Spatio-Temporal Reconstruction of MODIS LAI Using a Self-Supervised Framework for Vegetation Dynamics Monitoring Across China

Abstract

Leaf Area Index (LAI) is a key biophysical parameter for characterizing terrestrial vegetation dynamics and land surface processes. Time-series MODIS LAI products are widely used in ecological and land-related research, but cloud contamination and sensor noise lead to widespread spatio-temporal gaps, limiting their ability to support long-term, consistent vegetation monitoring over large areas. To address this issue, this study proposes a novel self-supervised LAI reconstruction framework (SSLAI) for generating gap-free and ecologically consistent LAI datasets across China. The framework integrates cross-modal environmental fusion, multi-scale spatio-temporal modeling, and adaptive phenological constraints to ensure the reconstructed LAI aligns with realistic vegetation growth rhythms. SSLAI outperforms seven traditional and state-of-the-art deep learning methods, maintaining a root mean square error (RMSE) below 0.20 even with 16 missing time windows. Field validation confirms its high accuracy, with a coefficient of determination (R2) of 0.885 and an RMSE of 0.477. Furthermore, SSLAI’s response to meteorological changes aligns with ecological principles, demonstrating favorable physical interpretability and ecological rationality. The reconstructed LAI exhibits superior spatial completeness and temporal consistency compared with MODIS, VIIRS, and GLASS products, and performs robustly under variable climatic conditions. This study provides an effective self-supervised solution for MODIS LAI gap-filling over large regions, and the generated high-quality LAI dataset can serve as a reliable data foundation for vegetation dynamics monitoring, land surface modeling, and global change research.

Plant phenotyping relevance

植物群落のLAIという明示的な植物形質を対象に、欠測補完の計算手法を開発し、既存手法との比較および野外検証を行っているため、方法開発・検証が中心である。

Code and data availability

The paper describes a self-supervised MODIS LAI reconstruction framework (SSLAI) but provides no public deposit of its reconstructed LAI dataset, field LAI measurements, or analysis code. The URLs in Table 1 are generic provider portals (NASA Earthdata, Copernicus CDS, SRTM) for standard input products, not paper-phenp

No evidence-backed public reproduction asset is currently recorded.