Soil Moisture and Vegetation Water Content Retrieval Using QuikSCAT Data

Remote Sensing

Joe Turk¹  Shadi Oveisgharan¹  Ernesto Rodriguez¹  Ziad Haddad¹  Li Li² 

[1] Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91109, USA;Naval Research Laboratory, 4555 Overlook Ave., SW, Washington, DC 20375, USA;

关键词: QuikSCAT;    vegetation water content;    soil moisture;   

DOI  :  10.3390/rs10040636

来源: DOAJ

【 摘 要 】

Climate change and hydrological cycles can critically impact future water resources. Uncertainties in current climate models result in disagreement on the amount of water resources. Soil moisture and vegetation water content are key environmental variables on evaporation and transpiration at the land–atmosphere boundary. Radar remote sensing helps to improve our estimate of water resources spatially and temporally. This work proposes a backscattered power formulation for the Ku-band. Li et al. (2010) retrieved soil moisture and vegetation water content values using Windsat data and simultaneous collocated QuikSCAT backscattered power are used to estimate different parameters of backscatter formulation. These parameters are used to estimate soil moisture and vegetation water content using QuikSCAT power everywhere and every day during the summer season. The 2-folded cross validation method is used to evaluate the performance of soil moisture and vegetation water content retrieval. A relatively large correlation is observed between vegetation water content using WindSat and QuikSCAT data in land classes of Evergreen Needleleaf, Evergreen Broadleaf, Deciduous Broadleaf, and Mixed Forests. Similarly, the retrieved soil moisture using QuikSCAT in areas with bare surface fraction of greater than 60% shows relatively high correlation with WindSat values. QuikSCAT satellite collects data over land globally almost every day. Therefore, QuikSCAT data can be used to generate a global map of soil moisture and vegetation water content daily from 2000 to 2009.

【 授权许可】

Unknown