HS6.2

The Soil Moisture and Ocean Salinity (SMOS) satellite, launched in 2009 by ESA, has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space with a spatial resolution of ~ 40 km. SMOS data are an essential component of the ESA Climate Change Initiative (CCI) for ocean salinity and soil moisture and they are used by the CCI biomass. A specific SMOS neural network soil moisture product is assimilated operationally at the European Centre for Medium Range Weather Forecasts (ECMWF). L-band surface SM measurements have also been used to estimate root zone soil moisture, to derive drought indices, to enable food security monitoring and to improve satellite precipitation estimates. SMOS data have also been used to detect frozen soils, thin ice-sheets over the ocean and ice melting in Antarctica and Greenland.

Different studies on scientific and operational applications of L-band radiometry have shown the need of the continuity of L-band observations with an increased resolution with respect to the current generation of sensors. Resolutions from 1 km to 10 km would be a breakthrough for many applications over ocean, land and ice. One approach to obtain those resolutions could be downscaling coarse resolution data using an auxiliary dataset with higher resolution. However, using airborne data, we will show that the accuracy of the data downscaled to 1 km decreases significantly when the initial native resolution is 40 km with respect to downscaling from initial resolutions of 5-10 km. We will present two instrumental concepts to reach native resolutions of 5-10 km.

The SMOS-HR mission project, completed the Phase 0 study at the French Centre National d’Etudes Spatiales (CNES) with contributions from Airbus Defence & Space and CESBIO. The goal was to ensure the continuity of L-band measurements while increasing the spatial resolution to ~10 km, which requires a typical antenna size of ~18 meters. Taking into account the difficulty of deploying a real aperture of this size in space and the successful alternative approach used by SMOS, SMOS-HR will perform aperture synthesis using an array of 230 small antennas distributed in a cross with four 12 m arms. During the Phase A study (ongoing at CNES) a mission concept with a central carrier surrounded by a swarm of nanosatellites will also be studied.

How to cite: Rodriguez-Fernandez, N., Kerr, Y., Anterrieu, E., Cabot, F., Boutin, J., Picard, G., Pellarin, T., Merlin, O., Escorihuela, M. J., Albitar, A., Richaume, P., Mialon, A., Palacin, B., Rodriguez Suquet, R., Amiot, T., Costeraste, J., Vivier, F., Vialard, J., Yu, L., and Decoopman, T. and the rest of the SMOS-HR team: A follow-up for the Soil Moisture and Ocean Salinity mission, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4796, https://doi.org/10.5194/egusphere-egu21-4796, 2021.

In this talk, we present the results of a recently published milestone publication for the validation of global coarse-scale satellite soil moisture products (doi:10.1016/j.rse.2020.111806). It is a community effort in which validation good practice guidelineshave been developed. We provide theoretical background, a review of state-of-the-art methodologies for estimating errors in soil moisture data sets, practical recommendations on data pre-processing and presentation of statistical results, and a recommended validation protocol that is supplemented with an example validation exercise focused on microwave-based surface soil moisture products. We conclude by identifying research gaps that should be addressed in the near future. The presented guidelines are endorsed by the Land Product Validation Subgroup of the Committee on Earth Observation Satellites (https://lpvs.gsfc.nasa.gov) and aim to serve as exemplary work for the development of similar best practice guidelines in other communities.

How to cite: Gruber, A. and the Validation Good Practice Team: Validation practices for satellite soil moisture retrievals: What are (the) errors?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12449, https://doi.org/10.5194/egusphere-egu21-12449, 2021.

To validate satellite soil moisture products and compare their quality with other products, standardized, fully traceable validation methods are required. The QA4SM (Quality Assurance for Soil Moisture; ) free online validation tool provides an easy-to-use implementation of community best practices and requirements set by the Global Climate Observing System and the Committee on Earth Observation Satellites. It sets the basis for a community wide standard for validation studies.

QA4SM can be used to preprocess, intercompare, store, and visualise validation results. It uses state-of-the-art open-access soil moisture data records such as the European Space Agency’s Climate Change Initiative (ESA CCI) and the Copernicus Climate Change Services (C3S) soil moisture datasets, as well as single-sensor products, e.g. H-SAF Metop-A/B ASCAT surface soil moisture, SMOS-IC, and SMAP L3 soil moisture. Non-satellite data include in-situ data from the International Soil Moisture Network (ISMN: ), as well as land surface model or reanalysis products, e.g. ERA5 soil moisture.

Users can interactively choose temporal or spatial subsets of the data and apply filters on quality flags. Additionally, validation of anomalies and application of different scaling methods are possible. The tool provides traditional validation metrics for dataset pairs (e.g. correlation, RMSD) as well as triple collocation metrics for dataset triples. All results can be visualised on the webpage, downloaded as figures, or downloaded in NetCDF format for further use. Archiving and publishing features allow users to easily store and share validation results. Published validation results can be cited in reports and publications via DOIs.

The new version of the service provides support for high-resolution soil moisture products (from Sentinel-1), additional datasets, and improved usability.

We present an overview and examples of the online tool, new features, and give an outlook on future developments.

Acknowledgements: This work was supported by the QA4SM & QA4SM-HR projects, funded by the Austrian Space Applications Programme (FFG).

How to cite: Scherrer, S., Preimesberger, W., Tercjak, M., Bakcsa, Z., Boresch, A., and Dorigo, W.: QA4SM – An online tool for satellite soil moisture data validation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7398, https://doi.org/10.5194/egusphere-egu21-7398, 2021.

Soil moisture is an Essential Climate Variable (ECV) that plays an important role in land surface-atmosphere interactions. Accurate monitoring of soil moisture is essential for many studies in water, energy and carbon cycles. However, soil moisture is characterized with high spatial and temporal variability, making conventional point-based in-situ measurements difficult to sufficiently capture these variabilities given the often lack of dense in-situ network for most regions. Considerable efforts have been made to explore satellite remote sensing, hydrological and land surface models in estimating and mapping soil moisture, leading to increasing availability of different gridded soil moisture products at various spatial and temporal resolutions. The accuracy of an individual product varies between regions and needs to be evaluated in order to guide the selection of the most suitable products for certain applications. Such evaluation will also benefit product development and improvements. The most common (traditional) evaluation method is to calculate error metrics of the evaluated products with in-situ measurements as ground truth. The triple collocation (TC) analysis has been widely used and demonstrated powerful in evaluation of various products for different geophysical variables when ground truth is not available.

The Integrated Carbon Observation System (ICOS) is a research infrastructure with aim to quantify the greenhouse gas balance of Europe and adjacent regions. A standardized network of more than 140 research stations in 13 member states has been established and is operated by ICOS to provide direct measurements of climate relevant variables. The ICOS Carbon Portal offers a 'one-stop shop' freely for all ICOS data products at https://www.icos-cp.eu/observations/carbon-portal. This study evaluates for the first time a large number of different satellite-based and reanalysis surface soil moisture products at varying spatial and temporal resolutions using ICOS measurements from 2015 over Sweden. Evaluated products include ESA CCI, ASCAT, SMAP, SMOS, Sentinel-1 derived, ERA5 and GLDAS products. In order to quantify spatial patterns of errors of each individual product, TC analysis is applied to different combinations of gridded products for spatial evaluation across entire Sweden. The performance of products in different seasons and years is evaluated. The similarity and difference among different products for the drought period in the year 2018 is particularly assessed. This study is expected to improve our understanding of the applicability and limitations of various gridded soil moisture products in the Nordic region.

How to cite: Duan, Z., del Rosario, N., Dong, J., Gao, H., Peng, J., Lu, Y., Liu, J., and Vermeulen, A.: Quantifying errors of multiple gridded soil moisture products in Sweden using triple collocation analysis and traditional evaluation method with ICOS data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8041, https://doi.org/10.5194/egusphere-egu21-8041, 2021.

Soil moisture (SM) products derived from the passive satellite missions have been extensively used in various hydrological and environmental processes. However, validation of the satellite derived product is crucial for its reliability in several applications. In this study, we present a comprehensive validation of the descending SM product from Soil Moisture Active Passive (SMAP) Enhanced Level-3 (L3) radiometer (SMAP L3-Version 3) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) Level-3 (Version 1), over the newly established Critical Zone Observatory (CZO) within the Ganga basin, North India. The AMSR2 soil moisture product used here, has been derived using the Land Parameter Retrieval Model (LPRM) algorithm. Four SM derived products from SMAP (L-band) and AMSR2 (C1- and C2- and X-band) are validated against the in-situ observations collected from 21 SM monitoring locations distributed over the CZO within a period from September 2017 to December 2019, for a total of 62 days. Since the remotely sensed SM product has a coarser spatial resolution (here 9 km for SMAP and 10 km for AMSR2), the assessment has been carried out for the temporal variation of the measured values. Four statistical metrics such as bias, root mean square error (RMSE), unbiased root-mean-square error (ubRMSE) and the correlation coefficient (R) have been used here for the evaluation. The SMAP Level-3 products are found to show a satisfactory correlation (R>0.6) compared to the other three SM product. Both the SMAP L3 and the AMSR2 C2 SM shows a negative bias, -0.05 m³/m³ and -0.04 m³/m³ respectively whereas these values are found to be 0.04 m³/m³ and 0.06 m³/m³ for C1 and X bands of AMSR2, respectively. Furthermore, the RMSE between the SMAP L3 and in-situ data is 0.07 m³/m³, which is slightly underperformed when considering the required accuracy of SMAP. This is possibly due to variation in the sampling depth along with the sampling day distribution over CZO. The AMSR2 SM products (C1-, C2- and X-bands) are found to have a higher RMSE than SMAP L3, ranging from 0.08-0.1 m³/m³. In addition, the ubRMSE for all remotely sensed soil moisture product range from 0.06-0.08 m³/m³ with the lowest value for the SMAP L3 and AMSR2 C1. The results in this study can be used further for relevant hydrological modelling along with evaluating various downscaling strategies towards improving the coarser resolution satellite soil moisture.

How to cite: Dash, S. and Sinha, R.: Validation of SMAP and AMSR2 satellite soil moisture data over the Critical Zone Observatory in central Ganga plains, North India using ground-based observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4667, https://doi.org/10.5194/egusphere-egu21-4667, 2021.

Vineyards are crops whose production has a major economic impact in the Portuguese economy (~750 million euros) being exported worldwide. As the climate models project a larger variability in precipitation regime, the water requirements of vineyards can change and drip irrigation can be responsible for salt accumulation in the root zone, especially when late autumn and winter precipitation is not enough to leach salts from the soil upper horizons, turning the soil unsuitable for grape production.

The aim of this work is to present a methodology to map surface soil moisture content (SMC) in a vineyard, (40 hectares) based on the application of two classification algorithms to satellite imagery (Sentinel 1 and Sentinel 2). Two vineyard plots were considered and three field campaigns (December 2017, January 2018 and May 2018) were conducted to measure soil moisture contents (SMC). A geostatistical method was used to estimate the SM class probabilities according to a threshold value, enlarging the training set (i.e., SMC data of the two plots) for the classification algorithms. Sentinel-1 and Sentinel-2 images and terrain attributes fed the classification algorithms. Both methods, Random Forest and Logistic Regression, classified the highest SMC areas, with probabilities above 14%, located close to a stream at the lower altitudes.

RF performed very well in classifying the topsoil zones with lower SMC during the autumn-winter period (F-measure=0.82).

This delineation allows the prevention of the occurrence of areas affected by salinization, indicating which areas will need irrigation management strategies to control the salinity, especially under climate change, and the expected increase in droughts.

How to cite: Mendes, M. P., Falcão, A. P., Matias, M., and Gomes, R.: Soil moisture assessment based on Sentinel 1/2 and in-situ data: The vineyard case study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16507, https://doi.org/10.5194/egusphere-egu21-16507, 2021.

Remote sensing has proven to be an irreplaceable tool for monitoring soil moisture. The European Space Agency (ESA), through the Climate Change Initiative (CCI), has provided one of the most substantial contributions in the soil water monitoring, with almost 4 decades of global satellite derived and homogenized soil moisture data for the uppermost soil layer. Yet, due to the inherent limitations of many of the remote sensors, only a limited soil depth can be monitored. To enable the assessment of the deeper soil layer moisture from surface remotely sensed products, the Soil Water Index (SWI) has been established as a convolutive transformation of the surface soil moisture estimation, under the assumption of uniform hydraulic conductivity and the absence of transpiration. The SWI uses a single calibration parameter, the T-value, to modify its response over time.

Here the Soil Water Index (SWI) is calibrated using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then use Artificial Neural Networks (ANNs) to find the best physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration of the T-value. The calibration is then used to assess a root zone related soil moisture for the period 2001 – 2018.

The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land reanalysis soil moisture dataset, showing a good agreement, mainly over mid-latitudes. The results indicate that there is added value to the results of the machine learning calibration, comparing to the uniform T-value. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large scale soil moisture related studies.

How to cite: Grillakis, M. G.: A regionally explicit, global SWI calibration based on ISMN observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-496, https://doi.org/10.5194/egusphere-egu21-496, 2021.

The quality of soil moisture retrievals from passive microwave satellite sensors is limited during certain conditions, e.g. snow coverage, radio-frequency interference and dense vegetation. Therefore, masking the retrievals in these conditions by data flagging algorithms is vital for the production of reliable satellite-based products. However, these products utilise different flagging methods. A clear overview and comparison of these methods and their impact on the data are lacking. For long-term soil moisture records such as the ESA CCI soil moisture products, the impact of any flagging inconsistency from combining multiple sensor datasets was not assessed.

Recently, Van der Vliet et al. (2020) provided a review of the data flagging system that is used within multi-sensor ESA CCI soil moisture products as well as the flagging systems of two other soil moisture datasets from sensors that are also used for the ESA CCI soil moisture products: The level 3 Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP). Substantial differences were detected between the SMOS and SMAP soil moisture flagging systems in terms of the number and type of conditions considered, critical flags, and data source dependencies. The impact on the data availability of the different flagging systems was shown to differ globally and especially for northern high latitudes, mountainous regions, and equatorial latitudes (up to 37%, 33%, and 32% respectively) with large seasonal variability. These results highlighted the relevance of a consistent and well-performing flagging approach that is applicable to all individual products used in long-term soil moisture data records.

Consequently, Van der Vliet et al. (2020) designed a consistent and model-independent flagging strategy to improve soil moisture climate records. For the snow cover, ice, and frozen conditions, which were found to have the highest impact on data availability, a uniform satellite driven flagging strategy was designed and evaluated against two ground observation networks. Compared to the individual flagging approaches adopted by the SMOS and SMAP soil moisture datasets, the new flagging approach was demonstrated to be a robust flagging alternative, with a similar performance, but with the applicability to the full ESA CCI historical record without the use of modelled approximations. 

A part of the designed flagging decision tree demonstrated to form a good base for the filtering of bare grounds and heavy precipitation events as well. A future extension of the flagging strategy is expected to mask these conditions, as well as other conditions such as radio frequency interference and dense vegetation.

How to cite: van der Vliet, M., de Jeu, R., Rodriguez-Fernandez, N., Scanlon, T., Colliander, A., Preimesberger, W., Dorigo, W., Madelon, R., and van der Schalie, R.: The development and relevance of a consistent flagging strategy for multi-sensor satellite soil moisture climate records, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10740, https://doi.org/10.5194/egusphere-egu21-10740, 2021.

Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling.

Here we introduce the advances in our work on the first operationally capable SWI-based root-zone soil moisture dataset from C3S Soil Moisture v201912 COMBINED product, spanning the period 2002-2020. The uniqueness of this dataset lies in the fact that T-values (temporal lengths ruling the infiltration) characteristic of SWI were translated into particular soil depths making it much more intuitive, user-friendly and easily applicable. Available are volumetric soil moisture values for the top 1 m of the soil profile at 10 cm intervals, where the optimal T-value (T-best) for each soil layer is selected based on a range of correlation metrics with in situ measurements from the International Soil Moisture Network (ISMN) and the relevant soil and climatic parameters.
Additionally we present the results of an extensive global validation against in situ measurements (ISMN) as well as the results of investigations into the relationship between a range of soil and climate characteristics and the optimal T-values for particular soil depths.

How to cite: Pasik, A., Preimesberger, W., Bauer-Marschallinger, B., and Dorigo, W.: Towards an operationally capable satellite-based 0-100 cm soil moisture dataset from C3S, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12268, https://doi.org/10.5194/egusphere-egu21-12268, 2021.

Merging data from different instruments is required to construct long time data records of soil moisture (SM). This is the goal of projects such as the ESA Climate Change Initiative (CCI) for SM (Gruber et al., 2019), which uses both active and passive microwave sensors. Currently, the GLDAS v2.1 model is used as reference to re-scale active and passive time series by matching their Cumulative Density Function (CDF) to that of the model. Removing the dependency on models is important, in particular for data assimilation applications into hydrological or climate models, and it has been proposed (Van der Schalie et al., 2018) to use L-band data from one of the two instruments specifically designed to measure SM, ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, as reference to re-scale other time series.
To investigate this approach, AMSR-2 SM time series obtained from C1-, C2- and X-band observations using LPRM (Land Parameter Retrieval Model) were re-scaled by CDF-matching (Brocca et al., 2011) using different SMAP and SMOS official (SMAP L2 V005, SMOS L3 V300, SMOS NRT V100&V200) and research (SMOS IC V103) SM products as well as the SMAP and SMOS LPRM v6 SM data used by the ESA CCI. The time series re-scaled using L-band remote sensing data were compared to those re-scaled using GLDAS and were evaluated against in situ measurements at several hundred sites retrieved from the International Soil Moisture Network (Dorigo et al., 2011). The results were analyzed as a function of the land cover class and the Koppen-Geiger climate classification.
Overall, AMSR-2 time series re-scaled using SMAP L2, SMAP LPRM and SMOS IC data sets as reference gave the best correlations with respect to in situ measurements, similar to those obtained by the time series re-scaled using GLDAS and slightly better than those of the original AMSR-2 time series. These results imply that different SMAP and SMOS products could actually be used to replace GLDAS as reference for the re-scaling of other sensors time series within the ESA CCI. However, one must bear in mind that this study is limited to the re-scaling of AMSR-2 data at a few hundred sites.
For a more detailed assessment of the L-band data set to be used for a global re-scaling, it is necessary to investigate other effects such as the spatial coverage or the time series length. SMAP spatial coverage is better than that of SMOS in regions affected by radio frequency interference. In contrast, the length of SMAP time series can be too short to capture the long term SM variability for climate applications in some regions. The CDF of SMOS time series computed from the date of SMAP launch is significantly different to those of the full length SMOS time series in some regions of the Globe. Possible ways of using a coherent SMAP/SMOS L-band data set will be discussed.

How to cite: Madelon, R., Rodriguez-Fernandez, N., Van Der Shalie, R., Kerr, Y., Scalon, T., De Jeu, R., and Dorigo, W.: Towards the removal of model bias from ESA CCI SM by using an L-band scaling reference, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4800, https://doi.org/10.5194/egusphere-egu21-4800, 2021.

The dynamic processes of mass and energy exchange on the soil surface are mainly influenced by plant cover, soil physical quantities and meteorological conditions. The aims of the research were: (a) to identify spatial and temporal changes in soil moisture (SM) obtained from satellite observations and ground measurements at the regional scale and (b) to determine the temporal variability of soil moisture in the soil profile with and bare soil (reference). The study area included 9 sites in the eastern part of Poland. Agro-meteorological stations in each site allowed monitoring soil moisture (SM). Satellite SM data (time series) for the years 2010–2016 (every week) obtained from the Soil Moisture and Ocean Salinity satellite (SMOS L2 v. 650 datasets) were gridded using the discrete global grid (DGG) with the nodes spaced at 15 km. Seven DGG pixels per each site were considered in a way that the central one (named S0) containing the agrometeorological station was bordered with 6 others (S1÷S6). The measurements of SM were performed at depths of 0.05, 0.1, 0.2, 0.3, 0.4, 0.5 and 0.8 m once a day in April-July in plots of spring barley, rye and bare soil. The temporal dependence of the SMOS surface soil moisture was observed in S0÷S6 with the radius of autocorrelation time from 8.1 to 25.2 weeks. The smallest autocorrelation time (3 weeks ) was found in pixels with dominance of arable lands and the largest one - with dominance of wetlands (16.8 weeks) and forests (from 12 to 15.6 weeks). The autocorrelation times in S0 were much greater for ground-based SM data (11.1 to 43.1 weeks) than those for SMOS SM data. The autocorrelations enabled satisfactory predicting changes in SM forwards and backwards using the kriging method and filling gaps in the SM time series. As to ground measurements the highest autocorrelation times were in the soil below the plough layer under rye (170 days) and the lowest in the surface soil under barley and bare soil (18 and 19 days). In the plot of rye with the highest soil density the autocorrelation radius was over 1.5 months. The fractal dimensions (D0) indicated a large randomness of the surface SMOS SM distribution (D0 1.86–1.95) and the ground SM measurements (D0 1.82–1.92). The D0 values clearly decreased with the depth (from 1.7 to 1.15) in plant-covered soil while in the bare soil they did not change much throughout the profile (D0 1.7–1.8). The D0 values indicated that the temporal distribution of SM in the soil profile was more random in bare than plant-covered soil. The results help to understanding autocorrelation time ranges in surface and deeper soil and spatial changes in soil moisture depending on plant cover.

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

How to cite: Usowicz, B. and Lipiec, J.: Assessment of spatial and temporal variations in soil moisture from satellite observations and ground-based measurements and their relationship with plant cover, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2138, https://doi.org/10.5194/egusphere-egu21-2138, 2021.

Soil drainage capacity is the degree and frequency at which the soil is free of saturation. It influences land use and management, soil nutrient cycling and greenhouse gas fluxes. Accurate information on drainage conditions is crucial for crop production and management and fundamental in developing strategies to adhere to environmental sustainability goals. This is particularly important in Ireland where approximately 50% of the soils are classified as “marginal”. These are mainly poorly drained soils which negatively impact plant growth and productivity.

Soil moisture acts as a proxy for drainage capacity. Timely and accurate information on soil moisture allows for precision management strategies. It aids in designing effective interventions on farms for artificial drainage works which are often assessed by information on soil moisture, soil type and hydrology. Such data are conventionally acquired by in-situ point sampling techniques which are costly and time consuming. Remote sensing has the potential to provide a solution by allowing simultaneous coverage of large geographic areas, quickly and in a cost effective manner.

This study uses optical remote sensing data from Sentinel 2 to derive information on soil moisture conditions on selected sites in Ireland.  We develop the OPTRAM model of Sadeghi et al (2017) by exploring the use of remote sensing based vegetation indices such as the Normalised Difference Vegetation index, Enhanced Vegetation Index and Normalised Difference Red Edge Index for the years 2015-2020 along with short wave transformed infrared reflectance to estimate soil moisture variations for our study areas. We show that  non-linear estimates of the wet and dry edge curves in the model are better suited for Ireland, which is dominated by wet conditions for most of the year and also identify the best vegetation indices for studying soil moisture variations.

How to cite: Basu, R., Brown, C., Tuohy, P., and Daly, E.: Characterising soil moisture regimes on poorly drained soils in Ireland using optical satellite derived vegetation indices and the OPTRAM model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3336, https://doi.org/10.5194/egusphere-egu21-3336, 2021.

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 is sensitive to variations 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 aim at investigating 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 different time scales. We decompose the data sets into different temporal frequencies from seasonal to sub-monthly signals and carry out the comparison with respect to spatial patterns and temporal variability. Level-3 (Surface SM up to 5 cm depth) and Level-4 (Root-Zone SM up to 1 m depth) data sets of the SMOS and SMAP missions as well as the ESA CCI data set are used in this investigation.
Since a direct comparison of the absolute values is not possible due to the different integration depths of the two data sets (SM and TWS), we will analyze their relationship using Pearson’s pairwise correlation coefficient. Furthermore, a time-shift analysis is carried out by means of cross-correlation to identify time lags between SM and TWS data sets that indicate differences in the temporal dynamics of SM storage change in varying depth layers.

How to cite: Blank, D., Eicker, A., Jensen, L., and Güntner, A.: Joint analysis of remotely sensed soil moisture and water storage variations from satellite gravimetry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2188, https://doi.org/10.5194/egusphere-egu21-2188, 2021.

Soil moisture (SM) is a key variable in the earth surface dynamics; however, long-term in situ measurements at the global scale are scarce. In the Mediterranean Chilean Andes (MA; 30°-37°S), Sclerophyllous Forest tree species such as Belloto del Norte (BN; Beilschmiedia miersii) can grow for more than two centuries in very scarce humid lowland geographical zones. At the present work, we assess the linkages between two BN tree-ring chronologies (BML and AGU sites; 70 cores) and daily high-resolution satellite-based surface soil moisture product v201812.0 from ESA and to reconstruct past SM variations in the MA region. Our findings exhibit strong relationships between tree-growth from BML and AGU sites and the SM from 32°-34°S and 71°-73°W spatial domain, especially from February to September. We found significant r Pearson correlations of 0.85 and 0.68 during 1983-2014 (P-value < 0.001), respectively. Based on these results, we reconstructed the SM between 1800 - 2014 period using multiple linear regression. Our model retains 71.4% of the total variance and exhibits an unprecedented SM reduction since 2006 in the context of the past two centuries. This work constitutes the first reconstruction of surface soil moisture variability derived from remote sensing carried out in Chile, and can provide new information to understand current environmental changes related to the severe mega-drought period experienced in  Chile since 2010, which has provoked water conflicts, the Sclerophyllous Forest decline and browning, and the intensification of climate extreme events such as heatwaves and wildfires in the MA. 

Acknowledgments

Álvaro González-Reyes wish to thank: ANID+PAI+CONVOCATORIA NACIONAL SUBVENCIÓN A INSTALACIÓN EN LA ACADEMIA CONVOCATORIA AÑO 2019 + PAI77190101. Ariel Muñoz and Isadora Schneider thanks to the FONDECYT 1201714 and the Center for Climate and Resilience Research (CR)2, FONDAP 15110009.   

How to cite: González-Reyes, Á., Venegas-González, A., Muñoz, A., and Schneider, I.: The first soil moisture reconstruction in the Mediterranean Chilean Andes region developed by tree rings and satellite observations to inform climate change impacts in South America, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13831, https://doi.org/10.5194/egusphere-egu21-13831, 2021.

The state of soil moisture is a key variable controlling surface water and energy balances. Nowadays remote sensors provide the unprecedented opportunity to monitor soil moisture at high time frequency on large spatial scales. The high spatial resolution of radar is a key element for soil moisture mapping of small hydrologic basins with strong spatial variability of physiographic and land cover properties, such as typical of Mediterranean basins. In addition, in the Mediterranean basins, soil moisture changes with strong dynamics, due to both interannual and seasonal rain variability, becoming a key term for water resources management and planning.

The new constellation of synthetic aperture radar (SAR) satellites, Sentinel-1 A and Sentinel-1B, provides images not only at the high spatial resolution (up to 10 m), typical of radar sensors, but also at high temporal resolutions (6-12 revisit days), with a major advance in the development of an operational soil moisture mapping at the plot.

Several models have been used for estimating soil moisture over bare soil surfaces from synthetic aperture radar satellites varying from physical models [e.g., the Integral Equation, the Advanced Integral Equation Model and the Integral Equation Model for Multiple Scattering, empirical models (e.g., Dubois model), and semi-empirical models. The main difficulty with SAR imagery is that soil moisture, surface roughness, and vegetation cover all have an important and nearly equal effect on radar backscatter.

In this work, the potentiality of Sentinel 1 for soil moisture retrieving in a water limited grass field have been tested using three common models for soil moisture retrieval from radar images: the empirical Change detection method, the semi-empirical Dubois model, and the physically based Fung model. For considering the growth vegetation effect on radar signal we propose an empirical model, which used simultaneously the optical Sentinel 2 images.

The case study is the Orroli site in Sardinia (Italy), a typical semi-arid Mediterranean ecosystem which is an experimental site for the ALTOS European project of the PRIMA MED program.

The 2016-2018 observation period was characterized by strong interannual rainfall variability, alternating wet and dry years, becoming an interesting opportunity for testing Sentinel 1 and 2 potentiality on soil moisture estimate in a wide range of climate conditions.

Using the Dubois model for soil moisture retrieval and the proposed model for accounting vegetation growth and surface roughness variability soil moisture was well estimated in both wet and dry conditions when compared with field observations

The unprecedented high temporal frequency of Sentinel 1 observations provides the opportunity to finally achieve operational procedures for soil moisture assimilation to guide ecohydrologic models. An operational procedure for assimilating soil moisture estimates from Sentinel 1 images in a land surface model using an Ensemble Kalman filter based assimilation scheme has been tested successfully, demonstrating the potentiality of the new generation of Satellite sensors for soil water balance predictions.

How to cite: Corona, R., Fois, L., and Montaldo, N.: The role of vegetation growth on the estimate of soil moisture in a grass field using Sentinel-1 and Sentinel-2 observations and data assimilation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15679, https://doi.org/10.5194/egusphere-egu21-15679, 2021.

Soil moisture controls hydrological processes in natural and agricultural systems. A clear understanding of their temporal dynamics and spatial variability is essential to control soil degradation processes, irrigation management and water use efficiency. In recent years, the measurement of soil water content (SWC) with ground-based neutron sensors and remote sensing products have become promising non-invasive methods for different spatial scales. In this study, we are investigating the sensitivity of using cosmic ray neutron sensor (CRNS) and Sentinel-2 SWC index for quantifying different dynamics of soil moisture along a toposequence with underlying contrasting parent materials. For this study, three sites were selected in the upper section (US) soils on limestones correspond to Muschelkalk facies, and another three in the lower section (LS) siliciclastic materials composed of low-permeability marls and claystone formation with primarily silty clay texture (Keuper facies). During two surveys, which correspond to wet (spring 2018/05/05) and dry conditions (summer 2018/08/05), a set of soil moisture data were obtained by using i) portable CRNS backpack, ii) satellite-based information and iii) HS200 sensor Delta-T Devices. The physical composition of the studied soils reflects the clear difference in parent material, with mean content of soil organic carbon of 6% in US against 1% in LW, while the mean clay content was lower in US (21%) than in LS (26%). The infiltration measurements also show different responses for water infiltration capacity, with a much higher mean value of hydraulic conductivity for the soils in the US (317 mm per day), reflecting the karst features, than in the LS (35 mm per day) corresponding to the siliciclastic materials. Our results show similar trends during the two surveys, obtaining significantly lower soil water content on limestones at the US where infiltration processes prevailed thus facilitating leaching and limiting runoff. In contrast, the higher soil water content was on siliciclastic soils at the LS where the low permeability of soils due to the clayed substrate promoted increased runoff. Focusing on the comparison of soil moisture data obtained during the wet and dry surveys, a soil characteristic dependency is observed, with a more different soil moisture state on siliciclastic soils (LS) between the two surveys than for the soils on limestones. Our preliminary results pinpoint that CRNS, Sentinel-2 index and field data captured soil moisture dynamics along the toposequence and demonstrated the sensitivity of neutron sensors and remote sensing products to investigate the effect of parent material on soil water content at sampling scale.

How to cite: Gaspar, L., Franz, T., Lizaga, I., Latorre, B., and Navas, A.: The sensitivity of CRNS and Sentinel-2 products to detect differences in soil water content along a toposequence with two contrasting parent materials, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5003, https://doi.org/10.5194/egusphere-egu21-5003, 2021.

The estimation of superficial soil moisture is performed with a Change Detection (CD) method applied over an agricultural area in Spain, in the basin of the Duero river. The CD method is applied on Sentinel-1 SAR images over a time period of three years. For the period  and area of interest are available in situ soil moisture measurments of the REMEDHUS network belonging to the International Soil Moisture Network (ISMN). Two years of data are used for the calibration procedure (2018 and 2019), one year (2020) for validation purposes.
According to the Corine Land Cover classification of 2018, the agricultural area is mainly coverd by low vegetation. The backscatterd SAR signal is indeed modelled as the inchoerent sum of the volumetric contribution of the canopy, and the soil attenuated contribution.
The Sentinel-1 VH polarized band is used for the classification of the areas with homogeneous volumetric contribution, where the condition of constant vegetation contribution is respected in order to apply the CD method. Furthermore, those areas will be identified exploiting the bimodal distribution of the VH band histogram in the upper phase of the vegetative stage of the crop.
The soil roughness contribution to the superficial component of the backscattered signal couldn’t be neglected due to agricultural practices such as tillage and harvesting. Furthermore the data are processed at a very high resolution, in order to exploit the full spatial resolution of the SAR data. The VV polarized band will be used to identify the variations of the SAR signal due to changes in the soil roughness, and time periods with constant roughness contribution will be identified in order to apply the CD method. It is expected that the variations of the VV backscattering coefficient due to changes in soil roughness are higher than the ones caused by soil moisture changes, except for meteoric events.
The CD is thus applied on areas and time intervals where only soil moisture content is supposed to vary, and the maximum variation is calculated in each time interval. Finally, the calculation of the soil moisture is performed by scaling the maximum difference of SAR signal with the maximum difference of the in situ data.
In previous studies performed on the same area, a SAR vegetation index was used to classify homogeneous volumetric contribution, and soil rougness was neglected. Even if the trend of the solution fits well the precipitation events and the trend of the in situ data (RMSE=0.096m³/m³, R=0.583m³/m³), the results presented singularities. The above presented method for the superficial soil moisture calculation is expected to smooth the singularities present in the results of our previous studies.

How to cite: Graldi, G. and Vitti, A.: Exploiting Sentinel-1 polarized data for the classification of areas and time intervals where coherently apply a change detection method for the retrieval of superficial soil moisture at the field scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4458, https://doi.org/10.5194/egusphere-egu21-4458, 2021.

The change detection method of multi-temporal analysis is often used to estimate surface soil moisture from Sentinel-1 SAR data. The underlying assumptions that vegetation cover and soil surface roughness vary significantly more slowly with time than soil moisture are problematic in areas under cultivation, which are characterised by seasonal cycles of rapid crop growth, senescence, harvesting and tillage. The issue becomes more acute when data is processed at the field scale. Other areas, where the vegetation cover is persistently high, also exhibit poor sensitivity of SAR backscatter to soil moisture. In general, the mean absolute error appears to be related to the relative fractions of photosynthetically active and inactive vegetation, and bare soil. Optical indices derived from Sentinel-2 data may be used with spectral unmixing to estimate these fractions as time series at field scale. Combined with knowledge of land use, confidence levels may be assigned to each field. The soil moisture may then be estimated by two dimensional interpolation using inverse distance squared weighting across a range of neighbouring fields within a local zone.  During the peak growing season, the mean absolute error in the soil moisture estimate for wheat fields is significantly reduced, in one example from around 20% volumetric water content to less than 5%. This will benefit users of such products in agriculture, for example, in determining actual soil moisture deficit in the growing season.

How to cite: Beale, J., Waine, T., Corstanje, R., and Evans, J.: Improved soil moisture estimation with Sentinel-1 for arable land at the field scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4537, https://doi.org/10.5194/egusphere-egu21-4537, 2021.

Agriculture is an important factor on water resources, given the constant population growth and the strong relationship between water availability and food production. In this context, root zone soil moisture (RZSM) measurements are used by modern irrigators in order to detect the onset of crop water stress and to trigger irrigations. Unfortunately, in situ RZSM measurements are costly; combined with the fact they are available only over small areas and that they might not be representative at the field scale, remote sensing is a cost-effective approach for mapping and monitoring extended areas. A recursive formulation of an exponential filter was used in order to derive 1 km resolution RZSM estimates from SMAP (Soil Moisture Active Passive) surface soil moisture (SSM) over the Ebro basin. The SMAP SSM was disaggregated to a 1 km resolution by using the DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) algorithm. The pseudodiffusivity parameter of the exponential filter was calibrated per land cover type, by using ISBA-DIF (Interaction Soil Biosphere Atmosphere) surface and root zone soil moisture data as an intermediary step. The daily 1 km RZSM estimates were then used to derive 1 km drought indices such as soil moisture anomalies and soil moisture deficit indices (SMDI), on a weekly time-scale, covering the entire 2020 year. Results show that both drought indices are able to capture rainfall and drying events, with the weekly anomaly being more responsive to sudden events such as heavy rainfalls, while the SMDI is slower to react do the inherent inertia it has. Moreover, a quantitative comparison with drought indices derived from a model-based RZSM estimates has also been performed, with results showing a strong correspondence between the different indices. For comparison purposes, the weekly soil moisture anomalies and SMDI derived using 1 km SMAP-derived SSM were also estimated. The analysis shows that the anomalies and SMDI based on the RZSM are more representative of the hydric stress level of the plants, given that the RZSM is better suited than the SSM to describe the moisture conditions at the deeper layers, which are the ones used by plants during growth and development.

The study provides an insight into obtaining robust, high-resolution remote-sensing derived drought indices based on remote-sensing derived RZSM estimates. The 1 km resolution proves an improvement from other currently available drought indices, such as the European Drought Observatory’s 5 km resolution drought index, which is not able to capture as well the spatial variability present within heterogeneous areas. Moreover, the SSM-derived drought indices are currently used in a drought observatory project, covering a region in the Tarragona province of Catalonia, Spain. The project aims at offering irrigation recommendations to water agencies, and the introduction of RZSM-derived drought indices will further improve such advice.

How to cite: Stefan, V.-G., Escorihuela, M.-J., and Quintana-Seguí, P.: High-resolution root zone soil moisture-based indices for drought monitoring in the Ebro basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5849, https://doi.org/10.5194/egusphere-egu21-5849, 2021.

Soil moisture content is an important hydrological and climatic variable with applications in a wide range of domains. The high spatial variability of soil moisture cannot be well captured from conventional point-based in-situ measurements. Remote sensing offers a feasible way to observe spatial pattern of soil moisture from regional to global scales. Microwave remote sensing has long been used to estimate Surface Soil Moisture Content (SSMC) at lower spatial resolutions (>1km), but few accurate options exist in the higher spatial resolution (<1km) domain. This study explores the capabilities of deep learning in the high-resolution domain of remotely sensed SSMC by using a Convolutional Neural Network (CNN) to estimate SSMC from Sentinel-1 acquired Synthetic Aperture Radar (SAR) imagery. The developed model incorporates additional SSMC predictors such as Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and soil type to yield a more accurate estimation than traditional empirical formulas that focus solely on the conversion of backscatter signals to relative soil moisture. This also makes the developed model less sensitive to site-specific conditions and increases the model applicability outside the training domain. The model is developed and tested with in-situ soil moisture measurements in Denmark from a dense network maintained by HOBE (Danish Hydrological Observatory). The unique advantage of the developed model is its transferability across climate zones, which has been historically absent in many prior models. This would open up opportunities for high-resolution soil moisture mapping through remote sensing in areas with relatively few soil moisture gauges. A reliable high-resolution soil moisture platform at good temporal resolution would allow for more precise erosion modelling, flood forecasting, drought monitoring, and precision agriculture.

How to cite: Simonsen, N. and Duan, Z.: Development of a deep learning-based method for estimating surface soil moisture at high spatial resolution from Sentinel-1 satellite data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9493, https://doi.org/10.5194/egusphere-egu21-9493, 2021.

Validation of remotely sensed soil moisture is a well-known issue. Reference data with the correct spatial and temporal resolution on large scales are sparse and lack spatial representativeness. Moreover, due to the heterogeneity of soil moisture in both space and time, even reference data cannot be considered to be “ground truth”. As such, uncertainties are difficult to quantify. Additionally, in remotely sensed soil moisture there are trade-offs between spatial resolution and temporal resolution, resolution and accuracy, and resolution and computing time. Here, we try to identify the best spatial resolution for Sentinel-1 based soil moisture estimation, considering the trade-off between product resolution and accuracy. We use the uncertainty  of the soil moisture estimate as a guide parameter, and focus on how product accuracy depends on factors as soil wetness, and characteristics of the vegetated canopy.  To this end, we compare Sentinel-1 soil moisture estimates to both in situ data and global reference data sets with a lower spatial resolution. Remotely sensed surface soil moisture data were obtained by applying the MULESME algorithm  (Pulvirenti et al., 2018) on Sentinel-1 data throughout 2020. An extensive field campaign was performed, where TDR data and volumetric soil samples were gathered. A nearby setup of permanent soil moisture probes additionally provided continuous measurements of soil moisture at different depths, from 10 to 60 centimetres. Global datasets were obtained from the SMOS satellite constellation, GLDAS, MERRA-2 and ESA CCI.

Pulvirenti, L., Squicciarino, G., Cenci, L., Boni, G., Pierdicca, N., Chini, M., Versace, P. & Campanella, P. (2018). A surface soil moisture mapping service at national (Italian) scale based on Sentinel-1 data. Environmental Modelling & Software, 102, 13-28.

How to cite: van Hateren, T. C., Chini, M., Matgen, P., Pulvirenti, L., Pierdicca, N., and Teuling, A. J.: Estimating the best spatial resolution of remotely sensed surface soil moisture based on their uncertainty, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12826, https://doi.org/10.5194/egusphere-egu21-12826, 2021.