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Title: ESTIMATING SOIL MOISTURE AT THE WATERSHED SCALE WITH SATELLITE-BASED RADAR AND LAND SURFACE MODELS 1528

Author
item Moran, Mary
item WATTS, J. - US ARMY
item PETERS-LIDARD, C. - NASA GODDARD
item McElroy, Stephen

Submitted to: Canadian Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2004
Publication Date: 10/1/2004
Citation: Moran, M.S., Watts, J.M., Peters-Lidard, C.D., McElroy S.A. 2004. Estimating soil moisture at the watershed scale with satellite-based radar and land surface models. Canadian Journal of Remote Sensing 30(5): 805-826.

Interpretive Summary: Information about soil moisture is critical for land management decisions related to drought or flooding, crop irrigation scheduling and pest management. Generally, information is need at fine resolutions (10 to 100 m) with extensive spatial coverage (1,000 to 25,000 km2). This can be accomplished through the use of sensors mounted on airplanes or satellites measuring radar backscatter and surface temperature. This review summarizes the state of the science using current satellite-based sensors to determine regional surface soil moisture distribution. Further, the review introduces the concept of combining remotely sensed information with Soil Vegetation Atmosphere Transfer (SVAT) models to estimate soil moisture within the entire root zone at daily time steps. The basic conclusion of this review is that currently orbiting sensors combined with available SVAT models could provide distributed, profile soil moisture information with known accuracy at the watershed scale. However, to realize a truly operational system for watershed management, it will be necessary to continue sensor development, improve image availability and timely delivery, and reduce image cost.

Technical Abstract: Distributed soil moisture information at the watershed scale would be useful for such critical applications as regional resource management during times of drought or flooding, crop irrigation scheduling and pest management, and determining mobility with lightweight vehicles. At this scale, the desired spatial resolution is on the order of 10 to 100 m and spatial coverage requirements range from 1,000 to 25,000 km2. The only feasible way to determine distributed soil moisture at this resolution and coverage is through the use of airborne sensors, particularly those measuring microwave backscatter and optical reflectance and emittance. This review summarizes the state of the science using current satellite-based sensors to determine regional surface soil moisture distribution. However, remote sensing alone can only provide surface soil moisture at depths ranging from 1-5 cm, whereas watershed management applications required soil moisture information to depths ranging from the sub-surface (15 cm) to the entire root zone (> 1 m). Furthermore, remotely sensed images are generally acquired at bi-weekly to monthly intervals that do not correspond well with day-to-day information requirements of resource managers. To fully meet the soil moisture information requirements for watershed management, remotely sensed information has been assimilated into Soil Vegetation Atmosphere Transfer (SVAT) models to estimate profile soil moisture at daily time steps. This review describes a number of SVAT models that have potential for this application and summarizes the theory and state of data assimilation for estimation of profile soil moisture. The basic conclusion of this review is that currently orbiting sensors combined with available SVAT models could provide distributed, profile soil moisture information with known accuracy at the watershed scale. The priority areas for future research should include active and passive microwave data fusion, determination of soil moisture in densely vegetated sites, image-based approaches for mapping surface roughness, and improved data assimilation approaches for a combined remote sensing/modeling system. However, to realize a truly operational system for watershed management, it will be necessary to continue sensor development, improve image availability and timely delivery, and reduce image cost.