Preprints
https://doi.org/10.5194/essd-2023-68
https://doi.org/10.5194/essd-2023-68
24 Feb 2023
 | 24 Feb 2023
Status: this preprint is currently under review for the journal ESSD.

Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020

Sen Cao, Muyi Li, Zaichun Zhu, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, and Yue Chen

Abstract. 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 1/12° 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 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 higher accuracy than its predecessor (GIMMS LAI3g) and two mainstream LAI products (Global LAnd Surface Satellite [GLASS] LAI and Long-term Global Mapping [GLOBMAP] LAI), with an R2 of 0.95, mean absolute error of 0.18 m2/m2, and mean absolute percentage error of 15 % which meet the accuracy target proposed by the Global Climate Observation System. It outperformed other LAI products for most vegetation biomes in a majority area of the land. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presented a better temporal consistency before and after the year 2000 and a more reasonable global vegetation trend. 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.

Sen Cao et al.

Status: open (until 21 Apr 2023)

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  • RC1: 'Comment on essd-2023-68', Shangrong Lin, 21 Mar 2023 reply

Sen Cao et al.

Data sets

Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020 Sen Cao, Muyi Li, Zaichun Zhu, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, and Yue Chen https://doi.org/10.5281/zenodo.7649108

Sen Cao et al.

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Short summary
The long-term global LAI products are critical supports to characterize vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It also presents a better temporal consistency before and after 2000 and a more reasonable global vegetation trend.