@Article{BegliominiBMNPMLOPL:2023:MaLeCy,
               author = "Begliomini, Felipe N. and Barbosa, Cl{\'a}udio Clemente Faria and 
                         Martins, Vitor S. and Novo, Evlyn M{\'a}rcia Le{\~a}o de Moraes 
                         and Paulino, Rejane de Souza and Maciel, Daniel Andrade and Lima, 
                         Thainara Munhoz Alexandre de and O'Shea, Ryan E. and Pahlevan, 
                         Nima and Lamparelli, Marta C.",
          affiliation = "{University of Cambridge} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Mississippi State University (MSU)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {NASA Goddard Space Flight Center} and {NASA 
                         Goddard Space Flight Center} and {Environmental Company of the 
                         State of S{\~a}o Paulo (CETESB)}",
                title = "Machine learning for cyanobacteria mapping on tropical urban 
                         reservoirs using PRISMA hyperspectral data",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2023",
               volume = "204",
                pages = "378--396",
                month = "Oct.",
             keywords = "C-Phycocyanin, Cyanobacteria, Inland water, Remote sensing, Urban 
                         reservoir, Water quality.",
             abstract = "Urban reservoirs are important for drinking water services and 
                         urban living. However, potentially toxic cyanobacteria blooms are 
                         frequently present due to human pollution and might threaten the 
                         urban water supply. Conveniently, cyanobacteria can be monitored 
                         by remote sensing-based approaches based on the spectral features 
                         of C-Phycocyanin (PC). Furthermore, methods leveraging Machine 
                         Learning Algorithms (MLA) for PC estimation from hyperspectral 
                         data have highlighted the potential to estimate PC more accurately 
                         - even at low concentrations. Since relatively few methodologies 
                         for PC retrieval in tropical environments have been developed or 
                         validated, this research evaluated PRISMA hyperspectral data 
                         processed with three MLA (Random Forest, Extreme Gradient Boost, 
                         and Support Vector Machines) to estimate PC concentrations in the 
                         Billings reservoir, Brazil. The same MLA were used to generate PC 
                         models using Wordview-3 and Landsat-8/OLI simulated data to assess 
                         the potential gain of using hyperspectral over multispectral data. 
                         A PRISMA image was processed with three atmospheric correction 
                         methods and validated with co-located in-situ data, where the best 
                         atmospherically corrected product was used to generate synthetic 
                         Landsat-8/OLI and Worldview-3 images. The PC models were 
                         calibrated and validated through Monte Carlo simulation using 
                         field radiometric and biological data (Chlorophyll-a, PC, and 
                         phytoplankton taxonomy) collected in eight field campaigns (N = 
                         115). The PRISMA and the synthetic multispectral images were used 
                         for a second round of models validation using co-located PC 
                         measurements (match-up window ± 4 h). The global PC Mixture 
                         Density Network was also applied to the PRISMA data, and the 
                         estimates were compared with the other MLA. The results showed 
                         that the standard PRISMA surface reflectance product provided the 
                         best atmospheric correction (MAE < 20% for the 500700 nm bands), 
                         while ACOLITE and 6SV underperformed it from two to more than 
                         ten-fold. Cyanobacteria species were abundant in 96% of the 
                         taxonomical samples, even though relatively low PC concentrations 
                         were found (PC from 0 to 301.81 \μg/L and median PC = 2.9 
                         \μg/L). The global Mixture Density Network sharply 
                         overestimated PC (MAE = 280% and Bias = 280%), potentially due to 
                         Billings reservoir's low PC:Chlorophyll-a ratio relative to the 
                         original training dataset. PRISMA/Random Forest (MAE = 45%) 
                         achieved the lowest error for orbital PC estimate, while Extreme 
                         Gradient Boost outperformed the other MLA using Worldview-3 (MAE = 
                         49%) and Landsat-8 (MAE = 74%) synthetic imagery. Therefore, the 
                         results suggest hyperspectral and multispectral orbital data 
                         aligned with MLA are feasible for monitoring PC, even for waters 
                         containing low PC concentrations and reduced PC:Chlorophyll-a 
                         ratios.",
                  doi = "10.1016/j.isprsjprs.2023.09.019",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2023.09.019",
                 issn = "0924-2716",
                label = "self-archiving-INPE-MCTIC-GOV-BR",
             language = "en",
           targetfile = "1-s2.0-S0924271623002617-main.pdf",
        urlaccessdate = "19 maio 2024"
}