融合哨兵1/2多时相数据，用深度学习精准估算叶面积指数。
Leveraging Multi-Temporal Sentinel 1 and 2 Satellite Data for Leaf Area Index Estimation With Deep Learning
- 用多尺度U-Net模型融合雷达与多光谱数据，实现像素级预测
- 在公开数据上达0.06 RMSE、0.93 R²，精度显著提升
- 适合遥感生态监测、植被动态研究者使用
叶面积指数（LAI）是理解生态系统健康与植被动态的关键参数。本文提出一种新颖的像素级LAI预测方法，充分利用哨兵1号雷达数据与哨兵2号多光谱数据在多个时间点的互补信息。所提方法基于多U-Net结构的深度神经网络，针对不同模态输入设计独立预训练模块，将各类数据映射至统一潜在空间；随后通过端到端微调，结合共同解码器与季节性特征建模，显著提升预测性能。实验结果显示，在公开数据集上达到0.06的RMSE和0.93的R²评分。相关代码已开源，供后续研究持续优化。
原文摘要 · Abstract (English)
The Leaf Area Index (LAI) is a critical parameter to understand ecosystem health and vegetation dynamics. In this paper, we propose a novel method for pixel-wise LAI prediction by leveraging the complementary information from Sentinel 1 radar data and Sentinel 2 multi-spectral data at multiple timestamps. Our approach uses a deep neural network based on multiple U-nets tailored specifically to this task. To handle the complexity of the different input modalities, it is comprised of several modules that are pre-trained separately to represent all input data in a common latent space. Then, we fine-tune them end-to-end with a common decoder that also takes into account seasonality, which we find to play an important role. Our method achieved 0.06 RMSE and 0.93 R2 score on publicly available data. We make our contributions available at https://github.com/valentingol/LeafNothingBehind for future works to further improve on our current progress.
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