Title

Soil moisture estimation in a semiarid watershed using RADARSAT-1 satellite imagery and genetic programming

Authors

Authors

A. Makkeasorn; N. B. Chang; M. Beaman; C. Wyatt;C. Slater

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

Water Resour. Res.

Keywords

SYNTHETIC-APERTURE RADAR; MICROWAVE RADIOMETER; SAR DATA; OCEAN; SALINITY; GREAT-BASIN; CALIBRATION; METHODOLOGY; NEVADA; MODEL; ESTAR; Environmental Sciences; Limnology; Water Resources

Abstract

[ 1] Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km(2) in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming ( GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements ( volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere.

Journal Title

Water Resources Research

Volume

42

Issue/Number

9

Publication Date

1-1-2006

Document Type

Article

Language

English

First Page

15

WOS Identifier

WOS:000240339000001

ISSN

0043-1397

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