Estimation of both optical and nonoptical surface water quality parameters using Landsat 8 OLI imagery and statistical techniques

Din, Essam Sharaf El; Zhang, Yun

doi:10.1117/1.jrs.11.046008

Cited by 15 publications

(11 citation statements)

References 32 publications

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“…As a result, they obtained algorithms that strongly estimated TSS and Chl-a (R 2 = 0.74 and 0.71, respectively), but were not as strong when estimating TP and TN (R 2 = 0.50 and 0.48, respectively). Due to this limitation, an indirect estimation approach has been taken by some authors in order to develop strong correlations that relate TP and TN to Chl-a concentrations and SDD [54,55].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

Arango

Nairn

2019

Drones

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The purpose of this study was to create different statistically reliable predictive algorithms for trophic state or water quality for optical (total suspended solids (TSS), Secchi disk depth (SDD), and chlorophyll-a (Chl-a)) and non-optical (total phosphorus (TP) and total nitrogen (TN)) water quality variables or indicators in an oligotrophic system (Grand River Dam Authority (GRDA) Duck Creek Nursery Ponds) and a eutrophic system (City of Commerce, Oklahoma, Wastewater Lagoons) using remote sensing images from a small unmanned aerial system (sUAS) equipped with a multispectral imaging sensor. To develop these algorithms, two sets of data were acquired: (1) In-situ water quality measurements and (2) the spectral reflectance values from sUAS imagery. Reflectance values for each band were extracted under three scenarios: (1) Value to point extraction, (2) average value extraction around the stations, and (3) point extraction using kriged surfaces. Results indicate that multiple variable linear regression models in the visible portion of the electromagnetic spectrum best describe the relationship between TSS (R2 = 0.99, p-value = <0.01), SDD (R2 = 0.88, p-value = <0.01), Chl-a (R2 = 0.85, p-value = <0.01), TP (R2 = 0.98, p-value = <0.01) and TN (R2 = 0.98, p-value = <0.01). In addition, this study concluded that ordinary kriging does not improve the fit between the different water quality parameters and reflectance values.

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Section: Discussionmentioning

confidence: 99%

Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

Arango

Nairn

2019

Drones

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“…Water quality monitoring using multispectral or hyperspectral remote sensing represents a great challenge in order to find the relationship between in situ data and reflectances obtained by various sensors. There are many optical and nonoptical parameters that affect water quality and can be estimated using satellite data [5,6]. The main research focus is on the optical properties of water such as chlorophyll [7], turbidity [8], total suspended matters [9], and colored dissolved organic matters [10], which affect the reflected radiation of the sea and, thus, can be measured remotely.…”

Section: Related Workmentioning

confidence: 99%

Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

et al. 2021

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In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in the Republic of Croatia. Chl-a is one of the parameters that indicates water quality and that can be measured by in situ measurements or approximated as an optical parameter with remote sensing. Remote sensing products for monitoring Chl-a are mostly based on the ocean and open sea monitoring and are not accurate for coastal waters. In this paper, we propose a method for remote sensing monitoring that is locally tailored to suit the focused area. This method is based on a data set constructed by merging Sentinel 2 Level-2A satellite data with in situ Chl-a measurements. We augmented the data set horizontally by transforming the original feature set, and vertically by adding synthesized zero measurements for locations without Chl-a. By transforming features, we were able to achieve a sophisticated model that predicts Chl-a from combinations of features representing transformed bands. Multiple Linear Regression equation was derived to calculate Chl-a concentration and evaluated quantitatively and qualitatively. Quantitative evaluation resulted in R2 scores 0.685 and 0.659 for train and test part of data set, respectively. A map of Chl-a of the case study area was generated with our model for the dates of the known incidents of algae blooms. The results that we obtained are discussed in this paper.

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“…Table 1 shows the acquisition dates of images from Landsat 8. Landsat-8 imagery at level 1T was rescaled to the top of atmosphere (TOA) reflectance using radiometric rescaling coefficients [22,33]. This radiometric rescaling was…”

Section: Satellite Imagery Acquisitionmentioning

confidence: 99%

“…Although significant advancements in mathematical models for surface water quality using reflectance values from satellite imagery are given, improving existing models using multiple linear regression to estimate water quality using Landsat 8 imagery remains an interesting pending research task. Recently, Sharaf El Din and Zhang [22] have proposed a regression-based technique to estimate surface water quality parameters using Landsat 8 OLI imagery. They propose a stepwise regression (SWR) to minimize the number of predictor variables and to maximize the precision of the water quality estimation.…”

Section: Introductionmentioning

confidence: 99%

Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression

Loaiza

Rangel‐Peraza

Monjardín-Armenta

et al. 2023

Water

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A methodology to estimate surface water quality using remote sensing is presented based on Landsat satellite imagery and in situ measurements taken every six months at four separate sampling locations in a tropical reservoir from 2015 to 2019. The remote sensing methodology uses the Box–Cox transformation model to normalize data on three water quality parameters: total organic carbon (TOC), total dissolved solids (TDS), and chlorophyll a (Chl-a). After the Box–Cox transformation, a mathematical model was generated for every parameter using multiple linear regression to correlate normalized data and spectral reflectance from Landsat 8 imagery. Then, significant testing was conducted to discard spectral bands that did not show a statistically significant response (α = 0.05) from the different water quality models. The r2 values achieved for TOC, TDS, and Chl-a water quality models after the band discrimination process were found 0.926, 0.875, and 0.810, respectively, achieving a fair fitting to real water quality data measurements. Finally, a comparison between estimated and measured water quality values not previously used for model development was carried out to validate these models. In this validation process, a good fit of 98% and 93% was obtained for TDS and TOC, respectively, whereas an acceptable fit of 81% was obtained for Chl-a. This study proposes an interesting alternative for ordered and standardized steps applied to generate mathematical models for the estimation of TOC, TDS, and Chl-a based on water quality parameters measured in the field and using satellite images.

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Estimation of both optical and nonoptical surface water quality parameters using Landsat 8 OLI imagery and statistical techniques

Cited by 15 publications

References 32 publications

Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression

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