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Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan
摘要： Concentrations of chlorophyll-a (Chl-a) and total suspended solids (TSS) are significant parameters used to assess water quality. The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds in Higashihiroshima, Japan, using field hyperspectral measurements and statistical analysis. Field experiments were conducted in six ponds and spectral readings for Chl-a and TSS were obtained from six field observations in 2014. For statistical approaches, we used two spectral indices, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI), and a partial least squares (PLS) regression. The predictive abilities were compared using the coefficient of determination (R2), the root mean squared error of cross validation (RMSECV) and the residual predictive deviation (RPD). Overall, iterative stepwise elimination based on PLS (ISE–PLS), using the first derivative reflectance (FDR), showed the best predictive accuracy, for both Chl-a (R2 = 0.98, RMSECV = 6.15, RPD = 7.44) and TSS (R2 = 0.97, RMSECV = 1.91, RPD = 6.64). The important wavebands for estimating Chl-a (16.97% of all wavebands) and TSS (8.38% of all wavebands) were selected by ISE–PLS from all 501 wavebands over the 400–900 nm range. These findings suggest that ISE–PLS based on field hyperspectral measurements can be used to estimate water Chl-a and TSS concentrations in irrigation ponds.
关键词： total suspended solids,partial least squares regression,irrigation ponds,hyperspectral,chlorophyll-a
更新于2025-09-23 15:21:01
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Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay
摘要： Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate.
关键词： water quality,total suspended solids,ocean color,satellite remote sensing,statistical analysis,Random Forest,Chesapeake Bay,multispectral,machine learning
更新于2025-09-19 17:15:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Retrieval of Suspended Solids from Landsat-8 and Sentinel-2: A Tool for Coastal Monitoring in Extremely Turbid Waters
摘要： This study is focused on presenting a methodology for estimating Total Suspended Solids (TSS) over two key strategic sites in SW Iberian Peninsula. The dataset used includes multi-temporal Landsat-8 and Sentinel-2A imagery during 2016. The application of the effective ACOLITE processor and the high quality of SWIR bands improve the atmospheric correction in these extremely turbid waters. A multi-conditional algorithm that uses both red and NIR reflectance is able to provide accurate TSS retrievals in the upper and lower reaches of the Guadalquivir estuary and Cadiz Bay. The level of spatial detail afforded by these observations at fine resolution (10-30m) allows a much in deep understanding of the timing and importance of turbidity plumes. The results indicate that the proposed approach is effective for fusing both missions (MAE of 5.21 mg/L and 3.89 mg/L in estuary and bay, respectively), producing more frequency for continuous monitoring, which is particularly valuable at these latitudes that are severely covered (~50%) by clouds and sunglint. These findings encourage further research with great implications to establish a continuous framework for the upcoming high-resolution services relying on both missions.
关键词： Turbidity Plumes,Landsat-8,Total Suspended Solids,Sentinel-2,Cadiz
更新于2025-09-10 09:29:36
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Bio-optical Modeling and Remote Sensing of Inland Waters || Bio-optical Modeling of Total Suspended Solids
摘要： Suspended solids play a fundamental role in the aquatic ecosystem as they regulate two major transport routes of materials and contaminants: the dissolved transport in the pelagic water and the particulate benthic sedimentation route (Wetzel, 1983; Ha?kanson, 2006). The presence of total suspended solids (TSS) in water has an impact on primary producers (Zhang et al., 2008), through affecting the amount of light penetrating through the water column that restricts the rate at which benthic algae, phytoplankton, and macrophytes can assimilate energy through photosynthesis.
关键词： Inland Waters,Bio-optical Modeling,Remote Sensing,Total Suspended Solids,Water Quality
更新于2025-09-10 09:29:36