用光学和雷达数据融合结果确定地平面物理参数的方法

M. Svideniuk
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引用次数: 1

摘要

提出了多光谱光学与双偏振雷达卫星数据融合估算土壤物理参数的方法。具体而言,该方法包括基于积分方程模型(IEM)的相对介电常数估计,该方法使用高分辨率Sentinel-1 GRDH雷达数据。在补偿土壤酸度和温度不稳定效应的基础上,提供了ε的定标。利用PlanetScope高分辨率多光谱图像进行植被指数和热发射率估算。对低分辨率MODIS和中分辨率Landsat-7/8 ETM+/TIRS热红外图像进行处理,估算地表热力温度。研究了一种基于局部信号偏差和表面粗糙度估计的雷达信号去极化补偿方法。基于中分辨率数字地形高程模型ALOS AWD3D复原地形非均质性。为了评估基于该方法设计的土壤水分估算模型的准确性,进行了地面真值测量。具体地说,他们包括土壤样本的重量土壤水分检索。此外,采用GM1312差示温度计和WALCOM多功能仪对土壤酸度和温度进行了测量。利用估计参数和地面真值数据,基于多元回归依赖关系反演土壤水分。土壤水分反演的均方根误差估计为4.73%。这样的精度对于自然保护区的土壤湿度监测是完全可以接受的
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Methodology for determining the physical parameters of ground plane by the results of the optical and radar data fusion
The methodology of multispectral optical and dual-polarized radar satellite data fusion for soils physical parameters estimation is developed. In particular, the methodology comprises relative permittivity estimation based on the Integral Equation Model (IEM) by using high resolution Sentinel-1 GRDH radar data. The calibration of ε was provided based on the compensation of soil acidity and temperature destabilizing effects. High-resolution multispectral images PlanetScope were used for vegetation indices and thermal emissivity estimation. Both, low-resolution MODIS and medium resolution Landsat-7/8 ETM+/TIRS thermal infrared images were processed in order to estimate ground plane thermodynamic temperature. An investigated approach for the radar signal depolarization compensation is based on local signal deviations and surface roughness estimation. The relief heterogeneity is restored based on the medium-resolution digital terrain elevation model ALOS AWD3D. Aiming to evaluate the accuracy of a soil moisture estimation model designed based on the presented methodology, ground truth measurements were carried out. Specifically, they included soil samples retrieving for the gravimetric soil moisture. In addition, the soil acidity and temperature were measured by applying the GM1312 differential thermometer and WALCOM multifunction device. The estimated parameters and ground truth data were used in order to retrieve the soil moisture based on the multivatiative regression dependence. Root mean square error of soil moisture retrieving was estimated as 4,73 %. Such accuracy is completely acceptable for the soil moisture monitoring of natural-reserved fund territories
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