TY - JOUR

T1 - Tighten the Bolts and Nuts on GPP Estimations from Sites to the Globe: An Assessment of Remote Sensing Based LUE Models and Supporting Data Fields

AU - Wang, Zhao

AU - Liu, Shuguang

AU - Wang, Ying-Ping

AU - Valbuena, Ruben

AU - Wu, Yiping

AU - Kutia, Mykola

AU - Zheng, Yi

AU - Lu, Weizhi

AU - Zhu, Yu

AU - Zhao, Meifang

AU - Peng, Xi

AU - Gao, Haiqiang

AU - Feng, Shuailong

AU - Shi, Yi

PY - 2021/1/6

Y1 - 2021/1/6

N2 - Gross primary production (GPP) determines the amounts of carbon and energy that enter terrestrial ecosystems. However, the tremendous uncertainty of the GPP still hinders the reliability of GPP estimates and therefore understanding of the global carbon cycle. In this study, using observations from global eddy covariance (EC) flux towers, we appraised the performance of 24 widely used GPP models and the quality of major spatial data layers that drive the models. Results show that global GPP products generated by the 24 models varied greatly in means (from 92.7 to 178.9 Pg C yr−1) and trends (from −0.25 to 0.84 Pg C yr−1). Model structure differences (i.e., light use efficiency models, machine learning models, and process-based biophysical models) are an important aspect contributing to the large uncertainty. In addition, various biases in currently available spatial datasets have found (e.g., only 57% of the observed variation in photosynthetically active radiation at the flux tower locations was explained by the spatial dataset), which not only affect GPP simulation but more importantly hinder the simulation and understanding of the earth system. Moving forward, research into the efficacy of model structures and precision of input data may be more important for global GPP estimation.

AB - Gross primary production (GPP) determines the amounts of carbon and energy that enter terrestrial ecosystems. However, the tremendous uncertainty of the GPP still hinders the reliability of GPP estimates and therefore understanding of the global carbon cycle. In this study, using observations from global eddy covariance (EC) flux towers, we appraised the performance of 24 widely used GPP models and the quality of major spatial data layers that drive the models. Results show that global GPP products generated by the 24 models varied greatly in means (from 92.7 to 178.9 Pg C yr−1) and trends (from −0.25 to 0.84 Pg C yr−1). Model structure differences (i.e., light use efficiency models, machine learning models, and process-based biophysical models) are an important aspect contributing to the large uncertainty. In addition, various biases in currently available spatial datasets have found (e.g., only 57% of the observed variation in photosynthetically active radiation at the flux tower locations was explained by the spatial dataset), which not only affect GPP simulation but more importantly hinder the simulation and understanding of the earth system. Moving forward, research into the efficacy of model structures and precision of input data may be more important for global GPP estimation.

U2 - 10.3390/rs13020168

DO - 10.3390/rs13020168

M3 - Article

VL - 13

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 2

M1 - 168

ER -