Share Email Print

Journal of Applied Remote Sensing

Improving long-term, retrospective precipitation datasets using satellite-based surface soil moisture retrievals and the Soil Moisture Analysis Rainfall Tool
Author(s): Fan Chen; Wade Crow; Thomas R. H. Holmes
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Using historical satellite surface soil moisture products, the Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve the submonthly scale accuracy of a multi-decadal global daily rainfall product that has been bias-corrected to match the monthly totals of available rain gauge observations. In order to adapt to the irregular retrieval frequency of heritage soil moisture products, a new variable correction window method is developed that allows for better efficiency in leveraging temporally sparse satellite soil moisture retrievals. Results confirm the advantage of using this variable window method relative to an existing fixed-window version of SMART over a range of one- to 30-day accumulation periods. Using this modified version of SMART and heritage satellite surface soil moisture products, a 1.0-deg, 20-year (1979 to 1998) global rainfall dataset over land is corrected and validated. Relative to the original precipitation product, the corrected dataset demonstrates improved correlation with a global gauge-based daily rainfall product, lower root-mean-square-error (-13%) on a 10-day scale and provides a higher probability of detection (+5%) and lower false alarm rates (-3.4%) for five-day rainfall accumulation estimates. This corrected rainfall dataset is expected to provide improved rainfall forcing data for the land surface modeling community.

Paper Details

Date Published: 28 November 2012
PDF: 16 pages
J. Appl. Rem. Sens. 6(1) 063604 doi: 10.1117/1.JRS.6.063604
Published in: Journal of Applied Remote Sensing Volume 6, Issue 1
Show Author Affiliations
Fan Chen, Agricultural Research Service (United States)
Wade Crow, Agricultural Research Service (United States)
Thomas R. H. Holmes, Agricultural Research Service (United States)

© SPIE. Terms of Use
Back to Top