Underestimates of Grassland Gross Primary Production in MODIS Standard Products

Zhu, Xiaoyan; Pei, Yanyan; Zheng, Zhaopei; Dong, Jinwei; Zhang, Yao; Wang, Junbang; Chen, Lajiao; Doughty, Russell; Zhang, Geli; Xiao, Xiangming

doi:10.3390/rs10111771

Underestimates of Grassland Gross Primary Production in MODIS Standard Products

Journal Article · Thu Nov 08 00:00:00 EST 2018 · Remote Sensing

DOI:https://doi.org/10.3390/rs10111771· OSTI ID:1567140

Zhu, Xiaoyan ^[1]; Pei, Yanyan ^[2]; Zheng, Zhaopei ^[3]; ^[2]; ^[4]; Wang, Junbang ^[2]; Chen, Lajiao ^[2]; ^[4]; Zhang, Geli ^[4]; ^[5]

Chinese Academy of Sciences (CAS), Beijing (China); Shandong Normal Univ., Jinan (China)
Chinese Academy of Sciences (CAS), Beijing (China)
Shandong Normal Univ., Jinan (China)
Univ. of Oklahoma, Norman, OK (United States)
Univ. of Oklahoma, Norman, OK (United States); Fudan Univ., Shanghai (China)

As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPP_MOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPP_EC) from a global network of eddy covariance towers (FluxNet), we compared GPP_MOD and GPP_EC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPP_MOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R²), and root mean square errors (RMSE). We found that GPP_MOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (R² = 0.66 (CI, 0.63–0.69), RMSE = 2.46 (2.33–2.58) g Cm^-2 day^-1) for the three grassland biomes. GPP_MOD had varied performances with R2 values of 0.72 (0.68–0.75) (temperate), 0.64 (0.59–0.68) (alpine), and 0.40 (0.27–0.52) (tropical). Thus, GPP_MOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPP_MOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (ε_max) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in ε_max, FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1567140

Journal Information:: Remote Sensing, Journal Name: Remote Sensing Journal Issue: 11 Vol. 10; ISSN 2072-4292

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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