Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data

Chunfeng Ma,Matthew F Mccabe,Xin Li

doi:10.3390/rs12142303

Abstract

Estimating soil moisture based on synthetic aperture radar (SAR) data remains challenging due to the influences of vegetation and surface roughness. Here we present an algorithm that simultaneously retrieves soil moisture, surface roughness and vegetation water content by jointly using high-resolution Sentinel-1 SAR and Sentinel-2 multispectral imagery, with an application directed towards the provision of information at the precision agricultural scale. Sentinel-2-derived vegetation water indices are investigated and used to quantify the backscatter resulting from the vegetation canopy. The proposed algorithm then inverts the water cloud model to simultaneously estimate soil moisture and surface roughness by minimizing a cost function constructed by model simulations and SAR observations. To examine the performance of VV- and VH-polarized backscatters on soil moisture retrievals, three retrieval schemes are explored: a single channel algorithm using VV (SCA-VV) and VH (SCA-VH) polarizations and a dual channel algorithm using both VV and VH polarizations (DCA-VVVH). An evaluation of the approach using a combination of a cosmic-ray soil moisture observing system (COSMOS) and Soil Climate Analysis Network measurements over Nebraska shows that the SCA-VV scheme yields good agreement at both the COSMOS footprint and single-site scales. The features of the algorithms that have the most impact on the retrieval accuracy include the vegetation water content estimation scheme, parameters of the water cloud model and the specification of initial ranges of soil moisture and roughness, all of which are comprehensively analyzed and discussed. Through careful consideration and selection of these factors, we demonstrate that the proposed SCA-VV approach can provide reasonable soil moisture retrievals, with RMSE ranging from 0.039 to 0.078 m3/m3 and R2 ranging from 0.472 to 0.665, highlighting the utility of SAR for application at the precision agricultural scale.

Highlights

Soil moisture plays a central role in both climate and hydrological systems [1,2,3] and represents a key link between the processes governing surface and atmosphere exchange
In addition to estimating the valid range of root mean of the surface height (RMSH) with physical and empirical backscattering models, such as the Advanced Integral Equation Model (AIEM) [19] and Oh model [35], we investigate ranges of RMSH measured from previous experiments in other regions, such as SMAPVEX12 [87] and SMAPVEX16-MB [88]
A simple regression analysis is first conducted to examine the response of Sentinel-1 at co- and cross-polarization to the variation of surface parameters, including soil moisture, RMSH and vegetation water content (VWC), to ensure that the parameters are retrievable from Sentinel-1 observations

Summary

Introduction

Soil moisture plays a central role in both climate and hydrological systems [1,2,3] and represents a key link between the processes governing surface and atmosphere exchange. The spatial distribution and temporal evolution of soil moisture can vary significantly [4] and as a consequence, traditional point-based measurements often provide limited insight into spatiotemporal patterns of behavior and response. Remote sensing data provide an opportunity for characterizing the spatial and temporal structure of soil moisture dynamics across a range of scales [5] but can be limited in terms of providing high-resolution detail. Among various remote sensing-based measurement approaches [6,7,8], Synthetic Aperture Radar (SAR) data have shown much potential in providing high-resolution soil moisture estimates at both the watershed and field scales [9,10]. Over the last few decades, SAR-based soil moisture estimation has received considerable research attention and seen much progress in terms of the development of new retrieval approaches, including the global optimization algorithm [13], Bayesian posterior estimation [14,15] and even machine learning techniques [16]

Results

Discussion

Conclusion