Next Article in Journal
Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping
Next Article in Special Issue
Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets
Previous Article in Journal
Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery
Previous Article in Special Issue
Simulation and Analysis of the Topographic Effects on Snow-Free Albedo over Rugged Terrain
Open AccessArticle

Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data

by 1,2, 1,2,*, 1,2,*, 1,2, 1,2, 1,2 and 1,2
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China
Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 327;
Received: 28 December 2017 / Revised: 4 February 2018 / Accepted: 18 February 2018 / Published: 22 February 2018
Accurately estimating vegetation productivity is important in research on terrestrial ecosystems, carbon cycles and climate change. Eight-day gross primary production (GPP) and annual net primary production (NPP) are contained in MODerate Resolution Imaging Spectroradiometer (MODIS) products (MOD17), which are considered the first operational datasets for monitoring global vegetation productivity. However, the cloud-contaminated MODIS leaf area index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrievals may introduce some considerable errors to MODIS GPP and NPP products. In this paper, global eight-day GPP and eight-day NPP were first estimated based on Global LAnd Surface Satellite (GLASS) LAI and FPAR products. Then, GPP and NPP estimates were validated by FLUXNET GPP data and BigFoot NPP data and were compared with MODIS GPP and NPP products. Compared with MODIS GPP, a time series showed that estimated GLASS GPP in our study was more temporally continuous and spatially complete with smoother trajectories. Validated with FLUXNET GPP and BigFoot NPP, we demonstrated that estimated GLASS GPP and NPP achieved higher precision for most vegetation types. View Full-Text
Keywords: GPP; NPP; MODIS; validation GPP; NPP; MODIS; validation
Show Figures

Graphical abstract

MDPI and ACS Style

Yu, T.; Sun, R.; Xiao, Z.; Zhang, Q.; Liu, G.; Cui, T.; Wang, J. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. Remote Sens. 2018, 10, 327.

AMA Style

Yu T, Sun R, Xiao Z, Zhang Q, Liu G, Cui T, Wang J. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. Remote Sensing. 2018; 10(2):327.

Chicago/Turabian Style

Yu, Tao; Sun, Rui; Xiao, Zhiqiang; Zhang, Qiang; Liu, Gang; Cui, Tianxiang; Wang, Juanmin. 2018. "Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data" Remote Sens. 10, no. 2: 327.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop