National mapping of wetland vegetation leaf area index in China using hybrid model with Sentinel-2 and Landsat-8 data

Abstract

Leaf area index (LAI) of wetland vegetation provides vital information for its growth condition, structure and functioning. Accurately mapping LAI at a broad scale is essential for conservation and rehabilitation of wetland ecosystem. However, owing to the spatial complexity and periodic inundation characteristics of wetland vegetation, retrieving LAI of wetlands remains a challenging task with significant uncertainty. Here, with 865 in-situ measurements across different wetland biomes in China during 2013–2023, we proposed a hybrid strategy that incorporated active learning (AL) technique, physically-based PROSAIL-5B model, and Random Forest machine learning algorithm to map wetland biomes LAI across China from Sentinel-2 and Landsat-8 imagery. The validation results showed that the hybrid approach outperformed physically-based and empirically-based methods and achieved higher accuracy (R² increased by 0.15–0.40, RMSE decreased by 0.02–0.27, and RRMSE reduced by 3.37–12.78 %). Additionally, three indices that we newly-developed (TBVI5, TBVI3 and TBVI1) exhibited superior potential for LAI inversion across different types of wetland vegetation. Our mapping results exhibited spatial details and consistency, and matched with in-situ observations from Sentinel-2 compared to Landsat-8 and the other MODIS-based products. In this study, we developed the first national-scale mapping of wetland vegetation LAI in China. The findings offer insights into accurate retrieval of LAI in wetland vegetation, providing valuable support for the scientific restoration of wetlands and assessing their responses to climate change.