用气候数据+卫星影像预测冰川退缩，效果优于纯图像模型
GlacierCastAI: Predicting Glacier Retreat from Multi-Modal Satellite Imagery and Climate Signals
- 融合卫星影像、气候变量与地形数据，构建多模态时序预测模型
- 加入气候信号后指标提升3.4%，轻量模型仅用1/85参数达98%效果
- 揭示春季太阳辐射是影响冰川退缩的关键气候因子
ERA5季节性气候变量包含超越卫星影像的未来冰川退缩预测信息，但现有深度学习方法多聚焦当前边界映射而非未来预测。本文提出GlacierCastAI，将冰川边界预测重构为多模态时空预测问题，融合多时相Landsat影像、ERA5再分析气候变量及Copernicus DEM地形特征，对跨越四种气候区的五个冰川进行边界预测。模型采用ResNet50空间编码器、ConvLSTM时序模块与交叉注意力气候融合单元。因预测固有不确定性，报告的IoU值（0.320-0.337）不直接可比现有映射模型。经预注册消融实验，加入ERA5气候信号使纯图像模型的IoU从0.326提升至0.337（+3.4%），表明大气强迫携带图像外预测信息。所有深度模型显著优于持续性与线性趋势基线（IoU分别为0.160和0.169），相对提升89%-99%。轻量级气候仅模型（661K参数）以85倍更少参数实现IoU 0.320（达图像模型98%性能），说明ERA5变量独立蕴含丰富预测信号。SHAP归因分析显示，春季太阳辐射（MAM）为关键气候驱动因子，与春季日照决定融雪季路径的已有认知一致。
原文摘要 · Abstract (English)
ERA5 seasonal climate variables contain predictive information about future glacier retreat beyond what satellite imagery alone provides, yet existing deep learning methods focus on mapping current boundaries rather than forecasting future ones. This paper presents GlacierCastAI, which reframes glacier boundary prediction as a multi-modal spatiotemporal forecasting problem, fusing multi-temporal Landsat imagery with ERA5 reanalysis climate variables and Copernicus DEM terrain features to forecast glacier boundaries across five glaciers spanning four climate regimes. The architecture couples a ResNet50 spatial encoder with a ConvLSTM temporal model and a cross-attention climate fusion module. Because forecasting is inherently more uncertain than mapping current boundaries, the reported IoU values (0.320-0.337) are not directly comparable to state-of-the-art mapping models. Comparisons are against traditional baselines and experimental conditions. Through a pre-registered ablation study, adding ERA5 climate signals improves image-only IoU from 0.326 to 0.337 (+3.4%), suggesting that atmospheric forcing carries predictive information beyond imagery alone. All deep learning models substantially outperform persistence and linear trend baselines (IoU 0.160 and 0.169 respectively), with improvements of 89-99% relative IoU. A lightweight climate-only MLP baseline (661K parameters) achieves an IoU of 0.320 (98% of image-only performance) using 85x fewer parameters, suggesting that ERA5 variables encode substantial predictive signal independently of satellite imagery. SHAP attribution analysis suggests that spring solar radiation (MAM) is the dominant climate driver, consistent with the known role of spring insolation in setting melt season trajectories.
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