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.
A total of eight global datasets were used in this study, namely, the PKU GIMMS NDVI, Landsat LAI sample dataset, MODIS Land-Cover Type, reprocessed MODIS LAI, GLASS LAI, GLOBMAP LAI, GIMMS LAI3g, and field LAI measurements. The PKU GIMMS NDVI was the primary data source from which the GIMMS LAI4g was generated. The Landsat LAI sample dataset was used as the LAI reference in machine learning model establishment and product evaluation. The field LAI measurements were also employed for product evaluation. The MODIS Land-Cover Type product provided vegetation biome types in the LAI modeling. The reprocessed MODIS LAI was used to extend the temporal coverage of the GIMMS LAI4g. The GLASS LAI, GLOBMAP LAI, and GIMMS LAI3g are three mainstream global LAI products that were included for an inter-comparison purpose.
The methodology includes three key steps : (1) generating the GIMMS LAI4g product from biome-specific BPNN models based on PKU GIMMS NDVI, Landsat LAI samples, and other explanatory variables; (2) consolidating the GIMMS LAI4g product with the reprocessed MODIS LAI product using a pixel-wise fusion method in their overlapping time span (2004–2015); and (3) evaluating the GIMMS LAI4g product using field LAI measurements and Landsat LAI samples and comparing it with other global LAI products.
The data quality is good
| # | number | name | type |
| 1 | 41901122 | National Natural Science Foundation of China |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ZENODO.DB6648.2024
10.12072/ncdc.zenodo.db6648.2024
