Downscaled Soil Moisture Research Articles

Roughness characterization and disaggregation of coarse resolution SMAP soil moisture using single-channel algorithm

Surface roughness is a crucial parameter for the estimation of soil moisture (SM). The present study attempted to optimize the surface roughness parameter (h) for the estimation of SM from Soil Moisture Active Passive (SMAP) using tau–omega (τ-ω) model and also downscaled the estimated SM product using a polynomial regression relation among Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and SM. The brightness temperature of SMAP available at two spatial resolutions (36 and 9 km) was used for two seasons intended for SM assessment. After assessment with in-situ SM data, 9-km SM data values were further used for spatial disaggregation to obtain the optimized downscaled soil moisture (ODSM) at 1 km. Results showed that the variation in the value of the roughness parameter strongly affects the performance of the τ-ω model and the downscaling performances. The investigation provided lowest values of root-mean-square error (RMSE) to be 0.0518 (at h = 0.35) and 0.0480 (at h = 0.25) for the SM estimation at 36 km for the different seasons used in this study while the lowest values of RMSE for ODSM were found to be 0.0365 (at h = 0.4) and 0.0252 (at h = 0.25, 0.3) for different seasons.

Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment

Global Navigation Satellite System—Reflectometry (GNSS-R) has already proven its potential for retrieving a number of geophysical parameters, including soil moisture. However, single-pass GNSS-R soil moisture retrieval is still a challenge. This study presents a comparison of two different data sets acquired with the Microwave Interferometer Reflectometer (MIR), an airborne-based dual-band (L1/E1 and L5/E5a), multiconstellation (GPS and Galileo) GNSS-R instrument with two 19-element antenna arrays with four electronically steered beams each. The instrument was flown twice over the OzNet soil moisture monitoring network in southern New South Wales (Australia): the first flight was performed after a long period without rain, and the second one just after a rain event. In this work, the impact of surface roughness and vegetation attenuation in the reflectivity of the GNSS-R signal is assessed at both L1 and L5 bands. The work analyzes the reflectivity at different integration times, and finally, an artificial neural network is used to retrieve soil moisture from the reflectivity values. The algorithm is trained and compared to a 20-m resolution downscaled soil moisture estimate derived from SMOS soil moisture, Sentinel-2 normalized difference vegetation index (NDVI) data, and ECMWF Land Surface Temperature.

Estimating catchment scale soil moisture at a high spatial resolution: Integrating remote sensing and machine learning

Soil moisture information is important for a wide range of applications including hydrologic modelling, climatic modelling and agriculture. L-band passive microwave satellite remote sensing is the most feasible option to estimate near-surface soil moisture (~0–5 cm soil depth) over large extents, but its coarse resolution (~10s of km) means that it is unable to capture the variability of soil moisture in detail. Therefore, different downscaling methods have been tested as a solution to meet the demand for high spatial resolution soil moisture. Downscaling algorithms based on the soil thermal inertia relationship between diurnal soil temperature difference (ΔT) and daily mean soil moisture content (μSM) have shown promising results over arid and semi-arid landscapes. However, the linearity of these algorithms is affected by factors such as vegetation, soil texture and meteorology in a complex manner. This study tested a (i) Regression Tree (RT), an Artificial Neural Network (ANN), and a Gaussian Process Regression (GPR) model based on the soil thermal inertia theory over a semi-arid agricultural landscape in Australia, given the ability of machine learning algorithms to capture complex, non-linear relationships between predictors and responses. Downscaled soil moisture from the RT, ANN and GPR models showed root mean square errors (RMSEs) of 0.03, 0.09 and 0.07 cm3/cm3 compared to airborne retrievals and unbiased RMSEs (ubRMSEs) of 0.07, 0.08 and 0.05 cm3/cm3 compared to in-situ observations, respectively. The study showed encouraging results to integrate machine learning techniques in estimating near-surface soil moisture at a high spatial resolution.

Drought monitoring using high spatial resolution soil moisture data over Australia in 2015–2019

Drought is one of the major hazards that could have a significant impact on agriculture. In this study, two drought indices at high spatial resolution: Soil Water Deficit Index (SWDI) and Soil Moisture Deficit Index (SMDI) were derived by 1 km downscaled Soil Moisture Active Passive (SMAP) soil moisture (SM), Global Land Data Assimilation System (GLDAS) long-term SM and soil attribute products, and used to analyze the drought conditions in Australia in 2015–2019. The SWDI was calculated from SMAP SM estimates and SM at field capacity/wilting point derived from soil attribute data, while the SMDI was calculated by integrating GLDAS and SMAP SM using a temporally incremental based method. We found that in the eastern and western coastal regions, the droughts occurred during spring and summer and were relieved in fall and winter. The temporal change pattern of drought conditions for the northern coastal regions was opposite of the eastern/western coasts. On the other hand, the inland regions always had more severe drought conditions. Additionally, the validation results for the 1 km SMAP SM using International Soil Moisture Network (ISMN) in situ data showed reliable accuracy and the Root Mean Square Deviation (RMSD) ranged from 0.02 to 0.09 m3/m3. Both SWDI and SMDI showed clear seasonal and interannual variability, and the drought conditions worsened in 2017–2019. From the 1 km SWDI/SMDI maps in the Murray-Darling River Basin, terrain and streamflow were found to be two deterministic factors for the drought conditions.

Downscaling SMAP Soil Moisture Products With Convolutional Neural Network

Soil moisture (SM) downscaling has been extensively investigated in recent years to improve coarse resolution of SM products. However, available methods for downscaling are generally based on pixel-to-pixel strategy, which ignores the information among pixels. Hence, a new downscaling method based on a convolutional neural network (CNN) is proposed to solve the problem. Furthermore, a weight layer is designed for the input, and residual SM is treated as the output of the CNN to improve the accuracy. This method is applied to downscale Soil Moisture Active Passive (SMAP) SM products (i.e., 36-km $\mathbf {L3{\_}SM{\_}P}$ and 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ ) from January 1, 2018 to December 30, 2018. Compared with 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ , the 9-km downscaling result is satisfactory with obtained correlation coefficient ( $R$ ), root mean square error (RMSE), and unbiased RMSE (ubRMSE) values of 95.81%, 2.77%, and 2.67%, respectively. Moreover, SMAP SM products (36 and 9 km) and downscaling SM (3 and 1 km) are validated by the in situ data, which are collected by the 109 stations of the Oklahoma Mesonet SM monitoring network. Mean $R$ , RMSE, and ubRMSE values are 67.92%, 7.94%, and 4.87% for 36-km $\mathbf {L3{\_}SM{\_}P}$ ; 67.78%, 8.35%, and 4.95% for 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ ; 67.28%, 8.34%, and 4.97% for 3-km downscaling SM; 65.90%, 8.40%, and 5.18% for 1-km downscaling SM, respectively. The 3-km downscaling SM generated by this method can improve the coarse resolution of 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ while preserving its accuracy. However, error will remarkably increase in the 1-km downscaling SM. Therefore, the proposed method provides a new strategy for SM downscaling and obtains satisfactory results in practice. Additional studies can be conducted in the future.

Disaggregating satellite soil moisture products based on soil thermal inertia: A comparison of a downscaling model built at two spatial scales

Lack of high spatial resolution soil moisture data is a major limitation in many regional scale hydrologic, climatic and agricultural applications. The available satellite soil moisture data products are too coarse and unable to cater for this resolution requirement. Downscaling coarse spatial resolution satellite soil moisture retrievals is a feasible option to meet the required level of spatial resolution for those applications. The main focus of this study is to compare two soil thermal inertia-based downscaling models, built by using long-term time records of (i) point scale in-situ data and, (ii) 25 km resolution Global Land Data Assimilation System (GLDAS) land surface model outputs. The developed models were tested over Goulburn River catchment in the Upper Hunter Region of NSW, Australia to downscale Soil Moisture Active Passive (SMAP) 36 km radiometric product into 1 km resolution.The downscaled SMAP product from both models produced encouraging results with unbiased root mean square errors (ubRMSEs) of 0.07–0.10 cm3/cm3, against in-situ field data, and an average ubRMSEs of 0.07 cm3/cm3 when compared to the National Airborne Field Experiment 2005 (NAFE'05) soil moisture retrievals. Both models showed promising results over semi-arid regions in estimating soil moisture at a high spatial resolution, but with their own strengths and weaknesses. The findings here provide useful insights on the robustness of the soil thermal inertia relationship across scales and the effects of the model resolution to the downscaled soil moisture estimates. The approach demonstrated encouraging results over semi-arid regions in estimating soil moisture at a high spatial resolution.

Snow and Ice Thickness Retrievals Using GNSS-R: Preliminary Results of the MOSAiC Experiment

The FSSCat mission was the 2017 ESA Sentinel Small Satellite (S⌃3) Challenge winner and the Copernicus Masters competition overall winner. It was successfully launched on 3 September 2020 onboard the VEGA SSMS PoC (VV16). FSSCat aims to provide coarse and downscaled soil moisture data and over polar regions, sea ice cover, and coarse resolution ice thickness using a combined L-band microwave radiometer and GNSS-Reflectometry payload. As part of the calibration and validation activities of FSSCat, a GNSS-R instrument was deployed as part of the MOSAiC polar expedition. The Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition was an international one-year-long field experiment led by the Alfred Wegener Institute to study the climate system and the impact of climate change in the Arctic Ocean. This paper presents the first results of the PYCARO-2 instrument, focused on the GNSS-R techniques used to measure snow and ice thickness of an ice floe. The Interference Pattern produced by the combination of the GNSS direct and reflected signals over the sea-ice has been modeled using a four-layer model. The different thicknesses of the substrate layers (i.e., snow and ice) are linked to the position of the fringes of the interference pattern. Data collected by MOSAiC GNSS-R instrument between December 2019 and January 2020 for different GNSS constellations and frequencies are presented and analyzed, showing that under general conditions, sea ice and snow thickness can be retrieved using multiangular and multifrequency data.

Downscaling Satellite Retrieved Soil Moisture Using Regression Tree‐Based Machine Learning Algorithms Over Southwest France

AbstractSatellite retrieved soil moisture (SM) shows great potential in hydrological, meteorological, ecological, and agricultural applications, while the coarse resolution limits its utilization in regional scale. The regression tree‐based machine learning algorithms reveal promising capability in SM downscaling. However, it lacks systematic study dedicated to intercomparisons of algorithms to explicitly illuminate their characteristics. In this study, comparisons are made to systematically evaluate performances of classification and regression tree (CART), random forest (RF), gradient boost decision tree (GBDT), and extreme gradient boost (XGB) in Soil Moisture Active Passive (SMAP) SM downscaling in southwest France. The results show that the four algorithms downscaled SM are capable of capturing spatial distribution features of the original SMAP SM. The downscaled regions with favorable accuracy are mostly situated in the dominant Mediterranean climate zone with moderate vegetation coverage and mild topography variation. The best results are obtained by GBDT in grassland with R value of 0.77 and ubRMSE value of 0.04 m3/m3. The RF and XGB also achieve good performances. On the whole, the GBDT approach is robust and reliable, which could downscale SM with superior correlation and smaller bias than the others. Besides, it achieves higher accuracy than the original SMAP in grassland and shrubland. The feature importance index of each explainable variable fluctuates regularly among different seasons and models. This study proves the outstanding performance of GBDT in SMAP SM downscaling and is expected to act as a valuable reference for studies focusing on SM scale conversion algorithms.

A New Approach for Soil Moisture Downscaling in the Presence of Seasonal Difference

The variation of soil moisture (SM) is a complex and synthetic process, which is impacted by numerous factors. The effects of these factors on soil moisture are dynamic. As a result, the relationship between soil moisture and explanatory variables varies with time and season. This kind of change should be considered in obtaining fine spatial resolution soil moisture products. We chose a study area with four distinct seasons in the temperate monsoon region. In this research, we established seasonal downscaling models to avoid the influence of seasonal differences. Precipitation, land surface temperature, evapotranspiration, vegetation index, land cover, elevation, slope, aspect and soil texture were taken as explanatory variables to produce fine spatial resolution SM. SM products derived from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) were downscaled with the help of machine learning algorithms. We compared three machine learning algorithms of random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) to determine the most suitable algorithm for this study. The results show that season-based downscaling is even better than continuous time series. In the analysis of seasonal differences, precipitation plays a dominant role, but its contribution rate is different in each season. Moreover, the influence of vegetation is more prominent in winter, while the influence of terrain is more important in the other three seasons. It could be noted that the accuracy of the RF model is the best among three machine learning algorithms, and the RF-downscaled products have superior matching performance to both AMSR (AMSR-E and AMSR2) SM products and in-situ measurements. The analysis indicates considering seasonal difference and the application of machine learning has high potential for spatial downscaling in remote sensing applications.

Exploiting High-Resolution Remote Sensing Soil Moisture to Estimate Irrigation Water Amounts over a Mediterranean Region

Despite irrigation being one of the main sources of anthropogenic water consumption, detailed information about water amounts destined for this purpose are often lacking worldwide. In this study, a methodology which can be used to estimate irrigation amounts over a pilot area in Spain by exploiting remotely sensed soil moisture is proposed. Two high-resolution DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled soil moisture products have been used: SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) at 1 km. The irrigation estimates have been obtained through the SM2RAIN algorithm, in which the evapotranspiration term has been improved to adequately reproduce the crop evapotranspiration over irrigated areas according to the FAO (Food and Agriculture Organization) model. The experiment exploiting the SMAP data at 1 km represents the main work analyzed in this study and covered the period from January 2016 to September 2017. The experiment with the SMOS data at 1 km, for which a longer time series is available, allowed the irrigation estimates to be extended back to 2011. For both of the experiments carried out, the proposed method performed well in reproducing the magnitudes of the irrigation amounts that actually occurred in four of the five pilot irrigation districts. The SMAP experiment, for which a more detailed analysis was performed, also provided satisfactory results in representing the spatial distribution and the timing of the irrigation events. In addition, the investigation into which term of the SM2RAIN algorithm plays the leading role in determining the amount of water entering into the soil highlights the importance of correct representation of the evapotranspiration process.

Estimation and evaluation of high spatial resolution surface soil moisture using multi-sensor multi-resolution approach

Various soil moisture products (AMSR-E at 25 km, SMAP at 9 km, CCI at 25 km) available from different space agencies, derived using the active microwave remote sensing, may not be suitable for studies at a local level due to their coarse spatial resolution. Hence, an attempt has been made in this study to estimate the surface soil moisture at high spatial resolution using different high-resolution multispectral imageries from Landsat 8 OLI (Optical Land Imager) and Sentinel 2A MSI (Multi Spectral Instrument), of similar date. The thermal (10.9 µm) and short wave infra-red (SWIR) (2.2 µm) bands of the Landsat image and the SWIR band (2.19 µm) of Sentinel image were used to estimate the soil moisture at a spatial resolution of 30 m and 20 m respectively, whereas the red and near infrared (NIR) bands of both the images were used to estimate the dryness. As the thermal band at high spatial resolution (at 20 m or below) is not available, this study attempted to bridge the gap and integrate multi-sensor and multi-resolution feature space utilizing various scalable procedures so as to estimate soil moisture and dryness. Limitation due to unavailability of the thermal bands in Sentinel multispectral image was overcome through a bridging procedure using Landsat thermal bands derived LST and Sentinel derived NDVI to estimate Sentinel LST at 20 m. The distribution pattern of high-resolution estimated soil moisture in each coarse spatial resolution grid (1 km) was taken into consideration to downscale the available CCI and SMAP soil moisture data. The estimated soil moisture and dryness were upscaled to 1 km and then validated using the downscaled soil moisture products (SMAP and CCI), and a significant correlation having R2 greater than 0.8, 0.5 and 0.4 was observed for the thermal band derived soil moisture, short wave band derived soil moisture, and the red and NIR band derived dryness, respectively. To understand the temporal variability of the soil moisture, data from three different seasons representing extreme soil moisture conditions (winter, summer and monsoon) were analyzed and found that the independently estimated soil moisture had errors in temporal continuity and spatial spread. Hence, the estimated soil moisture was standardized using annual information which resulted in more accurate products, and the SWIR Transformed Reflectance (STR) derived soil moisture was more accurate than LST derived product.

An Intercomparison Study of Algorithms for Downscaling SMAP Radiometer Soil Moisture Retrievals

AbstractIn the past decade, a variety of algorithms have been introduced to downscale passive microwave soil moisture observations. Some exploit the soil moisture information from optical/thermal sensing of land surface temperature (LST) and vegetation dynamics while others use active microwave (radar) observations. In this study, downscaled soil moisture data at 9- or 1-km resolution from several algorithms are intercompared against in situ soil moisture measurements to determine their reliability in an operational system. The finescale satellite data used here for downscaling the coarse-scale SMAP data are observations of LST from the Geostationary Operational Environmental Satellite (GOES) and vegetation index (VI) from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) for the warm seasons in 2015 and 2016. Three recently developed downscaling algorithms are evaluated and compared: a simple regression algorithm based on 9-km thermal inertial data, a data mining approach called regression tree based on 9- and 1-km LST and VI, and the NASA SMAP enhanced 9-km soil moisture product algorithm. Seven sets of in situ soil moisture data from intensive networks were used for validation, including 1) the CREST-SMART network in Millbrook, New York; 2) Walnut Gulch Watershed in Arizona; 3) Little Washita Watershed in Oklahoma; 4) Fort Cobb Reservoir Experimental Watersheds in Oklahoma; 5) Little River Watershed in Georgia; 6) the Tibetan Plateau network in China, and 7) the OzNet in Australia. Soil moisture measurements of the in situ networks were upscaled to the corresponding SMAP reference pixels at 9 km and used to assess the accuracy of downscaled products at a 9-km scale. Results revealed that the downscaled 9-km soil moisture products generally outperform the 36-km product for most in situ datasets. The linear regression algorithm using the thermal sensing based evaporative stress index (ESI) had the best agreement with the in situ measurements from networks in the contiguous United States according to the site-by-site comparison. In addition, the inertial thermal linear regression method demonstrated the lowest unbiased RMSE when comparing to the matched-up in situ datasets as well. In general, this method is promising for operational generation of fine-resolution soil moisture data product.

Mapping paddy rice with the random forest algorithm using MODIS and SMAP time series

Abstract Rice production is very important for national food security in China. Time series vegetation indices and phenology-based algorithms have been utilized to map paddy rice fields by identifying the flooding and seedling transplanting phases from multitemporal moderate-resolution (500 m to 1 km) images. Satellite imagery in other electromagnetic spectrum such as microwave region provides supplementary information about land surface characteristics such as soil moisture, which may improve the accuracy of rice mapping. In this study, we developed a method for identifying paddy rice at the regional scale based on the potential relationship between soil moisture and crop growth. Random forest classification optimized by the coordinate descent algorithm was used to generate a 500 m resolution paddy rice map with time series downscaled soil moisture based on Soil Moisture Active Passive (SMAP) images and Moderate Resolution Imaging Spectroradiometer (MODIS) images of the northern Songnen Plain in Northeast China-one of the major paddy rice cultivation regions in China. A combination of the Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), Phenological Parameters (PH), and Soil Moisture (SM) was used to identify the rice fields during the flooding/transplanting and ripening phases. Two scenarios (EVI+LSWI+PH and EVI+LSWI+PH+SM as the input parameters) were developed to extract the paddy rice from the images. A comparison of the two identification scenarios indicated that the addition of the soil moisture to the combination achieved higher identification accuracy, especially at the junction of the different crop phases. The proposed method could be beneficial to researchers attempting to improve the accuracy of paddy rice identification at regional scale.

Integration of Microwave and Optical/Infrared Derived Datasets from Multi-Satellite Products for Drought Monitoring

Drought is among the most common natural disasters in North China. In order to monitor the drought of the typically arid areas in North China, this study proposes an innovative multi-source remote sensing drought index called the improved Temperature–Vegetation–Soil Moisture Dryness Index (iTVMDI), which is based on passive microwave remote sensing data from the FengYun (FY)3B-Microwave Radiation Imager (MWRI) and optical and infrared data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and takes the Shandong Province of China as the research area. The iTVMDI integrated the advantages of microwave and optical remote sensing data to improve the original Temperature–Vegetation–Soil Moisture Dryness Index (TVMDI) model, and was constructed based on the Modified Soil-Adjusted Vegetation Index (MSAVI), land surface temperature (LST), and downscaled soil moisture (SM) as the three-dimensional axes. The global land data assimilation system (GLDAS) SM, meteorological data and surface water were used to evaluate and verify the monitoring results. The results showed that iTVMDI had a higher negative correlation with GLDAS SM (R = −0.73) than TVMDI (R = −0.55). Additionally, the iTVMDI was well correlated with both precipitation and surface water, with mean correlation coefficients (R) of 0.65 and 0.81, respectively. Overall, the accuracy of drought estimation can be significantly improved by using multi-source satellite data to measure the required surface variables, and the iTVMDI is an effective method for monitoring the spatial and temporal variations of drought.

Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach

Land surface temperature (LST) is among the most important variables in monitoring land surface processes. LST is often retrieved from thermal infrared remote sensing data, which have a tradeoff between the spatial and temporal resolutions and are spatially incomplete due to cloud contamination. Land surface model (LSM) output can reflect LST under all-weather conditions, but the spatial resolution is relatively coarse. In this study, a two-step LST data fusion framework was proposed for generating MODIS-like LST (at the satellite overpass time for each day) at a 1 km spatial resolution under all-weather conditions. First, MODIS LST on clear days (i.e., all MODIS LST pixels are cloud-free) for a given study region (e.g., 80 km × 80 km) and China Land Data Assimilation System (CLDAS) LST at a spatial resolution of ~7 km × 7 km were fused using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). Second, systematic biases of the fused LST estimates were corrected by MODIS LST for clear pixels on cloudy days. Results indicate that the fused LST after bias correction (fused LSTcorr) under all-weather conditions is highly consistent with in situ LST measurements made at three sites with different land cover types in the north of China, in terms of mean absolute errors of 2.20–3.08 K, root mean square errors of 2.77–3.96 K, and coefficients of determination of 0.93–0.95. The accuracy of these results is comparable to that of the MODIS LST retrievals at all testing sites. In addition, the fused LSTcorr can well reflect spatial heterogeneity and temporal variability in LST under all-weather conditions, and there is no significant difference in the accuracy of the fused LSTcorr between the clear and cloudy pixels on cloudy days. The developed approach maximizes the potential of quality MODIS LST retrievals and LSM LST output, and generates LST under all-weather conditions without using ancillary remote sensing and in situ data. The generated MODIS-like LST data under all-weather conditions are valuable in soil moisture downscaling and evapotranspiration estimation for better water resources management.

Enhanced hydrologic simulation may not improve downscaled soil moisture patterns without improved soil characterization

AbstractFine‐resolution soil moisture maps (10–30 m grid cells) are useful for many applications, including agricultural production and off‐road vehicle mobility. Fine‐resolution maps of soil moisture can be produced by downscaling coarse‐resolution soil moisture data from remote sensing. Soil moisture downscaling has been conducted by modeling key hydrologic processes in the soil, but current downscaling methods neglect two key aspects of soil hydrology: surface runoff and residual water content. The objective of this study is to understand how these hydrologic considerations affect spatial patterns of soil moisture and whether their inclusion improves downscaling performance. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) downscaling model is used to address these questions. Surface runoff is introduced to the model by implementing an infiltration capacity based on the saturated hydraulic conductivity. Residual water content is included by assuming that all hydrologic processes cease when soil moisture is at or below the residual water content. The pre‐existing EMT+VS model is compared with the new models using data from northeastern Colorado. The results show that both model additions introduce more complex dependence on the spatial variability of soil texture, which is difficult to estimate accurately. Including surface runoff provides only a small improvement in model performance, and residual water content shows almost no effect on the performance in this case. Thus, the simplifying assumptions of the pre‐existing model provide an appropriate level of complexity, given the uncertainties of the spatial input data.

Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates

Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Recent satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. This study presents a merging framework that combines a hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). The framework was demonstrated for soil moisture by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission. The brightness temperature from the HydroBlocks-RTM and SMAP L3 were merged to obtain updated 30-m soil moisture. We validated the downscaled soil moisture estimates at four experimental watersheds with dense in-situ soil moisture networks in the United States and obtained overall high correlations (> 0.81) and good mean KGE score (0.56). The downscaled product captures the spatial and temporal soil moisture dynamics better than SMAP L3 and L4 product alone at both field and watershed scales. Our results highlight the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications.

Stochastic analysis and probabilistic downscaling of soil moisture in small catchments

Many applications require fine-resolution soil-moisture maps that exhibit realistic statistical properties (e.g., spatial variance and correlation). Existing downscaling models can estimate soil moisture based on its dependence on topography, vegetation, and soil characteristics. However, observed soil-moisture patterns also contain stochastic variations around such estimates. The objectives of this research are to perform a geostatistical analysis of the stochastic variations in soil moisture and to develop a downscaling model that reproduces the observed statistical features while including the dependence on topography, vegetation, and soil properties. Extensive soil-moisture observations from two catchments (8.0 and 10.5 ha) are used for the geostatistical analysis and model development, and two other catchments (6.0 and 60 ha) are used for model evaluation. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT + VS) model is used to estimate soil moisture, and the difference between the point measurements and the EMT + VS estimates are considered to be the stochastic variations. The stochastic variations contain a temporally stable pattern along with temporally unstable patterns. All of these patterns include spatially correlated and uncorrelated variations. Moreover, the spatial variance of the stochastic patterns increases in absolute terms with the spatial-average moisture content. The EMT + VS model can reproduce the observed statistical features if it is generalized to include stochastic deviations from equilibrium soil moisture, variations in porosity, and measurement errors.

Comprehensive analysis of alternative downscaled soil moisture products

Recent advances in L-band passive microwave remote sensing provide an unprecedented opportunity to monitor soil moisture at ~40 km spatial resolution around the globe. Nevertheless, retrieval of the accurate high spatial resolution soil moisture maps that are required to satisfy hydro-meteorological and agricultural applications remains a challenge. Currently, a variety of downscaling, otherwise known as disaggregation techniques have been proposed as the solution to disaggregate the coarse passive microwave soil moisture into high-to-medium resolutions. These techniques take advantage of the strengths of both the passive microwave observations of soil moisture having low spatial resolution and the spatially detailed information on land surface features that either influence or represent soil moisture variability. However, such techniques have typically been developed and tested individually under differing weather and climate conditions, meaning that there is no clear guidance on which technique performs the best. Consequently, this paper presents a quantitative assessment of the existing radar-, optical-, radiometer-, and oversampling-based downscaling techniques using a singular extensive data set collected specifically for that purpose, being the Soil Moisture Active Passive Experiment (SMAPEx)-4 and -5 airborne field campaigns, and the OzNet in situ stations, to determine the relative strengths and weaknesses of their performances. The oversampling-based soil moisture product best captured the temporal and spatial variability of the reference soil moisture overall, though the radar-based products had a better temporal agreement with airborne soil moisture during the short SMAPEx-4 period. Moreover, the difference between temporal analysis of products against in situ and airborne soil moisture reference data sets pointed to the fact that relying on in situ measurements alone is not appropriate for validation of spatially enhanced soil moisture maps.

Downscaling of SMAP Soil Moisture in the Lower Mekong River Basin

In large river basins where in situ data were limited or absent, satellite-based soil moisture estimates can be used to supplement ground measurements for land and water resource management solutions. Consistent soil moisture estimation can aid in monitoring droughts, forecasting floods, monitoring crop productivity, and assisting weather forecasting. Satellite-based soil moisture estimates are readily available at the global scale but are provided at spatial scales that are relatively coarse for many hydrological modeling and decision-making purposes. Soil moisture data are obtained from NASA’s soil moisture active passive (SMAP) mission radiometer as an interpolated product at 9 km gridded resolution. This study implements a soil moisture downscaling algorithm that was developed based on the relationship between daily temperature change and average soil moisture under varying vegetation conditions. It applies a look-up table using global land data assimilation system (GLDAS) soil moisture and surface temperature data, and advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST). MODIS LST and NDVI are used to obtain downscaled soil moisture estimates. These estimates are then used to enhance the spatial resolution of soil moisture estimates from SMAP 9 km to 1 km. Soil moisture estimates at 1 km resolution are able to provide detailed information on the spatial distribution and pattern over the regions being analyzed. Higher resolution soil moisture data are needed for practical applications and modelling in large watersheds with limited in situ data, like in the Lower Mekong River Basin (LMB) in Southeast Asia. The 1 km soil moisture estimates can be applied directly to improve flood prediction and assessment as well as drought monitoring and agricultural productivity predictions for large river basins.