Cross-ECV consistency at global scale: LAI and FAPAR changes

Mota, Bernardo; Gobron, Nadine; Morgan, Olivier; Cappucci, Fabrizio; Lanconelli, Christian; Robustelli, Monica

doi:10.1016/j.rse.2021.112561

Cited by 6 publications

(3 citation statements)

References 71 publications

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“…As FAPAR is physically related to LAI (Mota et al, 2021), the same sample pixels in the GLASS V6 LAI algorithm were used in this study. These globally distributed representative 52 997 sample pixels (Fig.…”

Section: Creating Global Training Samplesmentioning

confidence: 99%

Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021

Liang

Xiong

et al. 2022

Earth Syst. Sci. Data

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Abstract. The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical land surface variable for carbon cycle modeling and ecological monitoring. Several global FAPAR products have been released and have become widely used; however, spatiotemporal inconsistency remains a large issue for the current products, and their spatial resolutions and accuracies can hardly meet the user requirements. An effective solution to improve the spatiotemporal continuity and accuracy of FAPAR products is to take better advantage of the temporal information in the satellite data using deep learning approaches. In this study, the latest version (V6) of the FAPAR product with a 250 m resolution was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and other information, as part of the Global LAnd Surface Satellite (GLASS) product suite. In addition, it was aggregated to multiple coarser resolutions (up to 0.25∘ and monthly). Three existing global FAPAR products (MODIS Collection 6; GLASS V5; and PRoject for On-Board Autonomy–Vegetation, PROBA-V, V1) were used to generate the time-series training samples, which were used to develop a bidirectional long short-term memory (Bi-LSTM) model. Direct validation using high-resolution FAPAR maps from the Validation of Land European Remote sensing Instrument (VALERI) and ImagineS networks revealed that the GLASS V6 FAPAR product has a higher accuracy than PROBA-V, MODIS, and GLASS V5, with an R2 value of 0.80 and root-mean-square errors (RMSEs) of 0.10–0.11 at the 250 m, 500 m, and 3 km scales, and a higher percentage (72 %) of retrievals for meeting the accuracy requirement of 0.1. Global spatial evaluation and temporal comparison at the AmeriFlux and National Ecological Observatory Network (NEON) sites revealed that the GLASS V6 FAPAR has a greater spatiotemporal continuity and reflects the variations in the vegetation better than the GLASS V5 FAPAR. The higher quality of the GLASS V6 FAPAR is attributed to the ability of the Bi-LSTM model, which involves high-quality training samples and combines the strengths of the existing FAPAR products, as well as the temporal and spectral information from the MODIS surface reflectance data and other information. The 250 m 8 d GLASS V6 FAPAR product for 2020 is freely available at https://doi.org/10.5281/zenodo.6405564 and https://doi.org/10.5281/zenodo.6430925 (Ma, 2022a, b) as well as at the University of Maryland for 2000–2021 (http://glass.umd.edu/FAPAR/MODIS/250m, last access 1 November 2022).

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Section: Creating Global Training Samplesmentioning

confidence: 99%

Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021

Liang

Xiong

et al. 2022

Earth Syst. Sci. Data

View full text Add to dashboard Cite

show abstract

“…Since FAPAR is physically related to LAI (Mota et al, 2021), the same sample pixels in the GLASS V6 LAI algorithm were used in this study. These globally distributed representative 52,997 sample pixels (Fig.…”

Section: Creating Global Training Samplesmentioning

confidence: 99%

Global land surface 250-m 8-day Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product from 2000 to 2020

Liang

Xiong

et al. 2022

Preprint

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Abstract. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a critical land surface variable for carbon cycle modeling and ecological monitoring. Several global FAPAR products have been released and have become widely used; however, spatiotemporal inconsistency remains a large issue for the current products, and their spatial resolutions and accuracies can hardly meet the user requirements. An effective solution to improve the spatiotemporal continuity and accuracy of FAPAR products is to take better advantage of the temporal information in the satellite data using deep learning approaches. In this study, the latest version (V6) of the FAPAR product with a 250-m resolution was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and other information, as part of the Global Land Surface Satellite (GLASS) products suite. In addition, it was aggregated to multiple coarser resolutions (up to 0.25° and monthly). Three existing global FAPAR products (MODIS Collection 6, GLASS V5, and PROBA-V V1) were used to generate the time series training samples, which were used to develop a Bidirectional Long Short-Term Memory (Bi-LSTM) model. Direct validation using high-resolution FAPAR maps from the Validation of Land European Remote sensing Instrument (VALERI) and ImagineS networks revealed that the GLASS V6 FAPAR product has a higher accuracy than PROBA-V, MODIS, and GLASS V5, with an R2 value of 0.80 and Root Mean Square Errors (RMSEs) of 0.10–0.11 at the 250-m, 500-m, and 3-km scales, and a higher percentage (72 %) of retrievals for meeting the accuracy requirement of 0.1. Global spatial evaluation and temporal comparison at the Ameriflux and National Ecological Observatory Network (NEON) sites revealed that the GLASS V6 FAPAR has a greater spatiotemporal continuity and reflects the variations in the vegetation better than the GLASS V5 FAPAR. The higher quality of the GLASS V6 FAPAR is attributed to the ability of the Bi-LSTM model, which involved high-quality training samples and combines the strengths of the existing FAPAR products, as well as the temporal and spectral information from the MODIS surface reflectance data and other information.

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“…LAI is commonly defined as the total singlesided green leaf area per unit horizontal ground area (Baret et al, 2007). Quantifying the drivers of temporal and spatial changes in vegetation is crucial due to its significant role in regulating climate change (Piao et al, 2020;Mota et al, 2021;Shabanov et al, 2021;Yan et al, 2021;Dai et al, 2022;Kobayashi et al, 2023;Pu et al, 2023), land surface modeling (Running et al, 1999;Albergel et al, 2018;Peng et al, 2021;Zhu et al, 2023), and vegetation dynamic monitoring (Iwahashi et al, 2021;Zhao et al, 2021;Abubakar et al, 2022;Amin et al, 2022;Zhang et al, 2022;Bajocco et al, 2022;Caballero et al, 2022). The variation in vegetation structure and function not only affects biodiversity and energy supply but also provides valuable insights into ecological feedback to climatic changes.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Hou,

Han

et al. 2024

Front. Environ. Sci.

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Introduction: The Leaf area index (LAI) of source region of yellow river basin is an important indicator for environmental sustainability. Most studies focus on the trend of LAI in Yellow River Source Region (YRSR) in accordance with both climate change and human actives. However, quantifying the effect of human activities on LAI is difficult but urgently needed. Specifically, Particle Matter 2.5 (PM2.5) can be an indirect indicator of human activities.Methods: In this study, we explored the potential dependence of LAI on temperature, precipitation, and PM2.5 in different land cover types in YRSR with linear regression and correlation analysis.Results: Over the period of 2001–2020, the climate in the region has been warming and becoming more humid, leading to overall improvements in vegetation. The mean LAI values varied between seasons, with summer having the highest and winter having the lowest LAI. The analysis of the LAI trends revealed that the mean LAI has been steadily increasing, particularly in the eastern region. The correlation analysis showed a significant positive correlation between annual average LAI and both annual precipitation and temperature, indicating that temperature has a greater impact on vegetation growth. The analysis of land cover types showed that most types exhibited a unimodal trend in LAI throughout the year, except for construction land which had two distinct peaks. Human-induced land cover change had a small impact on the overall increase in LAI. Furthermore, the interannual variation of PM2.5 showed a downward trend, with a strong correlation with the trend of LAI. Additionally, multiple linear regression analysis and residual trend analysis showed that climate factors had the strongest impact on LAI.Conclusion: The study highlights the spatiotemporal variations of LAI in the YRSR and its correlation with climatic and human factors. The findings suggest that climate change plays a crucial role in the vegetation growth and LAI in the region.

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Cross-ECV consistency at global scale: LAI and FAPAR changes

Cited by 6 publications

References 71 publications

Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021

Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021

Global land surface 250-m 8-day Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product from 2000 to 2020

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

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