Variation of intra-daily instantaneous FAPAR estimated from the geostationary Himawari-8 AHI data

Zhang, Yinghui; Fang, Hongliang; Wang, Yao; Li, Sijia

doi:10.1016/j.agrformet.2021.108535

Cited by 15 publications

(5 citation statements)

References 75 publications

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“…There are two typical types of algorithms for retrieving the FAPAR from satellite data: statistical-model-based and radiative-transfer-model-based algorithms. For example, several studies have estimated FAPAR by establishing the relationship between the vegetation index (VI) and in situ FA-PAR measurements using simple statistical methods or machine learning models (Gitelson et al, 2014;Muller et al, 2020;Camacho et al, 2021), while other studies have estimated FAPAR based on energy balance inside the canopy using radiative transfer models (Zhang et al, 2021;Xiao et al, 2015;Liu et al, 2019). Although many satellite products have been generated using these two types of methods, and their accuracies have been evaluated and intercompared in many studies (M. Tao et al, 2015;Xiao et al, 2018;Putzenlechner et al, 2019), these algorithms usu-ally only use single-phase remote sensing data, and the critical temporal information contained in the satellite signals is often ignored.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…These accumulated multiangle data are promising for revealing the intradaily variation in BRDF and all the daily clear-sky observations were input into the Ross-Li model to reconstruct BRDF and then used for retrieving the land surface albedo [33] and vegetation phenology [34] for the AHI. In addition, instantaneous bidirectional observations were also used for snow detection [40] and fraction of absorbed photosynthetically active radiation (FAPAR) estimation [41]. However, these simple compositions of daily-scale GEO satellite data for reconstructing BRDF in current studies may result in uncertainties because the different sun-viewing geometries would highly affect the accuracy of BRDF inversion [6,9,42].…”

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of BRDF Information Retrieved from Time-Series Multiangle Data of the Himawari-8 AHI

et al. 2021

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Recently, much attention has been given to using geostationary Earth orbit (GEO) meteorological satellite data for retrieving land surface parameters due to their high observation frequencies. However, their bidirectional reflectance distribution function (BRDF) information content with a single viewing angle has not been sufficiently investigated, which lays a foundation for subsequent quantitative estimation. In this study, we aim to comprehensively evaluate BRDF information from time-series observations from the Advanced Himawari Imager (AHI) onboard the GEO satellite Himawari-8. First, ~6.2 km monthly multiangle surface reflectances from POLDER onboard a low-Earth-orbiting (LEO) satellite with good angle distributions over various land types during 2008 were used as reference data, and corresponding 0.05° high-quality MODIS (i.e., onboard LEO satellites) and AHI datasets during four months in 2020 were obtained using cloud and aerosol property products. Then, indicators of angle distribution, BRDF change, and albedos were retrieved by the kernel-driven Ross-Li BRDF model from the three datasets, which were used for comparisons over different time spans. Generally, the quality of sun-viewing geometries varies dramatically for accumulated AHI observations according to the weight-of-determination, and wide-ranging anisotropic flat indices are obtained. The root-mean-square-errors of white sky albedos between AHI and MODIS half-month data are 0.018 and 0.033 in the red and near-infrared bands, respectively, achieving smaller values of 0.004 and 0.007 between the half-month and daily AHI data, respectively, due to small variances in sun-viewing geometries. The generally wide AHI BRDF variances and good consistency in albedo with MODIS show their potential for retrieving anisotropy information and albedo, while angle accumulation quality of AHI time-series observations must be considered.

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“…There are two typical types of algorithms for retrieving the FAPAR from satellite data: statistical model based and radiative transfer model based algorithms. For example, several studies have estimated FAPAR by establishing the relationship between the Vegetation Index (VI) and in-situ FAPAR measurements using simple statistical methods or machine learning models (Gitelson et al, 2014;Muller et al, 2020;Camacho et al, 2021), while other studies have estimated FAPAR based on energy balance inside the canopy using radiative transfer models (Zhang et al, 2021;Xiao et al, 2015;Liu et al, 2019). Although many satellite products have been generated using these two types of methods, and their accuracies have been evaluated and inter-compared in many studies (Weiss et al, 2014b;Tao et al, 2015;Xiao et al, 2018;Putzenlechner et al, 2019), these algorithms usually only use single-phase remote sensing data, and the critical temporal information contained in the satellite signals is often ignored.…”

Section: Introductionmentioning

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.

show abstract

Variation of intra-daily instantaneous FAPAR estimated from the geostationary Himawari-8 AHI data

Cited by 15 publications

References 75 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

Evaluation of BRDF Information Retrieved from Time-Series Multiangle Data of the Himawari-8 AHI

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

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