@Article{LimaGiBrBaFaPeBe:2023:CoSeMa,
               author = "Lima, Thainara Munhoz Alexandre de and Giardino, Claudia and 
                         Bresciani, Mariano and Barbosa, Cl{\'a}udio Clemente Faria and 
                         Fabbretto, Alice and Pellegrino, Andrea and Begliomini, Felipe 
                         Nincao",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {National 
                         Research Council of Italy} and {National Research Council of 
                         Italy} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {National Research Council of Italy} and {National Research 
                         Council of Italy} and {University of Cambridge}",
                title = "Assessment of estimated phycocyanin and chlorophyll-a 
                         concentration from PRISMA and OLCI in Brazilian inland waters: a 
                         comparison between semi-analytical and machine learning 
                         algorithms",
              journal = "Remote Sensing",
                 year = "2023",
               volume = "15",
               number = "5",
                pages = "e1299",
                month = "Mar.",
             keywords = "aquatic remote sensing, cyanobacteria, hyperspectral, machine 
                         learning, phycocyanin, semi-analytical model.",
             abstract = "The aim of this work is to test the state-of-the-art of water 
                         constituent retrieval algorithms for phycocyanin (PC) and 
                         chlorophyll-a (chl-a) concentrations in Brazilian reservoirs from 
                         hyperspectral PRISMA images and concurrent in situ data. One 
                         near-coincident Sentinel-3 OLCI dataset has also been considered 
                         for PC mapping as its high revisit time is a relevant element for 
                         mapping cyanobacterial blooms. The testing was first performed on 
                         remote sensing reflectance ((Formula presented.)), as derived by 
                         applying two atmospheric correction methods (6SV, ACOLITE) to 
                         Level 1 data and as provided in the corresponding Level 2 products 
                         (PRISMA L2C and OLCI L2-WFR). Since PRISMA images were affected by 
                         sun glint, the testing of three de-glint models was also 
                         performed. The applicability of Semi-Analytical (SA) and Mixture 
                         Density Network (MDN) algorithms in enabling PC and chl-a 
                         concentration retrieval was then tested over three PRISMA scenes; 
                         in the case of PC concentration estimation, a Random Forest (RF) 
                         algorithm was further applied. Regarding OLCI, the SA algorithm 
                         was tested for PC estimation; notably, only SA was calibrated with 
                         site-specific data from the reservoir. The algorithms were applied 
                         to the (Formula presented.) spectra provided by PRISMA L2C 
                         productsand those derived with ACOLITE, in the case of OLCIas 
                         these data showed better agreement with in situ measurements. The 
                         SA model provided low median absolute error (MdAE) for 
                         PRISMA-derived (MdAE = 3.06 mg.m\−3) and OLCI-derived (MdAE 
                         = 3.93 mg.m\−3) PC concentrations, while it overestimated 
                         PRISMA-derived chl-a (MdAE = 42.11 mg.m\−3). The RF model 
                         for PC applied to PRISMA performed slightly worse than SA (MdAE = 
                         5.21 mg.m\−3). The MDN showed a rather different 
                         performance, with higher errors for PC (MdAE = 40.94 
                         mg.m\−3) and lower error for chl-a (MdAE = 23.21 
                         mg.m\−3). The results overall suggest that the model 
                         calibrated with site-specific measurements performed better and 
                         indicates that SA could be applied to PRISMA and OLCI for remote 
                         sensing of PC in Brazilian reservoirs.",
                  doi = "10.3390/rs15051299",
                  url = "http://dx.doi.org/10.3390/rs15051299",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-15-01299-v2.pdf",
        urlaccessdate = "03 maio 2024"
}