利用辐射传输模型标定雷达极化分解

IF 4.4
Giovanni Anconitano;Lorenzo Giuliano Papale;Leila Guerriero;Mario Alberto Acuña;Nazzareno Pierdicca
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引用次数: 0

摘要

本文描述了一种基于辐射传递理论的程序,用于校准玉米田上广义Freeman-Durden (GFD)极化分解的散射贡献。利用Tor Vergata电磁模型(TOV)模拟了典型散射机制,并与采用GFD对模拟和l波段SAOCOM-1A数据进行了比较。该方法首先分析了模型与应用于模拟数据的GFD之间的误差。然后进行多元数据拟合,得到新的GFD幂表达式,并在l波段实际数据上进行了检验。GFD体积功率从校准中获得最大收益,将相对于相应TOV模型贡献的均方根误差(RMSE)降低到0.006(线性单位)。为了进一步验证这一过程,利用SAOCOM-1A实际数据校准的GFD功率,使用线性回归模型估计土壤湿度。通过对原位数据进行留一(LOO)交叉验证来评估检索性能,显示出显着的改进。校准的GFD功率导致线性相关性增加(从0.32到0.57),而RMSE降低(从0.096到0.055 m3/m3)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Calibration of a Radar Polarimetric Decomposition Using a Radiative Transfer Model
This letter describes a procedure based on the radiative transfer theory to calibrate the scattering contributions from the Generalized Freeman–Durden (GFD) polarimetric decomposition over corn fields. The Tor Vergata electromagnetic model (TOV) is used to simulate canonical scattering mechanisms that are compared with those obtained by applying GFD to both simulated and L-band SAOCOM-1A data. The proposed method first analyzes the error between the model and the GFD applied to the simulated data. A multivariate data fitting is then performed to derive a new expression of the GFD powers, which is tested on the L-band real data. The GFD volume power obtains the greatest benefit from the calibration, reducing the root mean square error (RMSE) with respect to the corresponding TOV model contribution to 0.006 in linear units. To further test the procedure, a linear regression model is used to estimate soil moisture using the calibrated GFD powers from SAOCOM-1A real data. The retrieval performance, evaluated through a Leave-One-Out (LOO) cross-validation against in-situ data, shows a significant improvement. The calibrated GFD powers lead to an increased linear correlation (from 0.32 to 0.57), while the RMSE is reduced (from 0.096 to 0.055 m3/m3).
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