Passive Soil Moisture Products Research Articles

The Verification and Fusion Analysis of Passive Microwave Soil Moisture Products in the Three Northeastern Provinces of China

The utilization of remote sensing soil moisture products in agricultural and hydrological studies is on the rise. Conducting a regional applicability analysis of these soil moisture products is essential as a preliminary step for their effective utilization. The triple collocation (TC) method enables the estimation of the standard deviation of errors in products when true soil moisture values are unavailable. It assesses data uncertainty and mitigates the influence of product errors on fusion, thereby enhancing product accuracy significantly. In this study, the TC uncertainty error analysis was employed to integrate Soil Moisture Active Passive (SMAP), the Advanced Microwave Scanning Radiometer 2 (AMSR-2), and the European Space Agency Climate Change Initiative (ESA CCI) active (ESA CCI A) and passive (ESA CCI P) products, with ground-based measurements serving as a reference. Traditional evaluation metrics, such as the correlation coefficient (R), bias, root mean square error (RMSE), and unin situed root mean square error (ubRMSE), were employed to evaluate the accuracy of the product. The findings indicate that SMAP and ESA CCI P products demonstrate strong spatiotemporal continuity within the research area and exhibit low uncertainty across various land types. The products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) exhibit a high level of temporal and spatial continuity; however, there is a requirement for enhancing their accuracy. The products of ESA CCI A exhibit notable spatiotemporal disjunction, contributing significantly to their elevated level of uncertainty. After fusion with TC analysis, the correlation coefficient (R = 0.7) of the TC-2 product derived from the fusion of SMAP, AMSR-2, and ESA CCI P products is significantly higher than the correlation coefficient of the TC-1 product (R = 0.65) obtained from the fusion of SMAP, AMSR-2, and ESA CCI A products at a 95% confidence level. The integration of data can efficiently mitigate the challenges associated with spatiotemporal gaps and inaccuracies in products, offering a dependable foundation for the subsequent utilization of remote sensing products.

IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product

Abstract The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.

Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach

Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.

Evaluation of SMAP-Enhanced Products Using Upscaled Soil Moisture Data Based on Random Forest Regression: A Case Study of the Qinghai–Tibet Plateau, China

The evaluation of satellite soil moisture is a big challenge owing to the large spatial mismatch between pixel-based satellite soil moisture products and point-based in situ measurements. Upscaling in situ measurements to obtain the “true value” of soil moisture content at the satellite grid/footprint scale can make up for the scale difference and improve the validation. Many existing upscaling methods have strict requirements regarding the spatial distribution and quantity of soil moisture sensors. However, in reality, soil-moisture-monitoring networks are commonly sparse with low sensor density, which increases the difficulty of obtaining accurate upscaled soil moisture data and limits the validation of satellite products. For this reason, this paper proposes a scheme to upscale in situ measurements using five machine learning methods along with Landsat 8 datasets and DEM data to validate the accuracy of a SMAP-enhanced passive soil moisture product for a sparse network on the Qinghai–Tibet Plateau. The proposed scheme realizes the upscaling of in situ soil moisture data to the pixel scale (30 m × 30 m) and then to the coarse grid scale (9 km × 9 km) by using multi-source remote sensing data as the bridge of scale conversion. The long-time SMAP SM products since April 2015 on the Qinghai–Tibet Plateau were validated based on upscaled soil moisture data. The results show that (1) random forest regression performs the best, and the upscaled soil moisture data reflect the region-average soil moisture conditions that can be used for evaluating SMAP data; (2) the SMAP product meets its scientific measurement requirements; and (3) the SMAP product generally underestimates the soil moisture in the study area.

Parameterization of soil evaporation and coupled transport of moisture and heat for arid and semiarid regions

Soil moisture is an important parameter in numerical weather forecasting and climate projection studies, and it is extremely important for arid and semiarid areas. Different from those in relatively wet areas, for arid and semiarid areas, mechanisms associated with the transportation, condensation, and evaporation of water vapor in soil interior cannot be neglected. In this paper, schemes associated with soil evaporation and coupled transport of soil moisture and heat were developed for the integrated urban land model (IUM) to improve the simulation of soil moisture in arid and semiarid areas. The whole layer soil evaporation (WSE) scheme was developed to improve the simulation of soil evaporation. The soil’s inner layer water vapor transport is considered a part of WSE. The transport of water and heat in the inner soil was linked to the phase change of water. The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture product and 10-cm volumetric soil moisture observations in 358 autonomic soil moisture observation sites were used for validating the simulation results. The results indicate that after using the WSE scheme and considering the coupled transport of moisture and heat in the soil interior, the simulation of soil moisture was improved definitely. For June, July, and August, the biases of soil moisture simulation decreased by approximately 22.5, 34.4, and 27.5%, respectively, while the RMSEs decrease by approximately 7.0, 8.7, and 9.6%, respectively. The improvement of soil moisture simulation indicates that in arid and semiarid areas soil water vapor transport is important and cannot be neglected.

Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia.

Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its application at the basin scale. This research, subsequently plans to; (1) Evaluate the capability of SAR for the retrieval of surface roughness variables in the Awash River basin; (2) Measure the performance of Random Forest (RF) regression model to downscale SMAP satellite soil moisture over the Awash River basin; (3) validate downscaled soil moisture data with In-situ measurements in the river basin. Random Forest (RF) based downscaling approach was applied to downscale satellite-based soil moisture product (36km x 36km) to fine resolution (1km x 1km). Fine spatial resolution (1km) soil moisture data for the Awash River basin was generated. The downscaled soil moisture product also has a strong spatial correlation with the original one, allowing it to deliver more soil moisture information than the original one. In-situ soil moisture and downscaled soil moisture had a 0.69 Pearson correlation value, compared to a 0.53 correlation between the original and In-situ soil moisture. In-situ soil moisture measurements were obtained from the Middle and Upper Awash sub-basins for validation purposes. In the case of Upper Awash, downscaled soil moisture shows a variation of 0.07 cm3 /cm3, -0.036 cm3 /cm3, and 0.112 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Following that, the accuracy of downscaled soil moisture against the Middle Awash Sub-basin reveals a variance of 0.1320 cm3 /cm3, -0.033 cm3 /cm3, and 0.148 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Future studies should take into account the temporal domain of Soil Moisture Active Passive satellite soil moisture product downscaling over the study region.

Calibration Strategy for Compact Polarimetric GNSS-R Instruments

Polarimetric Global Navigation Satellite System - Reflectometry (GNSS-R) is being proposed by different teams as a solution to directly estimate soil moisture. Up to date, two approaches using polarimetric GNSS-R have been proposed to estimate soil moisture, using the ratio between the right-hand and left-hand circularly polarized received signals, and using the ratio between the linear horizontal and vertical components at a set of incidence angles, known as Hybrid Compact Polarimetric (HCP) GNSS-R. In this manuscript, the necessary calibrations of a received HCP GNSS-R signal are presented. The Stokes parameters of the signal are required to compensate all non-idealities. A methodology to calibrate the receiver effects, scene-antenna polarimetric misalignment, Faraday rotation, and transmitter non-idealities is proposed. Finally, the calibration performance is evaluated using several statistical parameters and polarimetric ratio models based on two different soil moisture products. Results show a correlation coefficient increase of 4.7% with respect to the model derived from the Soil Moisture Active Passive (SMAP) soil moisture product, and a 6.5% improvement with respect to the model derived from the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA-5) monthly average soil moisture product. Furthermore, the proposed calibrations show an unbiased root-mean-square error reduction of 6.3% and 6.8% for the SMAP soil moisture model and the ERA-5 model, respectively. Due to the SMAP antenna and pointing design, SMAP-Reflectometry (SMAP-R) is implicitly insensitive to some of the corrections. More significant corrections should be expected for future polarimetric GNSS-R instruments as European Space Agency (ESA) HydroGNSS.

Refinement of NOAA AMSR-2 Soil Moisture Data Product—Part 2: Development With the Optimal Machine Learning Model

Advanced Microwave Scanning Radiometer-2 (AMSR2) is a successor of AMSR for Earth-Observation System (AMSR-E), while the third generation of AMSR (AMSR3) will be launched in the near future. The AMSR2 soil moisture product is also an important component of the Soil Moisture Operational Products System (SMOPS) datasets that are operationally produced by National Oceanic and Atmospheric Administration (NOAA). Refinement of the NOAA AMSR2 soil moisture data product can not only benefit the past AMSR-E and the upcoming AMSR3, but also improve the SMOPS data quality. In this second paper of the two-part series, the Extreme Gradient Boosting (XGB) model was trained using the AMSR2 6.925 GHz, 10.65 GHz, 18.7 GHz and 36.5 GHz brightness temperature (Tb) measurements in dual polarizations, ancillary maps and the vegetation index datasets, and in turn used to predict the daily global AMSR2 soil moisture retrievals from 2012 to 2021. Validation results show that the refined AMSR2 soil moisture retrievals (AMSRr) show an overwhelming advantage in data accuracy over the currently operational AMSR2 product (AMSRc). Comapred to the AMSRc, the developed AMSRr presents a significant improvement on data availability. Results also indicate that the refined AMSR2 datasets are comparable with the latest version Soil Moisture Active Passive soil moisture product. Based on this study, higher quality AMSRr soil moisture data product will be operationally produced in the NOAA, and will eventually benefit the NOAA SMOPS blended soil moisture product and its users.

Inter-comparison and integration of different soil moisture downscaling methods over the Qinghai-Tibet Plateau

Soil moisture (SM) is a key state variable in the water, energy cycle between atmosphere and land surface but existing passive microwave soil moisture products typically have spatial resolutions of tens of kilometers, which fails to meet the requirements of regional applications. Even though numerous machine/deep learning methods have been applied to downscale SM, few studies have investigated the performance differences of diverse approaches and attempted to integrate various methods to improve downscaling accuracy. Therefore, this study firstly evaluated and inter-compared the downscaling performances of six machine/deep learning approaches and further proposed a hybrid downscaling method based on Bayesian three-cornered hat merging (MATCH). The daily 1 km seamless soil moisture product during 2015–2019 over the Qinghai-Tibet Plateau was then obtained. Evaluation and inter-comparison results revealed that there was obvious performance discrepancy among different downscaling approaches and the gradient boosting decision tree (GBDT) and random forest (RF) were the best two methods, which performed best in the southern and eastern of the plateau, respectively. While the artificial neural network (ANN) outperformed other approaches in the northwestern areas. Validation against in-situ measurements showed that compared with SMAP SM, the MATCH SM exhibited comparable accuracy and lower error with mean R and ubRMSE values of 0.55 and 0.047 m3/m3. The mean R and ubRMSE values for SMAP SM were 0.67 and 0.056 m3/m3, respectively. In addition, the MATCH SM presented great improvement compared with any single downscaled SM data, having the highest correlation and the lowest ubRMSE scores. While, among different methods, the highest R was 0.50 (GBDT) and the lowest ubRMSE was 0.052 m3/m3 (residual network (ResNet)). Besides, the downscaled SM could accurately reflect the temporal variations of soil moisture and precipitation, and effectively represent the spatial patterns of soil moisture. Satisfactory downscaling results were achieved in arid and semi-arid areas whereas a certain degree of overestimation still existed in the eastern and southeastern regions with dense vegetation and high moisture conditions. Such overestimation was inherent from original SMAP SM but was mitigated after downscaling. In conclusion, performance differences existed among diverse downscaling approaches and the developed MATCH method could maximize the potentials of each method and show encouraging downscaling performances.

A machine learning-based approach for generating high-resolution soil moisture from SMAP products

The coarse resolutions of passive microwave surface soil moisture (SSM) products often hamper the applications of such products in regional or local studies. This study developed a machine learning-based approach for the downscaling of Soil Moisture Active and Passive (SMAP) SSM products at both ascending and descending passes based on the relationships between SSM and environmental variables, such as the land surface temperature (LST), enhanced vegetation index (EVI), surface albedo, cumulative precipitation, digital elevation model (DEM), and soil texture. Two machine learning algorithms—random forest (RF) and long short-term memory network (LSTM)—were applied to downscale the SMAP product from a coarse resolution (36 km) to a fine resolution (1 km). The downscaled SSM results were validated against ground observations. The contributions of environmental variables to the established model were also discussed. The results showed that both RF and LSTM models demonstrated satisfactory performance in SMAP SSM prediction, indicating that the downscaling models were able to learn the relationships between the environmental variables and SMAP SSM well at coarse resolution. The RF and LSTM-based downscaled SSM maps not only realized full spatial coverage but also reasonably maintained the spatiotemporal evolution trends of the original SMAP SSM products. Both downscaled results correlated well with the ground observations and indicated good temporal consistency with the daily precipitation. In addition, the variable importance analysis reveals that the DEM, precipitation, EVI, and surface albedo were dominant to establishing SSM regression models, particularly for DEM in regions with large height differences. Overall, RF and LSTM-based models are promising downscaling approaches for generating full spatial coverage and fine-resolution SSM data from passive microwave SSM.

An advanced change detection method for time-series soil moisture retrieval from Sentinel-1

The feasibility of soil moisture retrieval from C-band Sentinel-1 data has been widely acknowledged, with pre-operational 1-km products currently available at regional and/or continental scale using the long-term (LTCD) or short-term change detection (STCD) methods. Both algorithms share the same assumptions of time-invariant roughness and vegetation, which can be questionable even for a short period of 4 Sentinel-1 acquisitions (18–36 days). An advanced change detection (ACD) method is proposed in this study for an improved soil moisture retrieval from Sentinel-1 data, including two main modifications with respect to the existing STCD methods: i) approximating the effect of temporal varying vegetation on the Sentinel-1 backscatter as a variation in the two-way attenuation, and ii) a temporal soil moisture constraint based on the coarse Soil Moisture Active Passive (SMAP) soil moisture product to partly remove the uncertainty caused by vegetation and/or roughness changes. The evaluation, based on time-series observations from 34 OzNet stations and ground samples collected during the Fifth Soil Moisture Active and Passive Experiment (SMAPEx-5) showed that the ACD improved the correlation coefficient (R), root mean square error (RMSE) and un biased RMSE (ubRMSE), achieving 0.66, 0.071 m3/m3 and 0.071 m3/m3 at the point scale, 0.77, 0.063 m3/m3 and 0.051 m3/m3 at 1-km scale, 0.80, 0.055 m3/m3 and 0.050 m3/m3 at 3-km scale. The contribution of the two modifications was further investigated using 559 stations from 22 networks across the world, showing that: i) the two modifications can increase R by 0.08–0.13 and reduce the retrieval RMSE by 0.009–0.013 m3/m3 (10% - 15% relative), and ii) the retrieval over densely vegetated areas or areas with large temporal vegetation variation can benefit more from the proposed modifications. The ACD achieved stable performance for various Sentinel-1 orbits/passes and maintained a stable performance for retrieval windows up to 30 Sentinel-1 acquisitions, providing a promising alternative for achieving consistent soil moisture retrievals from Sentinel-1.

Evaluation of SMOS, SMAP, AMSR2 and FY-3C soil moisture products over China

Microwave remote sensing can provide long-term near-surface soil moisture data on regional and global scales. Conducting standardized authenticity tests is critical to the effective use of observed data products in models, data assimilation, and various terminal scenarios. Global Land Data Assimilation System (GLDAS) soil moisture data were used as a reference for comparative analysis, and triple collocation analysis was used to validate data from four mainstream passive microwave remote sensing soil moisture products: Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active and Passive (SMAP), Global Change Observation Mission–Water using the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Fengyun-3C (FY-3C). The effects of topography, land cover, and meteorological factors on the accuracy of soil moisture observation data were determined. The results show that SMAP had the best overall performance and AMSR2 the worst. Passive microwave detection technology can accurately capture soil moisture data in areas at high altitude with uniform terrain, particularly if the underlying surface is soil, and in areas with low average temperatures and little precipitation, such as the Qinghai–Tibet Plateau. FY-3C performed in the middle of the group and was relatively optimal in northeast China but showed poor data integrity. Variation in accuracy between products, together with other factors identified in the study, provides a baseline reference for the improvement of the retrieval algorithm, and the research results provide a quantitative basis for developing better use of passive microwave soil moisture products.

Resolution Enhancement of SMAP Passive Soil Moisture Estimates

The Soil Moisture Active Passive (SMAP) mission includes a unique combination of instruments intended to provide daily global soil moisture data with high accuracy and resolution. Due to radar instrument failure, the default resolution of the data product decreased from the intended 9 km to 36 km shortly after the mission started to return data. To improve this, we employed the Scatterometer Image Reconstruction algorithm in its radiometer form (rSIR) to enhance the resolution of the radiometer brightness temperature measurements from which the soil moisture was derived. This paper compares the soil moisture estimates created from the rSIR-enhanced brightness temperatures with SMAP project radiometer L2_SM_SP and SMAP-Sentinel L2_SM_P products reported on 9 km and 3 km grids, respectively. We find that the difference of the rSIR-enhanced passive soil moisture product is generally within 0.020 cm3 cm−3 RMS of the 9 km SMAP radiometer L2_SM_SP and 0.045 cm3 cm−3 RMS of the 3 km SMAP-Sentinel L2_SM_P soil moisture products. The accuracy of the rSIR soil moisture can be improved by including better antenna pattern correction methods applied to the input TB measurements.

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.

Fusing Active and Passive Remotely Sensed Soil Moisture Products Using an Improved Double Instrumental Variable Method

Highly quality soil moisture is significant for hydrological, meteorological and agricultural applications. At present, active and passive remote sensing are the only ways to monitor soil moisture directly at regional scale. However, the quality of single satellite-based soil moisture product is insufficient to meet the requirements of these applications. Hence, fusing these two soil moisture products to improve their quality of change capture ability and accuracy is a necessary and challenging work. This study proposes an improved double instrumental variable method to fuse active and passive soil moisture products. First, the method is improved in finding the best instrumental variables in time series based on correlation coefficient. Second, fused weights of input soil moisture products are estimated using the improved method. Finally, fused soil moisture products are obtained with higher change capture ability and higher accuracy. The Tibetan Plateau was selected as the study area to test the algorithm using both of the Climate Change Initiative (CCI) active and passive soil moisture products from the European Space Agency (ESA). The ground validation results show that, compared with the original soil moisture products, the change capture ability, expressed by the correlation coefficient (R), and the accuracy, expressed by the unbiased root mean square deviation (ubRMSD), have been both improved by about 10% on average. This study indicates that the proposed fusion method can effectively improve the quality of soil moisture products to further understand the global changing water cycle.

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.

Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China

Passive microwave surface soil moisture (SSM) products tend to have very low resolution, which massively limits their application and validation in regional or local-scale areas. Many climate and hydrological studies are urgently needed to evaluate the suitability of satellite SSM products, especially in alpine mountain areas where soil moisture plays a key role in terrestrial atmospheric exchanges. Aiming to overcome this limitation, a downscaling method based on random forest (RF) was proposed to disaggregate satellite SSM products. We compared the ability of the downscaled soil moisture active passive (SMAP) SSM and soil moisture and ocean salinity satellite (SMOS) SSM products to capture soil moisture information in upstream of the Heihe River Basin by using in situ measurements, the triple collocation (TC) method and temperature vegetation dryness index (TVDI). The results showed that the RF downscaling method has strong applicability in the study area, and the downscaled results of the two products after residual correction have more details, which can better represent the spatial distribution of soil moisture. The validation with the in situ SSM measurements indicates that the correlation between downscaled SMAP and in situ SSM is better than downscaled SMOS at both point and watershed scales in the Babaohe River Basin. From the TC method, the root mean square error (RMSE) of the CLDAS (CMA land data assimilation system), downscaled SMAP and downscaled SMOS were 0.0265, 0.0255 and 0.0317, respectively, indicating that the downscaled SMAP has smaller errors in the study area than others. However, the soil moisture distribution in the study area shown by the SMOS downscaled results is closer than the downscaled SMAP to the degree of drought reflected by TVDI. Overall, this study suggests that the proposed RF-based downscaling method can capture the variation of SSM well, and the downscaled SMAP products perform significantly better than the downscaled SMOS products after the accuracy verification and error analysis of the downscaled results, and it should be helpful to facilitate applications for satellite SSM products at small scales.

Expanding the Application of Soil Moisture Monitoring Systems through Regression-Based Transformation

AbstractRelative to other geophysical variables, soil moisture (SM) estimates derived from land surface models (LSMs) and land data assimilation systems (LDAS) are difficult to transfer between platforms and applications. This difficulty stems from the highly model-dependent nature of LSM SM estimates and differences in the vertical support of discretized SM values. As a result, operational SM estimates generated by one LSM (or LDAS) cannot generally be directly applied to a hydrologic monitoring or forecast system designed around a second LSM. This lack of transferability is particularly problematic for LDAS applications, where the time, expertise, and computational resources required to generate an operational LDAS analysis cannot be practically duplicated for every LSM-specific application. Here, we develop a set of simple regression tools for translating SM estimates between LSMs and multiple LDAS analyses. Results demonstrate that simple multivariate linear regression—utilizing independent variables based on multilayer and temporally lagged SM estimates—can significantly improve upon baseline transformation approaches using direct percentile matching. The proposed regression approaches are effective for both the LSM-to-LSM and LDAS-to-LDAS transformation of multilayer SM percentiles. Application of this approach will expand the utility of existing, high-quality (but LSM-specific) operational sources of SM information like the NASA Soil Moisture Active Passive Level-4 Soil Moisture product.

Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products

Spatial downscaling has recently become a crucial process in the regional application of coarse-resolution passive microwave surface soil moisture (SSM) products. Extensive gaps in auxiliary optical/thermal infrared observation data (mainly caused by cloud cover) and gaps in coarse-resolution passive microwave SSM data lead to spatiotemporal discontinuity in downscaled SSM maps, thereby limiting their applications. An improved downscaling method for the 25-km European Space Agency (ESA) Climate Change Initiative (CCI) SSM product was proposed to obtain daily seamless downscaled SSM series at a 1-km scale. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daily land surface temperature (LST) and normalized difference vegetation index (NDVI) products were used as auxiliary data for the downscaling process. Prior to the spatial downscaling, an annual temperature cycle model was applied to the 1-km daily daytime LST data to fill the data gaps caused by cloud cover and to derive the spatial-seamless LST (gap-filled). Subsequently, the gap-filled ESA CCI SSM was generated at the original resolution based on the relationships among the SSM, LST, and NDVI. Finally, these were utilized to obtain a seamless downscaled series SSM at 1-km spatial resolution with a value-consistent downscaling method. The proposed method was applied to data obtained for the Iberian Peninsula from January 1, 2016 to December 31, 2018. Based on the comparison with the precipitation dataset, the downscaled SSM exhibited strong temporal correlation with rainfall events. Evaluation using the in situ SSM from the REMEDHUS network highlighted the good performance of the downscaled SSM at network level with a correlation coefficient (R) of 0.820. The root-mean-square-error, unbiased root-mean-square error (ubRMSE), and bias were 0.091, 0.033, and 0.085 m3/m3, respectively. A comparison with an alternative downscaled SSM product produced by the Barcelona Expert Center, one of the soil moisture and ocean salinity mission (SMOS)–downscaled SSM datasets, also indicated that the downscaled SSM has better spatiotemporal coverage and performance in terms of R and ubRMSE with reference to the REMEDHUS network. These results confirmed that the proposed method is an efficient and convenient downscaling process that can be used to generate high-resolution SSM data without spatiotemporal gaps. The downscaled SSM data improves the accuracy of the original passive microwave SSM product in describing regional SSM variations and shows good potential for related applications at regional scale.

Extraction of Irrigation Signals by Using SMAP Soil Moisture Data

To allow extraction of irrigation signals from satellite-derived data on soil moisture, this study describes the development of an irrigation signal extraction method that takes into account multiple environmental factors in irrigation. Firstly, the fuzzy membership functions of irrigation relating to multiple environmental factors are constructed. Then, a model is built based on the fuzzy membership functions by using operation rules of fuzzy sets, which is used to infer the relevant degree of irrigation to nonirrigation. Finally, the irrigation signals in satellite-based soil moisture data are recognized according to the relevant degree. Taking Henan Province in the North China Plain as the study area, the proposed method is used to extract irrigation signals from the SMAP Level 3 Passive Soil Moisture Product. Extracted irrigation signals from two SMAP grids are validated using daily in situ soil moisture and precipitation data, with the results showing correct identification of most of the irrigation signals. By grading the membership degree of the extracted irrigation signals, irrigation frequency maps for the 2016–2017 winter crop growth season and the 2017 summer crop growth season are obtained for Henan Province. Compared to the irrigation frequency maps with data on the annual precipitation and the annual potential evapotranspiration, the irrigation frequency maps show a spatial pattern opposite that of the annual precipitation and a spatial pattern similar to that of the annual potential evapotranspiration. It is common sense that areas with low precipitation and high evapotranspiration need more irrigation frequency and irrigation water. Thus, the spatial patterns of irrigation frequency maps are reasonable in a sense. However, it should be noted that the observed irrigation data used in the qualitative assessments are rendered less convincing by the SMAP product’s coarse resolution. Quantitative validation of extracted irrigation signals remains a significant challenge, and small-scale irrigation cannot be captured by coarse-resolution satellite-based soil moisture products. Thus, a high-resolution soil moisture product should be used to extract irrigation signals in future.