Evaluation of Unified Algorithms for Remote Sensing of Chlorophyll-a and Turbidity in Lake Shinji and Lake Nakaumi of Japan and the Vaal Dam Reservoir of South Africa under Eutrophic and Ultra-Turbid Conditions

Sakuno, Yuji; Yajima, Hiroshi; Yoshioka, Yumi; Sugahara, Shogo; Elbasit, Mohamed A. M. Abd; Adam, Elhadi; Chirima, George

doi:10.3390/w10050618

Cited by 24 publications

(23 citation statements)

References 40 publications

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“…Table 4 shows the model performance comparisons for validation. This result agrees with the conclusions of previous studies [38,39] and indicates that the performance of three-band model is slightly better than that of two-band models and NDCI. In addition, the comparisons show that the RMSE of the best model is 6.37 mg·m −3 (Figure 4).…”

Section: Results Of Feature Optimizationsupporting

confidence: 93%

Spectral Feature Selection Optimization for Water Quality Estimation

Nguyen

Lin

Chu

et al. 2019

IJERPH

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The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 mg · m − 3 to 6.37 mg · m − 3 , and the Pearson’s correlation coefficients between the predicted and in situ Chl- a improve from 0.56 to 0.89.

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Section: Results Of Feature Optimizationsupporting

confidence: 93%

Spectral Feature Selection Optimization for Water Quality Estimation

Nguyen

Lin

Chu

et al. 2019

IJERPH

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“…The turbidity estimation algorithms via satellite data have often been carried out according to the types of coastal or oceanic water and to the range of turbidity [50] [51] [52]. The best linear regression is R 2 = 0.6382 (Figure 6) was established between the reflectance at 550 nm wavelength of the available EO1 bands and the in situ turbidity data.…”

Section: Estimation Of Tu Using Hyperion Eo1 Datamentioning

confidence: 99%

Estimation of Chlorophyll and Turbidity Using Sentinel 2A and EO1 Data in Kneiss Archipelago Gulf of Gabes, Tunisia

Katlane¹,

Dupouy²,

Kilani³

et al. 2020

IJG

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Multispectral and hyperspectral sensor data of the bio-optical parameters with a high spatial resolution are important for monitoring and mapping of the coastal ecosystems and estuarine areas, such as the Kneiss Islands in the Gulf of Gabes. Sentinel 2 S2A and Hyperion Earth observing-1 (EO1) imaging sensors reflectance data have been used for water quality determination and mapping of turbidity TU and chlorophyll Chl-a in shallow waters. First, we applied a tidal swing area mask based on uncorrelated pixel via 2D scatter plot between 665 nm and 865 nm to eliminate the overestimation of the concentration of water quality parameters due to the effect of the bottom reflection. The processing for mapping and validating Chl-a, Turbidity S2A, and EO1 were performed using a relation between reflectance bands and in situ measurements. Therefore, we were able to validate the performance of the case 2 regional coast colour processor (C2RCC) as well as our region-adapted empirical optical remote sensing algorithms. Turbidity was mapped based on the reflectance of 550 nm band for EO1 (R 2 = 0.63) and 665 nm band for S2A (R 2 = 0.70). Chlorophyll was mapped based on (457/528 nm) reflectance ratio (R 2 = 0.57) for EO1 and (705/665 nm) reflectance ratio (R 2 = 0.72) for the S2A.

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“…Sustainability 2021, 13, 6416 2 of 13 Furthermore, the estimation performances of the band ratio algorithms need to be examined to appropriately evaluate the potential of Sentinel-2 data for monitoring Chla in tropical inland lake waters [8].…”

Section: Introductionmentioning

confidence: 99%

“…Traditional global models in regression, e.g., linear regression, transform spectral image data to water quality information without the consideration of the spatial variation of model coefficients in regional areas [12]. However, it is extremely difficult to find a unified algorithm with the same model coefficients in the multiple reservoirs and lakes [13]. Spatial heterogeneity or dependency is a common characteristic of spatial data of water quality.…”

Section: Introductionmentioning

confidence: 99%

Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression

Chu

Chusnah

et al. 2021

Sustainability

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Regional water quality mapping is the key practical issue in environmental monitoring. Global regression models transform measured spectral image data to water quality information without the consideration of spatially varying functions. However, it is extremely difficult to find a unified mapping algorithm in multiple reservoirs and lakes. The local model of water quality mapping can estimate water quality parameters effectively in multiple reservoirs using spatial regression. Experiments indicate that both models provide fine water quality mapping in low chlorophyll-a (Chla) concentration water (study area 1; root mean square error, RMSE: 0.435 and 0.413 mg m−3 in the best global and local models), whereas the local model provides better goodness-of-fit between the observed and derived Chla concentrations, especially in high-variance Chla concentration water (study area 2; RMSE: 20.75 and 6.49 mg m−3 in the best global and local models). In-situ water quality samples are collected and correlated with water surface reflectance derived from Sentinel-2 images. The blue-green band ratio and Maximum Chlorophyll Index (MCI)/Fluorescence Line Height (FLH) are feasible for estimating the Chla concentration in these waterbodies. Considering spatially-varying functions, the local model offers a robust approach for estimating the spatial patterns of Chla concentration in multiple reservoirs. The local model of water quality mapping can greatly improve the estimation accuracy in high-variance Chla concentration waters in multiple reservoirs.

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Evaluation of Unified Algorithms for Remote Sensing of Chlorophyll-a and Turbidity in Lake Shinji and Lake Nakaumi of Japan and the Vaal Dam Reservoir of South Africa under Eutrophic and Ultra-Turbid Conditions

Cited by 24 publications

References 40 publications

Spectral Feature Selection Optimization for Water Quality Estimation

Spectral Feature Selection Optimization for Water Quality Estimation

Estimation of Chlorophyll and Turbidity Using Sentinel 2A and EO1 Data in Kneiss Archipelago Gulf of Gabes, Tunisia

Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression

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