基于时序Sentinel-1A数据的农田土壤水分变化检测分析

发布时间：2018-06-01 14:40

本文选题：合成孔径雷达 + 土壤水分　；参考：《遥感技术与应用》2017年02期

【摘要】：土壤水分是作物生长的基本条件,同时也是作物长势监测、估产和旱情监测的重要参数。基于变化检测模型,利用不同时相Sentinel-1A数据估算农田土壤水分变化信息。首先利用积分方程模型(Integral Equation Model,IEM)模拟数据分析雷达后向散射系数变化与土壤水分变化之间的关系,在土壤表面粗糙度恒定的情况下,土壤湿度变化与雷达后向散射系数变化具有高度相关性,验证了变化检测模型用于估算土壤水分变化的合理性。在此基础上,利用河北邯郸研究区时序Sentinel-1ASAR数据和现场实测数据构建土壤水分变化检测模型,从而利用雷达后向散射系数变化估算土壤水分变化信息。结合现场实测数据得到,最小二乘和支持向量回归模型反演结果的均方根误差(RMSE)分别为5.1vol%和4.6vol%,决定系数(R2)分别为0.65和0.73。验证了时序Sentinel-1A数据用于监测农田土壤水分变化的实用性。
[Abstract]:Soil moisture is not only the basic condition of crop growth, but also an important parameter for crop growth monitoring, yield estimation and drought monitoring. Based on the change detection model, the information of farmland soil moisture change was estimated by using different phase Sentinel-1A data. First, the integral Equation Model (IEM) is used to simulate the relationship between the change of radar backscattering coefficient and the change of soil moisture. When the roughness of soil surface is constant, the relationship between the change of radar backscattering coefficient and the change of soil moisture is analyzed. The variation of soil moisture is highly correlated with the change of radar backscattering coefficient, which verifies the rationality of the change detection model used to estimate the change of soil moisture. On this basis, a soil moisture change detection model was constructed by using the time-series Sentinel-1ASAR data and field measured data in Handan study area, Hebei Province, and the information of soil moisture change was estimated by the change of radar backscattering coefficient. Based on the field measured data, the root mean square error (RMSE) of the inversion results of the least squares and support vector regression models is 5.1 vol% and 4.6 vols respectively, and the determination coefficient (R2) is 0.65 and 0.73, respectively. The practicability of using time series Sentinel-1A data to monitor soil moisture change in farmland was verified.
【作者单位】：中国矿业大学环境与测绘学院;国土环境与灾害监测国家测绘地理信息局重点实验室中国矿业大学;国家基础地理信息中心;
【基金】：国家自然科学基金项目(41271116) 江苏省基础研究计划青年基金项目(BK20130174) 青年科学基金项目(41401426) 测绘地理信息公益性行业科研专项经费项目(201412016)
【分类号】：S152.7

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