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

被引:5
|
作者
Ma, Han [1 ]
Liang, Shunlin [1 ]
Xiong, Changhao [2 ]
Wang, Qian [3 ]
Jia, Aolin [4 ]
Li, Bing [5 ,6 ]
机构
[1] Univ Hong Kong, Dept Geog, Pokfulam, Hong Kong, Peoples R China
[2] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430010, Hubei, Peoples R China
[3] Beijing Normal Univ, Fac Geog, Beijing 100875, Peoples R China
[4] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
[5] Henan Univ, Key Res Inst Yellow River Civilizat & Sustainable, Kaifeng 475001, Peoples R China
[6] Henan Univ, Collaborat Innovat Ctr Yellow River Civilizat Hena, Kaifeng 475001, Peoples R China
基金
中国国家自然科学基金;
关键词
LEAF-AREA INDEX; ESSENTIAL CLIMATE VARIABLES; BIDIRECTIONAL LSTM; GEOV1; LAI; PART; VEGETATION; VALIDATION; MODIS; FCOVER; PRINCIPLES;
D O I
10.5194/essd-14-5333-2022
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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 & LCIRC; 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).
引用
收藏
页码:5333 / 5347
页数:15
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