Mitigation of Land Contamination in SMOS L1C Brightness Temperature Data Based on Convolutional Neural Networks

IF 4.7 2区地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-10-08 DOI:10.1109/JSTARS.2024.3476470

Ke Chen;Qian Yang;Yuanyuan Tian;Qingxia Li;Rong Jin

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引用次数: 0

Abstract

Due to the Gibbs oscillation effect of microwave aperture synthesis radiometers (ASRs) occurring near land/ocean transitions, soil moisture and ocean salinity (SMOS) brightness temperature (TB) measurements are subject to land contamination in ocean regions (within a few hundred kilometers of the shore). In this article, the magnitude of land contamination in the SMOS L1C TB is demonstrated by comparison with the results of collocated forward modeling. Then, a land contamination correction algorithm for microwave SARs based on a convolutional neural network is presented. The basic process of the algorithm is to design a land contamination mitigation network (LCMN) to learn the land contamination features from the simulated TB dataset of the microwave ASRs and then to use the well-trained network to remap the actual observed TB images to mitigate the land contamination error. Land contamination error correction experiments based on LCMN remapping were executed on SMOS L1C TB data, and the performance of the algorithm was verified by comparison with forward modeling and salinity retrieval. The experimental results show that the proposed LCMN algorithm can effectively mitigate land contamination at the SMOS L1C TB.

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基于卷积神经网络减轻 SMOS L1C 亮度温度数据中的土地污染

由于微波孔径合成辐射计（ASR）在陆地/海洋转换附近产生的吉布斯振荡效应，土壤水分和海洋盐度（SMOS）亮度温度（TB）测量在海洋区域（距海岸几百公里内）受到陆地污染。本文通过与同位前向建模结果的比较，证明了 SMOS L1C TB 中陆地污染的严重程度。然后，介绍了一种基于卷积神经网络的微波合成孔径雷达陆地污染校正算法。该算法的基本过程是设计一个土地污染减缓网络（LCMN），从微波合成孔径雷达的模拟 TB 数据集中学习土地污染特征，然后使用训练有素的网络重新映射实际观测到的 TB 图像，以减缓土地污染误差。在 SMOS L1C TB 数据上进行了基于 LCMN 重映射的土地污染误差修正实验，并通过与前向建模和盐度检索的比较验证了算法的性能。实验结果表明，所提出的 LCMN 算法能有效减轻 SMOS L1C TB 的土地污染。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 地学-成像科学与照相技术

CiteScore

9.30

自引率

10.90%

发文量

563

审稿时长

4.7 months

期刊介绍： The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.