利用半经验模型的土壤水分反演方法

发布时间：2018-01-26 16:58

本文关键词： RADARSAT- IEM模拟粗糙度定标土壤水分　出处：《测绘科学》2016年02期 　论文类型：期刊论文

【摘要】：针对土壤水分反演经验模型使用范围受限、而理论模型相对复杂的问题,该文利用积分方程模型及经验相关长度定标模型,分析均方根高度、地表相关长度、土壤体积含水量对雷达后向散射系数组合的影响;结合RADARSAT-2全极化C波段雷达数据和野外实测数据,构建研究区均方根高度反演模型;将该模型与Dubois模型、介电模型进行结合以反演表层土壤含水量,并对反演值进行线性校正,最终实现裸土区表层土壤水分反演。实验结果表明,HH、VV极化下土壤水分实测值与反演值绝对误差在0.06内的点数分别达到总点数的78%和68%,证明该方法具有一定的可靠性和实用性,可为地表均方根高度、土壤水分反演提供方法和借鉴。
[Abstract]:In order to solve the problem that the range of soil moisture inversion empirical model is limited but the theoretical model is relatively complex, this paper uses the integral equation model and empirical correlation length calibration model to analyze the root mean square height and surface correlation length. The effect of soil volume water content on the combination of radar backscattering coefficient; Based on the RADARSAT-2 fully polarized C-band radar data and field measured data, the RMS height inversion model of the study area is constructed. The model is combined with Dubois model and dielectric model to retrieve the surface soil water content, and the inversion value is corrected linearly. Finally, the surface soil moisture inversion in bare soil area is realized. The absolute error of soil moisture measured value and inversion value under HHV polarization reached 78% and 68 of the total points, respectively, which proved that this method has certain reliability and practicability. It can provide the method and reference for the retrieval of the root mean square (RMS) height and soil moisture.
【作者单位】：河南理工大学矿山空间信息技术国家测绘地理信息局重点实验室;中国科学院遥感与数字地球研究所;山东科技大学测绘科学与工程学院;
【基金】：国家自然科学基金项目(41471310) 高分辨率对地观测系统重大专项(08-Y30BO7-9001-13/15;09-Y30B03-9001-13/15) 河南省高校科技创新团队支持计划项目(14IRTSTHN026) 中科院遥感与数字地球研究所所长基金项目(Y5SJ0600CX)
【分类号】：S152.7
【正文快照】： 0引言我国是一个旱灾频发的国家,土壤水分严重影响着土壤的理化性质与植被的生长,监测土壤水分对农作物估产、旱情监测和蒸散量起到决定性作用[1]。主动微波遥感因其不受天气影响、对地物具有一定穿透性等优势,被广泛应用于土壤水分反演。在裸土区,影响微波后向散射系数强度的

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