用卷积LSTM模型实现美国南部1公里分辨率30天叶面积指数预测。
A Sequence-to-Sequence ConvLSTM Approach for Leaf Area Index Forecasting over the South-Central United States
- 基于序列到序列的卷积LSTM，融合气象数据与历史叶面积指数序列。
- 30天预报均方根误差为0.36，比基准方法低三分之一以上。
- 适用于森林、草原等多种植被类型，适合次季节气候建模应用。
叶面积指数（LAI）是调控陆气相互作用的基本生物物理变量；然而，高空间分辨率下的LAI预报仍是未解难题。尽管近年机器学习方法已在点或区域尺度实现了LAI估算，但尚无提供网格化、气象驱动的预报结果，难以满足次季节陆面与气候模拟需求。本文提出一种序列到序列的卷积长短期记忆网络（ConvLSTM）框架，利用历史LAI序列与每日气温、降水等气象强迫数据，生成未来30天内每日1公里分辨率的LAI预报。模型在气候梯度显著、植被类型多样的美国南部地区训练与评估，30天预报的全域平均均方根误差（RMSE）为0.36，优于持久性基准超过三分之一。预报性能在不同季节、地理分布及植物功能类型（包括森林、草原、灌丛和农田）下均保持稳健。据我们所知，这是首次在30天预报时长和1公里分辨率下实现有效LAI预报的成果。
原文摘要 · Abstract (English)
Leaf Area Index (LAI) is a fundamental biophysical variable governing land-atmosphere interactions; however, LAI forecasting at high spatial resolution remains an unsolved challenge. While recent machine learning approaches have demonstrated LAI estimation at point or regional scales, none provides a gridded, meteorology-driven prognostic forecast suitable for subseasonal land surface and climate modeling applications. Here we present a sequence-to-sequence Convolutional LSTM (ConvLSTM) framework that generates daily 1-km LAI forecasts up to 30 days ahead, driven by historical LAI sequences and daily meteorological forcing including temperature and precipitation. Trained and evaluated over the South-Central United States -- a region of strong climate gradients and diverse vegetation -- the model achieves a domain-averaged RMSE of 0.36 at a 30-day lead time, more than a third lower than the persistence baseline. Forecast skill remains robust across seasons, geographic distributions, and plant functional types, including forests, grasslands, shrublands, and croplands. To our knowledge, this is the first demonstration of skillful LAI forecasting at a 30-day horizon at 1-km resolution.
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