Abstract
Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed for the GF-3 satellite based on a backscattering coefficient simulation database. We adopted eight optical vegetation indices to determine the relationships between these indices and vegetation water content (VWC) by combining Landsat-8 data and field measurements. A backscattering coefficient database was built using an advanced integral equation model (AIEM). The effects of vegetation on backscattering coefficients were corrected using the water cloud model (WCM) to obtain the bare soil backscattering coefficient (). Then, soil moisture retrievals were obtained at HH, VV and HH+VV combination respectively by minimizing the observed bare soil backscattering coefficient () and the AIEM-simulated backscattering coefficient (). Finally, the proposed algorithm was validated in agriculture region of wheat and corn in China using ground soil moisture measurements. The results showed that the normalized difference infrared index (NDII) had the best fit with measured VWC values (R = 0.885) among the eight vegetation water indices; thus, it was adopted to correct the effects of vegetation. The proposed algorithm using GF-3 satellite data performed well in soil moisture retrieval, and the scheme combining HH and VV polarization exhibited the highest accuracy, with a root mean square error (RMSE) of 0.044 m3m−3, followed by HH polarization (RMSE = 0.049 m3m−3) and VV polarization (RMSE = 0.053 m3m−3). Therefore, the proposed algorithm has good potential to operationally estimate soil moisture from the new GF-3 satellite data.
Highlights
Surface soil moisture plays a key role in hydrologic, agronomic, and meteorological process, and controls evaporation and transpiration fluxes from bare soil and vegetated areas, respectively [1,2,3,4].In-situ measurements can provide highly accurate data on soil moisture; they are representative only over a very small spatial scale due to the strong heterogeneity of the land surface [5]
The results showed that the normalized difference infrared index (NDII) had the best fit with measured vegetation water content (VWC) values (R = 0.885) among the eight vegetation water indices; it was adopted to correct the effects of vegetation
To explore its capability for estimating soil moisture, we developed a soil moisture retrieval algorithm for agricultural fields estimating soil moisture, we developed a soil moisture retrieval algorithm for agricultural fields using GF-3 data based on the water cloud model (WCM) and a simulated backscattering coefficient database built using using GF-3 data based on the WCM and a simulated backscattering coefficient database built using
Summary
Surface soil moisture plays a key role in hydrologic, agronomic, and meteorological process, and controls evaporation and transpiration fluxes from bare soil and vegetated areas, respectively [1,2,3,4].In-situ measurements can provide highly accurate data on soil moisture; they are representative only over a very small spatial scale due to the strong heterogeneity of the land surface (e.g., vegetation, topography, and soil texture) [5]. Sensors 2018, 18, 2675 remote sensing, both active and passive, has demonstrated a great potential to provide surface soil moisture data at large scales in both time and space due to its high sensitivity to soil permittivity and because it provides all-time and all-weather coverage. Satellite mission developed by NASA was designed to make global mapping of high-resolution soil moisture using an L-band (active) radar and an L-band (passive) radiometer. Passive microwave remote sensing can observe soil moisture with higher temporal resolution (e.g., 1~3 days) [7] than active radar especially the synthetic aperture radar (SAR) (typically the temporal resolution of a single SAR system is several weeks). Passive radiometers often limits on spatial resolution (e.g., >25 km) while SAR can provide observations with much higher spatial resolution than the passive radiometers that can be used for many practical applications such as agricultural productivity estimation at a local scale [8]
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