Soil moisture estimation in a semiarid watershed using RADARSAT‐1 satellite imagery and genetic programming
Soil moisture estimation in a semiarid watershed using RADARSAT‐1 satellite imagery and genetic programming
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DOI：
10.1029/2005wr004033
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发表时间：
2006-09
影响因子：
5.4
通讯作者：
Ammarin Makkeasorn;N. Chang;M. Beaman;C. Wyatt;C. Slater
中科院分区：
文献类型：
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作者：
Ammarin Makkeasorn;N. Chang;M. Beaman;C. Wyatt;C. Slater
土壤水分是水文循环的关键因素，特别是在半干旱或干旱地区。由于流域土壤水分格局在时间和空间上存在较大的差异，因此对流域土壤水分的连续分布进行点测量是困难的。星载雷达成像卫星一直很受欢迎，因为它们有能力显示所有天气观测结果。然而，基于主动或被动卫星图像的土壤水分估算方法仍然不确定。本研究旨在提出一种系统的土壤水分估算方法，该方法适用于美国德克萨斯州南部面积超过14,200 km2的半干旱流域——咽喉峡谷水库流域（CCRW）。在5个角反射器的帮助下，对2004年4月和9月获得的研究区RADARSAT - 1合成孔径雷达（SAR）图像进行了辐射定标和几何定标。利用遗传规划（GP）技术建立了新的土壤水分估算模型，并应用于土壤水分分布分析。在演化过程中导出的基于GP的非线性函数独特地将一系列关键的地形和地理特征联系起来。这个过程包括坡度、坡向、植被覆盖和土壤渗透性，以补充校准良好的SAR数据。研究表明，GP的新应用被证明有助于在回归体系中生成高度非线性结构，该结构在模型估计与基于未见数据集的地面真值测量（体积含水量）之间表现出很强的统计相关性。在努力产生土壤水分随季节的分布时，它最终导致表征局部到区域尺度的土壤水分变化，并进行陆地水圈储水量的可能估计。
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 km2 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.