Suspended sediment monitoring and assessment for Yellow River estuary from Landsat TM and ETM+ imagery

Zhang, Minwei; Qian, Dong; Cui, Tingwei; Xue, Chao; Zhang, Songli

doi:10.1016/j.rse.2013.09.033

Cited by 120 publications

(92 citation statements)

References 28 publications

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“…TSSpB1q " 612.72ˆr rs pB1q´4.83, pR 2 " 0.85q (19) TSSpB1q " 2.41ˆexpr40.12ˆr rs pB1qs`0.89, pR 2 " 0.85q (20) The LOOCV method provides overall model accuracy but does not produce assessment of uncertainty in the results derived by the model [81]. The bootstrap method of Efron [82] as discussed in [81] provides a means to generate the confidence in models as a result of uncertain determination of model parameters, uncertainties in in situ measurements, and assumptions in the model formulations [81].…”

Section: Model Calibration and Model Uncertainty Estimatesmentioning

confidence: 99%

“…Remote sensing has been used to map a wide array of coastal water's constituents, such as phytoplankton for biomass and primary production [1][2][3][4], coloured dissolved organic matter (CDOM) for its effect on benthic habitats [5][6][7], and total suspended sediments (TSS) concentration as a measure of water quality [8][9][10][11][12]. Many studies have been performed to derive TSS concentration via satellite remote sensing using different platforms: Sea-viewing Wide Field-of-view Sensor (SeaWiFS) [13,14], Landsat series [15][16][17][18][19][20], Medium Resolution Imaging Spectrometer (MERIS) [21][22][23][24][25][26], Moderate Resolution Imaging Spectroradiometer (MODIS) [9,11,25,[27][28][29][30], "Système Pour l'Observation de la Terre" (SPOT) [31], and high resolution sensor IKONOS [32]. Most models are developed to estimate TSS concentration by directly relating the remotely sensed reflectance with in situ measurements of the TSS concentration using statistical analysis, linear and non-linear regression.…”

Section: Introductionmentioning

confidence: 99%

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A Semi-Analytic Model for Estimating Total Suspended Sediment Concentration in Turbid Coastal Waters of Northern Western Australia Using MODIS-Aqua 250 m Data

2016

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Abstract:Knowledge of the concentration of total suspended sediment (TSS) in coastal waters is of significance to marine environmental monitoring agencies to determine the turbidity of water that serve as a proxy to estimate the availability of light at depth for benthic habitats. TSS models applicable to data collected by satellite sensors can be used to determine TSS with reasonable accuracy and of adequate spatial and temporal resolution to be of use for coastal water quality monitoring. Thus, a study is presented here where we develop a semi-analytic sediment model (SASM) applicable to any sensor with red and near infrared (NIR) bands. The calibration and validation of the SASM using bootstrap and cross-validation methods showed that the SASM applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua band 1 data retrieved TSS with a root mean square error (RMSE) and mean averaged relative error (MARE) of 5.75 mg/L and 33.33% respectively. The application of the SASM over our study region using MODIS-Aqua band 1 data showed that the SASM can be used to monitor the on-going, post and pre-dredging activities and identify daily TSS anomalies that are caused by natural and anthropogenic processes in coastal waters of northern Western Australia.

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Section: Model Calibration and Model Uncertainty Estimatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Semi-Analytic Model for Estimating Total Suspended Sediment Concentration in Turbid Coastal Waters of Northern Western Australia Using MODIS-Aqua 250 m Data

2016

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“…The empirical regression algorithms take advantage of the linear or exponential relations between TSM and reflectance in certain spectral band(s) [12,22,23,26,28]. To date, there has been no standardized TSM-derived model because of the high level of variation in particle size, density and other optical complexities of different water bodies [46].…”

Section: Algorithm Development and Validation For Landsatmentioning

confidence: 99%

“…Over recent decades, a number of studies have demonstrated that TSM distribution in open sea and inland waters can be mapped from various types of satellite remote sensing data, such as the land satellite (Landsat), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) [12][13][14][15]. The first Landsat satellite was launched on 23 July 1972 [16], and the latest Landsat 8 was launched on 11 February 2013 [17] (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Landsat-Based Long-Term Monitoring of Total Suspended Matter Concentration Pattern Change in the Wet Season for Dongting Lake, China

et al. 2015

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Assessing the impacts of environmental change and anthropogenic activities on the historical and current total suspended matter (TSM) pattern in Dongting Lake, China, is a large challenge. We addressed this challenge by using more than three decades of Landsat data. Based on in situ measurements, we developed an algorithm based on the near-infrared (NIR) band to estimate TSM in Dongting Lake. The algorithm was applied to Landsat images to derive TSM distribution maps from 1978 to 2013 in the wet season, revealing significant inter-annual and spatial variability. The relationship of TSM to water level, precipitation, and wind speed was analyzed, and we found that: (1) sand mining areas usually coincide with regions that have high TSM levels in Dongting Lake; (2) water level and seven-day precipitation were both important to TSM variation, but no significant relationship was found between TSM and wind speed or other meteorological data; (3) the increased level of sand mining in response to rapid economic growth has deeply influenced the TSM pattern since 2000 due to the resuspension of sediment; and (4) TSM variation might OPEN ACCESSRemote Sens. 2015, 7 13976 be associated with policy changes regarding the management of sand mining; it might also be affected by lower water levels caused by the impoundment of the Three Gorges Dam since 2000.

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“…Data from Lake Taihu were obtained by Zhang et al [4,39,40] in January, July, and October 2006, and January, and April 2007. Data from Lake Chaohu (June 2009), Lake Taihu (March and April 2009), the Three Gorges Reservoir (August, 2009), and Lake Dianchi (September and December 2009) were obtained by Sun et al [41], and data from the Yellow River estuary were obtained by Zhang et al [42] in June 2012. An independent test data set, comprising 143 in situ Rrs (λ), was collected from the above areas during other cruises.…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

Shen

Zhang

et al. 2015

Remote Sensing

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Determining the dominant optically active substances in water bodies via classification can improve the accuracy of bio-optical and water quality parameters estimated by remote sensing. This study provides four robust centroid sets from in situ remote sensing reflectance (Rrs (λ)) data presenting typical optical types obtained by plugging different similarity measures into fuzzy c-means (FCM) clustering. Four typical types of waters were studied: (1) highly mixed eutrophic waters, with the proportion of absorption of colored dissolved organic matter (CDOM), phytoplankton, and non-living particulate matter at approximately 20%, 20%, and 60% respectively; (2) CDOM-dominated relatively clear waters, with approximately 45% by proportion of CDOM absorption; (3) nonliving solids-dominated waters, with approximately 88% by proportion of absorption of nonliving particulate matter; and (4) cyanobacteria-composed scum. We also simulated spectra from seven ocean color satellite sensors to assess their classification ability. OPEN ACCESSRemote Sens. 2015, 7 14732POLarization and Directionality of the Earth's Reflectances (POLDER), Sentinel-2A, and MEdium Resolution Imaging Spectrometer (MERIS) were found to perform better than the rest. Further, a classification tree for MERIS, in which the characteristics of Rrs (709)/Rrs (681), Rrs (560)/Rrs (709), Rrs (560)/Rrs (620), and Rrs (709)/Rrs (761) are integrated, is also proposed in this paper. The overall accuracy and Kappa coefficient of the proposed classification tree are 76.2% and 0.632, respectively.

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Suspended sediment monitoring and assessment for Yellow River estuary from Landsat TM and ETM+ imagery

Cited by 120 publications

References 28 publications

A Semi-Analytic Model for Estimating Total Suspended Sediment Concentration in Turbid Coastal Waters of Northern Western Australia Using MODIS-Aqua 250 m Data

A Semi-Analytic Model for Estimating Total Suspended Sediment Concentration in Turbid Coastal Waters of Northern Western Australia Using MODIS-Aqua 250 m Data

Landsat-Based Long-Term Monitoring of Total Suspended Matter Concentration Pattern Change in the Wet Season for Dongting Lake, China

Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

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