Effects of the Temporal Aggregation and Meteorological Conditions on the Parameter Robustness of OCO-2 SIF-Based and LUE-Based GPP Models for Croplands

Remote Sensing

Jing Chen¹  Baozhang Chen²  Huifang Zhang²  Yawen Kong²  Fei Wang²  Lifeng Guo²  Xiaofeng Lin² 

[1] Meteorological Information Center of Beijing, Beijing Meteorological Bureau, No. 44, Zizhuyuan Road, Haidian District, Beijing 100089, China;State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China;

关键词: light use efficiency;    solar-induced chlorophyll fluorescence;    eddy covariance;    photosynthesis;    gross primary productivity;   

DOI  :  10.3390/rs11111328

来源: DOAJ

【 摘 要 】

Global retrieval of solar-induced chlorophyll fluorescence (SIF) using remote sensing by means of satellites has been developed rapidly in recent years. Exploring how SIF could improve the characterization of photosynthesis and its role in the land surface carbon cycle has gradually become a very important and active area. However, compared with other gross primary production (GPP) models, the robustness of the parameterization of the SIF model under different circumstances has rarely been investigated. In this study, we examined and compared the effects of temporal aggregation and meteorological conditions on the stability of model parameters for the SIF model ( $\epsilon /SI{F}_{\mathrm{yield}}$ ), the one-leaf light-use efficiency (SL-LUE) model ( ${\epsilon }_{\max }$ ), and the two-leaf LUE (TL-LUE) model ( ${\epsilon }_{\mathrm{msu}}$ and ${\epsilon }_{\mathrm{msh}}$ ). The three models were parameterized based on a maize–wheat rotation eddy-covariance flux tower data in Yucheng, Shandong Province, China by using the Metropolis–Hasting algorithm. The results showed that the values of the $\epsilon /SI{F}_{\mathrm{yield}}$ and ${\epsilon }_{\max }$ were similarly robust and considerably more stable than ${\epsilon }_{\mathrm{msu}}$ and ${\epsilon }_{\mathrm{msh}}$ for all temporal aggregation levels. Under different meteorological conditions, all the parameters showed a certain degree of fluctuation and were most affected at the mid-day scale, followed by the monthly scale and finally at the daily scale. Nonetheless, the averaged coefficient of variation ( $CV$ ) of $\epsilon /SI{F}_{\mathrm{yield}}$ was relatively small (15.0%) and was obviously lower than ${\epsilon }_{\max }$ ( $CV$ = 27.0%), ${\epsilon }_{\mathrm{msu}}$ ( $CV$ = 43.2%), and ${\epsilon }_{\mathrm{msh}}$ ( $CV$ = 53.1%). Furthermore, the SIF model’s performance for estimating GPP was better than that of the SL-LUE model and was comparable to that of the TL-LUE model. This study indicates that, compared with the LUE-based models, the SIF-based model without climate-dependence is a good predictor of GPP and its parameter is more likely to converge for different temporal aggregation levels and under varying environmental restrictions in croplands. We suggest that more flux tower data should be used for further validation of parameter convergence in other vegetation types.

【 授权许可】

Unknown