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@MastersThesis{Maciel:2019:AbMu,
               author = "Maciel, Daniel Andrade",
                title = "Quantifica{\c{c}}{\~a}o remota da concentra{\c{c}}{\~a}o de 
                         s{\'o}lidos totais e inorg{\^a}nicos em suspens{\~a}o em lagos 
                         da plan{\'{\i}}cie de inunda{\c{c}}{\~a}o do Baixo Amazonas: 
                         uma abordagem multi-sensor",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2019",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2019-02-25",
             keywords = "Landsat-8, Sentinel-2, CBERS-4, corre{\c{c}}{\~a}o 
                         atmosf{\'e}rica, corre{\c{c}}{\~a}o de glnt, Landsat-8, 
                         Sentinel-2,CBERS-4, atmosphere correction, glint correction.",
             abstract = "A utiliza{\c{c}}{\~a}o de imagens de sensoriamento remoto {\'e} 
                         de fundamental import{\^a}ncia para aumentar o conhecimento sobre 
                         a din{\^a}mica da troca de sedimentos entre o Rio Amazonas e as 
                         plan{\'{\i}}cies de inunda{\c{c}}{\~a}o j{\'a} que ela pode 
                         ajudar a entender como as mudan{\c{c}}as clim{\'a}ticas e de uso 
                         da terra influenciam esse processo. Neste sentido, este trabalho 
                         investigou a acur{\'a}cia de algoritmos de estimativa de TSS 
                         (Total de S{\'o}lidos em Suspens{\~a}o) e TSI (Total de 
                         S{\'o}lidos Inorganicos em suspens{\~a}o) atrav{\'e}s da 
                         utiliza{\c{c}}{\~a}o de tr{\^e}s sensores de m{\'e}dia 
                         resolu{\c{c}}{\~a}o espacial (Landsat-8/OLI, Sentinel-2A/MSI e 
                         CBERS-4/WFI) em lagos na plan{\'{\i}}cie de 
                         inunda{\c{c}}{\~a}o do Baixo Amazonas. Atrav{\'e}s de 
                         simula{\c{c}}{\~a}o Monte Carlo, foram calibrados e validados 
                         algoritmos emp{\'{\i}}ricos e semi-anal{\'{\i}}ticos a partir 
                         dados de reflet{\^a}ncia de sensoriamento remoto (Rrs) medidos 
                         in-situ e simulados para os tr{\^e}s sensores (Rrs_sim) e dados 
                         de TSS e TSI coletados simultaneamente ao longo de quatro 
                         campanhas de campo em lagos do baixo Amazonas. Para 
                         calibra{\c{c}}{\~a}o dos algoritmos, tr{\^e}s conjuntos de 
                         dados foram avaliados: Conjunto completo, separados por campanhas 
                         e separados por lagos. Ap{\'o}s a calibra{\c{c}}{\~a}o dos 
                         algoritmos, estes foram aplicados a uma cena de agosto de 2017 de 
                         cada sensor para sua valida{\c{c}}{\~a}o com dados de TSS e TSI 
                         in-situ. Al{\'e}m da valida{\c{c}}{\~a}o dos dados de TSS e 
                         TSI, avaliou-se tamb{\'e}m o desempenho de diversos m{\'e}todos 
                         de corre{\c{c}}{\~a}o atmosf{\'e}rica para o OLI (6S, ACOLITE, 
                         L8SR), MSI (6S, ACOLITE, Sen2Cor) e WFI (6S) e tamb{\'e}m de 
                         corre{\c{c}}{\~a}o de glint para o OLI e MSI tomando-se as Rrs 
                         simuladas a partir das medidas de Rrs in-situ como 
                         refer{\^e}ncia. Finalmente, avaliou-se a congru{\^e}ncia entre 
                         os dados de TSS e TSI estimados pelos tr{\^e}s sensores em 
                         imagens adquiridas no mesmo dia da passagem dos tr{\^e}s 
                         sat{\'e}lites afim de avaliar a possibilidade da 
                         cria{\c{c}}{\~a}o de constela{\c{c}}{\~o}es virtuais com estes 
                         sensores. O desempenho dos algoritmos com os dados in-situ mostrou 
                         resultados similares para as faixas espectrais equivalentes dos 
                         tr{\^e}s sensores avaliados e tamb{\'e}m resultados semelhantes 
                         para os algoritmos emp{\'{\i}}ricos e semi-anal{\'{\i}}ticos 
                         que utilizam a mesma faixa espectral. A valida{\c{c}}{\~a}o das 
                         corre{\c{c}}{\~o}es atmosf{\'e}ricas mostrou uma 
                         depend{\^e}ncia da faixa espectral utilizada e melhores 
                         resultados utilizando o 6S. J{\'a} a corre{\c{c}}{\~a}o de 
                         glint se mostrou satisfat{\'o}ria e com grande influ{\^e}ncia 
                         principalmente sobre a acur{\'a}cia do sensor MSI 
                         (Redu{\c{c}}{\~a}o nos valores de MAPE > 100%). Os algoritmos 
                         emp{\'{\i}}ricos e semi-anal{\'{\i}}ticos de estimativa de TSS 
                         e TSI apresentaram melhores resultados de valida{\c{c}}{\~a}o 
                         usando a banda do verde do sensor OLI (561 nm), do red-edge do 
                         sensor MSI (704 nm) do vermelho do sensor WFI (660 nm) quando 
                         aplicado {\`a}s cenas de agosto de 2017 utilizando o os 
                         algoritmos calibrados com o conjunto completo (MAPE < 31%). A 
                         compara{\c{c}}{\~a}o das estimativas de TSS e TSI a partir de 
                         imagens simult{\^a}neas dos tr{\^e}s sensores indicou que eles 
                         permitiram estimar as concentra{\c{c}}{\~o}es de TSS e TSI com 
                         diferen{\c{c}}as entre as medianas das concentra{\c{c}}{\~o}es 
                         inferior a 1 mgL-1. Estes resultados permitiram, pela primeira 
                         vez, a calibra{\c{c}}{\~a}o e valida{\c{c}}{\~a}o de 
                         algoritmos emp{\'{\i}}ricos e semi-anal{\'{\i}}ticos de TSS e 
                         TSI em lagos da plan{\'{\i}}cie de inunda{\c{c}}{\~a}o do 
                         Baixo Amazonas utilizando sensores de m{\'e}dia 
                         resolu{\c{c}}{\~a}o espacial. ABSTRACT: Remote sensing (RS) is a 
                         key tool for deepening the knowledge on the spatial and temporal 
                         dynamics of sediment exchange between Amazon River and their 
                         floodplains. Moreover, RS image can help to understand how both 
                         climate change and land use and land cover changes influence the 
                         sediment exchange between the Amazon River and floodplain lakes. 
                         In that sense, this study investigates the accuracy of Total 
                         Suspended Solids (TSS) and Total Inorganic Suspended Solids (TSI) 
                         estimates of Amazon floodplain lakes derived from medium 
                         resolution sensors (Landsat-8/OLI, Sentinel-2A/MSI and CBERS- 
                         4/WFI). Empirical and semi-analytical algorithms were calibrated 
                         and validated through a robust Monte Carlo simulation using both 
                         in-situ simulated remote sensing reflectance (Rrs_sim) and 
                         simultaneous TSS/TSI dataset collected over four field campaigns 
                         in the lower Amazon floodplain lakes. For algorithm calibration, 
                         three different datasets were evaluated: Complete dataset; 
                         Campaign dataset and Lake dataset. After the calibration process, 
                         calibrated algorithms were applied to an august/2017 scene of each 
                         sensor for validation using in-situ TSS and TSI concentration 
                         measurements. Despite TSS and TSI validation, the performance of 
                         several atmosphere correction methodologies for OLI (L8SR, 6S, 
                         ACOLITE), MSI (6S, ACOLITE, Sen2Cor) and WFI (6S) in Rrs retrieval 
                         were evaluated using in-situ Rrs,sim as a reference. Furthermore, 
                         the impacts of glint correction on OLI and MSI Rrs retrieval were 
                         also evaluated. Finally, the consistency between TSS and TSI 
                         estimates by each sensor was accessed using near-simultaneous 
                         imagery aiming to create a virtualconstellation based on those 
                         three sensors to support the generation of sediment products. The 
                         performance of in-situ algorithms demonstrates similar estimates 
                         for similar spectral bands disregarding the sensor and the type of 
                         algorithm (empirical or semi-analytical). Atmosphere correction 
                         validation presented a dependency on the spectral bands used and 
                         better results were obtained using 6S, although satisfactory 
                         results were also observed with other methods. Moreover, glint 
                         correction presented good results and being fundamental to the 
                         accuracy of the algorithms based on MSI imagery, reducing MAPE 
                         values higher beyond 100%. Empirical and semi-analytical TSS and 
                         TSI algorithms best results varied for each sensor when applied to 
                         August/2017 scenes: for OLI the best result was for the green band 
                         (561 nm) while for MSI the best result was for the red-edge band 
                         (704 nm) and for WFI the red band (660 nm) presented best results 
                         (MAPE values lower than 31% for both TSS and TSI) using algorithms 
                         calibrated with the Complete dataset. The comparison between TSS 
                         and TSI estimates using the near-simultaneous overpass indicated 
                         that they allowed sediment estimates with median difference values 
                         lower than 1 mgL-1. These results demonstrated, for the first 
                         time, the calibration and validation of empirical and 
                         semi-analytical algorithms for TSS and TSI retrieval over lower 
                         Amazon Floodplain Lakes using medium-resolution sensors.",
            committee = "Kampel, Milton (presidente) and Novo, Evlyn M{\'a}rcia Le{\~a}o 
                         de Moraes (orientadora) and Carvalho, Lino Augusto Sander de 
                         (orientador) and Oliveira, Nat{\'a}lia Rudorff and Montanher, 
                         Ot{\'a}vio Cristiano and Costa, Maycira",
         englishtitle = "Remote quantification of inorganic and total suspended solids over 
                         Lower Amazon floodplain lakes: a multisensor aproach",
             language = "pt",
                pages = "194",
                  ibi = "8JMKD3MGP3W34R/3SLFNB5",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34R/3SLFNB5",
           targetfile = "publicacao.pdf",
        urlaccessdate = "02 dez. 2020"
}


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