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利用温度和植被指数进行地物分类和土壤水分反演
引用本文:刘良云,刘银年,等.利用温度和植被指数进行地物分类和土壤水分反演[J].红外与毫米波学报,2002,21(4):269-273.
作者姓名:刘良云  刘银年
作者单位:1. 中国科学院遥感应用研究所,北京,100101
2. 中国科学院上海技术物理研究所,上海,200083
3. 北京农业信息技术研究中心,北京,100089
基金项目:国家 973计划 (批准号G2 0 0 0 0 7790 0 ),北京精准农业示范资助项目~~
摘    要:首先利用地物表面温度和植被指数,成功地对北京精准农业示范区内生长旺盛小麦、稀疏小麦、池塘水体、水草、淤泥和裸露土壤等6种地物进行了分类,其次,利用地物表现温度(LST)和归一化植被指数(NDVI)作为坐标系,建立LST-NDVI三角形分布的散点图,分析了散点图的地物特征分布及其物理意义,与植被的红外和近红外两个特征波段构造的散射图相比,同类样本的离散度更小,不同类别样本之间的距离更大。最后,提出了植被指数--表面温度的土壤水分反演模型,结合地面采样数据成功地反演了实验区内作物地块的土壤水分。

关 键 词:地物分类  土壤水分反演  植被指数  陆地表面温度  实用模块化成像光谱仪  土壤水分  散点图
收稿时间:2001/8/13
修稿时间:2001年8月13日

TARGET CLASSIFICATION AND SOIL WATER CONTENT REGRESSION USING LAND SURFACE TEMPERATURE(LST)AND VEGETATION INDEX(VI)
LIU Liang Yun,ZHANG Bing,ZHENG Lan Fen,TONG Qing Xi,LIU Yin Nian,XUE Yong Qi,YANG Min Hua,ZHAO Chun Jiang.TARGET CLASSIFICATION AND SOIL WATER CONTENT REGRESSION USING LAND SURFACE TEMPERATURE(LST)AND VEGETATION INDEX(VI)[J].Journal of Infrared and Millimeter Waves,2002,21(4):269-273.
Authors:LIU Liang Yun  ZHANG Bing  ZHENG Lan Fen  TONG Qing Xi  LIU Yin Nian  XUE Yong Qi  YANG Min Hua  ZHAO Chun Jiang
Abstract:Land surface temperature(LST) and NDVI were used for classification and soil water content(SWC) regression. Firstly, the 6 kinds of targets, dense wheat, sparse wheat, naked soil, water in ponds, silt and aquatic plants, were well classified using LST and NDVI channels. Secondly, the triangle shaped scatter plot was built and analyzed using LST and NDVI channels. Compared with the scatter plot built by red and near infrared bands, the spectral distances between different clatssifications are larger, and the samples in the same classification are more convergent. Finally, a VIT model was presented to extract SWC using LST and NDVI channel, which predicts the moisture well. The mapping of soil's moisture in the wheat area was calculated and illustrated for scientific irrigation and precise agriculture.
Keywords:vegetation index  land surface temperature  operation modular imaging spectrometer  soil water content    
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