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

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

doi:10.5194/essd-2023-68

Cited by 7 publications

(7 citation statements)

References 37 publications

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“…Remote sensing products for vegetation greenness in this study can be downloaded online at: GLASS LAI (University of Maryland, 2021); GIMMS LAI (Cao et al, 2023b); MODIS/Terra + Aqua LAI/FPAR 4-Day (Myneni et al, 2021a); MODIS/Terra LAI/FPAR 8-Day (Myneni et al, 2021b); GLOBMAP LAI v3 (R. Liu et al, 2021); Himawari-8 AHI TOA (GeoNEX, 2021). Other data sets are available online at: gridded land surface CRU JRA (Harris, 2020), GLEAM daily soil moisture (Martens et al, 2017b), MODIS land cover (Friedl & Sulla-Menashe, 2019), MEI index (NOAA PSL, 2023).…”

Section: Data Availability Statementmentioning

confidence: 99%

“…Liu et al, 2012). GIMMS LAI4g (1982LAI4g ( -2020, half monthly at 0.083°spatial resolution; downloaded from https://doi.org/10.5281/zenodo.7649108) is generated from GIMMS normalized difference vegetation index (NDVI), Landsat LAI sample data set, and reprocessed MODIS LAI using the back propagation neural network model (Cao et al, 2023a). Lastly, we used MODIS C6 LAI from Terra at 8-day and 500 m resolution (Myneni et al, 2021b) for the year 2001 and MODIS LAI at 4-day and 500 m resolution from Terra + Aqua combined (Myneni et al, 2021a) for 2002 onwards.…”

Section: Vegetation Greennessmentioning

confidence: 99%

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Strong Green‐Up of Tropical Asia During the 2015/16 El Niño

Satriawan,

Luo,

Tian

et al. 2024

Geophysical Research Letters

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El Niño/Southern Oscillation (ENSO) is the main climate mode that drives the interannual variability in climate and consequently vegetation greenness. While widespread green‐up has been reported and examined in tropical America during El Niño, it remains unclear how vegetation in tropical Asia changes during the period. Here, we used four remote sensing‐based leaf area index (LAI) products to investigate changes in vegetation greenness during the 2015/16 El Niño in tropical Asia. We found a strong green‐up during the 2015/16 El Niño in tropical Asia, with its regional average LAI stronger than that of tropical America. The drivers for the green‐up vary across the region, with radiation being the main driver for continental tropical Asia, and temperature and soil water anomalies in the west and east parts of maritime tropical Asia, respectively. These findings provide important insights into the response of tropical Asia's vegetation to extreme climate anomalies.

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Section: Data Availability Statementmentioning

confidence: 99%

Section: Vegetation Greennessmentioning

confidence: 99%

Strong Green‐Up of Tropical Asia During the 2015/16 El Niño

Satriawan,

Luo,

Tian

et al. 2024

Geophysical Research Letters

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“…The spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) generated in this study is openly available at https://doi.org/10.5281/zenodo.7649107 (Cao et al, 2023). It covers the whole global vegetation area at a half-month temporal resolution and 1/12 • spatial resolution from 1982 to 2020.…”

Section: Data Availabilitymentioning

confidence: 99%

“…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).…”

mentioning

confidence: 99%

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

Cao,

Li,

Zhu

et al. 2023

Earth Syst. Sci. Data

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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).

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“…We used the PKU GIMMS NDVI4g dataset (Li et al, 2023) and PKU GIMMS LAI4g (Cao et al, 2023) (Yuan et al, 2011).…”

Section: Satellite Datasetsmentioning

confidence: 99%

CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization

Kang,

Gaber,

Bassiouni

et al. 2023

Preprint

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Gross primary productivity (GPP) is the largest carbon flux in the Earth system, playing a crucial role in removing atmospheric carbon dioxide and providing the sugars and starches needed for ecosystem metabolism. Despite the importance of GPP, however, existing estimates present significant uncertainties and discrepancies. A key issue is the underrepresentation of the CO2 fertilization effect, a major factor contributing to the increased terrestrial carbon sink over recent decades. This omission could potentially bias our understanding of ecosystem responses to climate change.Here, we introduce CEDAR-GPP, the first global upscaled GPP product that incorporates the direct CO2 fertilization effect on photosynthesis. Our product is comprised of monthly GPP estimates and their uncertainty at 0.05º resolution from 1982 to 2020, generated using a comprehensive set of eddy covariance measurements, multi-source satellite observations, climate variables, and machine learning models. Importantly, we used both theoretical and data-driven approaches to incorporate the direct CO2 effects. Our machine learning models effectively predicted monthly GPP (R2 ~ 0.74), the mean seasonal cycles (R2 ~ 0.79), and spatial variabilities (R2 ~ 0.67). Incorporation of the direct CO2 effects substantially improved the models’ ability to estimate long-term GPP trends across global flux sites. While the global patterns of annual mean GPP, seasonality, and interannual variability generally aligned with existing satellite-based products, CEDAR-GPP demonstrated higher long-term trends globally after incorporating CO2 fertilization, particularly in the tropics, reflecting a strong temperature control on direct CO2 effects. CEDAR-GPP offers a comprehensive representation of GPP temporal and spatial dynamics, providing valuable insights into ecosystem-climate interactions.

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

Cited by 7 publications

References 37 publications

Strong Green‐Up of Tropical Asia During the 2015/16 El Niño

Strong Green‐Up of Tropical Asia During the 2015/16 El Niño

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

CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization

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