@Article{PahlevanSAABBBCGGFJKLLMHOPSVVR:2022:SiReSe,
               author = "Pahlevan, Nima and Smith, Brandon and Alikas, Krista and Anstee, 
                         Janet and Barbosa, Cl{\'a}udio Clemente Faria and Binding, Caren 
                         and Bresciani, Mariano and Cremelha, Bruno and Giardino, Claudia 
                         and Gurlin, Daniela and Fernandez, Virginia and Jamet, C{\'e}dric 
                         and Kangro, Kersti and Lehmann, Moritz K. and Loisel, Hubert and 
                         Matshushita, Bunkei and H{\`a}, Nguy{\^e}n and Olmanson, Leif 
                         and Potvin, Genevi{\`e}ve and Simis, Stefan G. H. and 
                         VanderWoude, Andrea and Vantrepotte, Vincent and Ruiz-Verdù, 
                         Antonio",
          affiliation = "{NASA Goddard Space Flight Center} and {NASA Goddard Space Flight 
                         Center} and {University of Tartu} and {Commonwealth Scientific and 
                         Industrial Research Organization (CSIRO)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Environment and Climate Change 
                         Canada} and {National Research Council of Italy} and {University 
                         of Sherbrooke} and {National Research Council of Italy} and 
                         {Wisconsin Department of Natural Resources} and {Universidad la 
                         Republica} and {Univ. Littoral C{\^o}te d’Opale} and {University 
                         of Tartu} and {Xerra Earth Observation Institute and the 
                         University of Waikato} and {Univ. Littoral C{\^o}te d’Opale} and 
                         {University of Tsukuba} and {Vietnam National University} and 
                         {University of Minnesota} and {University of Sherbrooke} and 
                         {Plymouth Marine Laboratory} and {National Oceanic and Atmospheric 
                         Administration} and {Univ. Littoral C{\^o}te d’Opale} and 
                         {University of Valencia}",
                title = "Simultaneous retrieval of selected optical water quality 
                         indicators from Landsat-8, Sentinel-2, and Sentinel-3",
              journal = "Remote Sensing of Environment",
                 year = "2022",
               volume = "270",
                pages = "e112860",
                month = "Mar.",
             keywords = "Inland and coastal waters, Machine learning, MSI, OLCI, OLI, Water 
                         quality.",
             abstract = "Constructing multi-source satellite-derived water quality (WQ) 
                         products in inland and nearshore coastal waters from the past, 
                         present, and future missions is a long-standing challenge. Despite 
                         inherent differences in sensors spectral capability, spatial 
                         sampling, and radiometric performance, research efforts focused on 
                         formulating, implementing, and validating universal WQ algorithms 
                         continue to evolve. This research extends a recently developed 
                         machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) 
                         (Pahlevan et al., 2020; Smith et al., 2021), to the inverse 
                         problem of simultaneously retrieving WQ indicators, including 
                         chlorophyll-a (Chla), Total Suspended Solids (TSS), and the 
                         absorption by Colored Dissolved Organic Matter at 440 nm 
                         (acdom(440)), across a wide array of aquatic ecosystems. We use a 
                         database of in situ measurements to train and optimize MDN models 
                         developed for the relevant spectral measurements (400800 nm) of 
                         the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), 
                         and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, 
                         Sentinel-2, and Sentinel-3 missions, respectively. Our two 
                         performance assessment approaches, namely hold-out and 
                         leave-one-out, suggest significant, albeit varying degrees of 
                         improvements with respect to second-best algorithms, depending on 
                         the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements 
                         based on the hold-out method for Chla, TSS, and acdom(440), 
                         respectively from MSI-like spectra). Using these two assessment 
                         methods, we provide theoretical upper and lower bounds on model 
                         performance when evaluating similar and/or out-of-sample datasets. 
                         To evaluate multi-mission product consistency across broad spatial 
                         scales, map products are demonstrated for three near-concurrent 
                         OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and 
                         acdom(440) from these three missions are consistent within the 
                         uncertainty of the model, but Chla maps from MSI and OLCI achieve 
                         greater accuracy than those from OLI. By applying two different 
                         atmospheric correction processors to OLI and MSI images, we also 
                         conduct matchup analyses to quantify the sensitivity of the MDN 
                         model and best-practice algorithms to uncertainties in reflectance 
                         products. Our model is less or equally sensitive to these 
                         uncertainties compared to other algorithms. Recognizing their 
                         uncertainties, MDN models can be applied as a global algorithm to 
                         enable harmonized retrievals of Chla, TSS, and acdom(440) in 
                         various aquatic ecosystems from multi-source satellite imagery. 
                         Local and/or regional ML models tuned with an apt data 
                         distribution (e.g., a subset of our dataset) should nevertheless 
                         be expected to outperform our global model.",
                  doi = "10.1016/j.rse.2021.112860",
                  url = "http://dx.doi.org/10.1016/j.rse.2021.112860",
                 issn = "0034-4257",
             language = "en",
           targetfile = "pahlevan_2022.pdf",
        urlaccessdate = "25 jun. 2024"
}