SMAP 토양수분 이미지를 이용한 농업가뭄 평가 기법 개발
Development of Agricultural Drought Assessment Approach Using SMAP Soil Moisture Footprints
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In this study, we evaluated daily root zone soil moisture dynamics and agricultural drought using a near-surface soil moisture data assimilation schemewith Soil Moisture Active & Passive (SMAP, 3 km × 3 km) soil moisture footprints under different hydro-climate conditions. Satellite-based LANDSATand MODIS image footprints were converted to spatially-distributed soil moisture estimates based on the regression model, and the converted soilmoisture distributions were used for assessing uncertainties and applicability of SMAP data at fields. In order to overcome drawbacks of the discontinuityof SMAP data at the spatio-temporal scales, the data assimilation was applied to SMAP for estimating daily soil moisture dynamics at the spatial domain. Then, daily soil moisture values were used to estimate weekly agricultural drought based on the Soil Moisture Deficit Index (SMDI). The Yongdam-damand Soyan river-dam watersheds were selected for validating our proposed approach. As a results, the MODIS/SMAP soil moisture values wererelatively overestimated compared to those of the TDR-based measurements and LANDSAT data. When we applied the data assimilation scheme toSMAP, uncertainties were highly reduced compared to the TDR measurements. The estimated daily root zone soil moisture dynamics and agriculturaldrought from SMAP showed the variability at the sptio-temporal scales indicating that soil moisture values are influenced by not only the precipitation,but also the land surface characteristics. These findings can be useful for establishing efficient water management plans in hydrology and agriculturaldrought.
Abstract
In this study, we evaluated daily root zone soil moisture dynamics and agricultural drought using a near-surface soil moisture data assimilation schemewith Soil Moisture Active & Passive (SMAP, 3 km × 3 km) soil moisture footprints under different hydro-climate conditions. Satellite-based LANDSATand MODIS image footprints were converted to spatially-distributed soil moisture estimates based on the regression model, and the converted soilmoisture distributions were used for assessing uncertainties and applicability of SMAP data at fields. In order to overcome drawbacks of the discontinuityof SMAP data at the spatio-temporal scales, the data assimilation was applied to SMAP for estimating daily soil moisture dynamics at the spatial domain. Then, daily soil moisture values were used to estimate weekly agricultural drought based on the Soil Moisture Deficit Index (SMDI). The Yongdam-damand Soyan river-dam watersheds were selected for validating our proposed approach. As a results, the MODIS/SMAP soil moisture values wererelatively overestimated compared to those of the TDR-based measurements and LANDSAT data. When we applied the data assimilation scheme toSMAP, uncertainties were highly reduced compared to the TDR measurements. The estimated daily root zone soil moisture dynamics and agriculturaldrought from SMAP showed the variability at the sptio-temporal scales indicating that soil moisture values are influenced by not only the precipitation,but also the land surface characteristics. These findings can be useful for establishing efficient water management plans in hydrology and agriculturaldrought.
참고문헌 (26)
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