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A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America

by Ruonan Qiu 1, Ge Han 1,*, Xin Ma 2, Hao Xu 2, Tianqi Shi 2 and Miao Zhang 3
1
School of Remote Sensing and Information Engineering, Wuhan University, Luoyu Road No.129, Wuhan 430079, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road No.129, Wuhan 430079, China
3
School of Environmental Science and Tourism, Nanyang Normal University, Wolong Road No.1638, Nanyang 473061, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 258; https://doi.org/10.3390/rs12020258
Received: 19 November 2019 / Revised: 7 January 2020 / Accepted: 9 January 2020 / Published: 11 January 2020
(This article belongs to the Special Issue Global Biospheric Monitoring with Remote Sensing)
Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p < 0.001; OCO-2 SIF: R2 = 0.11–0.99, p < 0.001; MODIS GPP: R2 = 0.15–0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP. View Full-Text
Keywords: Gross primary production (GPP); sun-induced chlorophyll fluorescence (SIF); MODIS GPP (MYD17A2H); GOSIF; OCO-2 Gross primary production (GPP); sun-induced chlorophyll fluorescence (SIF); MODIS GPP (MYD17A2H); GOSIF; OCO-2
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Qiu, R.; Han, G.; Ma, X.; Xu, H.; Shi, T.; Zhang, M. A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America. Remote Sens. 2020, 12, 258.

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