| 基于FY3C地表温度重建的多云地区旱情监测评估 |
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| 引用本文： | 张德军,杨世琦,王永前,孙亮,高阳华,祝好,叶勤玉.基于FY3C地表温度重建的多云地区旱情监测评估[J].自然资源学报,2021,36(4):1047-1061. |
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| 作者姓名： | 张德军 杨世琦 王永前 孙亮 高阳华 祝好 叶勤玉 |
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| 作者单位： | 1.成都信息工程大学资源环境学院,成都 6102252.重庆市气象科学研究所,重庆 4011473.中国农业科学院农业资源与区划所,中国农业农村部农业遥感重点实验室,北京 100081 |
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| 基金项目： | 国家自然科学基金项目(41631180);重庆市气象部门业务技术攻关项目(YWJSGG-202001);重庆市科技厅项目(cstc2019jcyj-msxmX0649);四川省科技计划项目(19ZDYF0158) |
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| 摘 要： | 受热红外传感器无法探测云下地表信息的影响,热红外遥感数据失去了对多云地区旱情监测的能力。采用RSDAST（Remotely Sensed Daily Land Surface Temperature Reconstruction）模型实现了FY3C/VIRR（Visible and Infrared Radiometer）云像元LST值的重建,结合重建后LST和NDVI数据采用TVDI指数对2018年重庆市干旱进行监测分析,并通过对比土壤墒情数据与OTVDI（Original TVDI）和RTVDI（Reconstructed TVDI）间的相关性来评估RTVDI在多云条件下旱情监测的能力。评估结果表明：基于RSDAST模型扩大了多云地区遥感干旱监测的空间范围和时间连续性,提升了区域旱情监测的精度（长时间序列和空间分布上RTVDI与土壤墒情数据间的R值均高于OTVDI）,极大地提高热红外遥感数据在多云条件下的可用性和可靠性。
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| 关 键 词： | 可见光遥感 地表温度重建 RSDAST 干旱 |
| 收稿时间： | 2019-09-12 |
| 修稿时间： | 2020-01-07 |
Assessing drought conditions over cloudy regions based on reconstructed FY3C/VIRR LST |
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| ZHANG De-jun,YANG Shi-qi,WANG Yong-qian,SUN Liang,GAO Yang-hua,ZHU Hao,YE Qin-yu.Assessing drought conditions over cloudy regions based on reconstructed FY3C/VIRR LST[J].Journal of Natural Resources,2021,36(4):1047-1061. |
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| Authors: | ZHANG De-jun YANG Shi-qi WANG Yong-qian SUN Liang GAO Yang-hua ZHU Hao YE Qin-yu |
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| Institution: | 1. College of Resources and Environment, Chengdu University of Information technology, Chengdu 610225, China2. Chongqing Institute of Meteorological Sciences, Chongqing 401147, China3. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Beijing 100081, China |
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| Abstract: | The surface information in cloud-covered regions cannot be captured by thermal infrared sensors. Therefore, thermal infrared remote sensing product data have lost their ability to monitor drought in cloudy regions. In this paper, remotely sensed daily land surface temperature reconstruction (RSDAST) model is used to reconstruct LST value of cloud pixels in FY3C/VIRR LST product data, and the reconstructed LST and NDVI data are used to monitor drought in Chongqing in 2018 by TVDI index. And the correlation between soil moisture and OTVDI (original TVDI) and RTVDI (reconstructed TVDI) was examined in this study so that we can evaluate the ability of RTVDI to monitor drought under cloudy conditions. The evaluation results show that the RSDAST model not only expands the spatial scope and temporal continuity of drought monitoring in cloudy regions, but also raises the accuracy of regional drought monitoring (the R value between RTVDI and soil moisture in long time series and spatial distribution is higher than that of OTVDI), which greatly improves the availability and reliability of thermal infrared remote sensing data in cloudy conditions. |
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| Keywords: | thermal infrared data reconstructed land surface temperature RSDAST drought |
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