인공위성 기반 TRMM/GPM 강우 이미지를 이용한 농업 가뭄 평가: 충청북도 지역을 중심으로
Assessment of Agricultural Drought Using Satellite-based TRMM/GPM Precipitation Images: At the Province of Chungcheongbuk-do
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초록
In this study, we assessed meteorological and agricultural drought based on the SPI(Standardized Precipitation Index), SMP(Soil Moisture Percentile), and SMDI(Soil Moisture Deficit Index) indices using satellite-based TRMM(Tropical Rainfall Measuring Mission)/GPM(Global Precipitation Measurement) images at the province of Chungcheongbuk-do. The long-term(2000-2015) TRMM/GPM precipitation data were used to estimate the SPI values. Then, we estimated the spatially-/temporally-distributed soil moisture values based on the near-surface soil moisture data assimilation scheme using the TRMM/GPM and MODIS(MODerate resolution Imaging Spectroradiometer) images. Overall, the SPI value was significantly affected by the precipitation at the study region, while both the precipitation and land surface condition have influences on the SMP and SMDI values. But the SMP index showed the relatively extreme wet/dry conditions compared to SPI and SMDI, because SMP only calculates the percentage of current wetness condition without considering the impacts of past wetness condition. Considering that different drought indices have their own advantages and disadvantages, the SMDI index could be useful for evaluating agricultural drought and establishing efficient water management plans.
Abstract
In this study, we assessed meteorological and agricultural drought based on the SPI(Standardized Precipitation Index), SMP(Soil Moisture Percentile), and SMDI(Soil Moisture Deficit Index) indices using satellite-based TRMM(Tropical Rainfall Measuring Mission)/GPM(Global Precipitation Measurement) images at the province of Chungcheongbuk-do. The long-term(2000-2015) TRMM/GPM precipitation data were used to estimate the SPI values. Then, we estimated the spatially-/temporally-distributed soil moisture values based on the near-surface soil moisture data assimilation scheme using the TRMM/GPM and MODIS(MODerate resolution Imaging Spectroradiometer) images. Overall, the SPI value was significantly affected by the precipitation at the study region, while both the precipitation and land surface condition have influences on the SMP and SMDI values. But the SMP index showed the relatively extreme wet/dry conditions compared to SPI and SMDI, because SMP only calculates the percentage of current wetness condition without considering the impacts of past wetness condition. Considering that different drought indices have their own advantages and disadvantages, the SMDI index could be useful for evaluating agricultural drought and establishing efficient water management plans.
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