Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020

Abstract

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 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. Its validation against Landsat LAI samples revealed an R2 of 0.96, root mean square error of 0.32 m2 m−2, mean absolute error of 0.16 m2 m−2, and mean absolute percentage error of 13.6 % which meets 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. The consolidation with the reprocessed MODIS LAI allows the GIMMS LAI4g to extend the temporal coverage from 2015 to a recent period (2020), producing the LAI trend that maintains high consistency before and after 2000 and aligns with the reprocessed MODIS LAI trend during the MODIS era. 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. The GIMMS LAI4g product is open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.7649107 (Cao et al., 2023).

Terrestrial ecosystems enhance root zones in response to intensified drought

“…To further explore how plants adapt to climate change, we analyzed the trend of leaf area index (LAI) and arid index (AI) at the grid cell and global scale. The LAI data used in our study was obtained from the GIMMS LAI4g dataset which utilized spatiotemporally consistent BPNN models and the latest PKU GIMMS NDVI product and high-quality global Landsat LAI samples to remove the effects of satellite orbital drift and sensor degradation 54 . The GIMMS LAI4g dataset provided half-month temporal resolution for the period 1982-2020, with a spatial resolution of 1/12°.…”

Spatio-temporal analysis of LAI using multisource remote sensing data for source region of Yellow River Basin

“…The GIMSS LAI4g product is produced by fusing multiple remote sensing data through a deep learning algorithm (backpropagation neural network, BPNN) (Cao et al, 2023). It can be downloaded through the website https://doi.org/10.5281/ zenodo.7649108.…”

“…Most of studies highlight the importance of merging multiple sources data for generating high-resolution LAI products which is an important data foundation for analyzing LAI spatiotemporal characteristics. Cao et al (2023) used backpropagation neural network (BPNN) and data integration methods to generate a new version of the Global Inventory Modeling and Mapping Studies (GIMMS) LAI product, namely, GIMMS LAI4g, with a time span from 1982 to 2020. The importance of GIMMS LAI4g lies in the use of the latest PKU GIMMS NDVI product and 3.6 million high-quality global Landsat LAI samples to eliminate the effects of satellite orbit drift and sensor degradation and to develop a BPNN model with spatiotemporal consistency.…”

Future response of ecosystem water use efficiency to CO2 effects in the Yellow River Basin, China

“…In this study, the normalized difference vegetation index (NDVI) and the leaf area index (LAI) were selected as vegetation indicators to characterize the underlying surface conditions. The GIMMS3g NDVI and GIMMS4g LAI data, acquired from the Advanced Very High Resolution Radiometer (AVHRR) sensor of NOAA series of weather satellites, provide high spatiotemporal resolution and high-accuracy observed vegetation indicators (Pinzon and Tucker, 2014;Cao et al, 2023). These data are freely available from the website (https://www.earthdata.nasa.gov/; https://zenodo.org/records/8281930).…”