基于校准的积分方程模型（IEM）和杜波依斯模型并利用神经网络从哨兵-1合成孔径雷达（SAR）数据中反演裸土表面湿度

Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks.

作者信息

Mirsoleimani Hamid Reza, Sahebi Mahmod Reza, Baghdadi Nicolas, El Hajj Mohammad

机构信息

Faculty of Geodesy and Geomatics Engineering & Remote Sensing Institute, K. N. Toosi University of Technology, Tehran 19667-15433, Iran.

IRSTEA, UMR TETIS, University of Montpellier, 500 rue François Breton, 34093 Montpellier cedex 5, France.

出版信息

Sensors (Basel). 2019 Jul 21;19(14):3209. doi: 10.3390/s19143209.

DOI:10.3390/s19143209

PMID:31330897

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6679500/

Abstract

The main purpose of this study is to investigate the performance of two radar backscattering models; the calibrated integral equation model (CIEM) and the modified Dubois model (MDB) over an agricultural area in Karaj, Iran. In the first part, the performance of the models is evaluated based on the field measurement and the mentioned backscattering models, CIEM and MDB performed with root mean square error (RMSE) of 0.78 dB and 1.45 dB, respectively. In the second step, based on the neural networks (NNS), soil surface moisture is estimated using the two backscattering models, based on neural networks (NNs), from single polarization Sentinel-1 images over bare soils. The inversion results show the efficiency of the single polarized data for retrieving soil surface moisture, especially for VV polarization.

摘要

本研究的主要目的是调查两种雷达后向散射模型的性能；校准积分方程模型（CIEM）和改进的杜波依斯模型（MDB）在伊朗卡拉季的一个农业区域上的性能。在第一部分中，基于实地测量对模型的性能进行评估，上述后向散射模型CIEM和MDB的均方根误差（RMSE）分别为0.78 dB和1.45 dB。在第二步中，基于神经网络（NNS），利用这两种后向散射模型，从裸露土壤上单极化哨兵 -1图像中估算土壤表面湿度。反演结果表明单极化数据在反演土壤表面湿度方面的有效性，特别是对于垂直极化（VV）。