二轴土壤背景纠正的植被指数及其在华南水稻遥感估产中的应用

目前国内外学者提出了各种植被指数来进行作物遥感估产的定量研究。这些指数多是基于“土壤线”的存在来进行土壤背景消除的。但它们只消除了土壤背景中的含水量(沿“土壤线”方向)对遥感数据的影响,而没有消除由于不同土壤质地的变化(垂直于“土壤线”方向,如红壤、棕壤等不同的土壤类型)所造成的遥感数据的偏移。本文首次提出了能基本上完全消除土壤背景影响(包括土壤含水量、土壤类型等)的二轴土壤背景纠正的植被指数(TWVI)模型。该指数比目前使用的其它植被指数更适合于作为进行全球监测的植被指数。已成功地应用于华南地区的水稻遥感估产试验。

Two-axis Soil-adjusted Vegetation Index (TWVI) and Its Application in Remote Sensing Prediction of Rice Production in South China

Various kinds of vegetation index have been proposed to minimize soil background influences based on the existence of "soil line" for forest monitoring and crop production prediction. They can only minimize soil moisture inf luen ces on remote sensing data along one axis, "soil line", which fail when different soil types appear (in the direction perpendicular to "soil line"). A twoaxis soil-ajusted vegetation index is presented here to diminish most soil background influences, and it is more suitable as a global monitoring vegetation index than other index. TWVI model has been applied successful in rice production prediction in South China.