Soil Moisture Active And Passive Research Articles

Drought Monitoring of Winter Wheat in Henan Province, China Based on Multi-Source Remote Sensing Data

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran’s I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought.

Evaluation of Spire GNSS-R reflectivity from multiple GNSS constellations for soil moisture estimation

ABSTRACT Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a pivotal tool with different land applications, prominently encompassing soil moisture estimation. In contrast to conventional radiometer satellites commonly used for this purpose, GNSS-R offers higher spatiotemporal coverage while maintaining cost-effectiveness. The potential of using Global Positioning System (GPS) reflections measured by the Cyclone Global Navigation Satellite System (CYGNSS) mission to retrieve soil moisture has been previously demonstrated. In 2019, Spire Global Inc. launched their first GNSS-R satellites, which now comprise a constellation of four CubeSats. These satellites track reflections from multi-constellation, encompassing GPS, Galileo, BeiDou Navigation Satellite System (BDS), and Quasi-Zenith Satellite System (QZSS). In this study, an analysis and validation of Spire GNSS-R L1B surface reflectivity for soil moisture retrieval within east Australia during an eight-month period in 2021 is presented. A comparison of the estimated Spire surface reflectivity to that of CYGNSS is performed, unveiling analogous behavioural patterns and biases across both missions. Soil moisture is estimated using observations from Spire GPS-only, Spire multi-constellation, and CYGNSS. The Soil Moisture Active and Passive (SMAP) retrievals are used as the reference, presuming a linear relationship between changes in soil moisture and changes in reflectivity. Our results indicate that the Spire GNSS-R mission can detect variations in soil moisture with a performance comparable to that of CYGNSS. A median unbiased root-mean-square difference (ubRMSD) of 0.062 m3.m−3 is found for both Spire GPS and multi-constellation when using 9-km products and SMAP as the reference.

Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains

Large-area soil moisture (SM) data with high resolution and precision are the foundation for the research and application of hydrological and meteorological models, water resource evaluation, agricultural management, and warning of geological disasters. It is still challenging to downscale SM products in complex terrains that require fine spatial details. In this study, SM data from the Soil Moisture Active and Passive (SMAP) satellite were downscaled from 36 to 1 km in the summer and autumn of 2017 in Sichuan Province, China. Genetic-algorithm-optimized backpropagation (GABP) neural network, random forest, and convolutional neural network were applied. A fusion model between SM and longitude, latitude, elevation, slope, aspect, land-cover type, land surface temperature, normalized difference vegetation index, enhanced vegetation index, evapotranspiration, day sequence, and AM/PM was established. After downscaling, the in situ information was fused through a geographical analysis combined with a spatial interpolation to improve the quality of the downscaled SM. The comparative results show that in complex terrains, the GABP neural network better captures the soil moisture variations in both time and space domains. The GDA_Kriging method is able to merge in situ information in the downscaled SM while simultaneously maintaining the dynamic range and spatial details.

Estimation of Irrigation Water Use by Using Irrigation Signals from SMAP Soil Moisture Data

Accurate irrigation water-use data are essential to agricultural water resources management and optimal allocation. The obscuration presented by ground cover in farmland and the subjectivity of irrigation-related decision-making processes mean that effectively identifying regional irrigation water use remains a critical problem to be solved. In view of the advantages of satellite microwave remote sensing in monitoring soil moisture, previous studies have proposed a method for estimating irrigation water use using the satellite microwave remote sensing of soil moisture. However, the method is affected by false irrigation signals from soil moisture increases caused by non-irrigation factors, causing irrigation water use to be overestimated. Therefore, the purpose of this study is to improve the estimation of irrigation water use in drylands by using irrigation signals from SMAP soil moisture data. In this paper, the irrigation water use in Henan Province is estimated by using the irrigation signals from SMAP (soil moisture active and passive) soil moisture data. Firstly, a method for recognizing irrigation signals in soil moisture data obtained by microwave satellite remote sensing was used. Then, an estimation model of the amount of irrigation water (SM2Rainfall model) was built on each data pixel of the satellite microwave remote sensing of soil moisture. Finally, the amount of irrigation water utilized in Henan Province was estimated by combining the irrigation signals and irrigation water-use estimation model, and the results were evaluated. According to the findings, this study improved the estimation accuracy of irrigation water use by using the irrigation signals in Henan Province. The result of this study is of great importance to accurately obtain irrigation water use in the region.

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

In arid and semi-arid areas, soil moisture (SM) plays a crucial role in land-atmosphere interactions, hydrological processes, and ecosystem sustainability. SM data at large scales are critical for related climatic, hydrological, and ecohydrological research. Data fusion based on satellite products and model simulations is an important way to obtain SM data at large scales; however, little has been reported on the comparison of the data fusion methods in different categories. Here, we compared the performance of two widely used data fusion methods, the Ensemble Kalman Filter (EnKF) and the Back-Propagation Artificial Neural Network (BPANN), in the degraded grassland site (DGS) and the alpine grassland site (AGS). The SM data from the Community Land Model 5.0 (CLM5.0) and the Soil Moisture Active and Passive (SMAP) were fused and validated against the observations of the Cosmic-Ray Neutron Sensor (CRNS) to avoid the impacts of scale-mismatch. Results show that compared with the original data sets at both sites, the RMSE of the fused data by BPANN (FD-BPANN) and EnKF (FD-EnKF) had improved by more than 50% and 31%, respectively. Overall, the FD-BPANN performs better than the FD-EnKF because the BPANN method assigned higher weights to input data with better performance and the EnKF method is affected by the strong variabilities of both the fused CLM5.0 and SMAP data and the CRNS data. However, in terms of the percentile range, the FD-BPANN showed the worst performance, with overestimations in the low SM range of 25th percentile (<Q25), because the BPANN method tends to be trapped in a local minimum. The BPANN method performed better in humid areas, then followed by semi-humid areas, and finally arid and semi-arid areas. Moreover, compared with the previous studies in arid and semi-arid areas, the BPANN method in this study performed better.

Soil moisture dominates the variation of gross primary productivity during hot drought in drylands

The frequency and severity of hot drought will increase in the future due to impact of climate change and human activities, threatening the sustainability of terrestrial ecosystems and human societies. Hot drought is a typical type of drought event, high vapor pressure deficit (VPD) and low soil moisture (SM) are its main characteristics of hot drought, with increasing water stress on vegetation and exacerbating hydrological drought and ecosystem risks. However, our understanding of the effects of high VPD and low SM on vegetation productivity is limited, because these two variables are strongly coupled and influenced by other climatic drivers. The southwestern United States experienced one of the most severe hot drought events on record in 2020. In this study, we used SM and gross primary productivity (GPP) datasets from Soil Moisture Active and Passive (SMAP), as well as VPD and other meteorological datasets from gridMET. We decoupled the effects of different meteorological factors on GPP at monthly and daily scales using partial correlation analysis, partial least squares regression, and binning methods. We found that SM anomalies contribute more to GPP anomalies than VPD anomalies at monthly and daily scales. Especially at the daily scale, as the decoupled SM anomalies increased, the GPP anomalies increased. However, there is no significant change in GPP anomalies as VPD increases. For all the vegetation types and arid zones, SM dominated the variation in GPP, followed by VPD or maximum temperature. At the flux tower scale, decoupled soil water content (SWC) also dominated changes in GPP, compared to VPD. In the next century, hot drought will occur frequently in dryland regions, where GPP is one of the highest uncertainties in terrestrial ecosystems. Our study has important implications for identifying the strong coupling of meteorological factors and their impact on vegetation under climate change.

Uncertainty Quantification of Satellite Soil Moisture Retrieved Precipitation in the Central Tibetan Plateau

SM2RAIN is a well-established methodology for estimating precipitation from satellite or observed soil moisture and it has been applied as a complementary approach to conventional precipitation monitoring methods. However, satellite soil moisture retrievals are usually subject to various biases and limited number of retrievals (and therefore large intervals) in remote areas, such as the Tibetan Plateau (TP), and little is known about their potential impacts on precipitation estimation. This study seeks to quantify the uncertainties in Soil Moisture Active and Passive (SMAP) soil moisture estimated precipitation through the commonly used SM2RAIN by referring to in situ soil moisture observations from the central Tibetan Plateau soil moisture network. The estimated precipitation is evaluated against rain gauge observations. Additional attention is paid to different orbits of the SMAP retrievals. Results show that the original SM2RAIN algorithm tends to underestimate the precipitation amount in the central TP when using SMAP soil moisture retrievals as input. The retrieval accuracy and sampling interval of SMAP soil moisture from ascending (descending) orbits each count for 1.04 mm/5 d (−0.18 mm/5 d) and 1.67 mm/5 d (0.72 mm/5 d) of estimated precipitation uncertainties as represented by root mean square error. Besides, the descending product of SMAP with a relatively less sampling interval and higher retrieval accuracy outperforms the ascending one in estimating precipitation, and the combination of both two orbits does add value to the overall SM2RAIN estimation. This study is expected to provide guidance for future applications of SM2RAIN-derived precipitation. Meanwhile, more reliable SM2RAIN precipitation estimations are desired when using higher quality satellite soil moisture products with better retrieval accuracy and smaller intervals.

Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model.

This paper coupled a unified passive and active microwave observation operator-namely, an enhanced, physically-based, discrete emission-scattering model-with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture Active and Passive (SMAP) brightness temperature TBp (p = Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of TBH lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of TBV reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated.

An Improved Method for Water Body Removal in Spaceborne GNSS-R Soil Moisture Retrieval

The global soil moisture (SM) retrievals by the spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) are significantly influenced by the presence of water bodies. The traditional method is to build a grid based on the location of satellite sampling points and determine the presence or absence of water bodies. In this paper, we propose a water body removal method for global spaceborne GNSS-R SM retrievals that combines water bodies and buffers derived from the marginal areas around water bodies as mask data, thus achieving accurate removal of the water body and avoiding margin effects. To verify the effectiveness of the proposed method, the Cyclone GNSS (CYGNSS) data with two different spatial resolutions (36 km and 3 km) were used for SM retrieval, and the Soil Moisture Active and Passive (SMAP) Radiometer SM as well as the International Soil Moisture Network (ISMN) were used as references. Results show that the correlation coefficient (R) and root mean square error (RMSE) of the 36 km grid are 0.50 and 0.057 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , respectively, while the R and RMSE of the 3 km grid are 0.68 and 0.041 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , respectively. Such performances are better than the traditional method. Moreover, the method proposed in this paper preserves more grids. Take the 3 km spatial resolution for example, it preserves 2.2 fold grids more than the traditional water body removal method. In the comparison with SMAP SM, the overall improvement of RMSE by using the water body removal method proposed in this paper is 16.3% (8.2% for the traditional method). In the in-situ validation, the overall improvement of RMSE is 19.4% (-1.2% for the traditional method). Therefore, in the future high spatial resolution SM retrieval, the water body removal method proposed in this paper can preserve the maximum area and effectively eliminate the influence of water bodies on SM retrieval.

Comparison of SMOS, SMAP and In Situ Sea Surface Salinity in the Gulf of St. Lawrence

ABSTRACT The Gulf of St. Lawrence (GSL) is an Eastern Canada semi-enclosed sea under the influence of the freshwater discharge from the Great Lakes – St. Lawrence River drainage basin. Studying the variability of oceanographic conditions in the GSL under a changing climate is important for ecosystem and fisheries management. To supplement the available in situ sea surface salinity (SSS) measurements with satellite SSS data, this study compares all available years of Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) satellite SSS to in situ SSS observations. Despite the relatively cold water and proximity to land and sea ice, the satellite SSS is able to capture the interannual variability and annual cycle of SSS in the GSL, with correlations ranging from 0.80 to 0.85 in the Southern GSL, and 0.22 to 0.77 in the Northern GSL. All satellite SSS data products were able to detect the very low salinity year of 2017 in the Southern GSL.

Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture

Abstract. Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We designed an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The integrated model, LPJ-PM (LPJ-PT-JPLSM Model) as the underlying model, was coupled from the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM version 3.01) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the Global LAnd and Surface Satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 ( coefficient of determination) of 0.91 and 0.81 and an ubRMSD (unbiased root mean square deviation) reduced by 40.3 % and 29.9 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The GPP and ET resulting from the assimilation demonstrated a better performance in the arid and semi-arid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS data had a slight difference, and the SMAP soil moisture data performed better than SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, and GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (μ): 3.4 mm per month; estimated standard deviation of μ: 1.91 mm per month). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.

An Examination of the SMAP Operational Soil Moisture Products Accuracy at the Tibetan Plateau

Surface soil moisture (SSM) plays an essential role in the Earth’s water cycle and land surface processes as well as in vegetative growth, ecological health, and ecosystem properties. Particularly, information on this parameter’s spatiotemporal variability at the Tibetan Plateau is of key importance to the study of climate and the impact of climate change due to it is distinctive characteristics in this area. The present study assesses the operational SSM products provided by the SMAP (Soil Moisture Active and Passive) satellite at the Tibetan Plateau, Naqu observational station, China. In particular, the globally distributed Level 3 operational products, SPL3SMP_36km and the Enhanced Passive SSM Product SPL3SMP_9km, are evaluated in two-phases. SSM and the surface temperature estimates by SPL3SMP_36km and SPL3SMP_9km are compared against corresponding ground data available at the Naqu observation network. All in all, the examined products captured the SSM dynamics in the studied area. The results showed that precipitation is the key driving source of SSM variability. SSM fluctuated significantly and was dependent on precipitation in the studied region. Statistical metrics, such as the root mean square error (RMSE), varied for SPL3SMP_36km and SPL3SMP_9km in the ranges of 0.036–0.083 m3/m3 and 0.074–0.097 m3/m3, respectively. The unbiased RMSE (ubRMSE) was higher than the SMAP uncertainty limit (0.04 m3/m3) in most cases. This study establishes some of the causes for the different performances of SMAP products, mainly, the ancillary input dataset parameterizations, and, specifically, the surface temperature parameterization schemes of SMAP retrieval algorithm is analyzed and discussed. Our research findings highlight, among others, the usefulness of those SSM products from SMAP, particularly in mesoscale studies, providing additional useful insights into the use of those products in practice in China and globally.

Sea Surface Salinity Inversion Model for Changjiang Estuary and Adjoining Sea Area with SMAP and MODIS Data Based on Machine Learning and Preliminary Application

Sea surface salinity (SSS) is one of the most important basic parameters for studying the oceanographic processes and is of great significance in identifying oceanic currents. However, for a long time, the salinity observation in the estuary and coastal waters has not been well resolved due to the technology limitation. In this study, the SSS inversion models for the Changjiang Estuary and the adjacent sea waters were established based on machine learning methods, using SMAP (Soil Moisture Active and Passive) salinity data combined with the specific bands and bands ratios of MODIS (Moderate Resolution Imaging Spectroradiometer). The performance of the three machine learning methods (Random Forest, Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Automatic Machine Learning (TPOT)) are compared with accuracy verification by the in-situ measured SSS. Random Forest is proven to be effective for the SSS inversion in flood season, whereas TPOP performs the best for the dry season. The machine learning-based models effectively solve the problem of insufficient time span of SSS observation from salinity satellites. At the same time, an empirical algorithm was established for the SSS inversion for the sea areas with low salinity (&lt;30 psu) where the machine learning based model fails with great errors. The average deviation of the complex SSS inversion models is −0.86 psu, validated with Copernicus Global Ocean Reanalysis Data. The long term series SSS dataset of March and August from 2003 to 2020 was then constructed to observe the salinity distribution characteristics of the flood season and the dry season, respectively. It is indicated that the distribution pattern of CDW can be divided into three categories: northeast-oriented expansion pattern, multi direction isotropic expansion pattern, and a turn pattern of which CDW shows changing direction, namely the northeast-southeast expansion pattern. The pattern of CDW expansion is indicated to be the comprehensive effect of the interaction of different currents. In addition, it is noteworthy that CDW shows increasing expansion with decreasing SSS in the front plume, especially in the flood season. This study not only gives a feasible solution for effective SSS observation, but also provides a dataset of basic oceanographic parameters for studying the coastal biogeochemical processes, evolution of land-sea interaction, and changing trend of material and energy transport by the CDW in the west Pacific boundary.

Learning the Indicative Patterns of Simulated Force Changes in Soil Moisture by BP Neural Networks and Finding Differences with SMAP Observations

Soil moisture is a vital land surface variable that can influence climate change. Many problems in soil moisture data require the identification of signals obscured by anthropogenic external forces (including greenhouse gases such as CO2 and aerosol radiative force), natural forces (such as volcanic and solar activity), and internal variability (such as ENSO, NAO, and PDO). Although artificial neural networks (ANNs) have been widely studied in making accurate predictions, the studies of interpretation of ANNs in soil moisture are still rare. Hence, the proposed method aims to assist in the study of interpretating soil moisture data. Specifically, first, an ANN model is trained to predict the approximate year of the simulations by identifying the spatial patterns of qualitative changes in soil moisture. After accurately predicting the approximate year, the spatial patterns in the ANN model, acting as “reliable indicators” of the force changes, are the different natures of regional signals. Then, the simulated data and Soil Moisture Active and Passive (SMAP) observations are fed into the trained ANN separately, and the specific differences are observed by the Deep Taylor Decomposition (DTD) visualization tool. By comparing with the standard multiple linear regression method, the results of the ANN model can provide the reliable indicators of change for a specific year, thus providing meaningful information from the ANN model according to the common soil moisture data. The results show that a large correlation exists between eastern Asia and western North America during the 21st century, and the correlation increases with time in Australia. This also reflects the strong force signal due to a combination of anthropogenic and external forces that has played a role in soil moisture over the decades and can clearly discern the differences between model simulations and observed data. This study indicates that the proposed method using ANNs and visualization tools enables relatively accurate predictions and the discovery of unknown patterns within soil moisture data.

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.

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.

An Exponential Filter Model-Based Root-Zone Soil Moisture Estimation Methodology from Multiple Datasets

Modern smart agriculture initiative presents more requests for soil moisture (SM) monitoring over large agricultural areas. Remote sensing techniques facilitate high-resolution surface SM (SSM) estimation at a large scale but lack root zone SM (RZSM) information. Establishing the deduction method of RZSM from the SSM has long been the focus of most attention. Data assimilation methods are promising techniques for RZSM estimation, developing numerous assimilated reanalysis datasets, e.g., ERA5 and the latest Soil Moisture Active and Passive (SMAP) L4 SM product. However, data latency and large computation during data collecting and processing often inhibits further applications. This work proposes a rapid estimation scheme for estimating RZSM with short latency and small computations, based on the Exponential Filter (EF) method. The EF model with single parameter T was firstly calibrated and validated using the SSM and RZSM of ERA5 reanalysis dataset, obtaining the optimum parameter T map for each grid. Then, the fast-updating SMAP L3 SSM product together with the scale-matched optimum T were adopted as inputs into the EF model to retrieve RZSM estimation of each grid. Specifically, such estimation scheme was tested over the central and eastern agricultural areas of China, using a dense monitoring network of 796 SM observation sites, which contains various land uses, as well as meteorological and hydrological conditions. The calibrated optimum parameter T presented an increasing trend with good physical explanations. Furthermore, all the estimated RZSMs were found to have good performances on capturing the temporal-spatial variations of RZSM and well reflecting seasonal RZSM changes. Overall, such an estimation scheme was proven to be a desirable alternative for estimating RZSM over large agricultural areas.

Spatio-temporal Analysis of Surface and Root Zone Soil Moisture Derived from SMAP and SMOS Microwave Satellite Data Using in-situ Measurements in Senegal

Soil moisture plays a regulating role on Earth-Atmosphere flux and the water balance at the terrestrial surface. In recent years, significant progress has been made in quantifying soil moisture from satellite data. Particularly, microwave space remote sensing remains an efficient tool for estimating soil moisture at a large scale. However, the validation by in situ measurements is still needed in some areas such as the Sahel, especially for elaborated products such as soil moisture in the root zone. The present study is contributing to the validation of soil moisture products in the silvopastoral and agricultural system within the Sahelian area in Senegal. Soil moisture products derived from Soil Moisture Active and Passive (SMAP) sensors at 9 and 36 km and Soil Moisture Ocean Salinity (SMOS) at the resolution of 0.25° x 0.25° are compared with in-situ measurements acquired at different depths (from 0 to 100 cm) three validation sites. First, a validation of the in-situ measurements was done to visualize the performance of these sensors in situ for soil moisture recovery on the three study sites (Loumbi, Bawdi, and Niakhar). Second, an estimation of the SMAP, SMOS products of the surface area (5 cm depth) was made. Third, an estimation showed that these microwave sensors follow the soil moisture dynamics whatever the surface and climatic conditions and also despite RMSE higher or equal to the specifications, 0.04 m3/m3. Finally, the results from the soil moisture analysis of the root zone of the satellite and in-situ products are very important.

Assessment of SMAP and SMOS soil moisture products using triple collocation method over Inner Mongolia

Soil moisture (SM) is an indispensable variable in drought monitoring and weather forecast. L-band is found to be the most suitable band for retrieving surface SM. Here, we evaluate two L-band passive microwave SM products Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) in Inner Mongolia. The collected in-situ data from 24 measured sites and the triple collocation (TC) analysis method (using Global Land Data Assimilation System (GLDAS) Noah and the Advanced Scatterometer (ASCAT) SM data as a reference dataset) are used to evaluate SMAP and SMOS SM products. In the validation based on in-situ SM, SMAP shows the highest correlation with the in-situ SM, followed by GLDAS and SMOS. The root mean square error (RMSE) and bias value of SMAP is the lowest. The validation results of each in-situ station show that the median correlation coefficient of ASCAT is the highest, followed by SMAP, GLDAS, and SMOS. The median RMSE and bias of SMAP are the lowest, while SMOS performs the worst among the three products. TC analysis results show that SMAP has better performance with the majority of the random errors distributed between 0.02 and 0.03 m3/m3. The average random error of SMAP is small, while SMOS has relatively large random errors. Simultaneously, SMAP performs better than GLDAS and SMOS in grassland and low vegetation coverage areas. The research results and analysis of this paper are expected to provide some insights for improving the quality of SM products.

A Novel RFI Detection Method for Microwave Radiometers Using Multilag Correlators

Radio frequency interference (RFI) is an increasing problem in microwave radiometry, particularly for Earth observation, because the antennas are pointing toward the Earth. RFI has been observed at the L-band in European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) Earth Explorer mission, National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active and Passive (SMAP) and Aquarius missions, and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and WindSat missions at 10.7 and 18.7 GHz (Misra and de Matthaeis, 2014). Therefore, dedicated onboard systems are, nowadays, a must to detect and remove contaminated measurements, improving radiometric accuracy and increasing the spatial coverage. In this work, a novel detection technique, specially tailored for synthetic aperture interferometric radiometers (SAIRs), is proposed and its performance analyzed. It is based on the change of the shape of the cross correlation function at lags different from zero under the presence of RFI. The performance of the proposed technique is compared to other common RFI detection algorithms, and its limitations and advantages are discussed. Postcorrelation detection performance is found to outperform other commonly used algorithms, such as kurtosis. In addition, it presents some convenient properties for its practical application in correlation and real aperture radiometers.