Soil moisture product consistency for operational drought monitoring in Europe

Soil moisture (SM) is a key variable in hydrological processes, bio-ecological processes, and biogeochemical processes. Long-term observations of soil moisture over large areas are critical to research on flooding and drought monitoring, water resource management, and crop yield forecasts. In this paper, Fengyun (FY3B and FY3C) SM products, Japan Aerospace Exploration Agency (JAXA) SM products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Land Parameter Retrieval Model (LPRM) AMSR2 L3 SM products, the Version 2 (v2) global land parameter data record (LPDR) of SM products, Soil Moisture Ocean Salinity (SMOS) Centre Aval de Traitement des Donnees SMOS (CATDS) L3 SM products, the Soil Moisture Active Passive (SMAP) passive L3 SM products and the European Space Agency(ESA) Climate Change Initiative (CCI) SM products were evaluated using the ground-based observations in the Little Washita, Fort Cobb and Yanco networks. Long-time series comparison between measured and satellite products was conducted to evaluate the overall performance of FY3 series satellites SM products. Bias (mean bias), R (correlation coefficient), RMSE (root mean square error) and ubRMSE (unbiased root mean square error) were calculated to explore the agreement between satellite products and in-situ measurements. Taylor diagrams were used to compare the performance of various satellite products. The result showed that (1) FY3B, FY3C and LPRM AMSR2 ascending and descending products had an obvious overestimate with in-situ soil moisture in three networks. (2) JAXA AMSR2 ascending and descending products had considerable underestimation in three networks. (3) The validation result of SMOS ascending and descending products over the three networks was satisfactory, with a rather high correlation than other X-band products. (4) The validation result of SMAP ascending and descending products outperformed the other products over the Little Washita and Fort Cobb networks. (5) The ESA CCI product had the lowest values of RMSE and ubRMSE than the other products in the Yanco network, revealing the effectiveness of merging active and passive soil moisture products. (6)The LPDR descending SM products had better performance than the LPDR ascending SM products in the LW and FC networks.

Abstract. Soil moisture (SM) data records longer than 30 years are critical for climate change research and various applications. However, only a few such long-term global SM datasets exist, and they often suffer from large biases, low spatial resolution, or spatiotemporal incompleteness. Here, we generated a consistent and seamless global surface SM product (0–5 cm) spanning 1982–2021 using a deep learning (DL) model. The model was trained with the GLASS-MODIS SM product and was designed to integrate four decades of Advanced Very High Resolution Radiometer (AVHRR)-derived albedo and land surface temperature, the land component of the fifth generation of European ReAnalysis (ERA5-Land) SM, and terrain and soil texture datasets as input features. Considering the temporal autocorrelation of SM, we explored two types of DL models that are adept at processing sequential data, including three long short-term memory (LSTM)-based models, i.e., the basic LSTM, bidirectional LSTM (Bi-LSTM), and attention-based LSTM (AtLSTM), and a transformer model. We also compared the performance of the DL models with the tree-based eXtreme Gradient Boosting (XGBoost) model, known for its high efficiency and accuracy. Our results show that all four DL models outperformed the benchmark XGBoost model, with the AtLSTM model achieving the highest accuracy on the test set, particularly at high SM levels (>0.4m3m-3). These results suggest that under some challenging conditions, utilizing temporal information and adding an attention module can effectively enhance the estimation accuracy of SM. Subsequent analysis of attention weights revealed that the AtLSTM model could automatically learn the necessary temporal information from adjacent positions in the sequence, which is critical for accurate SM estimation. The best-performing AtLSTM model was then adopted to produce a four-decade seamless global SM dataset at 5 km spatial resolution, denoted as the GLASS-AVHRR SM product. Validation of the GLASS-AVHRR SM product using 45 independent International Soil Moisture Network (ISMN) stations prior to 2000 yielded a median correlation coefficient (R) of 0.73 and an unbiased root mean square error (ubRMSE) of 0.041 m3 m−3. When validated against SM datasets from three post-2000 field-scale COsmic-ray Soil Moisture Observing System (COSMOS) networks, the median R values ranged from 0.63 to 0.79, and the median ubRMSE values ranged from 0.044 to 0.065 m3 m−3. Further validation across 22 upscaled 9 km Soil Moisture Active Passive (SMAP) core validation sites indicated that it could well capture the temporal variations in measured SM and remained unaffected by the large wet biases present in the input ERA5-Land SM product. Moreover, characterized by complete spatial coverage and low biases, this four-decade, 5 km GLASS-AVHRR SM product exhibited high spatial and temporal consistency with the 1 km GLASS-MODIS SM product and contained much richer spatial details than both the long-term ERA5-Land SM product (0.1°) and European Space Agency Climate Change Initiative combined SM product (0.25°). The annual average GLASS-AVHRR SM dataset from 1982 to 2021 is available at https://doi.org/10.5281/zenodo.14198201 (Zhang et al., 2024b), and the complete product can be freely downloaded from https://glass.hku.hk/archive/SM/AVHRR/ (last access: 18 September 2025).

Abstract. Soil moisture (SM) plays a critical role in the water and energy cycles of the Earth system; consequently, a long-term SM product with high quality is urgently needed. In this study, five SM products, including one microwave remote sensing product – the European Space Agency's Climate Change Initiative (ESA CCI) – and four reanalysis data sets – European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis – Interim (ERA-Interim), National Centers for Environmental Prediction (NCEP), the 20th Century Reanalysis Project from National Oceanic and Atmospheric Administration (NOAA), and the ECMWF Reanalysis 5 (ERA5) – are systematically evaluated using in situ measurements during 1981–2013 in four climate regions at different timescales over the Chinese mainland. The results show that ESA CCI is closest to the observations in terms of both the spatial distributions and magnitude of the monthly SM. All reanalysis products tend to overestimate soil moisture in all regions but have higher correlations than the remote sensing product except in Northwest China. The largest inconsistency is found in southern Northeast China region, with an unbiased root mean square error (ubRMSE) value larger than 0.04. However, all products exhibit certain weaknesses in representing the interannual variation in SM. The largest relative bias of 144.4 % is found for the ERA-Interim SM product under extreme and severe wet conditions in northeastern China, and the lowest relative bias is found for the ESA CCI SM product, with the minimum of 0.48 % under extreme and severe wet conditions in northwestern China. Decomposing mean square errors suggests that the bias terms are the dominant contribution for all products, and the correlation term is large for ESA CCI. As a result, the ESA CCI SM product is a good option for long-term hydrometeorological applications on the Chinese mainland. ERA5 is also a promising product, especially in northern and northwestern China in terms of low bias and high correlation coefficient. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.

<p>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.<br>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.<br>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.<br>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.</p>

This study develops a triple-collocation (TC) based, multi-source shallow-soil moisture product for Oklahoma. The method uses a least squared weights (LSW) optimization to find the set of parameters that result in the lowest root mean squared error (RMSE) with respect to the “unknown truth”. Soil moisture information from multiple sources and resolutions, including the Soil Moisture Active Passive SMAP L3_SM_P_E (9 km, daily), the physically-based, land surface model (LSM) estimates from NLDAS_NOAH0125_H (1/8°, hourly), and the Oklahoma Mesonet ground sensor network (9 km interpolated from point, 30 min) is merged into a 9 km spatial and daily temporal resolution product across the state of Oklahoma from April 2015 to July 2019. This multi-sensor surface soil moisture (MSSM) product is assessed in terms of a state-wide benchmark and previously tested, in situ-based soil moisture product and SMAP L4. Results show that: (1) independent source products have differential values according to the regional conditions they represent, including land cover type, soils, irrigation, or climate regime; (2) beyond serving as validation sets, in situ measurements are of significant value for improving the accuracy of multi-sensor soil moisture datasets through TC; and (3) state-wide RMSE values obtained with MSSM are similar to the typical measurement error found on in situ ground measurements which provides some degree of confidence on the new product. MSSM is an improvement over currently available products in Oklahoma due to its minimized uncertainty, easiness of production, and continuous temporal and geographic coverage. Nevertheless, to exploit its utility, further tests of this methodology are needed in different climates, land cover types, geographic regions, and for other independent products and spatiotemporal resolutions.

The error characterization of soil moisture products, for example, obtained from microwave remote sensing data, is a key requirement for using these products in applications like numerical weather prediction. The error variance and root-mean-square error are among the most popular metrics: they can be estimated consistently for three datasets using triple collocation (TC) without assuming any dataset to be free of errors. This technique can account for additive and multiplicative biases; that is, it assumes that the three products are linearly related. However, its susceptibility to nonlinear relations (e.g., due to sensor saturation and scale mismatch) has not been addressed. Here, a simulation study investigates the impact of quadratic relations on the TC error estimates [also when the products are first rescaled using the nonlinear cumulative distribution function (CDF) matching technique] and on those by two novel methods. These methods—based on error-in-variables regression and probabilistic factor analysis—extend standard TC by also accounting for nonlinear relations using quadratic polynomials. The relative differences between the error estimates of the ASCAT remotely sensed product by the quadratic and the linear methods are predominantly smaller than 10% in a case study based on remotely sensed, reanalysis, and in situ measured soil moisture over the contiguous United States. Exceptions with larger discrepancies indicate that nonlinear relations can pose a challenge to traditional TC analyses, as the simulations show they can introduce biases of either sign. In such cases, the use of nonlinear methods may complement traditional approaches for the error characterization of soil moisture products.

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm3/cm3, 0.019 cm3/cm3, and 0.034 cm3/cm3, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm3/cm3 (ASCAT) and 0.0662 cm3/cm3 (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product.

Using dense soil moisture (SM) measurements in the upper Huai River basin of China, this study evaluated the spatial patterns of L-band satellite-based SM products, including Soil Moisture Active Passive (SMAP) L3, Soil Moisture and Ocean Salinity (SMOS) L3 and the European Space Agency’s Climate Change Initiative (ESA CCI) SM products. The mean difference (MD), root mean squared error (RMSE), unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (R), were used in the evaluation. The evaluation results presented that SMAP and ESA CCI products can well capture the temporal variation of SM at single points quite well, with average R values of 0.51 and 0.46, respectively. And SMAP had the highest overall accuracy among the three satellite-based products in study area. We also analyzed the correlations between the four accuracy indexes and six environmental factors including the proportions of five land use/land cover types (i.e. water bodies, paddy fields, construction land, dryland and forest) and the average NDVI (Normalized Difference Vegetation Index) in 2016 in each grid. Analysis showed that the proportions of paddy fields and water bodies in each grid had significant positive correlations with MD, RMSE and ubRMSE, while NDVI, and the proportions of dryland and construction land had significant negative correlations with these three indexes. The significant correlations between the accuracy of SMAP, SMOS and ESA CCI SM products and environmental factors indicate that there exist systematic biases in these products, which can provide valuable insights into algorithm improvements.

Time series of soil moisture (SM) data in the Qinghai–Tibet plateau (QTP) covering a period longer than one decade are important for understanding the dynamics of land surface–atmosphere feedbacks in the global climate system. However, most existing SM products have a relatively short time series or show low performance over the challenging terrain of the QTP. In order to improve the spaceborne monitoring in this area, this study presents a random forest (RF) method to rebuild a high-accuracy SM product over the QTP from 19 June 2002 to 31 March 2015 by adopting the advanced microwave scanning radiometer for earth observing system (AMSR-E), and the advanced microwave scanning radiometer 2 (AMSR2), and tracking brightness temperatures with latitude and longitude using the International Geosphere–Biospheres Programme (IGBP) classification data, the digital elevation model (DEM) and the day of the year (DOY) as spatial predictors. Brightness temperature products (from frequencies 10.7 GHz, 18.7 GHz and 36.5 GHz) of AMSR2 were used to train the random forest model on two years of Soil Moisture Active Passive (SMAP) SM data. The simulated SM values were compared with third year SMAP data and in situ stations. The results show that the RF model has high reliability as compared to SMAP, with a high correlation (R = 0.95) and low values of root mean square error (RMSE = 0.03 m3/m3) and mean absolute percent error (MAPE = 19%). Moreover, the random forest soil moisture (RFSM) results agree well with the data from five in situ networks, with mean values of R = 0.75, RMSE = 0.06 m3/m3, and bias = −0.03 m3/m3 over the whole year and R = 0.70, RMSE = 0.07 m3/m3, and bias = −0.05 m3/m3 during the unfrozen seasons. In order to test its performance throughout the whole region of QTP, the three-cornered hat (TCH) method based on removing common signals from observations and then calculating the uncertainties is applied. The results indicate that RFSM has the smallest relative error in 56% of the region, and it performs best relative to the Japan Aerospace Exploration Agency (JAXA), Global Land Data Assimilation System (GLDAS), and European Space Agency’s Climate Change Initiative (ESA CCI) project. The spatial distribution shows that RFSM has a similar spatial trend as GLDAS and ESA CCI, but RFSM exhibits a more distinct spatial distribution and responds to precipitation more effectively than GLDAS and ESA CCI. Moreover, a trend analysis shows that the temporal variation of RFSM agrees well with precipitation and LST (land surface temperature), with a dry trend in most regions of QTP and a wet trend in few north, southeast and southwest regions of QTP. In conclusion, a spatiotemporally continuous SM product with a high accuracy over the QTP was obtained.

Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation Index (NDVI) modulated relationships between day/night SM and temperature change at corresponding times. Land surface model output variables from the North America Land Data Assimilation System (NLDAS), remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) were used in this methodology. The functional relationships developed using NLDAS data at a grid resolution of 12.5 km were applied to downscale AMSR2 JAXA (Japan Aerospace Exploration Agency) SM product (25 km) using MODIS land surface temperature (LST) and NDVI observations (1 km) to produce the 1 km SM estimates. The downscaled SM estimates were validated by comparing them with ISMN (International Soil Moisture Network) in situ SM in the Black Bear–Red Rock watershed, central Oklahoma between 2015–2017. The overall statistical variables of the downscaled AMSR2 SM validation R2, slope, RMSE and bias, demonstrate good accuracy. The downscaled SM better characterized the spatial and temporal variability of SM at watershed scales than the original SM product.

Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation

The Soil Moisture and Ocean Salinity (SMOS) satellite is a 2D interferometer in L-band (1.4 GHz). Over land, it enables to probe the earth surface emissivity related to soil moisture in the first centimeters of the soil, with an aimed accuracy better than 4m³.m^-3 and an average spatial resolution of 40km. The European Space Agency's (ESA) ground segment provides half-orbit soil moisture products at level 2. The Centre National d'Etudes Spatiales (CNES) in France has developed the CATDS (Centre Aval de Traitement des Données SMOS) ground segment in order to produce global maps, known as level 3 and 4 products. Over land, the algorithm is based on the level 2 soil moisture ESA's prototype. The major enhancement of the CATDS concerns the use of multi-orbit retrieval. The level 3 Soil moisture (SM) products are global maps of soil moisture, and other geophysical products (vegetation optical thickness, albedo, soil dielectric constant or surface temperature). For a particular point, the revisit time is between 1 and 3 days, and the entire Earth's surface is covered by SMOS field of view in 3 days. Level 3 SM products are available over different time periods. First, a 1 day global product is generated for each day. Then, 1 day global maps are aggregated in 3-day global products, 10- day and monthly products.

Tremendous progress has been made in estimating soil moisture (SM) from satellite remote sensing data. Several global-scale coarse-resolution products have also been generated and released for various applications in the Earth system. However, high-resolution SM estimation is still in its infancy. Currently, two main methods are used for this purpose: downscaling approaches and direct retrieval from microwave and optical/thermal data. Several studies have attempted to comprehensively evaluate the performance of these approaches and have found that each method has its own strengths and weaknesses, with no single method outperforming the others. In this study, we aim to investigate the advantages of integrating optical, thermal, and microwave data to estimate SM by leveraging an intensive SM network and triple collocation (TC) method. First, we determined the best-performing coarse-resolution microwave SM product through the TC approach. Second, we generated 1-km SM using a downscaling approach based on land surface temperature and vegetation index, utilizing the best-performing SMAP L3 descending product. Third, we evaluated the high-resolution downscaled SM, Sentinel 1 SM, and SMAP/Sentinel 1 combined SM products using SM measurements from the REMEDHUS station network, ETOPO1 elevation, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation, and the European Space Agency (ESA) CCI land cover map. Finally, we investigated and demonstrated the advantages of merging these products through point-scale evaluation and large-scale spatial pattern comparison.

The proper determination of soil moisture on different scales is important for applications in a variety of fields. We aim to develop a high-level soil moisture product with high temporal and spatial resolution by assimilating the multilayer soil moisture product SCATSAR-SWI (Scatterometer Synthetic Aperture Radar Soil Water Index) into the surface model SURFEX. In addition, we probe the impact of the findings on the numerical weather prediction (NWP) in Austria. The data assimilation system consists of the NWP model AROME and the SURFEX Offline Data Assimilation, which provide atmospheric forcing and soil moisture fields as mutual input. To address the known sensitivity of the employed simplified extended Kalman filter to the specification of errors, we compute the observation error variances of the SCATSAR-SWI locally using Triple Collocation Analysis and implement them into the assimilation system. The verification of the forecasted 2 m temperature and relative humidity against measurements of Austrian weather stations shows that the actual impact of the local error approach on the atmospheric forecast is slightly positive to neutral compared to the standard error approach, depending on the time of the year. The direct verification of the soil moisture analysis against a gridded water balance product reveals a degradation of the unbiased root-mean-square error for small observation errors.

The inter-comparison of different soil moisture (SM) products over the Tibetan Plateau (TP) reveals the inconsistency among different SM products, when compared to in situ measurement. It highlights the need to constrain the model simulated SM with the in situ measured data climatology. In this study, the in situ soil moisture networks, combined with the classification of climate zones over the TP, were used to produce the in situ measured SM climatology at the plateau scale. The generated TP scale in situ SM climatology was then used to scale the model-simulated SM data, which was subsequently used to scale the SM satellite observations. The climatology-scaled satellite and model-simulated SM were then blended objectively, by applying the triple collocation and least squares method. The final blended SM can replicate the SM dynamics across different climatic zones, from sub-humid regions to semi-arid and arid regions over the TP. This demonstrates the need to constrain the model-simulated SM estimates with the in situ measurements before their further applications in scaling climatology of SM satellite products.