Monitoring Spatiotemporal Surface Soil Moisture Variations During Dry Seasons in Central America With Multisensor Cascade Data Fusion
Abbreviated Journal Title
IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.
Advanced microwave scanning radiometer on earth observing system; (AMSR-E); leaf area index (LAI); moderate resolution imaging; spectroradiometer (MODIS); normalized multiband drought index (NMDI); surface soil moisture (SSM); REMOTE-SENSING DATA; MICROWAVE SCANNING RADIOMETER; LEAF-AREA INDEX; AMSR-E; TIBETAN PLATEAU; BOREAL FOREST; MODIS; RETRIEVAL; PRODUCTS; MODEL; Engineering, Electrical & Electronic; Geography, Physical; Remote; Sensing; Imaging Science & Photographic Technology
Soil moisture is a critical element in the hydrological cycle, which is intimately tied to agriculture production, ecosystem integrity, and hydrological cycle. Point measurements of soil moisture samples are laborious, costly, and inefficient. Remote sensing technologies are capable of conducting soil moisture mapping at the regional scale. The advanced microwave scanning radiometer on earth observing system (AMSR-E) provides global surface soil moisture (SSM) products with the spatial resolution of 25 km which is not sufficient enough to meet the demand for various local-scale applications. This study refines AMSR-E SSM data with normalized multiband drought index (NMDI) derived from the moderate resolution imaging spectroradiometer (MODIS) data to provide fused SSM product with finer spatial resolution that can be up to 1 km. Practical implementation of this data fusion method was carried out in Central America Isthmus region to generate the SSM maps with the spatial resolution of 1 km during the dry seasons in 2010 and 2011 for various agricultural applications. The calibration and validation of the SSM maps based on the fused images of AMSR-E and MODIS yielded satisfactory agreement with in situ ground truth data pattern wise.
Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing
"Monitoring Spatiotemporal Surface Soil Moisture Variations During Dry Seasons in Central America With Multisensor Cascade Data Fusion" (2014). Faculty Bibliography 2010s. 5160.