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Drought monitoring with VHI computed from the improved MODIS LST

机译:使用改进的MODIS LST计算了与VHI的干旱监测

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Vegetation Health Index is computed based on the vegetation index and land surface temperature which are very important satellite products for drought monitoring with remote sensing. The quality of Temperature Condition Index and Vegetation Health Index deeply depends on the quality of the Land Surface Temperature (LST). The present retrieval algorithm of Moderate Resolution Imaging Spectroradiometer (MODIS) LST products is quite good globally but for a local area there is still a room to improve by taking the effects of aerosol, water vapor, view zenith and observing time into account. The paper compared the MODIS LST with the observed surface temperature in weather station in the farming area of Eastern China. The correlations between the MODIS LST and the MODIS TPW, MODIS AOD, observing time, observing angle and DEM, respectively, were analyzed. And finally a regression equation to correct the temperature difference is formed and used to correct the MODIS LST retrieved from MODIS satellite. The results showed that the corrected LST is close to the observed with the decreased root mean square error and the increased correlation coefficient. The Vegetation Health Index was further calculated and used to monitoring the winter wheat drought in Eastern China. The drought situation was better captured by the improved VHI.
机译:基于植被指数和陆地表面温度来计算植被健康指数,这是具有遥感干旱监测的非常重要的卫星产品。温度条件指标和植被健康指数的质量深深取决于土地表面温度(LST)的质量。当前分辨率成像光谱仪(MODIS)LST产品的目前检索算法全球相当不错,但对于当地区域仍然通过采取气溶胶,水蒸气,观看天顶和观察时间考虑时间来改善空间。本文将Modis LST与中国东部农业地区的田径驻地观察到的地表温度进行了比较。分析了MODIS LST和MODIS TPW,MODIS AOD,观察时间,观察角度和DEM之间的相关性。并且最后形成回归方程来纠正温度差,并用于校正从MODIS卫星检索的MODIS LST。结果表明,校正的LST接近于具有降低的根均方误差和增加的相关系数。植被健康指数进一步计算并用于监测中国东部冬小麦干旱。随着VHI的改进,干旱情况更好地捕获。

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