Paper Info
Title
Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields.
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 (sigma(soil)degrees). Then, soil moisture retrievals were obtained at HH, VV and HH+VV combination respectively by minimizing the observed bare soil backscattering coefficient (sigma(soil)degrees) and the AIEM-simulated backscattering coefficient (sigma(soil-simu)degrees ). 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 m(3)m(-3), followed by HH polarization (RMSE = 0.049 m(3)m(-3)) and VV polarization (RMSE = 0.053 m(3)m(-3)). Therefore, the proposed algorithm has good potential to operationally estimate soil moisture from the new GF-3 satellite data.
Year
DOI
Venue
2018
10.3390/s18082675
SENSORS
Keywords
Field
DocType
GF-3 satellite,soil moisture,simulation database,water cloud model
Satellite,3D optical data storage,Remote sensing,Electronic engineering,Agriculture,Engineering,Water content
Journal
Volume
Issue
Citations 
18
8.0
1
PageRank 
References 
Authors
0.35
19
7
Name
Order
Citations
PageRank
Linlin Zhang1215.88
qingyan meng255.90
Shun Yao331.75
Qiao Wang49721.94
Jiangyuan Zeng55111.97
Shaohua Zhao6192.24
jianwei ma772.52