Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29574
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM
Author(s): Montanher, Otávio Cristiano
Novo, Evlyn Márcia Leão Moraes
Barbosa, Cláudio Clemente Faria
Rennó, Camilo Daleles
Silva, Thiago Sanna Freire
Contact Email: thiago.sf.silva@stir.ac.uk
Keywords: band ratios
corresponding author
d
fluvial sediments
geology of
m
mrs
multiple regressions
otávio cristiano montanher
s institution
spectral bands
the amazon
top of atmosphere reflectance
universidade estadual de maringá
Issue Date: Jun-2014
Citation: Montanher OC, Novo EMLM, Barbosa CCF, Rennó CD & Silva TSF (2014) Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM. International Journal of Applied Earth Observation and Geoinformation, 29, pp. 67-77. https://doi.org/10.1016/j.jag.2014.01.001
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 R2 is 0.76, while the adjusted R2 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.
DOI Link: 10.1016/j.jag.2014.01.001
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