Remote Sensing-based Agricultural Drought Monitoring using Hydrometeorological Variables
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A new drought index, the agricultural dry condition index (ADCI), was developed to combine various hydrometeorological variables associated with agricultural droughts. It was calculated by applying weights to the soil moisture, vegetation activity, and land surface temperature data, which are used to monitor agricultural droughts. The vegetation health index (VHI) and microwave integrated drought index (MIDI) are also used to monitor agricultural droughts; these were calculated using satellite image data collected between 2001 and 2015 in South Korea and their spatiotemporal variations were analyzed. In order to compare the ADCI with actual agricultural drought conditions, land in South Korea was divided into two classes (rice paddies and croplands) and the ADCI values were compared to the corresponding crop yields (rice from rice paddies, potatoes and soybeans from croplands). There was no significant correlation between the ADCI and crop yield for the rice paddies because the water supply is controlled by irrigation. However, in the croplands there was a high degree of correlation with correlation coefficients of 0.83 and 0.80 for potatoes and soybeans, respectively. In order to confirm agreement with the actual affected areas, a receiver operating characteristic analysis was conducted for 2001 and 2015 when there was severe drought. This analysis found that the ADCI peaked at 0.68 in 2001 (June) 0.64 in 2015 (June). The ADCI was found to be highly applicable to the assessment of agricultural drought conditions. The VHI responded positively to land surface temperature while the MIDI responded to rainfall. However, the ADCI showed the best results because it is a weighted index of the input data, such as the land surface temperature, soil moisture, and vegetation activity, and their combination. The results confirmed that soil moisture, vegetation activity, and land surface temperature are the most important variables associated with droughts and that the ADCI can be effectively used to monitor agricultural droughts.
Keywordsagricultural drought remote sensing hydrometeorological variables crop yield ADCI
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This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1A02018546) and supported by Korea Environment Industry & Technology Institute (KEITI) though Water Management Research Program, funded by Korea Ministry of Environment (MOE) (79616).
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