Regional analysis of lake and reservoir water quality with multispectral satellite remote sensing images

Xu, Min; Liu, Hongxing.; Beck, Richard; Reif, Molly; Emery, Erich; Young, Jade

doi:10.21079/11681/34933

Cited by 9 publications

(13 citation statements)

References 26 publications

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“…The high absolute RMSE in Zhang et al can be attributed to the "extremely high" Chl-a concentration (over 200 µg/L) in some lakes of their study area. In a water quality assessment project in the U.S., Xu et al [69] manually corrected Landsat 8 OLI images for calibrating empirical models with the same set of water samples as in our study, and they reported a comparable RMSE of 6.17 µg/L. Therefore, our model evaluation shows that the quality of GEE public data assets is sufficient for mapping freshwater Chl-a in a large geographical region.…”

Section: Comparison With Other Studiessupporting

confidence: 56%

“…First, the SVM model accuracy under scenario S1 is better than that reported in [69]. For the match-up point formation, scenario S1 uses the same length of temporal search window (10 days) as in [64].…”

Section: Svm Model Performance Under Different Data Scenariosmentioning

confidence: 94%

“…Xu et al [69] used a much larger temporal window to form match-up points for constructing empirical models for predicting Chl-a concentration. For 97 water samples, Landsat 8 OLI images were manually searched to identify images closest in time to the samples.…”

Section: Scenario Tests Of the Multi-sensor Approach With Different Smentioning

confidence: 99%

“…Even though radiometric calibration and atmospheric correction were carefully applied, the RMSE of the prediction model (6.17 µg/L) is higher than the result in this research. Xu et al [69] only used one satellite sensor (Sentinel-2 MSI) to form match-up points for model construction. Because the search was constrained to a single sensor, the water samples and the paired satellite reflectance measurements may not capture the same water conditions.…”

Section: Scenario Tests Of the Multi-sensor Approach With Different Smentioning

confidence: 99%

“…Among these data scenarios, S1 is the baseline scenario because it uses cross-sensor data and a comparable time window with Xu et al [69]. The comparisons between S2 and S3 and between S4 and S5 scenarios show the effect of sensor differences on the performance of the prediction model, and the comparisons between S2 and S4 and between S3 and S5 show the effect of larger time gaps (> 2 days but up to 10 days) for pairing match-up points on the model performance.…”

Section: Scenario Tests Of the Multi-sensor Approach With Different Smentioning

confidence: 99%

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Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Wang

Liu

et al. 2020

Remote Sensing

Self Cite

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Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed.

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Section: Comparison With Other Studiessupporting

confidence: 56%

Section: Svm Model Performance Under Different Data Scenariosmentioning

confidence: 94%

Section: Scenario Tests Of the Multi-sensor Approach With Different Smentioning

confidence: 99%

Section: Scenario Tests Of the Multi-sensor Approach With Different Smentioning

confidence: 99%

Section: Scenario Tests Of the Multi-sensor Approach With Different Smentioning

confidence: 99%

See 3 more Smart Citations

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Wang

Liu

et al. 2020

Remote Sensing

Self Cite

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LiDAR topo‐bathymetry for riverbed elevation assessment: A review of approaches and performance for hydrodynamic modelling of flood plains

Frizzle,

Trudel,

Daniel

et al. 2024

Earth Surf Processes Landf

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Topo‐bathymetric LiDAR (TBL) can provide a continuous digital elevation model (DEM) for terrestrial and submerged portions of rivers. This very high horizontal spatial resolution and high vertical accuracy data can be promising for flood plain mapping using hydrodynamic models. Despite the increasing number of papers regarding the use of TBL in fluvial environments, its usefulness for flood mapping remains to be demonstrated. This review of real‐world experiments focusses on three research questions related to the relevance of TBL in hydrodynamic modelling for flood mapping at local and regional scales: (i) Is the accuracy of TBL sufficient? (ii) What environmental and technical conditions can optimise the quality of acquisition? (iii) Is it possible to predict which rivers would be good candidates for TBL acquisition? With a root mean square error (RMSE) of 0.16 m, results from real‐world experiments confirm that TBL provides the required vertical accuracy for hydrodynamic modelling. Our review highlighted that environmental conditions, such as turbidity, overhanging vegetation or riverbed morphology, may prove to be limiting factors in the signal's capacity to reach the riverbed. A few avenues have been identified for considering whether TBL acquisition would be appropriate for a specific river. Thresholds should be determined using geometric or morphological criteria, such as rivers with steep slopes, steep riverbanks, and rivers too narrow or with complex morphologies, to avoid compromising the quality or the extent of the coverage. Based on this review, it appears that TBL acquisition conditions for hydrodynamic modelling for flood mapping should optimise the signal's ability to reach the riverbed. However, further research is needed to determine the percentage of coverage required for the use of TBL as a source of bathymetry in a hydrodynamic model, and whether specific river sections must be covered to ensure model performance for flood mapping.

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Impact of anthropogenic pollution and artisanal fishing on the population of Tilapia spp. Oreochromis niloticus and Oreochromis amphimelas in Lake Manyara, northern Tanzania

Mataba,

Ojija,

Munishi

2024

Aquat Ecol

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Regional analysis of lake and reservoir water quality with multispectral satellite remote sensing images

Cited by 9 publications

References 26 publications

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

LiDAR topo‐bathymetry for riverbed elevation assessment: A review of approaches and performance for hydrodynamic modelling of flood plains

Impact of anthropogenic pollution and artisanal fishing on the population of Tilapia spp. Oreochromis niloticus and Oreochromis amphimelas in Lake Manyara, northern Tanzania

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