixed land cover pixels as well as when the maps are compared to those of SMAP+S1.

The high resolution products are also evaluated against in-situ measurements along with coarse scale SM data sets (SMAP, SMOS, ESA CCI). In general, the coarse resolution SM products show better correlation than the HR ones. However, the HR products, in particular S²MP, show lower STDD and bias.

How to cite: Madelon, R., Bazzi, H., Amin, G., Albergel, C., Baghdadi, N., Dorigo, W., Rodriguez-Fernandez, N., and Zribi, M.: Evaluating high resolution soil moisture maps in the framework of the ESA CCI, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2147, https://doi.org/10.5194/egusphere-egu22-2147, 2022.

Soil moisture is an essential parameter for a better understanding of water processes in the soil-vegetation-atmosphere interface. In this context, passive and active microwave remote sensing have enabled the development of various increasingly operational approaches, in particular for low spatial resolution products. Synthetic aperture radar (SAR) is particularly suitable for monitoring water content at fine spatial resolutions of the order of 1 km spatial resolution. Since the launch of Sentinel-1 in 2014, numerous methodologies have been proposed for estimating fine spatial resolutions soil moisture, especially in agricultural areas. Two approaches are often considered in the inversion of SAR signals: approaches based on machine learning methodologies, such as neural networks trained on scattering models, or approaches based on change detection, essentially validated on low spatial resolution products using scatterometers. In this study, we propose a hybrid approach combining both the neural networks and change detection approaches. The methodology was applied to Sentinel-1 and Sentinel-2 using numerous predictors; Vertical-Vertical (VV) polarization radar signal, incidence angle, Normalized Difference Vegetation Index (NDVI) optical index, VH/VV ratio, etc.

This hybrid approach is tested on the database of the international soil moisture network (ISMN) with moisture networks covering different climatic contexts. Results are very encouraging with 10% improvement in the accuracy of soil moisture estimates compared to the use one of each approach individually (Neural Network or change detection).

How to cite: Zribi, M., Nativel, S., Rodriguez Fernandez, N., Baghdadi, N., Madelon, R., and Albergel, C.: A hybrid methodology based on Neural Network and Change detection approaches and using Sentinel-1/Sentinel-2 for soil moisture estimation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3698, https://doi.org/10.5194/egusphere-egu22-3698, 2022.

Soil moisture is a critical component in many hydrological, agricultural, and meteorological applications and processes, and understanding the spatiotemporal dynamics and changes is critical to further their understanding. They are also an important parameter for use within soil- and land-based Natural Flood Management (NFM) schemes, to determine a relationship between surface wetness and soil water storage. Satellite-based remote sensing offers the ability to capture this spatiotemporal information on soil moisture on the synoptic scale; compared to more site-based in-situ field measurements, made up of numerous national and international soil moisture networks. In this study, we use Sentinel-1 SAR imagery over the course of six water years (from 2016 to 2021), utilizing the TU-Wein change-detection algorithm to calculate the relative Surface Soil Moisture (rSSM) across the River Thames Catchment in Southern England, equating to approximately 11,000 km². As part of this, two pairs of backscatter normalisation factors were considered, in order to negate the impact from varying local incidence angles: a simple direct-slope and a complex multiple regression slope, both calculated annually and monthly. Whilst the monthly normalisation factor does exhibit a seasonal cycle (attributed to the growth and harvest of arable crops within the study area) in both the simple and multiple regression methodology, the impact upon the rSSM, when compared to the traditional annual method is small. In order to assess the spatiotemporal patterns of soil moisture across the River Thames Catchment, the rSSM timeseries was calculated using multiple spatial scales (1km, 500m, 250m, and 100m), to effectively estimate the rSSM across the catchment, sub-catchment, inter-field, and intra-field spatial scales. Comparisons with the Cosmic-ray Soil Moisture Observing System, United Kingdom (COSMOS-UK), show that, although there is an overestimation in rSSM over the summer months during the growing season of Arable farmland, we were able to effectively capture the general temporal dynamics of the relative Surface Soil Moisture across the region, with an average uncertainty of 30%, across both pairs of backscatter normalisation factors, and across all four spatial scales. Having catchment-wide datasets of rSSM such as this would be advantageous for evaluating land- and soil-based NFM measures across catchment and sub-catchment scales and have the potential for further application to improve hydrological model outputs.

How to cite: Maslanka, W., Morrison, K., White, K., Verhoef, A., and Clark, J.: Monitoring Relative Surface Soil Moisture Using Sentinel-1 Across the River Thames Catchment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9798, https://doi.org/10.5194/egusphere-egu22-9798, 2022.

Active microwave remote sensing satellites allow to retrieve surface soil moisture (SSM) consistently and independently from sun illumination or cloud cover. The current generation of Synthetic Aperture Radars (SAR) on-board of the Sentinel-1A and 1B satellites, launched in 2014 and 2016 respectively, provide backscatter observations in their interferometric wide swath mode at 20 x 22 m resolution. These data are being used by the Copernicus Global Land Service (CGLS) for generating SSM data at kilometre-scale resolution using a change detection approach. The data are operationally and freely available from https://land.copernicus.eu/global/. The goal of this study was to assess the quality of the CGLS SSM retrieval algorithm over different land cover types and crop species. For this purpose, we compared the satellite retrievals against in-situ SSM from the International Soil Moisture Network (ISMN). The stations analyzed are located in France and Austria (SMOSMANIA and HOAL) and cover a wide range of land cover types, from cropland and grassland to forested areas. For each station, backscatter at 20m resolution was averaged over fields containing the ISMN station using Land Parcel Identification System (LPIS) data. The resampled field backscatter, which covers one specific land cover or crop type, was then used as input for the change detection model and compared to the in-situ SSM from ISMN. The study shows that the temporal correspondence of the resulting SSM with in-situ data is strongly varying between crop species and land cover type. The results suggest that crops with seasonal variations in vegetation structure (e.g. winter wheat stem elongation and heading), have a negative impact on the performance of the model. In comparison, the retrieved SSM is better correlated to in-situ data over land cover such as grasslands or maize fields with more homogeneous vegetation development. This study explores the potential and challenges posed by the high resolution of Sentinel-1 backscatter data for SSM retrieval. It demonstrates the effect changes in vegetation structure can have on S1 backscatter, which is important information to all retrieval algorithms for S1 SSM retrieval.  It also provides a first path forward to improve SSM using the TUWien change detection from Sentinel-1. 

How to cite: Massart, S., Vreugdenhil, M., Bauer-Marschallinger, B., Navacchi, C., Reuẞ, F. D., Quast, R., and Wagner, W.: Assessing the impact of land cover type on Sentinel-1 soil moisture retrievals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9883, https://doi.org/10.5194/egusphere-egu22-9883, 2022.

Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. Quantification of the spatial and temporal behaviour of SM at field scale is vital for understanding water availability in agriculture, ecosystems research, river basin hydrology and water resources management. Uncrewed Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, UAS data can help the construction of downscaling models which can link the land surface features and SM to identify the importance level of each predictor. To optimize the usage of data from UAS surveys for generating high-resolution SM at field scale, a comparative study of various SM retrieval or downscaling methods can be beneficial.

In this study, four methods, which include the apparent thermal inertia method, Kubelka–Munk method (KM), simplified temperature-vegetation triangle method, and random forest model (RF), were compared by theory background, data requirements, operation procedures and SM estimation results. The above-mentioned models have been tested using UAS data and point measurements collected on the Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). A number of long-term studies on the vadose zone have been conducted across a range of spatial scales. The thermal inertia model is built upon the dependence of the thermal diffusion on SM, which were inferred from diachronic thermal infrared data. The Kubelka–Munk Model is a spectral model to retrieve surface SM using optical data. The simplified temperature–vegetation triangle model, was used to map surface SM based on simultaneous information of the vegetation coverage and surface temperature. In addition, we also introduce an SM downscaling method using the RF model and SENTINEL-1 CSAR 1km SM product.

The study is concluded with the inter-comparison of methods. The results from KM have the highest resolution which is the same as the input multispectral data. The results of RF and KM provides information only for bare soil pixels according to the principle of the model. Results show good performances for all methods, but the simplified triangle and thermal inertia model provides better performances in terms of correlation coefficient and RMSE measured with respect to in-situ measurements. In addition, it is worthy to say that the RF downscaling method reveals the features controlling the spatial distributions of SM at a different scale.

This research is a part of EU COST-Action “HARMONIOUS” and waterJPI project “iAqueduct”.

How to cite: Zhuang, R., Manfreda, S., Zeng, Y., Szabó, B., Dal Sasso, S. F., Romano, N., Ben Dor, E., Nasta, P., Francos, N., Maltese, A., Ciraolo, G., Capodici, F., Paruta, A., Mészáros, J., Petropoulos, G. P., Zhang, L., Pizzolla, T., and Su, Z.: Soil Moisture Mapping Using Uncrewed Arial Systems (UAS), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10452, https://doi.org/10.5194/egusphere-egu22-10452, 2022.

Land surfaces are characterized by strong heterogeneities of soil texture, orography, land cover, soil moisture, snow and other variables. These are very challenging to represent accurately in radiative transfer models which have currently a still limited reliability over land. In this study, we compare two statistical modeling approaches: the traditional CDF-matching used routinely in NWP centers (used here as a normalization and as an inversion technique), and the Neural Network (NN) methods. NNs and CDF-matching are compared and combined. Two cases are considered: (1) the more traditional inversion scheme, and (2) the forward modelling that could be attractive for assimilation purposes. It is shown that in the context of ASCAT, the inversion approach seems better suited than the forward modelling but this could be different for another type of observations. It is also shown that it is possible to combine the global model obtained using the NN and the localized information of the LSM offered by the CDF-matching. A first assessment is performed over the USA using in situ soil measurements. Localization strategies for the NN models are introduced. Another necessity for the use of NN in an assimilation framework are estimations of NN uncertainties: this is unfortunately not available so far and we propose several schemes in order to obtain them. Finally, we will present future plans to develop a forward operator for low-frequency microwave channels (SMOS, AMSR-E, SMAP, CIMR) based on a statistical modeling of surface emissivities over continental, snow-ice and sea ice surfaces.

How to cite: Aires, F., Weston, P., De Rosnay, P., and Fairbairn, D.: Statistical approaches to assimilate soil moisture information: Methodology, first assessment and future plans, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8349, https://doi.org/10.5194/egusphere-egu22-8349, 2022.

Previous studies have shown that assimilating satellite soil moisture data in land surface models can improve the estimations of soil moisture. One of the limitations of these satellite soil moisture products is that there are often spatial gaps (in the horizontal direction) in data availability over certain areas due to issues such as dense vegetation or hilly terrain. These products are also limited in the vertical direction because, for the microwave-based products for example, the microwave radiation captured by the satellite sensors to estimate soil moisture is usually representative of a very thin top layer of soil (up to about 5 cm). Lastly, data over a specific watershed may not be available every day (i.e. temporal gaps) because of the orbital configuration of the satellite in question. From the existing literature, it is not clear what the benefits will be for soil moisture modeling, if these spatio-temporal gaps in satellite soil moisture datasets could somehow be minimized or eliminated. To answer this question, a synthetic assimilation study was carried out on the Noah-MP land surface model within the WRF-Hydro modeling system. The study was conducted with ERA5 forcing data on the Susquehanna River Basin and the Ensemble Kalman Filter was the chosen assimilation algorithm. Multiple scenarios were explored in which spatio-temporal gaps were introduced in the synthetic observations by mimicking the actual spatio-temporal gaps that are present in the SMAP soil moisture product. Results indicate that the model’s ability to accurately simulate soil moisture is much lower when assimilated observations have spatio-temporal gaps, compared to model simulations where there are no gaps in the assimilated observations. However, it was found that this lower model accuracy can be improved if the model grids with missing observations are updated based on the covariance between the soil moisture of those grids and their surrounding grids.

How to cite: Mohammed, K., Leconte, R., and Trudel, M.: Assessment of the impacts on data assimilation performance caused by spatio-temporal gaps in satellite soil moisture data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2084, https://doi.org/10.5194/egusphere-egu22-2084, 2022.

We use Sentinel-1 and Sentinel-2 images to study drainage congestion due to road networks on a large alluvial fan of the Kosi River. We have estimated the soil moisture from Sentinel-1 images by applying an empirical modified Dubois model (MDM), and a data-driven machine learning model based on the fully connected feed-forward artificial neural network (FC-FF-ANN). We observed that the MDM underestimates the soil moisture (R = 0.43, RMSE = 0.08 m³/m³, and bias = -0.10). The FC-FF-ANN accurately predicts the soil moisture (R = 0.85, RMSE = 0.05 m³/m³, and bias = 0) in our study area. 

We now used the soil moisture obtained from the FC-FF-ANN model to study the spatial pattern of the surface soil moisture in the proximity of road networks that act as drainage barriers. For this, we generated a buffer of 1 km along the road network. Within this, we extract the soil moisture value at the locations where the road network traverses in the vertical, inclined, and horizontal directions. We observed a clear accumulation of soil moisture near the road network that decreases gradually as we move farther from the road. We found that the impact of drainage congestion ranges between 320 to 760 m on either side of the road. This study is a step towards assessing the effect of structural interventions on drainage congestion and flood inundation.

How to cite: Singh, A., Naik, M. N., and Gaurav, K.: Assessing the spatial variability of soil moisture in the proximity of road networks on a large alluvial fan in the Himalayan Foreland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-96, https://doi.org/10.5194/egusphere-egu22-96, 2022.

Soil Moisture (SM) remains one of the inevitable geophysical land surface variables, influencing climatological and hydrological fluxes that can control the interaction between Earth's surface and atmosphere. It is also a crucial land surface parameter indicating drought conditions in agricultural areas, significantly impacting agricultural production. The temperature vegetation dryness index (TVDI), a simplified surface dryness index based on vegetation index (VI) - land surface temperature (LST) triangle/trapezoidal spectral space, can monitor SM conditions in vegetation-covered areas.

The present study estimated a high-resolution temperature vegetation dryness index (TVDI) for assessing SM over the largest river basin in India, Ganga Basin. Triangular feature space between LST and VI is generated to obtain the dry and wet edges to calculate TVDI over the Ganga basin for three years (2017, 2018, and 2019). Two different TVDI were developed using two vegetation indices, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Estimated TVDI is evaluated using ESA CCI SM product. The relation of subsurface SM with TVDI is investigated using GLDAS Noah LSM SM available at four different layer depths (0–10, 10–40, 40–100, and 100–200 cm).

The result shows that TVDI generated using EVI correlates better with SM than NDVI generated TVDI. The relationship between TVDI and SM was found to be closer in Summer (-0.49–0.62) than in post monsoon season. The applicability of TVDI in investigating SM at soil layer depth at 10-40 cm (r close to -0.6) was found to be better than that at depth 0-10 cm, especially during the summer season. The results reveal relevance of generated TVDI with satellite-derived information only, in SM monitoring and assessment, especially in the summer season, over the area of sparse in-situ SM network.

How to cite: Krishnan, S. and Jayaluxmi, I.: Land Surface Temperature/ Vegetation Index Space for Soil Moisture Assessment over Ganga Basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3332, https://doi.org/10.5194/egusphere-egu22-3332, 2022.