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
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 50 条
  • [41] RADARSAT-2 SAR image quality and calibration operations
    Luscombe, AP
    CANADIAN JOURNAL OF REMOTE SENSING, 2004, 30 (03) : 345 - 354
  • [42] Monitoring Soil Moisture to Support Risk Reduction for the Agriculture Sector Using RADARSAT-2
    McNairn, Heather
    Merzouki, Amine
    Pacheco, Anna
    Fitzmaurice, John
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2012, 5 (03) : 824 - 834
  • [43] Stochastic bias correction for RADARSAT-2 soil moisture retrieved over vegetated areas
    Lee, Ju Hyoung
    Budhathoki, Sujata
    Lindenschmidt, Karl-Erich
    GEOCARTO INTERNATIONAL, 2021, 37 (25) : 9190 - 9203
  • [44] POTENTIAL USE OF RADARSAT-2 POLARIMETRIC PARAMETERS FOR ESTIMATING SOIL MOISTURE IN PRAIRIE AREAS
    Bai, Xiaojing
    He, Binbin
    Xu, Dasong
    2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 3043 - 3046
  • [45] Soil Moisture Retrieval from Radarsat Data: A Neuro-Fuzzy Approach
    Lakhankar, Tarendra
    Ghedira, Hosni
    Khanbilvardi, Reza
    2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8, 2006, : 2328 - 2331
  • [46] MONITORING SOIL MOISTURE TO SUPPORT RISK REDUCTION FOR THE AGRICULTURE SECTOR USING RADARSAT-2
    McNairn, H.
    Merzouki, A.
    Pacheco, A.
    2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2011, : 3618 - 3621
  • [47] EVOLUTION OF TYPHOON SOUDELOR OBSERVED BY RADARSAT-2 SAR
    Xu, Qing
    Zhang, Guosheng
    Zhang, Shuangshang
    Cheng, Yongcun
    Perrie, W.
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 735 - 738
  • [48] Applications of RADARSAT-2 polarimetric data
    Caves, RG
    Scheuchl, B
    Staples, G
    IGARSS 2001: SCANNING THE PRESENT AND RESOLVING THE FUTURE, VOLS 1-7, PROCEEDINGS, 2001, : 2596 - 2598
  • [49] Coupling SAR and optical remote sensing data for soil moisture retrieval over dense vegetation covered areas
    Shi, Jiahao
    Yang, Huan
    Hou, Xinli
    Zhang, Honglu
    Tang, Guozhong
    Zhao, Heng
    Wang, Fuqiang
    PLOS ONE, 2025, 20 (01):
  • [50] Land Cover Classification of RADARSAT-2 SAR Data Using Convolutional Neural Network
    LIN Wei
    LIAO Xiangyong
    DENG Juan
    LIU Yao
    WuhanUniversityJournalofNaturalSciences, 2016, 21 (02) : 151 - 158