融合气象数据时滞与深层土壤传播，提升干旱区墒情预测精度
Deep Learning for Soil Moisture Estimation: Fusing Satellite Data with Optimally-Lagged Meteorological Features
- 用交叉相关函数确定气象与土壤湿度的最优时间延迟（0-30天）及垂直传播延迟（0-15天）
- 融合气象与深层数据后，模型最高达R²=0.930，较仅用卫星数据提升1.00点
- 适合需要精准墒情监测的智慧农业、遥感与气候建模研究者使用
在半干旱农业区精确估算土壤湿度需融合遥感与气象信息，并考虑土壤对大气强迫的滞后响应。本研究提出交叉相关函数（CCF）方法，确定气象变量与土壤湿度间最优时间延迟（0-30天），以及表层（10厘米）到深层（20-50厘米）的垂直传播延迟（0-15天）。在西班牙东南部七个农田地块验证。评估三种深度学习架构：针对单像素的CNN，针对每日地块均值的LSTM，以及基于滑动窗口的多块聚合训练的CNN-LSTM混合模型。五种特征配置从仅卫星数据到卫星-气象-深度全融合。模型在预留数据上评估以衡量真实泛化能力。气象变量显著优于仅卫星数据基线，深层深度信息对所有架构均具决定性作用。单块像素CNN表现最佳（R²=0.877，RMSE=2.28），七块平均R²=0.535，较卫星基线提升+1.00。混合模型整体性能最高（R²=0.930，CVRMSE=8.0%）。结果表明，显式建模大气与垂直延迟可显著提升土壤湿度估算，助力精准农业。
原文摘要 · Abstract (English)
Accurate soil moisture estimation in semi-arid agricultural regions requires integrating remote sensing and meteorological information while accounting for the delayed response of soil moisture to atmospheric forcing. This study introduces a Cross-Correlation Function (CCF) methodology to determine optimal temporal lags (0-30 days) between meteorological variables and soil moisture, as well as inter-depth lags (0-15 days) describing vertical moisture propagation from the surface (10 cm) to deeper layers (20-50 cm). The approach was validated across seven agricultural plots in southeastern Spain. Three deep learning architectures, each targeting a distinct prediction granularity, were evaluated under five feature configurations ranging from satellite-only to full satellite-meteorology-depth fusion: a CNN for per-pixel estimation within each plot, an LSTM for frame-level (daily plot-mean) prediction, and a CNN-LSTM hybrid operating on sliding windows with pooled multi-patch training. Models were assessed on held-out data to measure genuine generalisation. Meteorological variables improved performance over the satellite-only baseline, while subsurface depth information proved decisive across all architectures. The per-pixel CNN achieved the strongest single-patch result (R^2 = 0.877, RMSE = 2.28), with a seven-patch average R^2 of 0.535, representing an improvement of +1.00 over the satellite-only baseline. The pooled CNN-LSTM hybrid obtained the highest overall performance (R^2 = 0.930, CVRMSE = 8.0%). These results demonstrate that explicitly modelling atmospheric and vertical subsurface delays substantially improves soil moisture estimation for precision agriculture.
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