Soil Moisture Active Passive Soil Moisture Products Research Articles

Spatial downscaling of SMAP soil moisture estimation using multiscale geographically weighted regression during SMAPVEX16

Passive microwave remote sensing missions such as Soil Moisture Active Passive (SMAP) are widely used for global soil moisture (SM) estimation. But the low spatial resolution of passive microwave SM products limits their applications at regional and local scales (normally 1–10 km). Using high-resolution auxiliary data to downscale passive microwave SM products is the primary way to obtain high-resolution SM data. However, the existing SM downscaling methods do not entirely account for the scale differences in the impact of various variables on the distribution of SM. To solve this problem, this paper introduced MGWR (Multiscale Geographically Weighted Regression) as a novel way to analyze the scale differences of normalized difference vegetation index (NDVI) and land surface temperature (LST) on the spatial pattern of SM. Accordingly, a new spatial downscaling algorithm for SMAP SM products is proposed and compared with the geographically weighted regression (GWR) algorithm and moving window algorithm (MW). The algorithm was applied to data taken during the SMAP Validation Experiment 2016 (SMAPVEX16), and the downscaled SM evaluated with in situ SM and aircraft observations. These results show that: 1) the SM downscaling conversion function, based on the MGWR, effectively reveals how different surface parameters relate to SM, with LST influencing SM globally and NDVI affecting it locally, 2) compared with GWR and MW, the 1 km SM obtained by the MGWR-based downscaling method showed better spatial agreement with airborne SM in the semiarid region with the ubRMSE decreasing by 0.023 m3/m3 and 0.026 m3/m3 for GWR and MW respectively, 3) when evaluating the 1 km SM with in situ SM in the semiarid region, the SM obtained through MGWR exhibited lower ubRMSE values as compared to both GWR and MW. Specifically, the median ubRMSE values for MGWR, GWR, and MW were 0.043 m3/m3, 0.053 m3/m3, and 0.094 m3/m3, respectively. In conclusion, this study demonstrates that MGWR can effectively improve the accuracy of downscaled SM in semiarid regions.

Spatial downscaling of SMAP radiometer soil moisture using radar data: Application of machine learning to the SMAPEx and SMAPVEX campaigns

This study developed a random forest approach for downscaling the coarse-resolution (36 km) soil moisture measured by The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission to 1 km spatial resolution, utilizing airborne remotely sensed data (radar backscatter and radiometer retrieved soil moisture), vegetation characteristics (normalized difference vegetation index), soil properties, topography, and ground soil moisture measurements from before the launch of SMAP for training a random forest model. The 36 km SMAP soil moisture product was then downscaled by the trained model to 1 km resolution using the information from SMAP. The downscaled soil moisture was evaluated using airborne retrieved soil moisture observations and ground soil moisture measurements. Considering the airborne retrieved soil moisture as a reference, the results demonstrated that the proposed random forest model could downscale the SMAP radiometer product to 1 km resolution with a correlation coefficient of 0.97, unbiased Root Mean Square Error of 0.048 m3 m−3 and bias of 0.016 m3 m−3. Accordingly, the downscaled soil moisture captured the spatial and temporal heterogeneity and demonstrated the potential of the proposed machine learning model for soil moisture downscaling.

The Characterization of the Vertical Distribution of Surface Soil Moisture Using ISMN Multilayer In Situ Data and Their Comparison with SMOS and SMAP Soil Moisture Products

In this paper, we investigated the vertical distribution characteristics of surface soil moisture based on ISMN (International Soil Moisture Network) multilayer in situ data (5, 10, and 20 cm; 2, 4, and 8 in) and performed comparisons between the in situ data and four microwave satellite remote sensing products (SMOS L2, SMOS-IC, SMAP L2, and SMAP L4). The results showed that the mean soil moisture difference between layers can be −0.042~−0.024 (for the centimeter group)/−0.067~−0.044 (for the inch group) m3/m3 in negative terms and 0.020~0.028 (for the centimeter group)/0.036~0.040 (for the inch group) m3/m3 in positive terms. The surface soil moisture was found to have very significant stratification characteristics, and the interlayer difference was close to or beyond the SMOS and SMAP 0.04 m3/m3 nominal retrieval accuracy. Comparisons revealed that the satellite retrievals had a higher correlation with the field measurements of 5 cm/2 in, and SMAP L4 had the smallest difference with the in situ data. The mean difference caused by using 10 cm/4 in and 20 cm/8 in in situ data instead of the 5 cm/2 in data could be about −0.019~−0.018/−0.18~−0.015 m3/m3 and −0.026~−0.023/−0.043~−0.039 m3/m3, respectively, meaning that there would be a potential depth mismatch in the data validation.

Exploring the utility of the downscaled SMAP soil moisture products in improving streamflow simulation

Study regionThe Susquehanna and upper Susquehanna watersheds in the Northeastern of the United States of America (USA) Study focusThis study explored the utility of the Soil Moisture Active Passive (SMAP) soil moisture downscaled to a range of spatial resolutions for improving ensemble streamflow simulations. The SMAP level 3 soil moisture product with spatial resolution of roughly 40 km was downscaled to a range of spatial resolutions including 1, 3 and 9 km over the Susquehanna and upper Susquehanna watersheds. A set of experiments was conducted through direct insertion of the downscaled SMAP soil moisture into a physically-based distributed hydrological model. New hydrological insights for the regionThe updating of the model with the original and downscaled SMAP surface soil moisture markedly improved the accuracy of the ensemble streamflow simulations with the CRPSS and NRMSE values in the range of 0.10–0.17 and 0.79–0.85, respectively when compared to the non-updated model for the Susquehanna watershed. In addition, the ensemble spread was reduced, and the ensemble mean compares well with the observed streamflow. The 1 km downscaled SMAP soil moisture showed the highest accuracy in improving streamflow simulation with the CRPSS and NRMSE value of 0.21 and 0.72, respectively for the Upper Susquehanna watershed, whereas for the Susquehanna watershed downscaled SMAP at 9 km adequately improved the accuracy of the ensemble streamflow simulations with the CRPSS and NRMSE value of 0.17 and 0.80, respectively. Besides the top layer of the model, updating the second layer of the model with the vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the model.

Assessment of five SMAP soil moisture products using ISMN ground-based measurements over varied environmental conditions

Since the launch of Soil Moisture Active Passive (SMAP) mission in 2015, several advanced global surface soil moisture (SM) products derived from different retrieval schemes have been released. However, the factors contributing to the performance differences of these SMAP products have not been fully investigated. This study tries to fill the gap by assessing and intercomparing five SMAP products, namely, the single channel algorithm V/H-pol (SCA-V/H), dual channel algorithm (DCA), multi-temporal dual channel algorithm (MT-DCA) and the new SMAP-INRAE-BORDEAUX (SMAP-IB), using the International Soil Moisture Network (ISMN) ground measurements worldwide during 2016–2020. After reviewing the retrieval algorithms, the impact of five potential perturbing factors on the skills of all products is reported; these factors including mean surface soil temperature (MSST), surface roughness (log(SR)), vegetation density (characterized by vegetation optical depth (VOD)), mean surface soil wetness (MSSM) and soil organic matter (SOM). Results shows that the SMAP-IB product has the highest overall R value of 0.75 with ISMN ground SM, and the smallest unbiased root mean square difference (ubRMSD) values same as SCA-V and DCA (∼0.058 m3/m3). The performance of all five products in terms of R and ubRMSD rapidly degrades over the regions with high VOD, rough surface and low MSST. In contradiction with the fact that product performance degrades with increasing roughness, the ubRMSD of all five products increases with decreasing log(SR) on a relatively smooth surface (log(SR) < 6) with larger growth rates for SMAP-IB, SCA-H and MT-DCA. Regarding characterizing the temporal variations of in situ SM anomalies (Ranom), Ranom increases as the ground becomes wet for all products within a certain range (MSSM < 0.3 m3/m3). In particular, SMAP-IB and MT-DCA present higher Ranom values than the other three products under wet soil surfaces (MSSM > 0.3 m3/m3). The two metrics R and Ranom decrease significantly with increasing SOM for SMAP-IB compared to the other products, and SMAP-IB obtains the lowest values for both metrics when SOM > 20%. These findings are expected to provide useful information for appropriate product selection and improvement.

Indicator of Flood-Irrigated Crops From SMOS and SMAP Soil Moisture Products in Southern India

Spaceborne L-band data have the potential to monitor flooded and irrigated areas. However, further studies are needed to assess in real cases the impact of flood-irrigated crops on SMOS and SMAP surface soil moisture (SSM) data. This paper demonstrates the ability of SMOS/SMAP SSM retrievals to quantify the fraction of flood-irrigated area at the seasonal scale and at a 25 km resolution in the Telangana State in southern India. Over irrigated areas, both SMOS level 3 (L3) SSM and SMAP L3 enhanced SSM products present a bimodal annual cycle, with a peak of SSM during the monsoon (wet) season corresponding to rainfall and irrigation, and a peak during the dry season due to irrigation activities solely. The second peak is absent or has a very small amplitude in areas where rice represents a small fraction (typically below 5-10%). More importantly, the amplitude of the second SSM peak is significantly correlated to the rice cover fraction within 25×25 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> pixels (R=0.81 for SMOS and 0.77 for SMAP), showing its potential to assess crop fraction and hence the water used for irrigation. The SMOS/SMAP L3 SSM peak during the dry period occurs several months before the harvest, constituting an indicator for rice stocks at the end of the season. However the irrigation signature is absent from the SMAP level 4 SSM product derived from the assimilation of SMAP brightness temperatures in a land surface model, which indicates that the data assimilation scheme is inefficient to restitute irrigation information.

Reconstruct SMAP brightness temperature scanning gaps over Qinghai-Tibet Plateau

• SMAP brightness temperature ( TB ) scanning gaps were compensated. • Daily variation of TB was analyzed in function of influencing factors. • Seamless TB benefits the refinements of SMAP soil moisture products. Soil Moisture Active Passive (SMAP) satellite was used to monitor global soil moisture and freeze–thaw state using surface brightness temperature (TB). However, due to the limitation of scanning width and shifting track, the daily TB observation had stripe gaps in varying degrees, which imped scientific applications of SMAP official product. To solve this issue, we proposed a temporal fitting algorithm to produce daily seamless TB data over Qinghai-Tibet Plateau (QTP) from 2015 to 2021. This method was composed of two steps: (1) overall time trend fitting which used a spline function to fit the overall mean time trend for each pixel from 2015 to 2021; (2) daily variation correction which accounted for several influential factors including surface temperature, precipitation, and vegetation to establish an Ordinary Least Squares (OLS) model for predicting daily variation of TB in numerous time slices. Consequently, the missing TB observation was reconstructed by combining the overall temporal trend and daily variation. A 10-fold cross-validation and product comparison were carried out to validate the robustness of the proposed method. For cross-validation, we randomly removed available SMAP observations in certain periods as unavailable, and then compared the reconstructed values with the removed practical ones. The cross-validation results indicated the reconstructed TB agreed well with actual SMAP TB, with a high overall coefficient of determination (R 2 = 0.94) and low RMSE (RMSE = 3.7 K), implying the desirable performance of our proposed method. By comparison with Soil Moisture and Ocean Salinity (SMOS) TB, the reconstructed data had an approximate performance with actual SMAP TB. Furthermore, the effects of surface temperature and precipitation on TB in different periods (frozen and unfrozen) over QTP were also analyzed. Our method has the potential to generate seamless SMAP products.

Investigating multiple causes of time-varying SMAP soil moisture biases based on core validation sites data

• SMAP soil moisture (SM) biases are investigated based on core validation sites data. • The p.m. retrievals outperform a.m. retrievals due to better accuracy in clear days. • Dual channel algorithm has the potential to improve SM retrieval in bad weathers. • SM bias correlates negatively with soil temperature and positively with its bias. • Soil wetness, temperature and vegetation parameters jointly contribute to SM biases. The Soil Moisture Active Passive (SMAP) mission provides state-of-the-art global soil moisture (SM) datasets. However, seasonal SM biases and their contributing factors have not be systematically reviewed. This study evaluated the biases of SMAP V6 dual channel algorithm (DCA), single channel algorithm H-pol (SCA-H) and V-pol (SCA-V) SM products based on core validation sites data. All algorithms perform better under clear- than cloudy-sky, and in cloudless daytime than nighttime. Consecutive clear-sky benefits SM retrieval, progressively lowering the uncertainty of SM retrievals while at the cost of dry biases. Cloudy-sky deteriorates the quality of SM retrievals, and wet biases increase with the duration time of cloudy-sky. The modified V7 DCA has a major improvement, owning the potential to provide accurate retrievals under cloudy-sky. SMAP SM biases are co-determined by vegetation index, soil temperature and their biases, and a single factor only explains at most 54% variance in SM biases. Generally, SMAP SM bias is negatively correlated with soil temperature and positively correlated with its bias. SM bias correlates positively with vegetation index and its bias for single channel algorithms, and the underestimation of SM increases with vegetation density for DCA. To get a complete picture of SMAP SM biases, a total differential of radiative transfer equation is recommended for decomposing SMAP SM biases. To this end, more validation sites are required covering diverse land cover types and providing continuous data records.

A comprehensive assessment of SM2RAIN-NWF using ASCAT and a combination of ASCAT and SMAP soil moisture products for rainfall estimation

Rainfall estimation using remote sensing products is an alternative to in situ measurement rainfall due to their high temporal and spatial resolution. Using satellite soil moisture (SM) observations in the SM to Rain (SM2RAIN) algorithm have a great potential to estimate rainfall. SMA2RAIN-NWF algorithm is a reinforced version of a SMA2RAIN algorithm which was developed to estimate rainfall through the integration of the SM2RAIN algorithm and the net water flux (NWF) model. A new release of SMA2RAIN-NWF algorithm uses the Advanced Microwave Scanning Radiometer 2 (AMSR2) SM dataset as input datasets.The aim here is to assess the SMA2RAIN-NWF by using multiple SM products including ASCAT, and their integration in four aggregations (AGGR) periods (1, 7, 14, and 30 days) by comparing with rainfall observation of 15 stations over the Lake Urmia basin, Iran for the period January 2015 to December 2019. The Discrete Cosine Transform (DCT) method is applied to fill the gap in the satellite SM time series. Moreover, the effect of land cover classes (grasslands, croplands, and urban) on rainfall estimation is investigated. Considering the Kling-Gupta efficiency (KGE) and correlation coefficient (R) values in comparisons of calibration and validation revealed that urban areas experienced a minimum decrement rate (2–5 %). A comparison of three SM products (ASCAT, ASCAT+SMAP, and ASCAT+DCT) show that all products had a high performance on a daily time scale in term of the KGE and R. The results showed that algorithm performance gradually rose via an increase in AGGR levels, reaching KGE and R values of 0.8 and above. Furthermore, the comparison of SM2RAIN-NWF and SM2RAIN show an improvement of SM2RAIN-NWF performance across various AGGRs.

Bias correction of satellite soil moisture through data assimilation

Soil moisture exhibits great spatio-temporal heterogeneity and plays a critical part in land surface energy and water cycles, being identified as a terrestrial essential climate variable. Thus, it is urgently needed in a wide variety of environmental processes such as hydrology, meteorology, agriculture, and ecology. Microwave remote sensing has the potential to provide near real-time soil moisture estimates on large spatial scales according to the distinctive contrast between dielectric properties of water and dry soils. Thus, many space-borne microwave sensors have been launched for retrieving soil moisture. Especially, SMOS and SMAP at L-band frequency (1.4 GHz) supply an unprecedented opportunity for retrieving surface soil moisture due to their deeper penetration than instruments at other bands. However, these satellite soil moisture products need bias correction before application such as data assimilation. Two common correction methods require reliable land surface soil moisture simulations. However, the quality of these simulations relies heavily on model parameters, such as soil porosity and texture, which are almost unavailable in remote regions such as the Tibetan Plateau. In this study, a dual-cycle assimilation algorithm is taken to make on-line bias correction when assimilating SMAP soil moisture products. During the assimilation, a linear bias correction scheme is regarded as the observation operator to link the simulated soil moisture values and the satellite retrievals. In the inner cycle, a sequentially based assimilation algorithm is run with both model parameters and bias correction coefficients, which are provided by the outer cycle. At the same time, both the analyzed soil moisture and the innovation are reserved at each analysis moment. In the outer cycle, the innovation time series kept by the inner cycle are fed into a likelihood function to adjust the values of both model parameters and correction coefficients through an optimization algorithm. A series of numerical experiments are designed and conducted, indicating that the soil moisture estimates by the presented algorithm are superior to those with the existing bias correction schemes.

A deep neural network based SMAP soil moisture product

In this paper, it is demonstrated that while satellite soil moisture (SM) retrievals often have minimum biases, reanalysis data can capture more temporal variability of SM, especially for non-cropland areas – when validated against in situ measurements. Accordingly, this paper presents a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) – using NASA's Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The mean of the benchmark data is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables of DNN include polarized SMAP brightness temperatures, incidence angle, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015–03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land-cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.

Soil Moisture Retrieval from the CyGNSS Data Based on a Bilinear Regression

Soil moisture (SM) has normally been estimated based on a linear relationship between SM and the surface reflectivity (Γ) from the spaceborne Global Navigation Satellite System (GNSS)-Reflectometry, while it usually relies on inputs of SM data without considering vegetation optical depth (VOD/τ) effects. In this study, a new scheme is proposed for retrieving soil moisture from the Cyclone GNSS (CyGNSS) data. The variation of CyGNSS-derived ΔΓ is modeled as a function of both variations in SM and VOD (ΔSM and Δτ). For retrieving SM, ancillary τ data can be obtained from the Soil Moisture Active Passive (SMAP) mission. In addition to this option, a model for simulating Δτ is suggested as an alternative. Experimental evaluation is performed for the time span from August 2019 to July 2021. Excellent agreements between the final retrievals and referenced SMAP SM products are achieved for both training (1-year period) and test (1-year duration) sets. On the whole, overall correlation coefficients (r) of 0.97 and 0.95 and root-mean-square errors (RMSEs) of 0.024 and 0.028 cm3/cm3 are obtained based on models using the SMAP and simulated Δτ, respectively. The model without τ generates an r of 0.95 and an RMSE of 0.031 cm3/cm3. The efficiency and necessity of considering τ are thus confirmed by its enhancement based on correlation and RMSE against the one without τ, and the usefulness of approximating Δτ by sinusoidal functions is also validated. Influences of SM statistics in terms of mean and variance on the retrieval accuracy are evaluated. This work unveils the interaction between CyGNSS data, SM, and τ and demonstrates the feasibility of integrating the Δτ approximation function into a bilinear regression model to obtain SM results.

Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology.

A global 1‐km downscaled SMAP soil moisture product based on thermal inertia theory

AbstractMicrowave remote sensing technology has been applied to produce soil moisture (SM) retrievals on a global scale for various studies and applications. However, due to the limitations of current technology, the native spatial resolution of currently available passive microwave SM products is on the order of tens of kilometers, and this resolution cannot be used to characterize SM variability on a regional scale. To overcome this limitation, a downscaling algorithm based on the thermal inertia theory–derived relationship between SM and temperature difference was developed using outputs from the Global Land Data Assimilation System–Noah Land Surface Model and the land long‐term data record–Advanced Very High Resolution Radiometer normalized difference vegetation index (NDVI) dataset and applied to the Aqua Moderate Resolution Imaging Spectroradiometer land surface temperature/NDVI data to produce a downscaled 1‐km Soil Moisture Active Passive (SMAP) radiometer daily SM product, respectively, at 6:00 a.m. and 6:00 p.m. on a global scale from 2015 to 2020. The evaluation results reveal that the downscaling model performs better in the middle or low latitudes than in high latitudes. It also performs better in warm months than in cold months. The in situ SM observations from dense networks around the world were used to validate the 1‐km and enhanced 9‐km SMAP SM data. The validation metrics indicated that both the 1‐km and 9‐km SM data have overall overestimation trends, and the unbiased RMSE (0.063 m3 m–3 on average), mean absolute error (0.052 m3 m–3 on average), and spatial standard deviation (0.025 m3 m–3 on average) of the 1 km data are generally more accurate than the metrics of the 9‐km SM data, which indicates that the downscaled data provide reliable observed SM information.

Enhancing Spatial Resolution of SMAP Soil Moisture Products through Spatial Downscaling over a Large Watershed: A Case Study for the Susquehanna River Basin in the Northeastern United States

Soil moisture (SM) with a high spatial resolution plays a paramount role in many local and regional hydrological and agricultural applications. The advent of L-band passive microwave satellites allowed for it to be possible to measure near-surface SM at a global scale compared to in situ measurements. However, their use is often limited because of their coarse spatial resolution. Aiming to address this limitation, random forest (RF) models are adopted to downscale the SMAP level-3 (L3SMP, 36 km) and SMAP enhanced (L3SMP_E, 9 km) SM to 1 km. A suite of predictors derived from the Sentinel-1 C-band SAR and MODIS is used in the downscaling process. The RF models are separately trained and verified at both spatial scales (i.e., 36 and 9 km) considering two experiments: (1) using predictors derived from the MODIS and Sentinel-1 along with other predictors such as elevation and brightness temperature and (2) using all predictors of the first experiment except for the Sentinel-1 predictors. Only dates when the Sentinel-1 images were available are considered for the comparison of the two experiments. The comparison of the results of the two experiments indicates that the removal of Sentinel-1 predictors from the second experiment only reduces the R value from 0.84 to 0.83 and from 0.91 to 0.86 for 36 and 9 km spatial scales, respectively. Among the predictors used in the downscaling, the brightness temperature in VV polarization is identified as the most important predictor, followed by NDVI, surface albedo and API. On the contrary, the Sentinel-1 predictors play a less important role with no marked contribution in enhancing the predictive accuracy of RF models. In general, the two experiments have limitation, such as a small sample size for the training of the RF model because of the scarcity of Sentinel-1 images (i.e., revisit time of 12 days). Therefore, based on this limitation, a third experiment is proposed, in which the Sentinel-1 predictors are not considered at all in the training of the RF models. The results of the third experiment show a good agreement between the downscaled L3SMP and L3SMP_E SM, and in situ SM measurements at both spatial scales. In addition, the temporal availability of the downscaled SM increased. Moreover, the downscaled SM from both SMAP products presented greater spatial detail while preserving the spatial patterns found in their original products. The use of the two SMAP SM products as background fields for the downscaling process does not show marked differences. Overall, this study demonstrates encouraging results in the downscaling of SMAP SM products over humid climate with warm summers dominated by vegetation.

Evaluation of Sentinel-1, SMAP and SMOS surface soil moisture products for distributed eco-hydrological modelling in Mediterranean forest basins

Reliable distributed hydrological modeling, especially in semi-arid areas, must consider the inclusion of surface soil moisture (SSM) spatial information during the calibration process. This variable plays a key role in the evapotranspiration processes that determine the hydrological cycle. The coarse resolution of the SSM estimates by satellite remote sensing has restricted the application of this approach to only large basins, focusing most of the studies in the consideration of simply the temporal dynamics of this variable. The growing efforts in providing higher spatial resolution through disaggregating methodologies or new sensor estimates facilitates the application of this spatial approach to small basins. This paper explores the applicability of the currently available satellite surface soil moisture estimates for distributed eco-hydrological modelling in Mediterranean forest basins. On one hand, this study contributes to fill the existing research gap on the use of remote sensing SSM spatial patterns within the distributed hydrological modelling framework in small basins. On the other hand, it serves as an indirect validation method for the spatial performance of satellite SSM products. To achieve this goal, we implemented the eco-hydrological model TETIS in three case studies named: Hozgarganta (southern Spain), Ceira (western Portugal) and Carraixet (eastern Spain). The SSM estimates selected for comparison were Sentinel-1 SSM provided by the Copernicus Global Land Services (CGLS), SMAP SSM disaggregated using Sentinel-1 (SPL2SMAP_S) provided by the National Aeronautics and Space Administration (NASA), SMOS SSM provided by the Barcelona Expert Center (BEC), and SMOS and SMAP SSM disaggregated using the DISPATCH algorithm provided by Lobelia Earth. The methodology employed involved a multi-objective and multi-variable calibration in terms of remote sensing SSM spatial patterns and in-situ streamflow, using the Spatial Efficiency Metric (SPAEF) and the Nash-Sutcliffe efficiency index (NSE) respectively. Before model calibration a sensitivity analysis of the most influent variables was performed. The temporal and spatial comparison of the reference SSM products revealed inconsistencies amongst products. The disaggregating methodology determined the spatial agreement to a greater degree than the sensor itself (i.e. SMAP, SMOS). In spite of the differences amongst products, the multi-objective calibration approach proposed increased the robustness of the hydrological modelling.

Soil moisture content retrieval from Landsat 8 data using ensemble learning

Although detailed spatial and temporal distribution of soil moisture is crucial for numerous applications, current global soil moisture products generally have low spatial resolutions (25–50 km), which largely limit their application at local scales. In this study, we developed a high-resolution soil moisture retrieval framework based on ensemble learning by integrating Landsat 8 optical and thermal observations with multi-source datasets, including in-situ measurements from 1,154 stations in the International Soil Moisture Network, the Soil Moisture Active Passive (SMAP) soil moisture product, the ERA5-Land reanalysis dataset, and auxiliary datasets (terrain, soil texture, and precipitation). Two widely used ensemble learning models were explored and compared using ten-fold cross-validation. The extreme gradient boosting (XGBoost) model performed slightly better than the random forest (RF) model, with a root mean square error (RMSE) of 0.047 m3/m3 and correlation coefficient (R) of 0.952, respectively. Further validation using data from four independent soil moisture networks demonstrated that the prediction accuracy of the XGBoost model was comparable to the SMAP soil moisture product, but with a much higher spatial resolution. The model was finally used to map soil moisture over the high-altitude Tibetan Plateau, which is especially sensitive to climate change, from May to September of 2015. The comparison between our fine-scale soil moisture map at 30 m resolution and the coarse-scale SMAP soil moisture product (36 km) revealed high spatial consistency. These results suggest that there is potential to generate accurate soil moisture products globally at 30 m spatial resolution from the long-term Landsat archive. This finding has practical implications in scenarios requiring fine-scale soil moisture maps, such as climate change and permafrost modeling, hydrological and land surface modeling, and agriculture monitoring.

Validation of Soil Moisture Data Products From the NASA SMAP Mission

The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error &lt;0.04 m³&#x002F;m³). The validation approach also includes other (&#x201C;sparse network&#x201D;) <i>in situ</i> SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products&#x2019; performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the <i>L</i>-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio&#x2013;temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

Potential of Mapping Global Soil Texture Type From SMAP Soil Moisture Product: A Pilot Study

Soil texture and associated thermal and hydraulic parameters are key to land surface processes. Current global soil datasets are derived from limited soil samples, which are not only very costly but also prone to large uncertainties. While it is difficult to directly retrieve soil properties through satellite remote sensing, this study explores the feasibility of mapping global soil type and thereby corresponding soil texture through the Soil Moisture Active Passive (SMAP) soil moisture product without reference to soil samples. Specifically, for each grid-cell, 12 USDA (U.S. Department of Agriculture) soil types are first used to drive the Noah-MP land surface model and then the optimal one is obtained by referring to a four-year (2015-2018) SMAP soil moisture time series. The proposed scheme can reasonably map global distribution of soil types in terms of sand/clay content and porosity that are close to the Global Soil Dataset for Earth System Models (GSDE) dataset and outperform the one used in GLDAS/Noah model. The result of this pilot study is very encouraging as it purely relies on satellite data, which is especially important for remote areas where few soil samples are available and conventional soil datasets may have large biases. Further improvements may be achieved upon improved soil organic matter parameterization, through land data assimilation and by considering additional satellite information.

Assessment of ERA5-Land Volumetric Soil Water Layer Product Using In Situ and SMAP Soil Moisture Observations

In preparation for the NISAR mission soil moisture algorithm, this study performs the validation of the European Centre for Medium-Range Weather Forecast (ECMWF) ERA5-Land volumetric soil water (soil moisture) layer product with in-situ measurements from the Soil Moisture Active Passive (SMAP) core validation sites (CVS) for 2015 – 2021. The ERA5-Land soil water layer was also compared against the SMAP-enhanced radiometer soil moisture product (gridded at 9 km) at a global extent. For comparison between the ERA5-Land and the SMAP soil moisture products, a matching temporal dataset was generated from ERA5-Land based on the acquisition time of SMAP for each 9 km grid. In comparison with the CVS in-situ measurement, the ERA5-Land data exhibits an overall ubRMSE of about ~0.05 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> but has high wet bias over most of the sites except for sites in Australia, Denmark, and Argentina. The global comparison of the ERA5-Land soil moisture with the SMAP 9 km gridded product shows an overall wet bias with a high correlation in the tropical and temperate regions. The lowest bias was observed over the desert region but has poor correlation as it doesn’t have enough soil moisture variability. Poor correlation with high bias and high RMSD observed over dense vegetated and tundra regions is possibly due to the inferior performance of the SMAP soil moisture product, which has not been validated for these areas.