Surface Soil Moisture Retrieval Using the L-Band Synthetic Aperture Radar Onboard the Soil Moisture Active-Passive Satellite and Evaluation at Core Validation Sites

Theory of Microwave Remote Sensing of Vegetation Effects, SoOp and Rough Soil Surface Backscattering

In this paper, we provide updates on our recent work on the theory of microwave remote sensing for applications in remote sensing of soil moisture and snow water equivalent (SWE). The three topics are the following. (i) For the effects of forests and vegetation, we developed the hybrid method of NMM3D full-wave simulations over the vegetation field and forest canopies. In the hybrid method, we combined the use of commercial off-the-shelf software and wave multiple scattering theory (W-MST). The results showed much larger transmission than classical radiative transfer theory. (ii) In signals of opportunity at L-band and P-band, which are radar bistatic scattering in the vicinity of the specular direction, we developed the Analytical Kirchhoff solution (AKS) and Numerical Kirchhoff approach (NKA) in the calculations of coherent waves and incoherent waves. We also took into account of the effects of topographical elevations and slopes which have strong influences. (iii) In rough surface radar backscattering, we used the volume integral equation approach for NMM3D full-wave simulations for soil surfaces with kh up to 15. The simulations were calculated for the X-band and Ku-band and the results showed saturation effects. The simulation results can be applied to microwave remote sensing of SWE at these two frequencies.

GNSS-R Soil Moisture Retrieval for Flat Vegetated Surfaces Using a Physics-Based Bistatic Scattering Model and Hybrid Global/Local Optimization

This paper presents a soil moisture retrieval scheme from Cyclone Global Navigation Satellite System (CYGNSS) delay-Doppler maps (DDMs) over land. The proposed inversion method consists of a hybrid global and local optimization method and a physics-based bistatic scattering forward model. The forward model was developed for bare-to-densely vegetated terrains, and it predicts the circularly polarized bistatic radar cross section DDM of the land surface. This method was tested on both simulated DDMs and CYGNSS DDMs over the Soil Moisture Active Passive (SMAP) Yanco core validation site in Australia. About 250 CYGNSS DDMs from 2019 and 2020 over the Yanco site were used for validation. The simulated DDMs were for grassland and forest vegetation types. The vegetation type of the Yanco validation site was grassland. The vegetation water content (VWC) was 0.19 kgm−2 and 4.89 kgm−2 for the grassland and forest terrains, respectively. For the case when the surface roughness is known to the algorithm, the unbiased root mean square error (ubRMSE) of soil moisture estimates was less than 0.03 m3m−3 while it was approximately 0.06 m3m−3 and 0.09 m3m−3 for the validation results from 2019 and 2020, respectively. The retrieval algorithm generally had enhanced performance for smaller values of soil moisture. For the case when both the soil moisture and surface roughness are unknown to the algorithm and only a single DDM is used for retrieval, the validation results showed an expected reduced performance, with an an ubRMSE of less than 0.12 m3m−3.

Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation

Soil moisture is an essential parameter for a better understanding of water processes in the soil–vegetation–atmosphere continuum. Satellite synthetic aperture radar (SAR) is well suited for monitoring water content at fine spatial resolutions on the order of 1 km or higher. Several methodologies are often considered in the inversion of SAR signals: machine learning techniques, such as neural networks, empirical models and change detection methods. In this study, we propose two hybrid methodologies by improving a change detection approach with vegetation consideration or by combining a change detection approach together with a neural network algorithm. The methodology is based on Sentinel-1 and Sentinel-2 data with the use of numerous metrics, including vertical–vertical (VV) and vertical–horizontal (VH) polarization radar signals, the classical change detection surface soil moisture (SSM) index ISSM, radar incidence angle, normalized difference vegetation index (NDVI) optical index, and the VH/VV ratio. Those approaches are tested using in situ data from the ISMN (International Soil Moisture Network) with observations covering different climatic contexts. The results show an improvement in soil moisture estimations using the hybrid algorithms, in particular the change detection with the neural network one, for which the correlation increases by 54% and 33% with respect to that of the neural network or change detection alone, respectively.

High Spatial-Temporal Resolution Estimation of Ground-Based Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) Soil Moisture Using the Genetic Algorithm Back Propagation (GA-BP) Neural Network

Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays a vital role in climate change, crop growth, and environmental disaster event monitoring, and it is important to monitor soil moisture continuously. Recently, Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technology has become essential for monitoring soil moisture. However, the sparse distribution of GNSS-IR soil moisture sites has hindered the application of soil moisture products. In this paper, we propose a multi-data fusion soil moisture inversion algorithm based on machine learning. The method uses the Genetic Algorithm Back-Propagation (GA-BP) neural network model, by combining GNSS-IR site data with other surface environmental parameters around the site. In turn, soil moisture is obtained by inversion, and we finally obtain a soil moisture product with a high spatial and temporal resolution of 500 m per day. The multi-surface environmental data include latitude and longitude information, rainfall, air temperature, land cover type, normalized difference vegetation index (NDVI), and four topographic factors (elevation, slope, slope direction, and shading). To maximize the spatial and temporal resolution of the GNSS-IR technique within a machine learning framework, we obtained satisfactory results with a cross-validated R-value of 0.8660 and an ubRMSE of 0.0354. This indicates that the machine learning approach learns the complex nonlinear relationships between soil moisture and the input multi-surface environmental data. The soil moisture products were analyzed compared to the contemporaneous rainfall and National Aeronautics and Space Administration (NASA)’s soil moisture products. The results show that the spatial distribution of the GA-BP inversion soil moisture products is more consistent with rainfall and NASA products, which verifies the feasibility of using this experimental model to generate 500 m per day the GA-BP inversion soil moisture products.

Sensitivity of C-Band SAR Polarimetric Variables to the Directionality of Surface Roughness Parameters

Surface roughness is an important factor in many soil moisture retrieval models. Therefore, any mischaracterization of surface roughness parameters (root mean square height, RMSH, and correlation length, ʅ) may result in unreliable predictions and soil moisture estimations. In many environments, but particularly in agricultural settings, surface roughness parameters may show different behaviours with respect to the orientation or azimuth. Consequently, the relationship between SAR polarimetric variables and surface roughness parameters may vary depending on measurement orientation. Generally, roughness obtained for many SAR-based studies is estimated using pin profilers that may, or may not, be collected with careful attention to orientation to the satellite look angle. In this study, we characterized surface roughness parameters in multi-azimuth mode using a terrestrial laser scanner (TLS). We characterized the surface roughness parameters in different orientations and then examined the sensitivity between polarimetric variables and surface roughness parameters; further, we compared these results to roughness profiles obtained using traditional pin profilers. The results showed that the polarimetric variables were more sensitive to the surface roughness parameters at higher incidence angles (θ). Moreover, when surface roughness measurements were conducted at the look angle of RADARSAT-2, more significant correlations were observed between polarimetric variables and surface roughness parameters. Our results also indicated that TLS can represent more reliable results than pin profiler in the measurement of the surface roughness parameters.

Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors

This paper discusses the potential of L-band Advanced Land Observing Satellite-2 (ALOS-2) and C-band Sentinel-1 radar data for retrieving soil parameters over cereal fields. For this purpose, multi-incidence, multi-polarization and dual-frequency satellite data were acquired simultaneously with in situ measurements collected over a semiarid area, the Merguellil Plain (central Tunisia). The L- and C-band signal sensitivity to soil roughness, moisture and vegetation was investigated. High correlation coefficients were observed between the radar signals and soil roughness values for all processed multi-configurations of ALOS-2 and Sentinel-1 data. The sensitivity of SAR (Synthetic Aperture Radar) data to soil moisture was investigated for three classes of the normalized difference vegetation index (NDVI) (low vegetation cover, medium cover and dense cover), illustrating a decreasing sensitivity with increasing NDVI values. The highest sensitivity to soil moisture under the dense cover class is observed in L-band data. For various vegetation properties (leaf area index (LAI), height of vegetation cover (H) and vegetation water content (VWC)), a strong correlation is observed with the ALOS-2 radar signals (in HH(Horizontal-Horizontal) and HV(Horizontal-Vertical) polarizations). Different empirical models that link radar signals (in the L- and C-bands) to soil moisture and roughness parameters, as well as the semi-empirical Dubois modified model (Dubois-B) and the modified integral equation model (IEM-B), over bare soils are proposed for all polarizations. The results reveal that IEM-B performed a better accuracy comparing to Dubois-B. This analysis is also proposed for covered surfaces using different options provided by the water cloud model (WCM) (with and without the soil–vegetation interaction scattering term) coupled with the best accuracy bare soil backscattering models: IEM-B for co-polarization and empirical models for the entire dataset. Based on the validated backscattering models, different options of coupled models are tested for soil moisture inversion. The integration of a soil–vegetation interaction component in the WCM illustrates a considerable contribution to soil moisture precision in the HV polarization mode in the L-band frequency and a neglected effect on C-band data inversion.

Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy

The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). The proposed methodology is based on the change detection technique, applied to a series of measurements over a three-year period (2015 to 2018). The algorithm described here as “Soil Moisture Estimations from the Synergy of Sentinel-1 and optical sensors (SMES)” proposes different options, allowing information from vegetation densities and seasonal conditions to be taken into account. The output from this algorithm is a moisture index ranging between 0 and 1, with 0 corresponding to the driest soils and 1 to the wettest soils. This methodology has been tested at different test sites (South of France, Central Tunisia, Western Benin and Southwestern Niger), characterized by a wide range of different climatic conditions. The resulting surface soil moisture estimations are compared with in situ measurements and already existing satellite-derived soil moisture ASCAT (Advanced SCATterometer) products. They are found to be well correlated, for the African regions in particular (RMSE below 6 vol.%). This outcome indicates that the proposed algorithm can be used with confidence to estimate the surface soil moisture of a wide range of climatically different sites.

High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks

This paper presents a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The goal of the proposed novel method is to advance CYGNSS-based SM estimations, exploiting the spatio-temporal resolution of the GNSS reflectometry (GNSS-R) signals to its highest potential within a machine learning framework. The methodology employs a fully connected Artificial Neural Network (ANN) regression model to perform SM predictions through learning the nonlinear relations of SM and other land geophysical parameters to the CYGNSS observables. In situ SM measurements from several International SM Network (ISMN) sites are used as reference labels; CYGNSS incidence angles, derived reflectivity and trailing edge slope (TES) values, as well as ancillary data, are exploited as input features for training and validation of the ANN model. In particular, the utilized ancillary data consist of normalized difference vegetation index (NDVI), vegetation water content (VWC), terrain elevation, terrain slope, and h-parameter (surface roughness). Land cover classification and inland water body masks are also used for the intermediate derivations and quality control purposes. The proposed algorithm assumes uniform SM over a 0.0833 ∘ × 0.0833 ∘ (approximately 9 km × 9 km around the equator) lat/lon grid for any CYGNSS observation that falls within this window. The proposed technique is capable of generating sub-daily and high-resolution SM predictions as it does not rely on time-series or spatial averaging of the CYGNSS observations. Once trained on the data from ISMN sites, the model is independent from other SM sources for retrieval. The estimation results obtained over unseen test data are promising: SM predictions with an unbiased root mean squared error of 0.0544 cm 3 /cm 3 and Pearson correlation coefficient of 0.9009 are reported for 2017 and 2018.

Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics

The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation’s contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation.

Surface Parameters Retrieval from Fully Bistatic Radar Scattering Data

Fully bistatic radar scattering from rough surfaces is of vital importance in terrain remote sensing, but results in bulky data volume. The scattering is dependent on physical parameters of the media and is controlled by the radar observation geometry. Together, the two sets of parameters determine the scattering patterns in a bistatic plane confined by incident and polar angles in both incident and scattering directions. For radar remote sensing, it is desirable to infer surface parameters of interest, with satisfactory accuracy, from large volumes of measured data sets. This is essentially a task of data mining. In this paper, we present model-generated bistatic radar scattering data, followed by a sensitivity analysis, to identify a suitable configuration in terms of parameter inversion from fully bistatic measurements by a Kalman filter-trained dynamic learning neural network (DLNN). Results indicate that with bistatic observation, superior retrieval performance (as compared to backscattering observation) can be readily achieved.