Optmization of Bio-Optical Model Parameters for Turbid Lake Water Quality Estimation Using Landsat 8 and Wasi-2d

Manuel, A.; Blanco, A. C.; Tamondong, A. M.; Jalbuena, R.; Cabrera, Olivia C; Gege, Peter

doi:10.5194/isprs-archives-xlii-3-w11-67-2020

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Parameterizations of WASI employed in the processing of Taal Lake images were established using the inversion outputs derived from the field spectral measurements. These parameters serve as a crucial foundation for accurately characterizing and analyzing the optical properties of Taal Lake, enabling comprehensive and reliable assessments of its water quality, and associated environmental dynamics (Manuel, 2022).…”

Section: Data Processing and Analysismentioning

confidence: 99%

Comparative Analysis of Prisma Hyperspectral and Sentinel-2 Multispectral Images for Chlorophyll-a and Turbidity Mapping of Taal Lake

Hernando,

Piano,

Blanco

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Freshwater bodies like Taal Lake play a pivotal role in providing essential resources like fresh drinking water and in supporting local livelihoods. This study aimed to examine the environmental conditions of Taal Lake by quantifying chlorophyll-a (chl-a) and turbidity levels with Water Colour Simulator (WASI) and water quality indices, specifically with Sentinel-2 and PRISMA imagery. The results from the satellite image-derived data revealed discernible variations in chlorophyll-a and turbidity concentrations across different regions of Taal Lake. Higher chlorophyll-a was consistently observed in the western regions (1.3–5.9 μg/L for Sentinel-2; 1.4–5.4 μg/L for PRISMA), while lower concentrations were found in the south (1.7–3.4 μg/L for Sentinel-2; 1.9–3.3 μg/L for PRISMA). Meanwhile, turbidity values were higher in the eastern and northeastern parts (0.15–0.59 mg/L for Sentinel-2; 0.13–0.51 mg/L for PRISMA). Water quality indices (NDTI & NDCI) also supported these findings. The findings of this comparative analysis between PRISMA hyperspectral and Sentinel-2 multispectral imagery demonstrate the potential of both satellite systems in providing valuable insights into the spatial distribution of water quality parameters in Taal Lake. Nonetheless, discrepancies observed in the scatter plots and inversion failures in PRISMA underscore the need for further research and refinement in utilizing PRISMA data with the WASI model. This study highlights the significance of freshwater bodies and the importance of monitoring their health for the well-being of communities and the environment.

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Section: Data Processing and Analysismentioning

confidence: 99%

Comparative Analysis of Prisma Hyperspectral and Sentinel-2 Multispectral Images for Chlorophyll-a and Turbidity Mapping of Taal Lake

Hernando,

Piano,

Blanco

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…However, these physics-based models require knowledge of the absorption and backscatter of IOPs, which were not available in public water quality data records and were, therefore, not employed in this study. Additionally, various bio-optical models require specifically centred bands which are not provided for by Landsat and require spectral calibration using in situ reflectances [93]. Alternative empirical methods such as machine learning, Empirical Orthogonal Function (EOF) analysis, and line-height algorithms options may also provide improvement to chl-a retrieval in optically complex waters [7,90,91,94].…”

Section: Comparison Of Global Algorithms To Owtsmentioning

confidence: 99%

Optimization of Landsat Chl-a Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types

Dallosch

Creed

2021

Remote Sensing

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The application of remote sensing data to empirical models of inland surface water chlorophyll-a concentrations (chl-a) has been in development since the launch of the Landsat 4 satellite series in 1982. However, establishing an empirical model using a chl-a retrieval algorithm is difficult due to the spatial heterogeneity of inland lake water properties. Classification of optical water types (OWTs; i.e., differentially observed water spectra due to differences in water properties) has grown in favour in recent years over traditional non-turbid vs. turbid classifications. This study examined whether top-of-atmosphere reflectance observations in visible to near-infrared bands from Landsat 4, 5, 7, and 8 sensors can be used to identify unique OWTs using a guided unsupervised classification approach in which OWTs are defined through both remotely sensed reflectance and surface water chemistry data taken from samples in North American and Swedish lakes. Linear regressions of algorithms (Landsat reflectance bands, band ratios, products, or combinations) to lake surface water chl-a were built for each OWT. The performances of chl-a retrieval algorithms within each OWT were compared to those of global chl-a algorithms to test the effectiveness of OWT classification. Seven unique OWTs were identified and then fit into four categories with varying degrees of brightness as follows: turbid lakes with a low chl-a:turbidity ratio; turbid lakes with a mixture of high chl-a and turbidity measurements; oligotrophic or mesotrophic lakes with a mixture of low chl-a and turbidity measurements; and eutrophic lakes with a high chl-a:turbidity ratio. With one exception (r2 = 0.26, p = 0.08), the best performing algorithm in each OWT showed improvement (r2 = 0.69–0.91, p < 0.05), compared with the best performing algorithm for all lakes combined (r2 = 0.52, p < 0.05). Landsat reflectance can be used to extract OWTs in inland lakes to provide improved prediction of chl-a over large extents and long time series, giving researchers an opportunity to study the trophic states of unmonitored lakes.

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“…As a result, many studies investigated the suspended sediment concentrations in rivers using remote-sensing images, exploiting the ability of suspended sediment to increase reflectance values at visible and near-infrared spectra [45][46][47][48][49][50]. The correlation between suspended sediment concentration and reflectance of satellite images was performed by semi-empirical approach [46,51,52], bio-optical modeling approach [53,54], or machine-learning algorithms [55][56][57].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Sediment Discharge in Rivers Using Sentinel-2 Images and Machine-Learning Algorithms

2022

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The spatio-temporal dynamism of sediment discharge (Qs) in rivers is influenced by various natural and anthropogenic factors. Unfortunately, most rivers are only monitored at a limited number of stations or not gauged at all. Therefore, this study aims to provide a remote-sensing-based alternative for Qs monitoring. The at-a-station hydraulic geometry (AHG) power–law method was compared to the at-many-stations hydraulic geometry (AMHG) method; in addition, a novel AHG machine-learning (ML) method was introduced to estimate water discharge at three gauging stations in the Tisza (Szeged and Algyő) and Maros (Makó) Rivers in Hungary. The surface reflectance of Sentinel-2 images was correlated to in situ suspended sediment concentration (SSC) by support vector machine (SVM), random forest (RF), artificial neural network (ANN), and combined algorithms. The best performing water discharge and SSC models were employed to estimate the Qs. Our novel AHG ML method gave the best estimations of water discharge (Szeged: R2 = 0.87; Algyő: R2 = 0.75; Makó: R2 = 0.61). Furthermore, the RF (R2 = 0.9) and combined models (R2 = 0.82) showed the best SSC estimations for the Maros and Tisza Rivers. The highest Qs were detected during floods; however, there is usually a clockwise hysteresis between the SSC and water discharge, especially in the Tisza River.

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Optmization of Bio-Optical Model Parameters for Turbid Lake Water Quality Estimation Using Landsat 8 and Wasi-2d

Cited by 7 publications

References 19 publications

Comparative Analysis of Prisma Hyperspectral and Sentinel-2 Multispectral Images for Chlorophyll-a and Turbidity Mapping of Taal Lake

Comparative Analysis of Prisma Hyperspectral and Sentinel-2 Multispectral Images for Chlorophyll-a and Turbidity Mapping of Taal Lake

Optimization of Landsat Chl-a Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types

Remote Sensing of Sediment Discharge in Rivers Using Sentinel-2 Images and Machine-Learning Algorithms

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