Abstract

Soil moisture is an essential variable in the land surface ecosystem, which plays an important role in agricultural drought monitoring, crop status monitoring, and crop yield prediction. High-resolution radar data can be combined with optical remote-sensing data to provide a new approach to estimate high-resolution soil moisture over vegetated areas. In this paper, the Sentinel-1A data and the Moderate Resolution Imaging Spectroradiometer (MODIS) data are combined to retrieve soil moisture over agricultural fields. The advanced integral equation model (AIEM) is utilized to calculate the scattering contribution of the bare soil surface. The water cloud model (WCM) is applied to model the backscattering coefficient of vegetated areas, which use two vegetation parameters to parameterize the scattering and attenuation properties of vegetation. Four different vegetation parameters extracted from MODIS products are combined to predict the scattering contribution of vegetation, including the leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI). The effective roughness parameters are chosen to parameterize the AIEM. The Sentinel-1A and MODIS data in 2017 are used to calibrate the coupled model, and the datasets in 2018 are used for soil moisture estimation. The calibration results indicate that the Sentinel-1A backscattering coefficient can be accurately predicted by the coupled model with the Pearson correlation coefficient (R) ranging from 0.58 to 0.81 and a root mean square error (RMSE) ranging from 0.996 to 1.401 dB. The modeled results show that the retrieved soil moisture can capture the seasonal dynamics of soil moisture with R ranging from 0.74 to 0.81. With the different vegetation parameter combinations used for parameterizing the scattering contribution of the canopy, the importance of suitable vegetation parameters for describing the scattering and attenuation properties of vegetation is confirmed. The LAI is recommended to characterize the scattering properties. There is no obvious clue for selecting vegetation descriptors to characterize the attenuation properties of vegetation. These promising results confirm the feasibility and validity of the coupled model for soil moisture retrieval from the Sentinel-1A and MODIS data.

Highlights

  • Soil moisture plays an important role in the water and energy cycles of the land surface ecosystem and is considered as an important variable in Earth Science [1,2]

  • The roughness parameters needed by the advanced integral equation model (AIEM) are parameterized by the effective roughness parameters

  • The root mean square error (RMSE) between the retrieved soil moisture and the in-situ measurements ranges from 0.069 to 0.092 m3 /m3, and the R ranges from 0.74 to 0.81. These results indicate that the trend of retrieved soil moisture is consistent with the in-situ soil moisture, and the seasonal dynamics of soil moisture was captured

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Summary

Introduction

Soil moisture plays an important role in the water and energy cycles of the land surface ecosystem and is considered as an important variable in Earth Science [1,2]. Due to the dynamic variability of soil moisture in time and space, dense spatial and temporal measurements at a regional and global scale remain difficult. Remote sensing provides an operational method for soil moisture monitoring and estimation. Due to the high sensitivity to soil moisture and the ability of all-time and all-weather observation, microwave remote sensing has been widely applied. Water 2020, 12, 1726 in soil moisture retrieval over the bare or vegetated land surface [5,6]. This includes the synthetic aperture radar (SAR), which, due to its high spatial resolution, can be potentially used for providing soil moisture products with high spatiotemporal resolution

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