Upscaling Solar-Induced Chlorophyll Fluorescence from an Instantaneous to Daily Scale Gives an Improved Estimation of the Gross Primary Productivity

Jiaochan Hu,Shanshan Du,Liangyun Liu,Xinjie Liu,Jian Guo

doi:10.3390/rs10101663

Abstract

Solar-induced chlorophyll fluorescence (SIF) is closely linked to the photosynthesis of plants and has the potential to estimate gross primary production (GPP) at different temporal and spatial scales. However, remotely sensed SIF at a ground or space level is usually instantaneous, which cannot represent the daily total SIF. The temporal mismatch between instantaneous SIF (SIFinst) and daily GPP (GPPdaily) impacts their correlation across space and time. Previous studies have upscaled SIFinst to the daily scale based on the diurnal cycle in the cosine of the solar zenith angle ( cos ( SZA ) ) to correct the effects of latitude and length of the day on the variations in the SIF-GPP correlation. However, the important effects of diurnal weather changes due to cloud and atmospheric scattering were not considered. In this study, we present a SIF upscaling method using photosynthetically active radiation (PAR) as a driving variable. First, a conversion factor (i.e., the ratio of the instantaneous PAR (PARinst) to daily PAR (PARdaily)) was used to upscale in-situ SIF measurements from the instantaneous to daily scale. Then, the performance of the SIF upscaling method was evaluated under changing weather conditions and different latitudes using continuous tower-based measurements at two sites. The results prove that our PAR-based method can reduce not only latitude-dependent but also the weather-dependent variations in the SIF-GPP model. Specifically, the PAR-based method gave a more accurate prediction of diurnal and daily SIF (SIFdaily) than the cos ( SZA ) -based method, with decreased relative root mean square error (RRMSE) values from 42.2% to 25.6% at half-hour intervals and from 25.4% to 13.3% at daily intervals. Moreover, the PAR-based upscaled SIFdaily had a stronger correlation with the daily absorbed PAR (APAR) than both the SIFinst and cos ( SZA ) -based upscaled SIFdaily, especially for cloudy days with a coefficient of determination (R2) that increased from approximately 0.5 to 0.8. Finally, the PAR-based SIFdaily was linked to GPPdaily and compared to the SIFinst or cos ( SZA ) -based SIFdaily. The results indicate that the SIF-GPP correlation can obviously be improved, with an increased R2 from approximately 0.65 to 0.75. Our study confirms the importance of upscaling SIF from the instantaneous to daily scale when linking SIF with GPP and emphasizes the need to take diurnal weather changes into account for SIF temporal upscaling.

Highlights

Photosynthesis, which supports most life on earth [1], is a vital process driving the global carbon cycle [2]
The predicted Solar-induced chlorophyll fluorescence (SIF) values using the two upscaling methods were compared with the measured SIF values to investigate whether the predicted SIF reliably tracked the diurnal cycles of SIF measurements
Long-term SIF and gross primary productivity (GPP) measurements have validated that this photosynthetically active radiation (PAR)-based method can reduce the latitude dependency and the weather dependency of the SIF-GPP model, which is likely to improve the accuracy of GPP estimation at the ground and spatial scales

Summary

Introduction

Photosynthesis, which supports most life on earth [1], is a vital process driving the global carbon cycle [2]. The explicit estimation of the photosynthetic exchange flux, known as gross primary productivity (GPP), can offer key information for exploring and quantifying terrestrial carbon fixation by plants [3]. Remote sensing offers the unique possibility of deriving the continuous global monitoring of carbon exchange. Global GPP can be estimated using a light use efficiency (LUE) model, which combines reflectance-based remote sensing and climate variables [5,6]; a data-driven method, which integrates EC flux observations, remote sensing and climate data [2,7]; process-based models [8,9]. Uncertainties in the diagnostic models, measured variables, and auxiliary products all propagate into the global GPP estimation, which limits the monitoring power of these methods [2,10]

Results

Discussion

Conclusion