Work

A dataset of 0.05-degree leaf area index in China during 1983–2100 based on deep learning network

Year: 2024

Type: data-paper

Abstract: 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.

Source: Scientific Data

DOI: 10.1038/s41597-024-03948-z

Authors: Hao Li, Yuyu Zhou, Xiang Zhao, Xin Zhang, Shunlin Liang

Institutions: Beijing Normal University, State Key Laboratory of Remote Sensing Science, University of Hong Kong, Manchester Metropolitan University

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,290