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

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.

[1]  E. Middleton,et al.  First observations of global and seasonal terrestrial chlorophyll fluorescence from space , 2010 .

[2]  Atul K. Jain,et al.  A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis , 2012 .

[3]  John E. Hay,et al.  A revised method for determining the direct and diffuse components of the total short‐wave radiation , 1976 .

[4]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[5]  C. Frankenberg,et al.  Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2 , 2013 .

[6]  P. Zarco-Tejada,et al.  Relationships between net photosynthesis and steady-state chlorophyll fluorescence retrieved from airborne hyperspectral imagery , 2013 .

[7]  Michele Meroni,et al.  Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies , 2011, Sensors.

[8]  Liangyun Liu,et al.  Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence , 2017 .

[9]  Jihua Wang,et al.  Detecting solar-induced chlorophyll fluorescence from field radiance spectra based on the Fraunhofer line principle , 2005, IEEE Trans. Geosci. Remote. Sens..

[10]  M. Schaepman,et al.  Far-red sun-induced chlorophyll fluorescence shows ecosystem-specific relationships to gross primary production: An assessment based on observational and modeling approaches , 2015 .

[11]  H. Walz.  Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges , 2014 .

[12]  I. Mammarella,et al.  Solar‐induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO‐2 and flux tower observations , 2018, Global change biology.

[13]  Liangyun Liu,et al.  Assessing the wavelength-dependent ability of solar-induced chlorophyll fluorescence to estimate the GPP of winter wheat at the canopy level , 2017 .

[14]  A. Huete,et al.  Estimation of vegetation photosynthetic capacity from space‐based measurements of chlorophyll fluorescence for terrestrial biosphere models , 2014, Global change biology.

[15]  Shaomin Liu,et al.  A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem , 2011 .

[16]  C. Frankenberg,et al.  Prospects for Chlorophyll Fluorescence Remote Sensing from the Orbiting Carbon Observatory-2 , 2014 .

[17]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[18]  Liangyun Liu,et al.  Evaluating the Performance of the SCOPE Model in Simulating Canopy Solar-Induced Chlorophyll Fluorescence , 2018, Remote. Sens..

[19]  Benjamin Y. H. Liu,et al.  The interrelationship and characteristic distribution of direct, diffuse and total solar radiation , 1960 .

[20]  P. Gentine,et al.  Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence , 2018, Geophysical research letters.

[21]  J. Markwell,et al.  Calibration of the Minolta SPAD-502 leaf chlorophyll meter , 2004, Photosynthesis Research.

[22]  J. Moreno,et al.  Remote sensing of sun‐induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP) , 2010 .

[23]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[24]  S. W. Maier,et al.  Sun-induced fluorescence: A new tool for precision farming , 2003 .

[25]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[26]  C. Frankenberg,et al.  New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity , 2011, Geophysical Research Letters.

[27]  J. M. Krijger,et al.  Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence , 2014 .

[28]  C. Justice,et al.  The generation of global fields of terrestrial biophysical parameters from the NDVI , 1994 .

[29]  W. Verhoef,et al.  A model and measurement comparison of diurnal cycles of sun-induced chlorophyll fluorescence of crops , 2016 .

[30]  F. Woodward,et al.  Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate , 2010, Science.

[31]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[32]  R. Myneni,et al.  On the relationship between FAPAR and NDVI , 1994 .

[33]  Liangyun Liu,et al.  Effects of spectral resolution and SNR on the vegetation solar-induced fluorescence retrieval using FLD-based methods at canopy level , 2015 .

[34]  L. Guanter,et al.  Consistency Between Sun-Induced Chlorophyll Fluorescence and Gross Primary Production of Vegetation in North America , 2016 .

[35]  L. Guanter,et al.  Assessing the potential of sun-induced fluorescence and the canopy scattering coefficient to track large-scale vegetation dynamics in Amazon forests , 2016 .

[36]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[37]  J. Moreno,et al.  Global sensitivity analysis of the SCOPE model: What drives simulated canopy-leaving sun-induced fluorescence? , 2015 .

[38]  A. Arneth,et al.  Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .

[39]  M. Rossini,et al.  Solar‐induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest , 2015 .

[40]  Emilio A. Laca,et al.  Inverse estimation of Vcmax, leaf area index, and the Ball-Berry parameter from carbon and energy fluxes , 2006 .

[41]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[42]  M. S. Moran,et al.  Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence , 2014, Proceedings of the National Academy of Sciences.

[43]  Liangyun Liu,et al.  Response of Canopy Solar-Induced Chlorophyll Fluorescence to the Absorbed Photosynthetically Active Radiation Absorbed by Chlorophyll , 2017, Remote. Sens..

[44]  Philip Lewis,et al.  Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements , 2012 .

[45]  T. Vesala,et al.  On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm , 2005 .

[46]  W. Verhoef,et al.  An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance , 2009 .

[47]  Javier Pacheco-Labrador,et al.  EUROSPEC: at the interface between remote-sensing and ecosystem CO2 flux measurements in Europe , 2015 .

[48]  J. Randerson,et al.  Global net primary production: Combining ecology and remote sensing , 1995 .

[49]  Pierre Gentine,et al.  A global spatially Continuous Solar Induced Fluorescence (CSIF) dataset using neural networks , 2018 .

[50]  J. Joiner,et al.  Retrieval of sun-induced chlorophyll fluorescence from space , 2014 .

[51]  W. Cohen,et al.  Site‐level evaluation of satellite‐based global terrestrial gross primary production and net primary production monitoring , 2005 .

[52]  W. W. Adams,et al.  Photosynthesis: Harvesting sunlight safely , 2000, Nature.

[53]  L. Guanter,et al.  On the relationship between sub-daily instantaneous and daily total gross primary production: Implications for interpreting satellite-based SIF retrievals , 2018 .

[54]  D. Lamb,et al.  The impact of solar illumination angle when using active optical sensing of NDVI to infer fAPAR in a pasture canopy , 2015 .

[55]  Ü. Rannik,et al.  Gap filling strategies for defensible annual sums of net ecosystem exchange , 2001 .