利用地表温度与LAI的新型土壤湿度监测方法

地表温度（T_s）是土壤湿度和植被生长状态等因素的综合反映，利用植被指数和T_s能够监测土壤湿度的时空分布特征。利用农田气候模型CUPID的T_s模拟结果，发展了利用温度与叶面积指数（LAI）的新型土壤水分反演方法（advanced temperature vegetation dryness index, ATVDI）。前人研究表明归一化植被指数（NDVI）容易达到饱和状态，因此利用LAI代替NDVI开展土壤水分反演。利用CUPID模型模拟结果构建LAI-T_s散点图，分析T_s随LAI与土壤湿度的变化特征，利用对数关系式改进了温度植被干旱指数（TVDI）中相同土壤湿度时T_s与植被指数之间的线性关系，建立了ATVDI方法。在实际应用中，首先利用LAI与T_s的散点图确定对数曲线的上边界与下边界，然后采用查找表的方法将每个像元对应的T_s变换为研究区最小叶面积指数对应的T_s。以陕西省关中作为研究区，利用MODIS的LAI和T_s产品（MOD11A2和MOD15A2）以及野外观测土壤湿度数据对ATVDI模型进行验证，结果表明该方法具有较高的监测精度，R2达到0.62。此外，ATVDI的计算结果具有一定的物理意义，使得不同时期的监测结果具有一致性，因而可更好地满足不同空间尺度土壤湿度的动态监测。

A Novel Method of Soil Moisture Content Monitoring by Land Surface Temperature and LAI

Land surface temperature (T_s) is influenced by soil background and vegetation growing conditions, and the combination of T_s and vegetation indices (Vis) can indicate the status of surface soil moisture content (SMC). In this study, Advanced Temperature Vegetation Dryness Index (ATVDI) used for monitoring SMC was proposed on the basis of the simulation results with agricultural climate model CUPID. Previous studies have concluded that Normalized Difference Vegetation Index (NDVI) easily reaches the saturation point, andLeaf Area Index (LAI) was then used instead of NDVI to estimate soil moisture content in the paper. With LAI-T_s scatter diagram established by the simulation results of CUPID model, how T_s varied with LAI and SMC was found. In the case of the identical soil background, the logarithmic relations between T_s and LAI were more accurate than the linear relations included in Temperature Vegetation Dryness Index (TVDI), based on which ATVDI was then developed. LAI-T_s scatter diagram with satellite imagery were necessary for determining the expression of the upper and lower logarithmic curves while ATVDI was used for monitoring SMC. T_s derived from satellite imagery were then transformed to the T_svalue which has the same SMC and the minimum LAI in study area with look–up table. The measured SMC from the field sites in Weihe Plain, Shanxi Province, China, and the products of LAI and T_s (MOD15A2 and MOD11A2, respectively) produced by the image derived from Moderate Resolution Imaging Spectrometer (MODIS) were collected to validate the new method proposed in this study. The validation results shown that ATVDI (R2=0.62) was accurate enough to monitor SMC, and it achieved better result than TVDI.Moreover, ATVDI-derived result were T_s values with some physical meanings, which made it comparative in different periods. Therefore, ATVDI is a promising method for monitoring SMC in different time-spatial scales in agricultural fields.