SOIL MOISTURE RETRIEVAL IN WELL COVERED FARMLAND BY RADARSAT-2 SAR DATA

被引:2
作者
Yue, Jibo [1 ,2 ]
Yang, Guijun [2 ]
Qi, Xiudong [1 ]
Wang, Yanjie [1 ]
机构
[1] Henan Polytech Univ, Surveying & Land Informat Engn, Jiaozuo 454000, Peoples R China
[2] Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
来源
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2016年
关键词
Soil moisture; Water Cloud Model; Radarsat-2; Winter wheat; Radar vegetation index;
D O I
10.1109/IGARSS.2016.7729434
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Crop drought is a terrible agricultural disaster across the globe, which has been widely studied with remote sensing optical data. However, soil moisture, a key parameter in crop drought monitoring which was hard for optical remote sensing data to estimate. SAR (Synthetic Aperture Radar) observation system is very sensitive to moisture in the soil, more importantly, the microwave which SAR systems used could penetrate the crop canopy into the soil. Water Cloud Model (WCM) is a common method of estimating soil moisture, which needs descriptor of the canopy. In order to reduce descriptor of the canopy error in the WCM, crop parameters are instead by Radar Vegetation Index (RVI). A new method was proposed to soil moisture estimation and application based on WCM and bare soil model. In the new model, crop parameter input ware replaced by RVI, which was calculated by Radarsat-2 SAR data. The result shows a good performance with no crop parameter was used.
引用
收藏
页码:1699 / 1702
页数:4
相关论文
共 4 条
[1]   Analysis of Local Variation of Soil Surface Parameters With TerraSAR-X Radar Data Over Bare Agricultural Fields [J].
Anguela, Thais Paris ;
Zribi, Mehrez ;
Baghdadi, Nicolas ;
Loumagne, Cecile .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2010, 48 (02) :874-881
[2]   VEGETATION MODELED AS A WATER CLOUD [J].
ATTEMA, EPW ;
ULABY, FT .
RADIO SCIENCE, 1978, 13 (02) :357-364
[3]   Estimation of Leaf Area Index (LAI) in corn and soybeans using multi-polarization C- and L-band radar data [J].
Hosseini, Mehdi ;
McNairn, Heather ;
Merzouki, Amine ;
Pacheco, Anna .
REMOTE SENSING OF ENVIRONMENT, 2015, 170 :77-89
[4]   A Time-Series Approach to Estimate Soil Moisture Using Polarimetric Radar Data [J].
Kim, Yunjin ;
van Zyl, Jakob J. .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2009, 47 (08) :2519-2527