Drought is an important natural disaster that causes devastating impacts on the ecosystem, livestock, environment, and society. So far, various remote-sensing methods have been developed to estimate drought conditions, each of which has advantages and restrictions. This study aims to monitor the real-time drought indices at the field scales via the integration of various earth observations. Our proposed method consists of two steps. In the first step, the relationships between long-term standardized precipitation indices (SPI) derived from PERSIANN-CDR rainfall data and two drought-dependent parameters derived from MODIS products, including normalized NDVI and soil-air temperature gradient, are obtained at the spatial resolution of PERSIANN-CDR grid (approximately 25 km). As the next step, the corresponding relationships are applied to estimate the drought index maps at the spatial resolution of MODIS products (1 km). Numerous analyses are carried out to evaluate the proposed method. The results revealed that, from various drought indices, including SPIs of different timescales (1, 3, 6, and 12-months), SPI-3 and SPI-6 are more appropriate to the proposed method in terms of correlation with temperature and vegetation parameters. The findings also demonstrate the competency of the proposed method in estimating SPI indices with average RMSE 0.67 and the average correlation coefficient of 0.74.

干旱是重要的自然灾害，会对生态系统，牲畜，环境和社会造成破坏性影响。迄今为止，已经开发了各种遥感方法来估计干旱状况，每种方法都有其优点和局限性。这项研究旨在通过整合各种地球观测资料来监测田间尺度的实时干旱指数。我们提出的方法包括两个步骤。第一步，在空间上获得从PERSIANN-CDR降雨数据得出的长期标准化降水指数（SPI）与从MODIS产品得出的两个干旱相关参数之间的关系，包括归一化NDVI和土壤温度梯度。 PERSIANN-CDR网格的分辨率（约25 km）。下一步，应用相应的关系，以MODIS产品的空间分辨率（1 km）估算干旱指数图。进行了大量分析以评估所提出的方法。结果表明，从各种干旱指数（包括不同时间尺度（1、3、6和12个月）的SPI）来看，SPI-3和SPI-6与温度和植被的相关性更适合于该方法。参数。研究结果还证明了该方法在估计SPI指数（平均RMSE为0.67，平均相关系数为0.74）中的能力。和12个月），就温度和植被参数的相关性而言，SPI-3和SPI-6更适合于所提出的方法。研究结果还证明了该方法在估计SPI指数（平均RMSE为0.67，平均相关系数为0.74）中的能力。和12个月），就温度和植被参数的相关性而言，SPI-3和SPI-6更适合于所提出的方法。研究结果还证明了该方法在估计SPI指数（平均RMSE为0.67，平均相关系数为0.74）中的能力。