中文摘要：

通过获取多时相试验区TM影像和大田同步调查棉花黄萎病,将TM影像光谱指数与病害严重度进行相关分析,建立棉花病害严重度估测模型.结果表明：随着病害严重度的增加,TM影像光谱指数B2,B4,SATVI,OSAVI,MSAVI,TSAVI,SVNSWI,SNSWIa,SNSWIb,SVNI,DNSIa,DNSIb,NDSWIa,NDSWIb,RNSWIa,RNSWIb,DVNI,EVI,TVI,SAVI,DVI,NDVI,RVI和PVI逐渐减小,B1,B3,B7和RI逐渐增加,NDGI呈现先增后降的趋势,而B5呈现先降后增的趋势.病害严重度与TM影像光谱指数B1,B3和RI均呈极显著正相关,与B4,OSAVI,MSAVI,TSAVI,SVNSWI,SNSWIa,SNSWIb,SVNI,DNSIa,DNSIb,NDSWIa,NDSWIb,RNSWIa,RNSWIb,DVNI,EVI,TVI,NDGI,SAVI,DVI,NDVI,RVI和PVI均呈极显著负相关,与SATVI呈显著负相关,与B2,B5和B7均未达显著相关.建立的8个TM影像光谱指数估算棉花黄萎病模型均通过显著性检验,且以DVI和DNSIb为自变量的线性模型精度最高,表明利用多时相TM卫星影像光谱指数进行棉田病害定量诊断是可行的.

英文摘要：

The cotton field infected by Verticillum wilt was investigated with both the multi-temporal TM images and the field survey simultaneously.A model of evaluating disease severity of the cotton was established by analyzing the correlation between spectral indices of TM image and severity level（SL） of the disease.The results indicated that with an increase of disease SLs,the values of spectral indices B2,B4,SATVI,OSAVI,MSAVI,TSAVI,SVNSWI,SNSWIa,SNSWIb,SVNI,DNSIa,DNSIb,NDSWIa,NDSWIb,RNSWIa,RNSWIb,DVNI,EVI,TVI,SAVI,DVI,NDVI,RVI and PVI declined slowly,B1,B3,B7 and RI increased gradually,NDGI increased at first and then decreased,while B5 exhibited a trend of decrease-increase with an increase of disease SLs.The SLs of disease were highly significantly positive correlated with the spectral indices values of B1,B3 and RI,highly significantly negative correlated with the spectral indice values of B4,OSAVI,MSAVI,TSAVI,SVNSWI,SNSWIa,SNSWIb,SVNI,DNSIa,DNSIb,NDSWIa,NDSWIb,RNSWIa,RNSWIb,DVNI,EVI,TVI,NDGI,SAVI,DVI,NDVI,RVI and PVI,significantly negative correlated with the spectral indice values of SATVI,and no significantly correlated with the spectral indice values of B2,B5 and B7.All of the eight spectral indices of TM image selected achieved significant correlation level.However,the linear models on the basis of DVI and DNSIb had the best estimating precision.This study demonstrated that it is feasible to estimate quantitatively the SL of cotton disease using the spectral indices of TM satellite image.