Microwave Soil Moisture Research Articles

Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review

Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuous global-scale SM information. However, the coarse spatial resolution of these L-band microwave SM retrievals poses uncertainties in many regional- and local-scale SM applications which require a high amount of spatial details. Numerous studies have been conducted to develop downscaling algorithms to enhance the spatial resolution of coarse-resolution satellite-derived SM datasets. Machine Learning (ML)-based downscaling models have gained prominence recently due to their ability to capture non-linear, complex relationships between SM and its driving factors, such as vegetation, surface temperature, topography, and climatic conditions. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling. The usage of classical, ensemble, neural nets, and deep learning methods to downscale SM products and the comparison of multiple algorithms are detailed in this paper. Insights into the significance of surface ancillary variables for model accuracy and the improvements made to ML-based SM downscaling approaches are also discussed. Overall, this paper provides useful insights for future studies on developing reliable, high-spatial-resolution SM datasets using ML-based algorithms.

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.

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.

Comparison Between Thermal-Optical and L-Band Passive Microwave Soil Moisture Remote Sensing at Farm Scales: Towards UAV-Based Near-Surface Soil Moisture Mapping

Comparison Between Thermal-Optical and L-Band Passive Microwave Soil Moisture Remote Sensing at Farm Scales: Towards UAV-Based Near-Surface Soil Moisture Mapping

Disaggregation of remote sensing and model-based data for 1 km daily seamless soil moisture

High-resolution soil moisture (SM) products are crucial for managing water in agricultural regions, irrigation scheduling, and land–atmosphere model simulations. Most satellite-based SM products have spatial resolutions in the tens of kilometers, which cannot satisfy the aforementioned applications, and there is an urgent need to disaggregate them to fine spatial resolutions. Currently, disaggregation method based on multi-source data fusion tend to be robust, but the following difficulties remain: 1) satellite products (microwave, optical/infrared) are somewhat missing in space and time which hinder the applications; 2) only one model-based product is used in the fusion process and different model-based products were not sufficiently evaluated. To overcome these problems, this work developed an SM disaggregation methodology. The best model-based SM product was first assessed using the triple collocation (TC) method, which allows us to obtain the weight of individual product. The model-based and microwave SM products were combined based on weight to produce daily seamless SM data at 25 km. Second, the iterative multi-temporal reconstruction and the ESTARFM method were combined to solve the invalid grids problem of optical/infrared product, thus obtaining 1 km daily seamless auxiliary data. Finally, daily seamless SM data at 1 km were obtained by geographically weighted regression (GWR) method. The methodology proposed in this study had a well-defined mechanism, and the resulting SM product achieved a satisfied accuracy (ubRMSE = 0.044 m3/m3), with characteristics of spatiotemporal continuity as well as fine spatial detail. The disaggregated SM product makes full use of model-based, microwave, and optical/infrared products, which is valuable in areas such as agricultural management policy, high-resolution hydro-meteorological simulations, and disaster monitoring.

Retrieval and evaluation of surface soil moisture from CYGNSS using blended microwave soil moisture products

The Global Navigation Satellite System-Reflectometry (GNSS-R) can estimate land surface soil moisture (SSM) as a viable and promising approach. However, it has some large uncertainty in retrieving SSM. In this study, the SSM is retrieved from different Cyclone GNSS (CYGNSS) SSM retrieval models formed with different SSM reference data products, including two blended microwave SSM products from the European Space Agency's Climate Change Initiative (CCI) and the National Oceanic and Atmospheric Administration's Soil Moisture Operational Product System (SMOPS), and a single microwave sensor-derived Soil Moisture Active Passive (SMAP) Level-3 product. The performance of the developed retrieval models, characterized by spatial resolutions of 36 km × 36 km and 0.25° × 0.25°, is evaluated using K-fold cross-validation. Furthermore, the accuracy of these models is validated against ground measurements acquired from Chinese automated soil moisture observation network. In order to alleviate the impact of spatial mismatching between the predicted gridded SSM and the point-scale in-situ measurements, a cumulative distribution function (CDF) rescaling strategy is applied. The results indicated that all models are effective at capturing spatial variations in SSM, and the SMOPS-based model achieves the highest correlation coefficient (0.930) and the lowest root mean square error (RMSD, 0.028 cm3/cm3), followed by the CCI-based model (0.906 and 0.042 cm3/cm3). The SMAP-based model performs poorly in the comparison. The suboptimal performance of models evaluated with Chinese automated soil moisture measurements is largely attributed to the insufficient calibration of the original reference data in the region.

On the potential of Sentinel-1 for sub-field scale soil moisture monitoring

Soil moisture (SM) datasets at high spatial resolutions are beneficial for a wide range of applications, such as monitoring and prediction of hydrological extremes, numerical weather prediction, and precision agriculture. For large scale applications in particular, remotely sensed SM has advantages over in situ data because it provides gridded estimates and because it is less labour-intensive. However, until present, active microwave SM data have not been presented at their native spatial resolution, since the quality of these data is limited by speckle. We explored the potential and limits of high spatial resolution of active microwave SM observations. We used a Sentinel-1 C-band SAR SM dataset at six spatial resolutions ranging from 20 × 20 to 120 × 120 m2. This was compared to a closely spaced (20 m) in situ dataset collected on a non-irrigated agricultural field (±2.5 ha) in the Southeast of Luxembourg. A comparison of the field and satellite datasets demonstrated how Sentinel-1 data with a high spatial resolution can be used to quantify temporal within-field SM variability. SM was accurately estimated at spatial resolutions of 60 × 60 m2 and coarser, where the temporal correlation was found to be 0.67 and sub-field variations in SM were still detected. Spatial correlation was limited by the absence of SM variability within the field. These results indicate that high spatial resolution SM estimates from Sentinel-1 data can be valuable for monitoring temporal SM variations within agricultural fields.

Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning

Abstract. Motivated by the lack of long-term global soil moisture products with both high spatial and temporal resolutions, a global 1 km daily spatiotemporally continuous soil moisture product (GLASS SM) was generated from 2000 to 2020 using an ensemble learning model (eXtreme Gradient Boosting – XGBoost). The model was developed by integrating multiple datasets, including albedo, land surface temperature, and leaf area index products from the Global Land Surface Satellite (GLASS) product suite, as well as the European reanalysis (ERA5-Land) soil moisture product, in situ soil moisture dataset from the International Soil Moisture Network (ISMN), and auxiliary datasets (Multi-Error-Removed Improved-Terrain (MERIT) DEM and Global gridded soil information (SoilGrids)). Given the relatively large-scale differences between point-scale in situ measurements and other datasets, the triple collocation (TC) method was adopted to select the representative soil moisture stations and their measurements for creating the training samples. To fully evaluate the model performance, three validation strategies were explored: random, site independent, and year independent. Results showed that although the XGBoost model achieved the highest accuracy on the random test samples, it was clearly a result of model overfitting. Meanwhile, training the model with representative stations selected by the TC method could considerably improve its performance for site- or year-independent test samples. The overall validation accuracy of the model trained using representative stations on the site-independent test samples, which was least likely to be overfitted, was a correlation coefficient (R) of 0.715 and root mean square error (RMSE) of 0.079 m3 m−3. Moreover, compared to the model developed without station filtering, the validation accuracies of the model trained with representative stations improved significantly for most stations, with the median R and unbiased RMSE (ubRMSE) of the model for each station increasing from 0.64 to 0.74 and decreasing from 0.055 to 0.052 m3 m−3, respectively. Further validation of the GLASS SM product across four independent soil moisture networks revealed its ability to capture the temporal dynamics of measured soil moisture (R=0.69–0.89; ubRMSE = 0.033–0.048 m3 m−3). Lastly, the intercomparison between the GLASS SM product and two global microwave soil moisture datasets – the 1 km Soil Moisture Active Passive/Sentinel-1 L2 Radiometer/Radar soil moisture product and the European Space Agency Climate Change Initiative combined soil moisture product at 0.25∘ – indicated that the derived product maintained a more complete spatial coverage and exhibited high spatiotemporal consistency with those two soil moisture products. The annual average GLASS SM dataset from 2000 to 2020 can be freely downloaded from https://doi.org/10.5281/zenodo.7172664 (Zhang et al., 2022a), and the complete product at daily scale is available at http://glass.umd.edu/soil_moisture/ (last access: 12 May 2023).

Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing

Abstract. Earth's drylands are home to more than two billion people, provide key ecosystem services, and exert a large influence on the trends and variability in Earth's carbon cycle. However, modeling dryland carbon and water fluxes with remote sensing suffers from unique challenges not typically encountered in mesic systems, particularly in capturing soil moisture stress. Here, we develop and evaluate an approach for the joint modeling of dryland gross primary production (GPP), net ecosystem exchange (NEE), and evapotranspiration (ET) in the western United States (US) using a suite of AmeriFlux eddy covariance sites spanning major functional types and aridity regimes. We use artificial neural networks (ANNs) to predict dryland ecosystem fluxes by fusing optical vegetation indices, multitemporal thermal observations, and microwave soil moisture and temperature retrievals from the Soil Moisture Active Passive (SMAP) sensor. Our new dryland ANN (DrylANNd) carbon and water flux model explains more than 70 % of monthly variance in GPP and ET, improving upon existing MODIS GPP and ET estimates at most dryland eddy covariance sites. DrylANNd predictions of NEE were considerably worse than its predictions of GPP and ET likely because soil and plant respiratory processes are largely invisible to satellite sensors. Optical vegetation indices, particularly the normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv), were generally the most important variables contributing to model skill. However, daytime and nighttime land surface temperatures and SMAP soil moisture and soil temperature also contributed to model skill, with SMAP especially improving model predictions of shrubland, grassland, and savanna fluxes and land surface temperatures improving predictions in evergreen needleleaf forests. Our results show that a combination of optical vegetation indices and thermal infrared and microwave observations can substantially improve estimates of carbon and water fluxes in drylands, potentially providing the means to better monitor vegetation function and ecosystem services in these important regions that are undergoing rapid hydroclimatic change.

Passive Only Microwave Soil Moisture Retrieval in Indian Cropping Conditions: Model Parameterization and Validation

The present study carried out to parameterize the single channel soil moisture active passive (SMAP) passive soil moisture (SM) retrieval algorithm, over Indian conditions. The moderate resolution imaging spectroradiometer (MODIS) data products and soil texture data were used for an improved parameterization of the algorithm. The bias correction was applied to the MODIS leaf area index (LAI) for accurate computation of vegetation optical depth. The necessary vegetation and roughness parameter were calibrated through minimization of the error between model retrieved and ground measured SM. The value of root mean square error (RMSE) for retrieved SM was found as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.059\,\,m^{3}m^{-3}$ </tex-math></inline-formula> with bias and correlation coefficients of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.036\,\,m^{3}m^{-3}$ </tex-math></inline-formula> and 0.724 for ascending overpass, respectively, while a lower value was recorded (RMSE = <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.059\,\,m^{3}m^{-3}$ </tex-math></inline-formula> , bias = <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.024\,\,m^{3}m^{-3}$ </tex-math></inline-formula> , and correlation coefficients = 0.752) for descending overpass. The same method is also implemented on two other test sites in different regions of India to check the model robustness, which indicates that the current parameterization provides a better estimate of SM over croplands in India. The overall performance of new parameterized model is found as (RMSE = 0.052 and bias = 0.034) for ascending and descending (RMSE = 0.048 and bias = 0.026) satellite overpasses for all the three test sites. Additionally, the intercomparing of various operational SM products SMAP SM (L2_SM_P), Soil Moisture and Ocean Salinity (SMOS) SM (SMOS_L3_SM), and SMOS-IC data products was carried out with the SAC-ISRO PAN India SM network, which showed a significant RMSE, dry and wet biases over all three test sites as compared to the developed improved parameterized algorithm.

Gap Filling of the ESA CCI Soil Moisture Data Using a Spatiotemporal Attention-Based Residual Deep Network

As an essential climate variable, soil moisture (SM) exerts an indispensable influence on numerous disciplines. However, various degrees of data gaps exist in current microwave SM products. Therefore, this article proposed a spatiotemporal attention-based residual deep network (STARN) to reconstruct gaps of the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) over the Qinghai–Tibet Plateau (QTP) during unfrozen seasons (May to September) from 2001 to 2021. The developed model is an end-to-end residual network embedded with three attention modules to comprehensively consider the potential relationship between SM and surface variables. Evaluation results revealed that the proposed model could well reconstruct SM gaps with an overall median <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> and unbiased RMSE (ubRMSE) values of 0.52 and 0.054 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> , while the overall median <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> and ubRMSE values for the ESA CCI SM were 0.41 and 0.058 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> . Besides, comparison with five baseline methods (e.g., the artificial neural network, convolutional neural network, extreme gradient boosting, long-short term memory, and DCT-PLS model) indicated that the STARN model had certain advantages over the five baseline models with higher correlation and more reasonable distribution patterns. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> /ubRMSE values for the five models were 0.38/0.057, 0.34/0.058, 0.40/0.058, 0.41/0.056, and 0.41/0.058, respectively. The pretraining using the ERA5-Land SM data could further improve the accuracy of generated seamless SM data since the ERA5-Land and ESA CCI SM complemented each other to a certain extent on the QTP. In summary, by leveraging the spatiotemporal information and attention modules, the STARN model showed great potentials in SM gap filling.

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 pixel-wise calculation of soil evaporative efficiency with thermal/optical remote sensing and meteorological reanalysis data for downscaling microwave soil moisture

Soil evaporative efficiency (SEE) was estimated as the relative difference of soil temperature (Ts) to its maximum (Tsmax) and minimum (Tsmin) values at ‘minimum and maximum’ soil moisture (SM), i.e., SEE= (Tsmax−Ts)/(Tsmax−Tsmin). This thermal indicator of SM (SMI) has been proven effective in downscaling satellite microwave SM in some local areas. However, the Tsmax and Tsmin were usually determined in an empirical way which is not conducive to a wider range of applications or global downscaling. In addition to the Ts-based SMI (SEE), solo-LST (Land Surface Temperature)-based SMI and composite index of LST and optical SMIs have also been widely used in downscaling microwave SM. However, they have rarely been evaluated with spatially distributed SM at a similar spatial resolution, leading to a vague understanding of their pros and cons in the downscaling. In this study, we first implemented a pixel-wise and theoretically calculating of SEE with LST/FVC space and meteorological reanalysis data. Then, a critical evaluation with satellite, aircraft, and in-situ SM observations was made via the SMPAVEX15 and SMAPVEX16 aircraft-based field experiments. Results indicated that the thermal SMIs presented a better performance than the optical SMIs for depicting the variations of SM. Among the thermal SMIs, Ts-based SEE showed better performance than the solo-LST-based index and the composite index temperature-vegetation-soil moisture dryness index (TVMDI). The results demonstrate the validity and necessity of obtaining the Ts-based SEE for downscaling satellite microwave SM with thermal/optical data. The provided pixel-wise method was validated in this study for calculating the Ts-based SEE, which is suggested for future downscaling of satellite microwave SM with microwave and thermal/optical fusion. However, the disadvantages of cloud contamination, the decoupling effect, and uncertainties brought by complex meteorological conditions and reanalysis data urgently need more research efforts.

Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma—Part I: Individual Product Assessment

Improvements in soil moisture observations and modeling play a vital role in drought, water resources, flooding, and landslide management and forecasting. However, the lack of multisensor products that integrate different spatial scales (i.e., from 1 m2 to 102 km2) is a pressing need in the management and forecasting chain. Up to date, surface soil moisture estimates could be obtained through three primary approaches: (1) in situ measurements and their interpolations, (2) remote sensing observations, and (3) land surface model (LSM) outputs. Each source of soil moisture has its own spatiotemporal resolution, strengths, and weaknesses. Therefore, their correct interpretation and application require an in-depth understanding of their accuracy and appropriateness. In this study, we explore the utility of the triple collocation (TC) method for an independent assessment of three soil moisture products to characterize their uncertainty structures and make recommendations toward a potential product merge. The state of Oklahoma is an ideal domain to test the hypotheses of this work because of the presence of marked west-to-east gradients in climate, vegetation, and soils. The three target soil moisture products include (1) the remotely sensed microwave soil moisture active passive (SMAP) L3_SM_P_E (9 km, daily), (2) the physically based LSM estimates from NLDAS_NOAH0125_H (1/8°, hourly; Noah), and (3) the Oklahoma Mesonet ground sensor network (point, 30 min). The product assessment was conducted from April 2015 to July 2019. The results indicate that, in general, Mesonet and Noah are the most reliable products, although their performance varies geographically and by land cover type, reflecting the main spatiotemporal characteristics and scope of each product. Specifically, Mesonet provides the best estimates of volumetric soil moisture with a mean Pearson correlation coefficient of 0.805, followed by Noah with 0.747. However, Noah represents the true soil moisture variation better than the interpolated Mesonet product on the mesoscale, with an averaged RMSE of 0.026 m3⁄m3. Over different land cover types, Mesonet had the best performance in shrub/scrub, herbaceous, hay/pasture, and cultivated crops with an average correlation coefficient of 0.79, while Noah achieved the best performance in evergreen, mixed, and deciduous forests, with an average correlation coefficient of 0.74. The period-integrated TC intercomparison results over nine climate divisions indicated that Noah outperformed in the central, northeast, and east-central regions. TC provides not only a new perspective for comparatively assessing multisource soil moisture products but also a basis for objective data merging to capitalize on the strengths of multisensor, multiplatform soil moisture products.

Microwave-based soil moisture improves estimates of vegetation response to drought in China

The increased frequency and severity of drought has heightened concerns over the risk of hydraulic vegetative stress and the premature mortality of ecosystems globally. Unfortunately, most land surface models (LSMs) continue to underestimate ecosystem resilience to drought – which degrades the credibility of model-predicted ecohydrological responses to climate change. This study investigates the response of vegetation gross productivity to water-stress conditions using microwave-based vegetation optical depth (VOD) and soil moisture retrievals. Based on the estimated isohydric/anisohydric spectrum, we find that vegetation at isohydric state exhibits a larger decrease in gross primary productivity and higher water use efficiency than anisohydric vegetation due to their more rigorous stomatal control and higher tolerance of carbon starvation risk. In addition, the introduction of microwave soil moisture improves the accuracy of isohydricity/anisohydricity estimates compared to those obtained using microwave VOD alone (i.e., increases their Spearman rank correlation versus the benchmark of Global Biodiversity Information Facility dataset from 0.12 to 0.63). Results of this study provide clear justification for the use of microwave-based soil moisture retrievals to enhance stomatal conductance parameterization within LSMs.

Characterizing natural variability in complex hydrological systems using passive microwave-based climate data records: a case study for the Okavango Delta

Abstract. The Okavango River system in southern Africa is known for its strong interannual variability of hydrological conditions. Here, we present how this is exposed in surface soil moisture, land surface temperature, and vegetation optical depth as derived from the Land Parameter Retrieval Model, using an inter-calibrated, long-term, multi-sensor passive microwave satellite data record (1998–2020). We also investigate how these interannual variations relate to state-of-the-art climate reanalysis data from ERA5-Land. We analysed both the upstream river catchment and the Okavango delta, supported by independent data records of discharge measurements, precipitation, and vegetation dynamics observed by optical satellites. The seasonal vegetation optical depth anomalies have a strong correspondence with the MODIS leaf area index (correlation catchment: 0.74, delta: 0.88). Land surface temperature anomalies derived from passive microwave observations match best with those of ERA5-Land (catchment: 0.88, delta: 0.81) as compared to MODIS nighttime land surface temperature (LST) (catchment: 0.70, delta: 0.65). Although surface soil moisture anomalies from passive microwave observations and ERA5-Land correlate reasonably well (catchment: 0.72, delta: 0.69), an in-depth evaluation over the delta uncovered situations where passive microwave satellites record strong fluctuations, while ERA5-Land does not. This is further analysed using information on inundated area, river discharge, and precipitation. The passive microwave soil moisture signal demonstrates a response to both the inundated area and precipitation. ERA5-Land however, which, by default, does not account for any lateral influx from rivers, only shows a response to the precipitation information that is used as forcing. This also causes the reanalysis model to miss record low land surface temperature values as it underestimates the latent heat flux in certain years. These findings demonstrate the complexity of this hydrological system and suggest that future land surface model generations should also include lateral land surface exchange. Also, our study highlights the importance of maintaining and improving climate data records of soil moisture, vegetation, and land surface temperature from passive microwave observations and other observation systems.

A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau

The coarse scale of passive microwave surface soil moisture (SSM) is not suitable for regional agricultural and hydrological applications such as drought monitoring and irrigation management. The optical/thermal infrared (OTI) data-based passive microwave SSM downscaling method can effectively improve its spatial resolution to fine scale for regional applications. However, the estimation capability of SSM with long time series is limited by OTI data, which are heavily polluted by clouds. To reduce the dependence of the method on OTI data, an SSM retrieval and spatio-temporal fusion model (SMRFM) is proposed in the study. Specifically, a model coupling in situ data, MODerate-resolution Imaging Spectro-radiometer (MODIS) OTI data, and topographic information is developed to retrieve MODIS SSM (1 km) using the least squares method. Then the retrieved MODIS SSM and the spatio-temporal fusion model are employed to downscale the passive microwave SSM from coarse scale to 1 km. The proposed SMRFM is implemented in a grassland dominated area over Naqu, central Tibet Plateau, for Advanced Microwave Scanning Radiometer—Earth Observing System sensor (AMSR-E) SSM downscaling in unfrozen period. The in situ SSM and Noah land surface model 0.01° SSM are used to validate the estimated MODIS SSM with long time series. The evaluations show that the estimated MODIS SSM has the same temporal resolution with AMSR-E and obtains significantly improved detailed spatial information. Moreover, the temporal accuracy of estimated MODIS SSM against in situ data (r = 0.673, μbRMSE = 0.070 m3/m3) is better than the AMSR-E (r = 0.661, μbRMSE = 0.111 m3/m3). In addition, the temporal r of estimated MODIS SSM is obviously higher than that of Noah data. Therefore, this suggests that the SMRFM can be used to estimate MODIS SSM with long time series by AMSR-E SSM downscaling in the study. Overall, the study can provide help for the development and application of microwave SSM-related scientific research at the regional scale.

Downscaling Microwave Soil Moisture Products with SM-RDNet for Semiarid Mining Areas

Surface soil moisture (SM), as a crucial ecological element, is significant to monitor in semiarid mining areas characterized by aridity and little rainfall. The passive microwave remote sensing, which is not affected by weather, provides more accurate SM information, but the resolution is too coarse for mining areas. The existing downscaling method is usually pointed to natural scenarios like agricultural fields rather than mining areas with high-intensity mining. In this paper, combined with geoinformation related to SM, we designed a convolutional neural network (SM-Residual Dense Net, SM-RDNet) to downscale SMAP/Sentinel-1 Level-2 radiometer/radar soil moisture data (SPL2SMAP_S SM) into 10 m spatial resolution. Based on the in-site measured data, the root mean square error (RMSE) was utilized to verify the downscaling accuracy of SM-RDNet. In addition, we analyzed its performance for different data combinations, vegetation cover types and the advantages compared with random forest (RF). Experimental results show that: (1) The downscaling from the 3 km product with the combination of auxiliary data NDVI + DEM + slope performs best (RMSE 0.0366 m3/m3); (2) Effective data combinations can improve the downscaling accuracy at the range of 0.0477–0.1176 m3/m3 (RMSE); (3) The SM-RDNet shows better spatial completeness, details and accuracy than RF (RMSE improved by 0.0905 m3/m3). The proposed SM-RDNet can effectively obtain the fine-grained SM in semiarid mining areas. Our method bridges the gap between coarse-resolution microwave SM products and ecological applications of small-scale mining areas, and provides data and technical support for future research to explore how the mining effect SM in semiarid mining areas.

Widespread occurrence of anomalous C-band backscatter signals in arid environments caused by subsurface scattering

Backscatter measured by scatterometers and Synthetic Aperture Radars is sensitive to the dielectric properties of the soil and normally increases with increasing soil moisture content. However, when the soil is dry, the radar waves penetrate deeper into the soil, potentially sensing subsurface scatterers such as near-surface rocks and stones. In this paper we propose an exponential model to describe the impact of such subsurface scatterers on C-Band backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on board of the METOP satellites. The model predicts an increase of the subsurface scattering contributions with decreasing soil wetness that may counteract the signal from the soil surface. This may cause anomalous backscatter signals that deteriorate soil moisture retrievals from ASCAT. We test whether this new model is able to explain ASCAT observations better than a bare soil backscatter model without a subsurface scattering term, using k-fold cross validation and the Bayesian Information Criterion for model selection. We find that arid landscapes with Leptosols and Arenosols represent ideal environmental conditions for the occurrence of subsurface scattering. Nonetheless, subsurface scattering may also become important in more humid environments during dry spells. We conclude that subsurface scattering is a widespread phenomenon that (i) needs to be accounted for in active microwave soil moisture retrievals and (ii) has a potential for soil mapping, particularly in arid and semi-arid environments.

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.