Bio-optical characterization of Amazon floodplain lakes and evaluation of the retrieval of optically active constituent using remote sensing

• Ano de defesa: 2016
• Autor(a) principal: Lino Augusto Sander de Carvalho
• Orientador(a): Cláudio Clemente Faria Barbosa, Evlyn Márcia Leão de Moraes Novo
• Banca de defesa: João Antonio Lorenzetti, Natália de Moraes Rudorff, Mauricio Almeida Noernberg, Emmanuel Boss
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Instituto Nacional de Pesquisas Espaciais (INPE)
• Programa de Pós-Graduação: Programa de Pós-Graduação do INPE em Sensoriamento Remoto
• País: BR
• Link de acesso: http://urlib.net/sid.inpe.br/mtc-m21b/2016/06.08.16.27

Amazon floodplain lakes play a substantial role in global and regional biogeochemical Amazonian processes. Due to their size, sampling strategies usually applied in limnological studies are not suitable and therefore, Remote Sensing (RS) techniques figure as an alternative due to the high temporal and synoptic characteristics. However, the use of RS demands precise lake bio-optical characterization in order to provide reliable estimates of optical active components (OAC).This work focused on the study of Curuai Lake which is a suitable example of a Brazilian Amazon floodplain lake. Curuai lake was sampled in four field campaigns (September/2012, February and August/2013 and April/2014) where Apparent Optical Properties-AOP (R$_{rs}$ and K-functions), in situ Inherent Optical Properties-IOP (Attenuation, Absorption, Backscattering profiles and Particle Size Distribution (PSD)) as well as laboratory analysis (AOC concentration and absorption) were measured. A data quality assessment was performed to test the suitability of commercial instrumentation (ACS and Hydroscat) for turbid environments as well as commonly used AOP/IOP measurement methodologies. The optical characterization compared datasets from each fieldcampaign for surface and profile measurements. Also three semi-analytical inverse models (Nechad Algorithm (NECHAD et al., 2010), Quasi-Analytical Algorithm (QAA) (LEE et al., 2002) and Generalized ocean color inversion model (GIOP) (WERDELL et al., 2013)) were tested using measured AOP and IOPs. Data quality assessment show that sun/skyglint effects have the highest impact on above water remote sensing measurements R$_{rs}$ (R$_{Ab}$$^{rs}$ ). Highest errors were found for In-water derived AOPs (R$_{lw}$$^{rs}$ and K-functions), but despite the the different tested approaches their differences are commonly in the 10 to 15 \% interval. For in situ IOPs, the Hydrolight IOP/AOP closure experiments resulted in mismatches from 50 \% to 100\% depending on the field campaign. Among the corrections tested for ACS/Hydroscat errors, the Doxaran (DOXARAN et al., 2013) and Rottgers (RöTTGERS et al., 2013) methods were the most suitable.