%0 Dataset
%T Global Monitoring System Leaf Area Index (GIMMS LAI4g) spatiotemporal consistent dataset (1982-2020) (V1.2)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/70012119-92cf-468d-b802-a06ed70791af
%W NCDC
%R 10.5281/zenodo.8281930
%A Cao Sen
%A Zhu Zaichun
%K GIMMS Leaf Area Index;Vegetation Dynamics;Normalized Vegetation Index (NDVI)
%X This dataset is based on the backpropagation neural network (BPNN) model and pixel integration method, and a new generation of GIMMS LAI product (GIMMS LAI4g, 1982-2020) has been developed. The feature of GIMMS LAI4g is the use of Peking University GIMMS NDVI products and a large number of high-quality Landsat LAI samples. The recently released PKU GIMMS NDVI effectively eliminates the effects of NOAA orbital drift and AVHRR sensor degradation, which have been key issues with existing LAI products. The total number of high-quality global land satellite LAI samples reaches 3.6 million, covering the period from 1984 to 2015, which provides convenience for creating spatiotemporal consistent BPNN models. The GIMMS LAI4g product, which is consistent in time and space, covers the time span from 1982 to 2020 with a time resolution of 15 days. It can provide strong data support for long-term vegetation monitoring and high-precision, high reliability model development.