Soil Moisture Mapping Research Articles

A web-based system for satellite-based high-resolution global soil moisture maps

Nowadays, a wide range of applications require near-real-time Surface Soil Moisture (SSM) data at high spatial resolution. However, operational passive microwave systems like SMOS and SMAP can only acquire such information at a relatively coarser resolution. Therefore, several downscaling algorithms have been developed to address this issue and provide SSM maps at a finer spatial scale. Users may, however, find it difficult to implement the downscaling algorithm due to the complexity of integrating various data sources. Disaggregation based on Physical and Theoretical scale Change (DisPATCh) is one of the algorithms that is widely accepted to downscale passive microwave SSM observations. But the complexity of modeling, the variety of data sources and formats of input data make it very difficult for users to implement the algorithm. Thus, we developed a Satellite-based Hydrological Monitoring System (SHMS), which facilitates this gap through the implementation of the DisPATCh algorithm to generate large-scale SSM maps with high resolution, which is achieved by combining SMAP and MODIS products. The System Usability Scale (SUS) method was used to evaluate the system's strengths and weaknesses. The SUS evaluation results show that 74.75% of SHMS users are satisfied with the system's performance.

High-resolution soil moisture mapping using PSO-based optimized cerebellar model articulation controller (CMAC)

A good knowledge in eco-hydrological processes requires significant understanding of geospatial distribution of soil moisture (SM). However, SM monitoring remains challenging due to its large spatial variability and its dynamic time response. This study was performed to assess the performance of a particle swarm optimization (PSO)-based optimized Cerebellar Model Articulation Controller (CMAC) in generating high-resolution surface SM estimates using sentinel-2 imagery over a Mediterranean agro-ecosystem. Furthermore, the results were compared with those of PSO-based optimized group method of data handling (GMDH) as a more common data-driven method. Two different modeling approaches i.e. modeling in homogenous clusters (local approach) and modeling in entire area as an entity (global approach) were examined. Candidate predictors namely sentinel-2 spectral bands, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), digital elevation model (DEM), slope and aspect were used as the input variables to estimate SM. An intensive field survey had been done to gather in-situ SM data using a time-domain reflectometer (TDR). K-fold validation based on in-situ SM measurements demonstrated the reasonability of the SM estimation of the proposed methodology. Detecting homogeneous areas was done using genetic and particle swarm optimization algorithms. Synthesized SM product of PSO-GMDH showed a mean Normalized Root-Mean-Square Error (NRMSE) of 13.6 to 8.91 for global and local approaches in the test phase. PSO-CMAC method with an average NRMSE of 12.47 to 8.72 for global and local approaches shows the highest accuracy and outperforms the PSO-GMDH method at both local and global approaches. Overall, results revealed that clustering study area prior to running machine learning (ML) models coupled with optical satellite imagery and geophysical properties, boosts their predictive performance and can lead to more accurate mapping of SM with more heterogeneity. The results also showed that the global approach had a moderate performance in capturing the SM heterogeneity.

Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices

Abstract. Soil moisture has important implications for drought and flooding forecasting, forest fire prediction and water supply management. However, mapping soil moisture has remained a scientific challenge due to forest canopy cover and small-scale variations in soil moisture conditions. When accurately scaled, terrain indices constitute a good candidate for modelling the spatial variation of soil moisture conditions in many landscapes. In this study, we evaluated seven different terrain indices at varying digital elevation model (DEM) resolutions and user-defined thresholds as well as two available soil moisture maps, using an extensive field dataset (398 plots) of soil moisture conditions registered in five classes from a survey covering a (68 km2) boreal landscape. We found that the variation in soil moisture conditions could be explained by terrain indices, and the best predictors within the studied landscape were the depth to water index (DTW) and a machine-learning-generated map. Furthermore, this study showed a large difference between terrain indices in the effects of changing DEM resolution and user-defined thresholds, which severely affected the performance of the predictions. For example, the commonly used topographic wetness index (TWI) performed best on a resolution of 16 m, while TWI calculated on DEM resolutions higher than 4 m gave inaccurate results. In contrast, depth to water (DTW) and elevation above stream (EAS) were more stable and performed best on 1–2 m DEM resolution. None of the terrain indices performed best on the highest DEM resolution of 0.5 m. In addition, this study highlights the challenges caused by heterogeneous soil types within the study area and shows the need of local knowledge when interpreting the modelled results. The results from this study clearly demonstrate that when using terrain indices to represent soil moisture conditions, modelled results need to be validated, as selecting an unsuitable DEM resolution or user-defined threshold can give ambiguous and even incorrect results.

Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products

SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.

From point to field scale - indirect monitoring of soil moisture variations at the DWD test site in Falkenberg

Information regarding the spatial distribution of soil water content is key in many disciplines and applications including soil and atmospheric sciences, hydrology, and agricultural engineering. Thus, within the past decades various experimental methods and strategies have been developed to map spatial variations in soil moisture distribution and to monitor temporal changes. Our study examines the combination of electrical resistivity mapping and point observations of soil moisture to infer the spatial and the temporal variability of soil moisture. Over a period of around two years, we performed field measurements on six days to collect repeated electrical resistivity mapping data for a nine-hectare test site south-east of Berlin, Germany. Permanently installed TDR probes, temporary TDR measurements within permanently installed tubes, and gravimetric measurements using soil samples provided soil moisture data at various selected points. In addition, soil analysis and classification results are available for 132 regularly distributed positions up to depths of 1.2 m. We compare and link three-dimensional resistivity models obtained via data inversion to soil composition and soil moisture as provided by our point data. Both the soil samples and the resistivity models indicate a two-layer medium characterized by a sandy top layer with varying thickness and a loamy bottom soil. For all six field campaigns, we observe similar resistivity patterns reflecting the temporally stable influence of soil texture. While the overall patterns are stable, the range of resistivity values changes with soil moisture. Finally, to estimate spatial models of soil moisture, we link our soil moisture and resistivity data using empirical petrophysical models relying on a second order polynomial function. We observe a mean prediction error for soil moisture of +/- 0.034 m3 • m−3 using all observation points while we notice that point-specific models further reduce the error. Thus, we conclude that our experimental and data analysis strategies represent a reliable approach to establish site-specific models and to estimate three-dimensional moisture distribution including temporal variations.

Development of Soil Moisture Content and Soil Matric Suction Model Based on Field Instrumentation and Electrical Resistivity Imaging (ERI) for Highway Slopes Constructed on High Expansive Clay Soil

In highway slopes (HWS) constructed on high expansive clay soil (HECS), in situ moisture variation is an environmentally driven variable that can significantly impact the safety of the constructed soil. Electrical resistivity imaging (ERI) is a non-destructive method with a considerable potential for subsurface soil moisture mapping, which can be correlated with volumetric soil moisture content (VSMC) and soil matric suction (SMS) of HECS to remarkably enhance the evaluation of the performance of the HWS. However, limited datasets are available to evaluate the accuracy and feasibility of the available correlative field-based models for the HECS under various field conditions. The objective of the current study is to develop a field-based model of VSMC and SMS using real-time field monitoring and ERI data. Six HWS located in the Jackson metro area in Mississippi (MS), USA were considered as reference slopes in this study. Comprehensive field instrumentation was executed at the six HWS to monitor the VSMC, SMS and rainfall intensity. The sensors were installed at the crest, middle and toe of the slope. The 2D ERI test was conducted using a dipole–dipole array with multiple electrodes at 5 ft (1.5 m) spacing. The ERI survey was conducted at the crest and middle of the six HWS to image the continuous soil subsurface profile in terms of moisture variation. The developed models indicated a good agreement between instrumented and ERI data. The developed models will facilitate the estimation of VSMC and SMS variations and aid in performance monitoring of the HWS built on HECS such as Yazoo clay.

Local Scale (3-m) Soil Moisture Mapping Using SMAP and Planet SuperDove

A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10 s of kilometers. Hence, a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine-learning (ML), and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing absolute bias and RMSE by ~0.01 cm3/cm3 over the original SMAP data in relation to in situ soil moisture measurements for the Australian Yanco region while preserving the high sampling frequency (1–3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses.

Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images

Remote sensing tools have been extensively used for large-scale soil moisture (SM) mapping in recent years, using Landsat satellite images. Rainfall, soil clay percentage, and the standardized precipitation index play key roles in determining the moisture content of crop fields. The objective of this study was to (i) calculate and determine the effectiveness of moisture-related indices in predicting surface SM, (ii) predict surface SM from satellite images using the Optical Trapezoid Model (OPTRAM), and (iii) evaluate if the OPTRAM predictions can be improved by incorporating weather station, soil, and crop data with a random forest algorithm. The ENVI® platform was used to create moisture-related indices maps, and the Google Earth Engine (GEE) was used to prepare OPTRAM maps. The results showed a very weak relationship between the moisture-related indices and surface SM content where r2 and slopes were ˂0.10 and ˂0.20, respectively. OPTRAM SM, when compared with in situ surface moisture, showed weak relationship with regression values ˂0.2. Surface SM was then predicted using random forest regression using OPTRAM moisture values, rainfall, and the standardized precipitation index (SPI), and percent clay showed high goodness of fit (r2 = 0.69) and low root mean square error (RMSE = 0.053 m3 m−3).

The use of remote sensing indices to understand flood‐recharged soil moisture impacts on trees in semi‐arid floodplains: A review

AbstractRemote sensing indices (RSIs) are essential for detection, mapping and monitoring of floods, soil moisture and tree characteristics. An understanding of the nexus between flood‐recharged soil moisture (FRSM) and endangered biodiversity tree hotspots in semi‐arid floodplains is critical. Specifically, the influence of FRSM on biophysical characteristics of keystone tree species with social, economic, ecological and environmental functions is crucial. However, the use of RSIs to understand the link between episodic floods, FRSM and trees, as well as their respective spatiotemporal dynamics within the semi‐arid floodplains, remains largely unexplored. Hence, this study reviewed literature on the adoption of RSIs in understanding short‐term phenological and long‐term tree structural changes arising from flood‐related infiltration. The reviewed literature shows the predominance of the normalised difference water index, the land surface water index and normalised difference vegetation index for flood, FRSM and tree mapping. The review also shows the emergence of SAR‐based normalised difference flood index for understanding the impacts of FRSM on riparian trees. Although the review identified an increase in data availability, however, there are high costs of active commercial remote sensing data of superior quality and computing resources. Furthermore, available free data are incapable of penetrating canopies and have coarse spatial resolution constraining the use of RSIs to understand the impacts of FRSM on trees. In view of increasing free SAR data and cloud‐based computing resources, application of emerging deep learning algorithms offers prospects for the use of RSIs to understand the impacts of FRSM on riparian trees.

Crop-CASMA: A web geoprocessing and map service based architecture and implementation for serving soil moisture and crop vegetation condition data over U.S. Cropland

• A data service system for the selected SMAP data and its derivative data products over CONUS is developed. • All map and geoprocessing services are implemented via the OGC standard interface. • The data service system is interoperable with web and desktop clients. • Monitoring and analyzing U.S. cropland soil moisture conditions are significantly simplified. Soil moisture is an essential parameter to understand crop conditions throughout the growing season. Collecting soil moisture data by field observation is labor-intensive, especially when attempting to obtain Conterminous United States (CONUS) geographic coverage. In addition, using soil moisture for assessing current and future crop conditions is best realized by combining soil moisture estimates with concurrent observations of crop conditions. However, until recently, this capability has not been available to the public. In this paper, we present an interoperable data service application system, the Crop Condition and Soil Moisture Analytics (Crop-CASMA) system, that facilitates the retrieval, analysis, visualization, and sharing of soil moisture data for the CONUS. This system delivers a variety of satellite remote sensing based data products that are derived from Soil Moisture Active Passive (SMAP) Level-4 data and SMAP Thermal Hydraulic disaggregation of Soil Moisture (THySM) data, as well as vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. To make services interoperable and reusable, all data products are disseminated via Open Geospatial Consortium (OGC) Web Map Service (WMS) and Web Coverage Service (WCS) interface standards. Additionally, a suite of geoprocessing operations, such as geospatial statistics, time-series profile generation, PDF map production, and image composition, has been implemented in the OGC Web Processing Service (WPS) interface standard. The implementation shows the proposed web service system can significantly simplify the mapping and quantitative analysis of soil moisture and crop condition over U.S. cropland. In addition, it is interoperable with GIS software and has been successfully integrated with web-based applications.

Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping

Soil moisture is one of the core hydrological and climate variables that crucially influences water and energy budgets. The spatial resolution of available soil moisture products is generally coarser than 25 km, which limits their hydro-meteorological and eco-hydrological applications and the management of water resources at watershed and agricultural scales. A feasible solution to overcome these limitations is to downscale coarse soil moisture products with the support of higher-resolution spatial information. Although many auxiliary variables have been used for this purpose, few studies have analyzed their applicability and effectiveness in arid regions. To this end, we comprehensively evaluated four commonly used auxiliary variables, including NDVI (Normalized Difference Vegetation Index), LST (Land Surface Temperature), TVDI (Temperature Vegetation Dryness Index), and SEE (Soil Evaporative Efficiency), against ground-based soil moisture observations during the vegetation growing season in the Heihe River Basin, China. Performance metrics indicated that SEE is most sensitive (R2 ≥ 0.67) to soil moisture because it is controlled by soil evaporation limited by the available soil moisture. The similarity of spatial patterns also showed that SEE best captures soil moisture changes, with the STD (standard deviation) of the HD (Hausdorff Distance) less than 0.058 when compared with PLMR (Polarimetric L-band Multi-beam Radiometer) soil moisture products. In addition, soil moisture was mapped by RF (Random Forests) using both single auxiliary variables and 11 types of multiple auxiliary variable combinations. SEE was found to be the best auxiliary variable for scaling and mapping soil moisture with accuracy of 0.035 cm3/cm3. Among the multiple auxiliary variables, the combination of LST, NDVI, and SEE was found to best enhance the scaling and mapping accuracy of soil moisture with 0.034 cm3/cm3.

Daily soil moisture mapping at 1 km resolution based on SMAP data for desertification areas in northern China

Abstract. Land surface soil moisture (SM) plays a critical role in hydrological processes and terrestrial ecosystems in desertification areas. Passive microwave remote-sensing products such as the Soil Moisture Active Passive (SMAP) satellite have been shown to monitor surface soil water well. However, the coarse spatial resolution and lack of full coverage of these products greatly limit their application in areas undergoing desertification. In order to overcome these limitations, a combination of multiple machine learning methods, including multiple linear regression (MLR), support vector regression (SVR), artificial neural networks (ANNs), random forest (RF) and extreme gradient boosting (XGB), have been applied to downscale the 36 km SMAP SM products and produce higher-spatial-resolution SM data based on related surface variables, such as vegetation index and surface temperature. Desertification areas in northern China, which are sensitive to SM, were selected as the study area, and the downscaled SM with a resolution of 1 km on a daily scale from 2015 to 2020 was produced. The results showed a good performance compared with in situ observed SM data, with an average unbiased root mean square error value of 0.057 m3 m−3. In addition, their time series were consistent with precipitation and performed better than common gridded SM products. The data can be used to assess soil drought and provide a reference for reversing desertification in the study area. This dataset is freely available at https://doi.org/10.6084/m9.figshare.16430478.v6 (Rao et al., 2022).

Comparison of bagging, boosting and stacking algorithms for surface soil moisture mapping using optical-thermal-microwave remote sensing synergies

Soil moisture information is key to irrigation water management, drought monitoring, and yield prediction. It plays a vital role in the water cycle and energy budget between the earth’s surface and atmosphere. Hence, its monitoring is crucial for both natural and anthropogenic environments. While the current remote sensing-based global SM products available at coarser resolution (3/15 km) are unsuitable for field-level operations, the most widely used microwave remote sensing suffers from model complexities and in-situ data requirements. Weather conditions limit the alternate approaches such as optical/thermal. This study aims to map surface soil moisture (SSM) at 30 m spatial resolution in a semi-arid region by fusing optical, thermal, and microwave remote sensing data using bagging, boosting, and stacking machine learning approaches. The reference data were collected using a soil moisture meter. The covariates included radar backscatter from Sentinel-1, visible, near-infrared, short-wave infrared, land surface temperature, and spectral indices derived from Landsat 8. Boruta algorithm was used for feature selection which identified radar backscatter, modified normalized difference water index, and land surface temperature as the most critical covariates impacting the SSM. The random forest (RF) showed the highest correlation coefficient (r = 0.71), and least root mean square error (RMSE = 5.17%). The cubist model had the least mean bias error (MBE = 0.21%) during independent validation. Stacking of cubist, gradient boosting machine (GBM), and RF using elastic net (ELNET) as meta-learner further reduced the MBE (0.18%) and RMSE (5.03%) during the validation. Overall, stacking multiple machine learning models improved model prediction and can be recommended to improve the digital soil moisture mapping.

A Heterogeneous Access Metamodel for Efficient IoT Remote Sensing Observation Management: Taking Precision Agriculture as an Example

Standard remote sensing observation (RSO) access and formulization is essential to Internet of Things (IoT) data management, such as in precision agriculture (PA). Because of the heterogeneous characteristics and the petabyte data size of RSO, massive remote sensing processing in RSO management has been hampered. Here, we present a heterogeneous access metamodel for efficient RSO management (HAMERM) and verify it in PA. The structure of basic metadata components is defined. A five-tuple metadata structure based on the metaobject facility is designed. HAMERM consists of identification, platform, observation, product, and access, which represent the five aspects of RSO metadata information. In addition, the flatMap/reduceByKey algorithms and the table structure have been proposed under Sensor Web and Geographic Information Science (GIS) techniques. Intensive experiments in Guangdong Province, China are conducted to test the proposed method. Two RSO metadata formulization instances were conducted to examine the ability of sheltering the differences of multisource and heterogeneous RSO. Experiments containing data storage and data soil moisture (SM) mapping were performed. The results suggest that the HAMERM method achieved a performance 30.1 times higher than that of Hadoop and three times higher than that of Spark (stand-alone). Consequently, the proposed HAMERM can be applied to achieve efficient SM mapping within PA, which is helpful for efficient RSO management for the IoT.

Contactless Soil Moisture Mapping Using Inexpensive Frequency-Modulated Continuous Wave RADAR for Agricultural Purposes

Soil Moisture (SM) is one of the most critical factors for a crop’s growth, yield, and quality. Although Ground-Penetrating RADAR (GPR) is commonly used in satelite observation to analyze soil moisture, it is not cost-effective for agricultural applications. Automotive RADAR uses the concept of Frequency-Modulated Continuous Wave (FMCW) and is more competitive in terms of price. This paper evaluates the viability of using a cost-effective RADAR as a substitute for GPR for soil moisture content estimation. The research consisted of four experiments, and the results show that the RADAR’s output signal and the soil moisture sensor SEN0193 have a high correlation with values as high as 0.93 when the SM is below 15%. Such results show that the tested sensor (and its cost-effective working principle) are able to determine soil water content (with certain limitations) in a non-intrusive, proximal sensing manner.

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation.

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m3/m3). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.

Quasi-global machine learning-based soil moisture estimates at high spatio-temporal scales using CYGNSS and SMAP observations

Global soil moisture mapping at high spatial and temporal resolution is important for various meteorological, hydrological, and agricultural applications. Recent research shows that the land surface reflection in the forward direction of Global Navigation Satellite System (GNSS) signals at L-band can convey high-resolution land surface information, including surface soil moisture. However, these signals are often affected by complex land surface characteristics and the bistatic nature of the GNSS-Reflectometry (GNSS-R) technique, resulting in a nonlinear relationship between the signals and surface soil moisture. In this work, a machine learning (ML) approach is used to map quasi-global soil moisture using bistatic reflectance observations acquired from the recently launched Cyclone GNSS (CYGNSS) mission. Specifically, several land surface parameters are obtained from remote sensing products and integrated with Soil Moisture Active Passive (SMAP) enhanced soil moisture retrievals to facilitate daily quasi-global CYGNSS soil moisture mapping at 9 km. Based on cross-validation against SMAP data, the ML algorithm is shown to be suitable for retrieving soil moisture from CYGNSS. Median values of unbiased root-mean-square-difference for the quasi-global coverage or regions with vegetation water content less than 5 kg/m2 are 0.0395 cm3/cm3 and 0.0320 cm3/cm3, respectively. Likewise, via independent evaluation against more than 100 in-situ sites, the algorithm is shown to have an unbiased root-mean-square-error of 0.0543 cm3/cm3. CYGNSS-based retrievals contain similar spatial variability as SMAP across different seasons. Moreover, through a robust triple collocation technique, the accuracy of CYGNSS soil moisture is relatively high over moderately vegetated regions with correlations ranging from 0.4 to 0.8. Based on these validation results, we argue that derived CYGNSS soil moisture estimates can supplement current global soil moisture databases and provide more frequent retrievals at 9 km.

Field-scale soil moisture estimation using sentinel-1 GRD SAR data

Soil moisture is a critical land variable that controls the energy and mass balance in land–atmosphere interactions. Spaceborne Synthetic Aperture Radar (SAR) sensors offer an efficient way to map and monitor soil moisture because of their sensitivity towards the dielectric and geometric properties of the target. In addition, SAR acquisitions are weather-independent, providing a significant advantage over optical imaging during periods of cloud cover. However, vegetation cover makes these processes more complex and influences the interaction of SAR backscatter resulting from combined soil matrix and vegetation cover. Therefore, using SAR data, it is necessary to compensate for vegetation contribution in total backscatter while estimating soil moisture over the vegetated soil surface. This study presents a technique that utilizes a vegetation index derived from SAR data to generate high-resolution soil moisture maps. It is noteworthy that this proposed soil moisture retrieval method uses only the dual-polarimetric Ground Range Detected (GRD) SAR product, i.e., only backscatter intensities. Hence, the proposed method has a high potential for operational soil moisture monitoring globally. We validated over 34 soil moisture stations of the Texas Soil Observation Network (TxSON) using time-series Sentinel-1 SAR data. The Root Mean Square Error (RMSE) values for estimated volumetric soil moisture are within the range of 0.048 m3 m−3 to 0.055 m3 m−3 with the Pearson correlation coefficient r>0.79. The code to generate DpRVIc in Google Earth Engine is available at: https://github.com/Narayana-Rao/dual_pol_descriptors.

Saline soil moisture mapping using Sentinel-1A synthetic aperture radar data and machine learning algorithms in humid region of China's east coast

Accurate and timely information on soil moisture (SM) is crucial for better understanding the hydrological and ecological processes in humid saline regions. This study investigated the potential of using Sentinel-1A Synthetic Aperture Radar (SAR) imagery and Machine Learning Algorithms for SM mapping in China's east coast. This study used the recursive feature elimination (RFE) technique to select optimum SAR indices from the backscattering coefficients of the sampling date as well as the averaged backscattering coefficients of the neighbouring dates. The newly developed support vector regression (SVR) model provided more accurate SM estimation than that from the random forest regression (RFR) and multivariate linear regression (MLR) models. By using the leave-one-out cross-validation (LOOCV), the achieved coefficient of determination (R2), root mean square error (RMSE), and the relative RMSE (RRMSE) in SM estimation were 0.77, 1.82%, and 10.29%, respectively. The SVR model was able to map the SM effectively over a large area under the condition that the information of soil surface roughness, soil salinity, and land cover types was unknown. Results from this study demonstrate that high-temporal-resolution Sentinel-1A SAR, with its free-accessibility, offers a great source for the SVR model to provide frequent and accurate SM mapping over coastal saline regions.

Evaluating the effect of DEM resolution on performance of cartographic depth-to-water maps, for planning logging operations

Reliable and accurate soil moisture maps are needed to minimise the risk of soil disturbance during logging operations. Depth-to-water (DTW) maps extracted from digital elevation models have shown potential for identifying water flow paths and associated wet and moist areas, based on surface topography. We have examined whether DEMs from airborne LiDAR data with varying point density can improve performance of DTW maps in planning logging operations. Soil moisture content was estimated on eight sites after logging operations and compared to DTW maps created from DEMs with resolutions of 2 m, 1 m, and 0.5 m. Different threshold values for wet soil (1 m and 1.5 m depth to water) were also tested. The map performances, measured by accuracy (ACC) and Matthews Correlation Coefficient (MCC), changed slightly (79%, 81% and 82% and 0.33, 0.26 and 0.30 respectively) when DEM resolutions varied from 2 m to 1 m, and 0.5 m. The corresponding values when the DTW threshold value for wet/dry soil changed from 1 m to 1.5 m were 70%, 72%, 71% and 0.38, 0.41 and 0.39. LiDAR-based DEM resolutions of 1–2 m were found to be sufficient for extraction of DTW maps during planning of logging operations, when knowledge about soil hydrological features, associated wet and moist areas, and their connectivity is beneficial.