TY - JOUR
T1 - Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula
AU - Park, Seonyoung
AU - Im, Jungho
AU - Park, Sumin
AU - Rhee, Jinyoung
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/5/1
Y1 - 2017/5/1
N2 - Droughts, typically caused by the deficiencies of precipitation and soil moisture, affect water resources and agriculture. As soil moisture is of key importance in understanding the interaction between the atmosphere and Earth's surface, it can be used to monitor droughts. In this study, a High resolution Soil Moisture Drought Index (HSMDI) was proposed and evaluated for meteorological, agricultural, and hydrological droughts. HSMDI was developed using the 1 km downscaled soil moisture data produced from the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) from 2003 to 2011 (March to November) over the Korean peninsula. Seven products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellite sensors were used to downscale AMSR-E soil moisture based on random forest machine learning. The downscaled 1 km soil moisture was correlated well with both in situ and AMSR-E soil moisture with the mean coefficient of determination (R2) of 0.29 and 0.59, respectively. The Standardized Precipitation Index (SPI) with time scales from 1 to 12 months, crop yields (for sesame, highland radish, and highland napa cabbage) and streamflow data were used to validate HSMDI for various types of droughts. The results showed that HSMDI depicted meteorological drought well, especially during the dry season, with a similar pattern with the 3-month SPI. However, the performance fluctuated a bit during the wet season possibly due to the limited availability of optical sensor data and heterogeneous land covers around the stations. HSMDI also showed high correlation with crop yield data, in particular the highland radish and napa cabbage cultivated in non-irrigated regions with a mean R2 of 0.77. However, HSMDI did not monitor streamflow well for hydrological drought presenting a various range of correlations with streamflow data (from 0.03 to 0.83).
AB - Droughts, typically caused by the deficiencies of precipitation and soil moisture, affect water resources and agriculture. As soil moisture is of key importance in understanding the interaction between the atmosphere and Earth's surface, it can be used to monitor droughts. In this study, a High resolution Soil Moisture Drought Index (HSMDI) was proposed and evaluated for meteorological, agricultural, and hydrological droughts. HSMDI was developed using the 1 km downscaled soil moisture data produced from the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) from 2003 to 2011 (March to November) over the Korean peninsula. Seven products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellite sensors were used to downscale AMSR-E soil moisture based on random forest machine learning. The downscaled 1 km soil moisture was correlated well with both in situ and AMSR-E soil moisture with the mean coefficient of determination (R2) of 0.29 and 0.59, respectively. The Standardized Precipitation Index (SPI) with time scales from 1 to 12 months, crop yields (for sesame, highland radish, and highland napa cabbage) and streamflow data were used to validate HSMDI for various types of droughts. The results showed that HSMDI depicted meteorological drought well, especially during the dry season, with a similar pattern with the 3-month SPI. However, the performance fluctuated a bit during the wet season possibly due to the limited availability of optical sensor data and heterogeneous land covers around the stations. HSMDI also showed high correlation with crop yield data, in particular the highland radish and napa cabbage cultivated in non-irrigated regions with a mean R2 of 0.77. However, HSMDI did not monitor streamflow well for hydrological drought presenting a various range of correlations with streamflow data (from 0.03 to 0.83).
KW - AMSR-E
KW - High resolution soil moisture drought index (HSMDI)
KW - MODIS
KW - Random forest
KW - Soil moisture downscaling
KW - TRMM
UR - http://www.scopus.com/inward/record.url?scp=85013940148&partnerID=8YFLogxK
U2 - 10.1016/j.agrformet.2017.02.022
DO - 10.1016/j.agrformet.2017.02.022
M3 - Article
AN - SCOPUS:85013940148
SN - 0168-1923
VL - 237-238
SP - 257
EP - 269
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
ER -
