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<strong class="journal-contentHeaderColor">Abstract.</strong> Root-zone soil moisture (RZSM) is crucial for water resource management, drought monitoring and sub-seasonal flood climate forecast. RZSM is not directly observable from space but various model-derived RZSM products are available at the global scale and are widely used. In this paper, a comprehensive quantitative evaluation of eight RZSM products is made over the Huai river basin (HRB) in China. A direct validation is performed using observations from 58 in situ soil moisture stations from 1 April 2015 to 31 March 2020. Attention is drawn to the potential factors increasing uncertainties of model-generated RZSM, such as errors on atmospheric forcings (precipitation, air temperature), soil properties, and model parameterizations. Results indicate that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) performs best among all RZSM products with the highest correlation coefficient (R) and lowest unbiased root-mean square error (ubRMSE): 0.503 and 0.031 m³ m^&minus;3, respectively. All RZSM products tend to overestimate the in situ soil moisture values, except for the Soil Moisture and Ocean Salinity (SMOS) L4 product, which underestimates RZSM. The underestimated SMOS L3 SSM associated with low physical surface temperature triggers the underestimation of RZSM in SMOS L4. The RZSM overestimation by other products can be explained by the overestimation of precipitation amount, precipitation event frequency (drizzle effects) and by the underestimation of air temperature. Besides, the overestimation of the soil clay content and the underestimation of the soil sand content in different LSMs leads to larger soil moisture values. The intercomparison of the eight RZSM products shows that MERRA-2 and SMAP L4 RZSM are the most correlated with one another. These products are based on the same LSM and on the same surface meteorological forcing generated from the National Aeronautics and Space Administration (NASA) GEOS-5. In addition, model parameterizations in different LSMs vary considerably, affecting the transfer and exchange of water and heat in the vadose zone.

Abstract. Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. While RZSM is not directly observable from space, several RZSM products are available and widely used at global and continental scales. This study conducts a comprehensive and quantitative evaluation of eight RZSM products using observations from 58 in situ soil moisture stations over the Huai River basin (HRB) in China. Attention is drawn to the potential factors that contribute to the uncertainties of model-based RZSM, including the errors in atmospheric forcing, vegetation parameterizations, soil properties and spatial scale mismatch. The results show that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) outperforms the other RZSM products with the highest correlation coefficient (R= 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.018 m3 m−3), while SMOS Level 4 (L4) RZSM shows the worst performance among eight RZSM products. The RZSM products based on land surface models generally perform better in the wet season than in the dry season due to the enhanced ability to capture of the temporal dynamics of in situ observations in the wet season and the inertia of remaining high soil moisture values even in the dry season, while the SMOS L4 RZSM product, derived from SMOS L3 surface moisture (SSM) combined with an exponential filter method, performs better in the dry season due to the attenuated ground microwave radiation signal caused by the increased water vapour absorption and scattering in the wet season. The underestimated SMOS L3 SSM triggers the underestimation of RZSM in SMOS L4. The overestimated RZSM products based on land surface models could be associated with the overestimated precipitation amounts and frequency, the underestimated air temperature, and the underestimated ratio of transpiration to the total terrestrial evapotranspiration. In addition, the biased soil properties and flawed vegetation parameterizations affect the hydrothermal transport processes represented in different land surface models (LSMs) and lead to inaccurate soil moisture simulation. The scale mismatch between point and footprint also introduces representative errors. The comparison of frequency of normalized soil moisture between RZSM products and in situ observations indicates that the LSMs should focus on reducing the frequency of wet soil moisture, increasing the frequency of dry soil moisture and the ability to capture the frequency peak of soil moisture. The study provides some insights into how to improve the ability of land surface models to simulate the land surface states and fluxes by taking into account the issues mentioned above. Finally, these results can be extrapolated to other regions located in similar climate zones, as they share similar precipitation patterns that dominate the terrestrial water cycle.

Root-zone soil moisture estimation from assimilation of downscaled Soil Moisture and Ocean Salinity data

Inversion of soil hydraulic properties from the DEnKF analysis of SMOS soil moisture over West Africa

Abstract. The increasing frequency of drought events has expanded the research interest in drought monitoring. In this regard, remote sensing is a useful tool to globally mapping the agricultural drought. While this type of drought is directly linked to the availability of root zone soil moisture (RZSM) for plants growth, current satellite soil moisture observations only characterize the water content of the surface soil layer (0–5 cm). In this study, two soil moisture-based agricultural drought indices were obtained at a weekly rate from June 2010 to December 2016, using RZSM estimations at 1 km from the Soil Moisture and Ocean Salinity (SMOS) satellite, instead of surface soil moisture (SSM). The RZSM was estimated by applying the Soil Water Index (SWI) model to the SMOS SSM. The Soil Moisture Agricultural Drought Index (SMADI) and the Soil Water Deficit Index (SWDI) were assessed over the Castilla y León region (Spain) at 1 km spatial resolution. They were compared with the Atmospheric Water Deficit (AWD) and the Crop Moisture Index (CMI), both computed at different weather stations distributed over the study area. The level of agreement was analyzed through statistical correlation. Results showed that the use of RZSM does not influence the characterization of drought, both for SMADI and SWDI.

Root zone soil moisture (RZSM) is a vital variable for agricultural production, water resource management and runoff prediction. Satellites provide large-scale and long-term near-surface soil moisture retrievals, which can be used to estimate RZSM through various methods. In this study, we tested the utility of an exponential filter (ExpF) using in situ soil moisture by optimizing the optimal characteristic time length T_opt for different soil depths. Furthermore, the parameter analysis showed that T_opt correlated negatively with precipitation and had no significant correlation with selected soil properties. Two approaches were taken to obtain T_opt: (1) optimization of the Nash–Sutcliffe efficiency coefficient (NSE); (2) calculation based on annual average precipitation. The precipitation-based T_pre outperformed the station-specific T_opt and stations-averaged T_opt. To apply the ExpF on grid scale, the precipitation-based T_pre considering spatial variability was adopted in the ExpF to obtain RZSM from a new soil moisture dataset RF_SMAP_L3_P (Random Forest Soil Moisture Active Passive_L3_Passive) continuous in time and space over Huai River Basin. Finally, the performance of RF_SMAP_L3_P RZSM (0–100 cm) was evaluated using in situ measurements and compared with mainstream products, for instance, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity Level 4 (SMOS L4) RZSM. The results indicated that RF_SMAP_L3_P RZSM could captured the temporal variation of measured RZSM best with R value of 0.586, followed by SMAP L4, which had the lowest bias value of 0.03, and SMOS L4 significantly underestimated the measured RZSM with bias value of −0.048 in the basin. Higher accuracy of RF_SMAP_L3_P RZSM was found in the flood period compared with the non-flood period, which indicates a better application for ExpF in wetter weather conditions.

&amp;lt;p&amp;gt;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.&amp;lt;br&amp;gt;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&amp;amp;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.&amp;lt;br&amp;gt;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.&amp;lt;br&amp;gt;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.&amp;lt;/p&amp;gt;

Crop growth depends on the root-zone soil moisture (RZSM) (~top 1m). Accurate estimation of RZSM is vital to optimize irrigation management for saving water and energy while sustaining crop yield. The High-Resolution Land Assimilation System (HRLDAS) from NCAR can generate RZSM at field scales for irrigation management. The soil moisture data from various agriculture sites in the AmeriFlux network, U.S. Climate Reference Network (USCRN), and Soil Climate Analysis Network (SCAN) are used to verify the soil moisture products generated by HRLDAS. Although the HRLDAS products is not location specific and could be applied nationwide, this study will focus on Nebraska for evaluation, validation, and further calibration. We also compared NASA's SMAP surface soil moisture products to HRLDAS surface layer soil moisture. Since the accuracy of the SMAP product is known, this comparison directly validates the HRLDAS surface soil moisture product and indirectly validate its RZSM products. Results from these two validation methods show a good accuracy of HRLDAS soil moisture products. The conspicuous differences between HRLDAS and SMAP products indicate that HRLDAS omits the irrigation activities as its simulation is based on weather variables and energy balance. It's hard for HRLDAS to consider and include the irrigation actions in its results, while as SMAP products remotely sense the soil moisture as it is, the changes caused by irrigation are clearly reflected. Therefore, a simple calibration is applied to the HRLDAS products by including irrigation amount as its variables.

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve.

This paper presents the Soil Moisture and Ocean Salinity (SMOS) mission data assimilation activities conducted at the European Centre for Medium-Range Weather Forecasts (ECMWF) to analyse soil moisture for Numerical Weather Prediction (NWP) applications. Two different approaches are presented based on SMOS brightness temperature and SMOS neural network soil moisture data assimilation, respectively. For the first approach, SMOS brightness temperature data assimilation relies on forward modelling. Long term results, spanning the SMOS period, of SMOS forward modelling, monitoring and data assimilation are presented. They emphasize the relevance of SMOS data for monitoring and to support NWP model developments. For the second approach, a SMOS soil moisture product has been produced based on a Neural Network (NN) trained on ECMWF soil moisture. So, the SMOS-ECMWF NN soil moisture product captures the SMOS signal variability in time and space, while by design its climatology is consistent with that of the ECMWF soil moisture, which makes it suitable for data assimilation purpose. This approach, initially tested for 2012 in a global scale stand alone approach, shows that SMOS NN data assimilation slightly improves the two-metre air temperature forecast in the short range at regional scale. For NWP applications this approach has been further developed with a near real time production of the SMOS-ECMWF NN soil moisture product, with the implementation of the SMOS NN data assimilation in the ECMWF Integrated Forecasting System (IFS), and with high resolution (9km) global scale testing compatible with the current ECMWF NWP system.

Soil moisture is an important hydrologic variable due to its influence on plant growth and the surface energy budget. Most current soil moisture networks measure soil moisture at single points; these point measurements of soil moisture may not relate to the soil moisture of the larger, surrounding areas. Crop models and weather and climate models, which are sensitive to soil moisture, could be improved by having accurate soil moisture data that is relevant at scales larger than a point. Microwave C–band and X–band sensing satellites have attempted to retrieve soil moisture at relevant, large spatial scales needed for models, but these satellites are limited in their sensing depth, sensitivity to vegetation, and sensitivity to scattering by precipitation. Two recent satellite missions, the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission and NASA’s Soil Moisture Active Passive (SMAP) mission, retrieve soil moisture from L–band measurements of radiation, which improve upon the limitations of previous satellites. The retrieval of soil moisture from microwave satellite measurements requires knowledge of the vegetation optical thickness (τ), which is related to the vegetation water content (VWC). The multi-incidence angular design of SMOS provides its retrieval algorithm the advantage of retrieving soil moisture and τ simultaneously. SMAP will measure at a single incidence angle and will require an ancillary source of τ data. The current plan is for SMAP to use a climatology of normalized difference vegetation index (NDVI) data to generate a τ climatology. This dissertation analyzed this SMAP τ climatology by first validating SMOS τ and then by comparing SMOS τ to SMAP τ . The validation of SMOS τ was done by comparing SMOS τ during the growing season to crop yield estimates for each Iowa county. The result was that larger values of SMOS τ were correlated with larger crop yields, which suggested that SMOS τ is related to the growth of vegetation. SMOS τ was also found to be noisy and to behave strangely outside of the growing

The Soil Moisture and Ocean Salinity (SMOS) mission delivers global surface soil moisture fields at high temporal resolution which is of major relevance for water management and climate predictions. Between April 26 and May 9, 2010, an airborne campaign with the L-band radiometer EMIRAD-2 was carried out within one SMOS pixel (44 × 44 km) in the Skjern River Catchment, Denmark. Concurrently, ground sampling was conducted within three 2 × 2 km patches (EMIRAD footprint size) of differing land cover. By means of this data set, the objective of this study is to present the validation of SMOS L1C brightness temperatures <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> of the selected node. Data is stepwise compared from point via EMIRAD to SMOS scale. From ground soil moisture samples, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's are pointwise estimated through the L-band microwave emission of the biosphere model using land cover specific model settings. These <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's are patchwise averaged and compared with EMIRAD <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's. A simple uncertainty assessment by means of a set of model runs with the most influencing parameters varied within a most likely interval results in a considerable spread of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's (5-20 K). However, for each land cover class, a combination of parameters could be selected to bring modeled and EMIRAD data in good agreement. Thereby, replacing the Dobson dielectric mixing model with the Mironov model decreases the overall RMSE from 11.5 K to 3.8 K. Similarly, EMIRAD data averaged at SMOS scale and corresponding SMOS <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's show good accordance on the single day where comparison is not prevented by strong radio-frequency interference (RFI) (May 2, avg. RMSE = 9.7 K). While the advantages of solid data sets of high spatial coverage and density throughout spatial scales for SMOS validation could be clearly demonstrated, small temporal variability in soil moisture conditions and RFI contamination throughout the campaign limited the extent of the validation work. Further attempts over longer time frames are planned by means of soil moisture network data as well as studies on the impacts of organic layers under natural vegetation and higher open water fractions at surrounding grid nodes.

Real surface soil moisture retrieved from the Soil Moisture and Ocean Salinity (SMOS) satellite is downscaled and assimilated in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer (MIKE SHE SW‐ET) model using a bias aware ensemble transform Kalman filter (Bias‐ETKF). Satellite‐derived soil moisture assimilation in a catchment scale model is typically restricted by two challenges: (1) passive microwave is too coarse for direct assimilation and (2) the data tend to be biased. The solution proposed in this study is to disaggregate the SMOS bias using a higher resolution land cover classification map that was derived from Landsat thermal images. Using known correlations between SMOS bias and vegetation type, the assimilation filter is adapted to calculate biases online, using an initial bias estimate. Real SMOS‐derived soil moisture is assimilated in a precalibrated catchment model in Denmark. The objective is to determine if any additional gains can be achieved by SMOS surface soil moisture assimilation beyond the optimized model. A series of assimilation experiments were designed to (1) determine how effectively soil moisture corrections propagate downward in the soil column, (2) compare the efficacy of in situ versus SMOS assimilation, and (3) determine how soil moisture assimilation affects fluxes and discharge in the catchment. We find that assimilation of SMOS improved R 2 soil moisture correlations in the upper 5 cm compared to a network of 30 in situ sensors for most land cover classes. Assimilation also brought modest gains in R 2 at 25 cm depth but slightly degraded the correlation at 50 cm depth. Assimilation overcorrected discharge peaks.

Soil moisture (SM) plays an important role in the water and energy exchanges that occur in the terrestrial surface. Soil moisture can be retrieved at a larger scale by using the visible and InfraRed (IR) bands as well as through the microwave remote sensing. Because of very high importance of SM for variety of applications, there are now two dedicated microwave satellites in the Earth’s orbit for soil moisture retrieval from space. The first, Soil Moisture and Ocean Salinity (SMOS) satellite has been launched by the European Space Agency in November 2009 and second is Soil Moisture Active and Passive (SMAP) launched by the National Aeronautics and Space Administration (NASA) in January 2015. In this review, brief background of soil moisture retrieval algorithms are presented with different applications in the area of water resources. The first section provides the introduction of the soil moisture, presents several in situ techniques for measurement of soil moisture and soil moisture retrieval algorithms from visible/IR and microwave remote sensing. Section 2 describes the satellite soil moisture applications in water resources.

For several decades, satellite-based microwave sensors have provided valuable soil moisture monitoring in various surface conditions. We have first developed a modeled aerosol optical depth (AOD) dataset by utilizing Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Global Land Data Assimilation System (GLDAS) soil moisture datasets in order to estimate dust outbreaks over desert areas of East Asia. Moderate Resolution Imaging Spectroradiometer- (MODIS-) based AOD products were used as reference datasets to validate the modeled AOD (MA). The SMOS-based MA (SMOS-MA) dataset showed good correspondence with observed AOD (R-value: 0.56) compared to AMSR2- and GLDAS-based MA datasets, and it overestimated AOD compared to observed AOD. The AMSR2-based MA dataset was found to underestimate AOD, and it showed a relatively lowR-value (0.35) with respect to observed AOD. Furthermore, SMOS-MA products were able to simulate the short-term AOD trends, having a highR-value (0.65). The results of this study may allow us to acknowledge the utilization of microwave-based soil moisture datasets for investigation of near-real time dust outbreak predictions and short-term dust outbreak trend analysis.