基于LST_LAI特征空间的农田干旱监测研究

农田干旱具有范围广且对农业生产影响巨大的特点，对农田干旱的遥感实时动态监测是目前公认的难题。利用MODIS的地表温度(LST)产品和叶面积指数(LAI)产品，构建LST-LAI光谱特征空间，提出温度—叶面积干旱指数(temperature LAI drought index，TLDI)监测农田水分含量，并利用宁夏实测的0～10 cm平均土壤含水量验证该指数的精度，结果表明：它们之间具有良好的相关性，R2的变化范围为0.43～0.86。与TVDI相比，TLDI弥补了作物封垄后TVDI因归一化植被指数(NDVI)饱和对农田水分监测精度降低的缺陷。此外，利用MODIS数据产品LST和LAI进行农田干旱监测，避免了使用MODIS原始数据的繁杂处理过程，初步为MODIS数据产品在农田干旱监测业务化运行探索出一条技术流程。

Monitoring of Farmland Drought Based on LST-LAI Spectral Feature Space

Farmland drought has the characteristics of wide range and seriously affecting on agricultural production, so real-time dynamic monitored has been a challenging problem. By using MODIS land products, and constructing the spectral space of LST and LAI, the temperature LAI drought index (TLDI) was put forward and validated using ground-measured 0~10 cm averaged soil moisture of Ningxia farmland. The results show that the coefficient of determination (R2) of both them varies from 0.43 to 0.86. Compared to TVDI, the TLDI has higher accuracy for farmland moisture monitoring, and solves the saturation of NDVI during the late development phases of the crop. Furthermore, directly using MODIS land products LST and LAI and avoiding the complicated process of using the original MODIS data provide a new technical process to the regular operation of farmland drought monitoring.