TY  - Data
T1  - Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) （1982—2020 ）
A1  - None
DO  - 10.12072/ncdc.zenodo.db6648.2024
PY  - 2024
DA  - 2024-11-28
PB  - National Cryosphere Desert Data Center
AB  - Leaf area index (LAI) with an explicit biophysical meaning is a critical variable to characterize terrestrial ecosystems. Long-term global datasets of LAI have served as fundamental data support for monitoring vegetation dynamics and exploring its interactions with other Earth components. However, current LAI products face several limitations associated with spatiotemporal consistency. In this study, we employed the back propagation neural network (BPNN) and a data consolidation method to generate a new version of the half-month  Global Inventory Modeling and Mapping Studies (GIMMS) LAI product, i.e., GIMMS LAI4g, for the period 1982–2020. The significance of the GIMMS LAI4g was the use of the latest PKU GIMMS normalized difference vegetation index (NDVI) product and 3.6 million high-quality global Landsat LAI samples to remove the effects of satellite orbital drift and sensor degradation and to develop spatiotemporally consistent BPNN models. The results showed that the GIMMS LAI4g exhibited overall higher accuracy and lower underestimation than its predecessor (GIMMS LAI3g) and two mainstream LAI products (Global LAnd Surface Satellite (GLASS) LAI and Long-term Global Mapping (GLOBMAP) LAI) using field LAI measurements and Landsat LAI samples.The GIMMS LAI4g product could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and could also benefit the model development in earth and environmental sciences.
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/14ada18e-6851-4382-81e4-7703795cabf9
ER  -