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Integrating UAV-Based Multispectral Data and Transfer Learning for Soil Moisture Prediction in the Black Soil Region of Northeast China

文献类型： 外文期刊

作者： Zhou, Tong 1 ; Ma, Shoutian 1 ; Liu, Tianyu 1 ; Yao, Shuihong 2 ; Li, Shenglin 1 ; Gao, Yang 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Farmland Irrigat, Xinxiang 453002, Peoples R China

2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China

关键词： UAV; remote sensing; transfer learning; soil moisture; cross-region

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 3 期

页码：

收录情况： SCI

摘要： The rapid and accurate acquisition of soil moisture (SM) information is essential. Although Unmanned Aerial Vehicle (UAV) remote sensing technology has made significant advancements in SM monitoring, existing studies predominantly focus on developing models tailored to specific regions. The transferability of these models across different regions remains a considerable challenge. Therefore, this study proposes a transfer learning-based framework, using two representative small agricultural watersheds (Hongxing region and Woniutu region) in Northeast China as case studies. This framework involves pre-training a model on a source domain and fine-tuning it with a limited set of target domain samples to achieve high-precision SM inversion. This study evaluates the performance of three algorithms: Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) network. Results show that the fine-tuned model significantly mitigates the decline in prediction accuracy caused by regional differences. The fine-tuned LSTM model achieved the highest retrieval accuracy, with the following results: 10% samples (R = 0.615, RRMSE = 15.583%), 30% samples (R = 0.682, RRMSE = 13.97%), and 50% samples (R = 0.767, RRMSE = 16.321%). Among these models, the LSTM model exhibited the most significant performance improvement and the best transferability. This study underscores the potential of transfer learning for enhancing cross-regional SM monitoring and providing valuable insights for future UAV-based SM monitoring.

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