中文摘要：

利用K-T变换提取TM和MODIS遥感影像的绿度、湿度分量,在不同的分辨率尺度下监测小麦覆盖地表参数：土壤湿度（Ms）、等效水厚度（EWT）和叶面积指数（LAI）,并与NDVI（归一化植被指数）、NDWI（归一化水分指数）和EVI（增强植被指数）监测结果比较。湿度分量监测Ms效果更好,TM和MODIS遥感影像反演精度分别为6.08%、7.37%（RMSE）,相关系数R2分别为0.49、0.31,基于绿度和湿度分量建立土壤湿度多元线性回归反演模型,利用TM影像反演土壤湿度RMSE为4.91%,反演土壤湿度和实测土壤湿度R2达0.63;绿度分量监测EWT效果更好,TM和MODIS遥感影像反演精度分别为0.37 kg/m2、0.43 kg/m2,R2分别为0.51、0.28;绿度分量反演LAI精度更好,TM和MODIS遥感影像反演精度分别为0.66、0.83,R2分别为0.64、0.35。

英文摘要：

The TM imagery and MODIS imagery were converted into greenness and wetness variables using the Cretaceous-Tertiary（K-T） transformation.The linear regression model was built to estimate the land surface parameters including soil moisture（Ms）,equivalent water thickness（EWT） and leaf area index（LAI） based on the greenness or wetness variable.The inversion results were compared to the results estimated by the Normalized Difference Vegetation Index（NDVI）,Normalized Difference Water Index（NDWI）,and Enhanced Vegetation Index（EVI）.The Ms was estimated by the wetness variable is better than other variables,the inversion accuracy by the TM imagery and MODIS imagery is 6.08%,7.37%（RMSE） respectively,the correlation coefficient（R2） is 0.49,0.31 respectively.Then a multi linear regression model to estimate Ms was built based on wetness and greenness,the Ms estimation result shows that the RMSE reaches 4.91% based on TM imagery,the correlation coefficient is 0.63 between estimation and measurement Ms.The greenness variable and EWT shows a better correction than other variables,the inversion accuracy by the TM imagery and MODIS imagery is 0.37 kg/m2,0.43 kg/m2 respectively,the correlation coefficient is 0.51,0.28 respectively.The greenness variable and LAI shows a better correction than other variables,the inversion accuracy by the TM imagery and MODIS imagery is 0.66,0.83 respectively,the correlation coefficient（R2） is 0.64,0.35 respectively.