Li Hao, Zhou Yuyu, Zhao Xiang, Zhang Xin, Liang Shunlin
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
Sci Data. 2024 Oct 11;11(1):1122. doi: 10.1038/s41597-024-03948-z.
Leaf Area Index (LAI) is a critical parameter in terrestrial ecosystems, with high spatial resolution data being extensively utilized in various research studies. However, LAI data under future scenarios are typically only available at 1° or coarser spatial resolutions. In this study, we generated a dataset of 0.05° LAI (F0.05D-LAI) from 1983-2100 in a high spatial resolution using the LAI Downscaling Network (LAIDN) model driven by inputs including air temperature, relative humidity, precipitation, and topography data. The dataset spans the historical period (1983-2014) and future scenarios (2015-2100, including SSP-126, SSP-245, SSP-370, and SSP-585) with a monthly interval. It achieves high accuracy (R² = 0.887, RMSE = 0.340) and captures fine spatial details across various climate zones and terrain types, indicating a slightly greening trend under future scenarios. F0.05D-LAI is the first high-resolution LAI dataset and reveals the potential vegetation variation under future scenarios in China, which benefits vegetation studies and model development in earth and environmental sciences across present and future periods.
叶面积指数（LAI）是陆地生态系统中的一个关键参数，高空间分辨率数据在各种研究中得到了广泛应用。然而，未来情景下的LAI数据通常仅在1°或更低的空间分辨率下可用。在本研究中，我们使用由气温、相对湿度、降水和地形数据驱动的叶面积指数降尺度网络（LAIDN）模型，在高空间分辨率下生成了1983 - 2100年的0.05°LAI数据集（F0.05D-LAI）。该数据集涵盖历史时期（1983 - 2014年）和未来情景（2015 - 2100年，包括SSP-126、SSP-245、SSP-370和SSP-585），时间间隔为每月。它具有高精度（R² = 0.887，RMSE = 0.340），并捕捉了不同气候区和地形类型的精细空间细节，表明在未来情景下有轻微的绿化趋势。F0.05D-LAI是首个高分辨率LAI数据集，揭示了中国未来情景下潜在的植被变化，这有利于当前和未来时期地球与环境科学中的植被研究和模型开发。
