用多模态迭代能量模型，从卫星数据同时精准反演土壤湿度、叶面积和株高。
An iterative energy-based multimodal transformer for joint retrieval of wheat soil moisture, leaf area index, and plant height from Sentinel-1 and Sentinel-2 time series
- 构建共享序列的迭代能量框架，通过梯度更新优化三参数联合估计。
- 在印度瓦拉纳西数据上实现0.854的R²均值，土壤湿度与叶面积指数精度超0.84。
- 自带质量诊断功能，能自动筛选低置信度样本，适合小农户农田监测场景。
田块尺度上同步反演地表土壤湿度（SM）、叶面积指数（LAI）和植株高度（PH）对精准农业至关重要，但仍是病态逆问题。土壤湿度与冠层密度的同步变化导致雷达后向散射与光谱响应存在显著混淆，削弱了传统前馈回归模型在异质小农户种植系统中的效果。本研究提出迭代能量基变压器（iEBT），用于从哨兵-1 C波段SAR与哨兵-2多光谱时序数据中联合反演耦合的土-冠状态。iEBT不直接回归，而是将多模态预测因子嵌入共享序列，生成初始状态估计，并通过归一化梯度下降迭代更新目标[SM, LAI, PH]向量，以最小化学习得到的标量兼容性能量函数。基于印度瓦拉纳西700个高质量田间测量数据，iEBT在随机测试集上取得最高学习模型性能，四种子集平均R²为0.854±0.012（R_SM²=0.841，R_LAI²=0.905，R_PH²=0.821）。保留WCM和PROSAIL作为物理解释性参考模型进行对比。模态消融实验表明，哨兵-1主导土壤湿度反演，哨兵-2主导叶面积指数，而植株高度依赖结构-物候特征融合。关键的是，模型终止时的能量函数可作为无校准后处理质量诊断工具；筛选出10%高能量样本后，目标层级均方根误差显著降低。尽管留一任务外验证揭示了因局部管理差异引起的跨季节域偏移挑战，但兼容性引导的多模态融合为可靠生物物理参数估计提供了结构化自诊断路径。
原文摘要 · Abstract (English)
Field-scale retrieval of surface soil moisture (SM), leaf area index (LAI), and plant height (PH) is essential for precision agriculture, yet it remains an ill-posed inverse problem. Concurrent variations in soil moisture and canopy density generate substantial ambiguities in radar backscatter and spectral responses, which reduces the effectiveness of traditional feedforward regression models in heterogeneous smallholder cropping systems. This study presents the Iterative Energy-Based Transformer (iEBT) for the joint retrieval of coupled soil-canopy states from Sentinel-1 C-band SAR and Sentinel-2 multispectral time series. Instead of direct regression, iEBT embeds multi-modal predictors within a shared sequence, produces an initial state estimate, and iteratively updates the target [SM, LAI, PH] vector through normalized gradient descent to minimize a learned scalar compatibility energy function. Using 700 quality-controlled field measurements from Varanasi, India, iEBT achieved the highest learned-model performance on the random test split, with a four-seed mean R^2 of 0.854 \pm 0.012 (R_SM^2 = 0.841, R_LAI^2 = 0.905, R_PH^2 = 0.821). WCM and PROSAIL were retained as physically interpretable SAR and optical reference models for comparison. Modality ablations confirmed that Sentinel-1 drives SM retrieval, while Sentinel-2 dominates LAI, whereas PH relies on combined structural-phenological signatures. Crucially, the model's terminal energy functions as an uncalibrated post-retrieval quality diagnostic; screening the 10% highest-energy samples markedly reduced target level root-mean-square errors. While leave-one-campaign-out validation highlights persistent cross-season domain shift challenges due to localized management variations, compatibility-guided multimodal fusion offers a structured self-diagnostic path toward reliable biophysical parameter estimation
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