@Article{WuLiDuYaPaOdKo:2021:SoFlMo,
               author = "Wu, Dien and Lin, John C. and Duarte, Henrique Ferro and Yadav, 
                         Vineet and Parazoo, Nicholas C. and Oda, Tomohiro and Kort, Eric 
                         A.",
          affiliation = "{University of Utah} and {University of Utah} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {California Institute 
                         of Technology} and {California Institute of Technology} and {NASA 
                         Goddard Space Flight Center} and {University of Michigan}",
                title = "A model for urban biogenic CO2 fluxes: Solar-Induced Fluorescence 
                         for Modeling Urban biogenic Fluxes",
              journal = "Geoscientific Model Development",
                 year = "2021",
               volume = "14",
               number = "6",
                pages = "3633--3661",
                month = "June",
             abstract = "When estimating fossil fuel carbon dioxide (FFCO2) emissions from 
                         observed CO2 concentrations, the accuracy can be hampered by 
                         biogenic carbon exchanges during the growing season, even for 
                         urban areas where strong fossil fuel emissions are found. While 
                         biogenic carbon fluxes have been studied extensively across 
                         natural vegetation types, biogenic carbon fluxes within an urban 
                         area have been challenging to quantify due to limited observations 
                         and differences between urban and rural regions. Here we developed 
                         a simple model representation, i.e., Solar-Induced Fluorescence 
                         (SIF) for Modeling Urban biogenic Fluxes ({"}SMUrF{"}), that 
                         estimates the gross primary production (GPP) and ecosystem 
                         respiration (Reco) over cities around the globe. Specifically, we 
                         leveraged space-based SIF, machine learning, eddy-covariance (EC) 
                         flux data, and ancillary remote-sensing-based products, and we 
                         developed algorithms to gap-fill fluxes for urban areas. 
                         Grid-level hourly mean net ecosystem exchange (NEE) fluxes are 
                         extracted from SMUrF and evaluated against (1) non-gap-filled 
                         measurements at 67 EC sites from FLUXNET during 2010-2014 (r>0.7 
                         for most data-rich biomes), (2) independent observations at two 
                         urban vegetation and two crop EC sites over Indianapolis from 
                         August 2017 to December 2018 (rCombining double low line0.75), and 
                         (3) an urban biospheric model based on fine-grained land cover 
                         classification in Los Angeles (rCombining double low line0.83). 
                         Moreover, we compared SMUrF-based NEE with inventory-based FFCO2 
                         emissions over 40 cities and addressed the urban-rural contrast in 
                         both the magnitude and timing of CO2 fluxes. To illustrate the 
                         application of SMUrF, we used it to interpret a few summertime 
                         satellite tracks over four cities and compared the urban-rural 
                         gradient in column CO2 (XCO2) anomalies due to NEE against XCO2 
                         enhancements due to FFCO2 emissions. With rapid advances in 
                         space-based measurements and increased sampling of SIF and CO2 
                         measurements over urban areas, SMUrF can be useful to inform the 
                         biogenic CO2 fluxes over highly vegetated regions during the 
                         growing season.",
                  doi = "10.5194/gmd-14-3633-2021",
                  url = "http://dx.doi.org/10.5194/gmd-14-3633-2021",
                 issn = "1991-959X",
             language = "en",
           targetfile = "wu_solar.pdf",
        urlaccessdate = "09 maio 2024"
}