Next Article in Journal
High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests
Next Article in Special Issue
Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin, China
Previous Article in Journal
An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor
Previous Article in Special Issue
Evaluation of Sub-Kilometric Numerical Simulations of C-Band Radar Backscatter over the French Alps against Sentinel-1 Observations
 
 
Article

An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images

1
Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China
2
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
3
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
4
Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, China
5
Low Carbon Energy Institute, China University of Mining and Technology, Xuzhou 221008, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(3), 349; https://doi.org/10.3390/rs11030349
Received: 21 January 2019 / Accepted: 1 February 2019 / Published: 10 February 2019
(This article belongs to the Special Issue Microwave Remote Sensing for Hydrology)
As an essential ecological parameter, soil moisture is important for understanding the water exchange between the land surface and the atmosphere, especially in the Loess Plateau (China). Although Synthetic Aperture Radar (SAR) images can be used for soil moisture retrieval, it is still a challenge to mitigate the impacts of complex terrain over hilly areas. Therefore, the objective of this paper is to propose an improved approach for soil moisture estimation in gully fields based on the joint use of the Advanced Integral Equation Model (AIEM) and the Incidence Angle Correction Model (IACM) from Sentinel-1A observations. AIEM is utilized to build a simulation database of microwave backscattering coefficients from various radar parameters and surface parameters, which is the data basis for the retrieval modeling. IACM is proposed to correct the deviation between the local incidence angle at the scatterer and the radar viewing angle. The study area is located in the Loess Plateau of China, where the main land cover is mostly bare land and the terrain is complex. The Sentinel-1A SAR data in C-band with dual polarization acquired on October 19th, 2017 was adopted to extract the VV&VH polarimetric backscattering coefficients. The in situ measurements of soil moisture were collected on the same day of the SAR acquisition, for evaluating the accuracy of the SAR-derived soil moisture. The results showed that, firstly, the estimated soil moisture with volumetric content between 0% and 20% was in the majority. Subsequently, both the RMSE of estimation values (0.963%) and the standard deviation of absolute errors (0.957%) demonstrated a good accuracy of the improved approach. Moreover, the evaluation of IACM confirmed that the improved approach coupling IACM and AIEM was more efficient than employing AIEM solely. In conclusion, the proposed approach has a strong ability to estimate the soil moisture in the gully fields of the Loess Plateau from Sentinel-1A data. View Full-Text
Keywords: soil moisture estimation; AIEM; IACM; Sentinel-1A; the Loess Plateau soil moisture estimation; AIEM; IACM; Sentinel-1A; the Loess Plateau
Show Figures

Figure 1

MDPI and ACS Style

Guo, S.; Bai, X.; Chen, Y.; Zhang, S.; Hou, H.; Zhu, Q.; Du, P. An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images. Remote Sens. 2019, 11, 349. https://doi.org/10.3390/rs11030349

AMA Style

Guo S, Bai X, Chen Y, Zhang S, Hou H, Zhu Q, Du P. An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images. Remote Sensing. 2019; 11(3):349. https://doi.org/10.3390/rs11030349

Chicago/Turabian Style

Guo, Shanchuan, Xuyu Bai, Yu Chen, Shaoliang Zhang, Huping Hou, Qianlin Zhu, and Peijun Du. 2019. "An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images" Remote Sensing 11, no. 3: 349. https://doi.org/10.3390/rs11030349

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop