Spatial and temporal monitoring of drought conditions using the satellite rainfall estimates and remote sensing optical and thermal measurements |
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Authors: | Farzane Mohseni Maryam Kiani Sadr Saeid Eslamian Atta Areffian Ali Khoshfetrat |
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Institution: | 1. K.N. TOOSI University of Technology, Faculty of Geodesy and Geomatics Engineering, Tehran 88888445, Iran;2. Islamic Azad University, Najafabad Branch, Department of Civil Engineering, Najafabad 8514143131, Iran;3. Department of Environment, College of Basic Sciences, Hamedan Branch, Islamic Azad University, Hamedan, Iran;4. Isfahan University of Technology, Collage of Agriculture, Department of Water Engineering, Isfahan 84156 83111, Iran;5. Islamic Azad University, Isfahan (Khorasgan) Branch, Department of Technical & Engineering, Isfahan 81595158, Iran |
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Abstract: | 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. |
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Keywords: | Drought Remote sensing PERSIANN-CDR SPI Optical and thermal products |
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