A Change Detection Algorithm for Retrieving High-resolution Surface Soil Moisture from Smap L-band Radar and Radiometer Observations

Soil moisture is a critical hydrological variable that links the terrestrial water, energy and carbon cycles. Global and regional observations of soil moisture are needed to estimate the water and energy fluxes at the land surface, to quantify the net carbon flux in boreal landscapes, to enhance weather and climate forecast skill and to develop improved flood prediction and drought monitoring capability. Active and Passive L-band microwave remote sensing provide a unique ability to monitor global soil moisture over land surfaces with an acceptable spatial resolution and temporal frequency [1]. Radars are capable of a very high spatial resolution (∼ 3km) but, since radar backscatter is hightly influenced by surface roughness, vegetation canopy structure and water content, they have a low sensitivity to soil moisture, and the algorithms developed for retrieval of soil moisture from radar backscattering are only valid in low-vegetation water content conditions [3]. In contrast, the spatial resolution of radiometers is typically low (∼ 40km), they have a high sensitivity to soil moisture, and the retrieval of soil moisture from radiometers is well established [4]. To overcome the individual limitations of active and passive approaches, the Soil Moisture Active and Passive (SMAP) mission of the NASA, scheduled for launch in the 2010-2013 time frame, is combining these two technologies [2]. The SMAP mission payload consists on an approximately 40-km L-band microwave radiometer measuring hh and vv brightness temperatures and a 3-km L-band synthetic aperture radar sensing backscatter cross-sections at hh, vv and hv polarizations. It will provide global scale land surface soil moisture observations with a three day revisit time and its key derived products are: soil moisture at 40-km for hydroclimatology, obtained from the radiometer measurements; soil moisture at 10-km resolution for hydrometeorology obtained by combining the radar and radiometer measurements in a joint retrieval algorithm; and freeze/thaw state at 3-km resolution from the radar measurements. A downscaling algorithm has been developed for combining the high radar resolution and the high radiometer accuracy into an optimal blend for the SMAP 10-km soil moisture product.