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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM

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Author(s):
Montanher, Otavio C. [1, 2] ; Novo, Evlyn M. L. M. [1] ; Barbosa, Claudio C. F. [1] ; Renno, Camilo D. [1] ; Silva, Thiago S. F. [3]
Total Authors: 5
Affiliation:
[1] Inst Nacl Pesquisas Espaciais, Div Sensoriamento Remoto, BR-12201970 Sao Jose Dos Campos, SP - Brazil
[2] Univ Estadual Maringa, Dept Tecnol, BR-87506370 Umuarama, PR - Brazil
[3] Univ Estadual Paulista UNESP, IGCE, Dept Geog, Rio Claro, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: International Journal of Applied Earth Observation and Geoinformation; v. 29, p. 67-77, JUN 2014.
Web of Science Citations: 26
Abstract

Suspended sediment yield is a very important environmental indicator within Amazonian fluvial systems, especially for rivers dominated by inorganic particles, referred to as white water rivers. For vast portions of Amazonian rivers, suspended sediment concentration (SSC) is measured infrequently or not at all. However, remote sensing techniques have been used to estimate water quality parameters worldwide, from which data for suspended matter is the most successfully retrieved. This paper presents empirical models for SSC retrieval in Amazonian white water rivers using reflectance data derived from Landsat 5/TM. The models use multiple regression for both the entire dataset (global model, N=504) and for five segmented datasets (regional models) defined by general geological features of drainage basins. The models use VNIR bands, band ratios, and the SWIR band 5 as input. For the global model, the adjusted R-2 is 0.76, while the adjusted R-2 values for regional models vary from 0.77 to 0.89, all significant (p-value <0.0001). The regional models are subject to the leave-one-out cross validation technique, which presents robust results. The findings show that both the average error of estimation and the standard deviation increase as the SSC range increases. Regional models were more accurate when compared with the global model, suggesting changes in optical proprieties of water sampled at different sampling stations. Results confirm the potential for the estimation of SSC from Landsat/TM historical series data for the 1980s and 1990s, for which the in situ database is scarce. Such estimates supplement the SSC temporal series, providing a more comprehensive SSC temporal series which may show environmental dynamics yet unknown. (C) 2014 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 10/11269-2 - Modeling of the spatial dynamics of macrophyte communities in the Amazon floodplain
Grantee:Thiago Sanna Freire Silva
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 11/23594-8 - Remote sensing applications for modeling human impacts on the ecological properties of wetland and aquatic environments in the Solimões/Amazon floodplain
Grantee:Evlyn Márcia Leão de Moraes Novo
Support type: Regular Research Grants