基于冠层温度棉花水分胁迫指数与高光谱植被指数的关系

Correlation between Cotton Canopy Crop Water Stress Index from Infrared Thermography and Hyperspectral Vegetation Index

【目的】用红外热图像提取棉花水分胁迫指数(CWSI),并用高光谱遥感植被指数对棉花的CWSI进行遥感估算,为红外热图像和高光谱遥感监测作物水分状况提供科学依据。【方法】通过Fluke热像仪和ASD非成像高光谱仪,分别获取棉花2品种4水平水分处理5个关键生育时期冠层的红外热图像和高光谱数据;对红外热图像进行技术处理,基于Jones定义的作物水分胁迫指数CWSI公式,计算CWSI;与高光谱数据转换得到的4种高光谱植被指数进行回归分析。【结果】CWSI与4种高光谱植被指数均达到了1%极显著的线性相关关系;其中红边归一化植被指数RENDVI与CWSI呈最高的线性负相关关系,利用它们的相关模型方程,估算CWSI,实测值与估算值之间呈极显著的线性相关(r=0.839 9**,n=30,α=1%)。【结论】红外热图像与高光谱遥感技术的结合,可以精确地对棉花水分胁迫指数CWSI进行遥感估算,更好的诊断棉花水分状况。

[ Objective ] The present study was to calculate crop water stress index （CWSI） based on the thermal images, and estimation of CWSI by using hyperspectral vegetation index,which intends to provide the scientific evidence to monitor cotton canopy water status by combining infrared thermography and hyperspectral techniques in the fields. [ Method ] With Fluke infrared thermal camera and ASD portable non - imaging hy- perspectral spectrometer, canopy infrared thermal images and hyperspectral reflectance data were obtained, re- spectively, at five key growth stages of cotton in an open experimental field including 2 cotton cultivars with 4 level water treatments. Thermal image was processed and the crop water stress index CWSI was calculated ac- cording to Jones＇formula. Regression analysis was carried out of the four vegetation indices derived from hyper- spectral reflectance data. [ Result] It was significant for the four linear regression function models at 1% lev- el, among the four vegetation indices, RENDVI （Red Normalize Vegetation Index） had the highest negative linear correlation with CWSI, according to their model function, estimation of CWSI, correlation between measured CWSI and the estimated CWSI was significant （ r = 0.839 9＊＊, n = 30, ot = 1% ）. [ Conclusion ]Combination of infrared thermography and hyperspectral remote sensing technology can precisely estimate CW- SI of cotton, which is helpful to have a better diagnosis of water status of cotton canopy.