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土壤水分是评估地表旱情最直接的环境指标。以海南省文昌市沿海地区为研究区，构建雷达后向散射耦合模型，发展适用于海水倒灌地区的土壤水分反演算法。结合水云模型和裸土后向散射模型构建后向散射耦合模型，选取4种植被指数构建植被含水量估算模型。实验随机选取50%的地面实测数据，结合高分辨率三号(GF-3)卫星数据对后向散射耦合模型进行参数化，利用余下的50%实测数据进行后向散射系数的模拟，并与卫星提取的后向散射系数之间构建代价函数，采用最小二乘法实现土壤水分和地表粗糙度的协同反演。结果显示，土壤水分和地表粗糙度的反演精度R分别为0.470和0.562,RMSE分别为0.022 cm3/cm3和0.071 cm,Bias分别为-0.001 cm3/cm3和-0.003 cm。该研究对于昼夜温差大、风速快的沿海地区的土壤水分和地表粗糙度反演具有参考价值。
Abstract：Soil moisture is the most direct environmental index to evaluate surface drought. Taken the coastal area of Wenchang City, Hainan Province as the research area, and the coupled model of radar backscattering is constructed, and the soil moisture inversion algorithm suitable for seawater intrusion area is developed.Combined with water cloud model and bare soil backscattering model, a coupled backscattering model was constructed, and four vegetation indices were selected to construct a vegetation water content estimation model. In the experiment, 50% of the measured data on the ground were randomly selected, and the coupled backscattering model was parameterized with the high-resolution GF-3 satellite data. The backscattering coefficient was simulated with the remaining 50% of the measured data, and the cost function was constructed with the backscattering coefficient extracted by the satellite. The cooperative inversion of soil moisture and surface roughness was realized by the least square method. The results indicated that the inversion accuracies for soil moisture and surface roughness achieved R values of 0.470 and 0.562, RMSE values of 0.022 cm3/cm3 and 0.071 cm, and Bias values of-0.001 cm3/cm3 and-0.003 cm, respectively. This study provides valuable insights for the inversion of soil moisture and surface roughness, particularly in coastal regions characterized by large diurnal temperature variations and high wind speeds.
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基本信息:
DOI：10.20203/j.cnki.2095-8919.2025.01.002
中图分类号:P237;S812.2
引用信息:
[1]翁昊哲,陈思,郭君达等.基于后向散射耦合模型和高分数据的草地土壤水分反演[J].吉林建筑大学学报,2025,42(01):8-16.DOI:10.20203/j.cnki.2095-8919.2025.01.002.
基金信息:
国家自然科学基金项目（42201435）; 国家民用基础设施陆地观测卫星共性支撑平台（CASPLOS-CCSI）; 吉林省科技发展计划项目（YDZJ202301ZYTS230）; 吉林省教育厅科学技术研究项目（JJKH20220259KJ）