@InProceedings{BontempoVale:2019:SuFlCo,
               author = "Bontempo, Edgard and Valeriano, Dalton de Morisson",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Sun-Induced Fluorescence's Correlation to Carbon-Flux Increases 
                         When Raw Data is Adjusted to Account for Vegetation Biochemistry 
                         and Structure",
                 year = "2019",
         organization = "AGU Fall Meeting",
             abstract = "The quantification and monitoring of photosynthesis are essential 
                         to understand the global carbon cycle and vegetation's responses 
                         to climate. Among the different remotely-sensed 
                         photosynthesis-related variables, Sun-Induced chlorophyll a 
                         Fluorescence (SIF) is especially promising since it results 
                         directly from photochemical energy conversion but uncertainties 
                         still complicate its interpretation. Recent studies have pointed 
                         to the influences of vegetation biochemistry and structure on 
                         radiative transfer as the main confounding factors for the use of 
                         SIF as a photosynthesis proxy. Leaf-level fluorescence research 
                         has shown that such influences may be removed by adjusting the raw 
                         fluorescence signal to the emitting leafs spectra and we suggest 
                         that this can be upscaled to the landscape level. In this study we 
                         present and test new Spectrally-Adjusted SIF formulations 
                         (SASIFs), along with previously proposed SIF modifications and 
                         other acknowledged photosynthesis productivity proxies, against 
                         carbon-flux data from vegetation of diverse structure. 
                         Accordingly, we used Gross Primary Productivity (GPP) data 
                         spanning periods from two to seven years, from 27 FLUXNET sites 
                         classified into different Land Cover Classes (LCCs) as defined by 
                         the International Geosphere-Biosphere Programme (IGBP). The data 
                         tested against GPP was calculated with GOME-2 SIF data, MODIS 
                         reflectance and spectral vegetation indices, and it included: 
                         NIRV, SIF from the red and the far-red frequency peaks, SIF 
                         normalized by the cosine of the Suns zenith angle, SIF-yield, new 
                         SASIFs and FLUXCOM GPP. The relationships between all variables 
                         and FLUXNET GPP were tested using time-series decomposition, site- 
                         and LCC-specific Kendalls rank correlation tests and linear mixed 
                         model analysis. Results show that one of our new SASIFs has the 
                         best overall correlation to FLUXNET GPP among all tested data. Our 
                         LCC-specific analysis demonstrates the influences of biochemistry, 
                         phenology, temporal resolution and vegetation structure on the 
                         relationships between the tested variables. Results support the 
                         idea that chlorophyll fluorescence can be complemented with 
                         reflectance data improving our ability to monitor vegetation 
                         productivity and predict climate-driven changes to standing 
                         biomass in spite of their particular limitations.",
  conference-location = "San Francisco, CA",
      conference-year = "09-13 dec.",
             language = "en",
           targetfile = "bontempo_sun.pdf",
        urlaccessdate = "25 abr. 2024"
}