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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.)

Single tuned algorithm to estimate the SPM concentration in a cascade reservoir system using OLI/L8 images

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Author(s):
Bernardo, Nariane [1] ; Carmo, Alisson [1] ; Rotta, Luiz [1] ; Alcantara, Enner [2]
Total Authors: 4
Affiliation:
[1] Sao Paulo State Univ UNESP, Dept Cartog, BR-19060900 Presidente Prudente - Brazil
[2] Sao Paulo State Univ Unesp, Dept Environm Engn, BR-12247004 Sao Jose Dos Campos - Brazil
Total Affiliations: 2
Document type: Journal article
Source: Advances in Space Research; v. 66, n. 11, p. 2583-2596, DEC 1 2020.
Web of Science Citations: 0
Abstract

Suspended particulate matter (SPM) affecting light propagation in the water column, which, in turn, affects primary production, is considered an important water quality indicator. Monitoring SPM in cascading reservoir system is of a particular challenge due to the widely differing optical properties. The Tiete River Cascade System (TRCS) is considered a representative case that comprises different water types with varying optical properties. At 1150 km long, the Tiete River crosses the State of Sao Paulo from East to West across a variety of land uses and land covers, which makes SPM monitoring of this system extremely challenging via traditional methods. This research aims to investigate the relationship between heterogeneous SPM configuration in the TRCS and the remote sensing reflectance (R-rs) by identifying the most suitable empirical model to quantify a wide range of SPM concentrations. Empirical models based on single-band and band ratios were tuned which is then applied to OLI/Landsat-8 images to estimate the SPM concentrations. We tested three approaches to obtain the best fit to retrieve the SPM concentration: the first approach (i) the dataset from the first fieldwork of each reservoir was used do calibrate and the others fieldworks data were used do validate the algorithm; the second (ii) all data from a single reservoir were selected for calibration and the others for validation and the third (iii) approach we used methods for randomly divide the calibration and validation dataset. The results showed that only approach (iii) returned significant results. The best algorithm to estimate the SPM concentration was based on the band ratio B3/B2 (SPM = 10.34 x {[}561 nm/482 nm]-12.32; with an r = 0.65). This algorithm resulted in the lowest error on average (RMSE = 6.5 mg/L, nRMSE = 32.97% and MAPE = 47.05%). The highest errors in retrieve the SPM were observed for reservoirs with dominance of phytoplankton; in fact, considering the dataset from all reservoirs the correlation between SPM and Chl-a was 0.95, proving that there are influence from the Chl-a pigment on the signal which increases the error. Therefore, a method to reduce the influence of phytoplankton on the remote sensing reflectance should be developed in order to reduce the error in retrieve the SPM concentration. (C) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 19/00259-0 - Algorithms development to retrieve water quality parameters from space
Grantee:Enner Herenio de Alcântara
Support Opportunities: Regular Research Grants