数字农科院2.0

基于深度学习的被动微波遥感土壤水分反演

文献类型： 中文期刊

关键词： 被动微波遥感;;深度学习;;AMSR2;;土壤水分;;数据优化

期刊名称：西北工程技术学报（中英文）

ISSN： 2097-5090

年卷期： 2025 年

页码：

收录情况： 无来源刊（2025版）

摘要： 针对地形复杂或植被密集的区域，为了提高被动微波遥感数据土壤水分反演的精度，提出了一种基于对抗神经网络（GAN）的优化方法。以北美南部（北纬13°—50°，西经65°—125°）为研究区域，采用全连接神经网络（FCNN）对AMSR2 亮温数据和AMSR2 土壤水分值进行预训练，并结合对抗神经网络思想的迭代数据优化方法，利用神经网络隐藏层的特征信息逐步调整目标值。结果表明，对于地表覆盖和气象条件具有显著异质性的区域，优化后的土壤水分反演模型在不同反演精度指标上均有显著提升，最优模型的平均绝对误差（MAE）为0.023 m3/m3，均方根误差（RMSE）为0.028 m3/m3，相关系数（R）为0.944。该研究为遥感数据反演技术在农业、水资源管理和气候变化研究中的应用提供了新方案。

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