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Drought monitoring with VHI computed from the improved MODIS LST

机译:通过改进的MODIS LST计算的VHI进行干旱监测

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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:植被健康指数是根据植被指数和地表温度计算得出的,而植被指数和地表温度是遥感监测干旱的重要卫星产品。温度条件指数和植被健康指数的质量在很大程度上取决于陆地表面温度(LST)的质量。目前,中分辨率成像光谱仪(MODIS)LST产品的检索算法在全球范围内是相当不错的,但是对于局部区域,仍然需要通过考虑气溶胶,水蒸气,观测天顶和观察时间的影响来提高空间。本文将MODIS LST与中国东部耕作区气象站的观测表面温度进行了比较。分析了MODIS LST与MODIS TPW,MODIS AOD,观测时间,观测角度和DEM之间的相关性。最后,形成用于校正温度差的回归方程,并将其用于校正从MODIS卫星获取的MODIS LST。结果表明,校正后的LST接近于实测值,均方根误差减小,相关系数增大。进一步计算了植被健康指数,并将其用于监测中国东部地区冬小麦的干旱。 VHI的改善可以更好地反映干旱情况。

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