Atmospheric correction issues for retrieving total suspended matter concentrations in inland waters using OLI/Landsat-8 image

• Autor(a) principal: Bernardo, Nariane [UNESP]
• Data de Publicação: 2017
• Outros Autores: Watanabe, Fernanda [UNESP], Rodrigues, Thanan [UNESP], Alcantara, Enner [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.asr.2017.02.017; http://hdl.handle.net/11449/162749
• topic: Atmospheric correction; Inland water; Water quality; Eutrophic environment

The atmospheric effects that influence on the signal registered by remote sensors might be minimized in order to provide reliable spectral information. In aquatic systems, the application of atmospheric correction aims to minimize such effects and avoid the under or overestimation of remote sensing reflectance (R-rs). Accurately R-rs provides better information about the state of aquatic system, it means, establishing the concentration of aquatic compounds more precisely. The aim of this study is to evaluate the outputs from several atmospheric correction methods (Dark Object Subtraction DOS; Quick Atmospheric Correction - QUAC; Fast Line-of-sight Atmospheric Analysis of Hypercubes - FLAASH; Atmospheric Correction for OLI 'lite' - ACOLITE, and Provisional Landsat-8 Surface Reflectance Algorithm - L8SR) in order to investigate the suitability of R-rs for estimating total suspended matter concentrations (TSM) in the Barra Bonita Hydroelectrical Reservoir. To establish TSM concentrations via atmospherically corrected Operational Land Imager (OLI) scene, the TSM retrieval model was calibrated and validated with in situ data. Thereby, the achieved results from TSM retrieval model application demonstrated that L8SR is able to provide the most suitable R-rs values for green and red spectral bands, and consequently, the lowest TSM retrieval errors (Mean Absolute Percentage Error about 10% and 12%, respectively). Retrieved R-rs from near infrared band is still a challenge for all the tested algorithms. (C) 2017 COSPAR. Published by Elsevier Ltd. All rights reserved.