Estimation of total suspended matter concentration from MODIS data using a neural network model in the China eastern coastal zone

Quan, Wenting; Cui, Tingwei; Song, Qingjun

doi:10.1016/j.ecss.2015.01.018

Cited by 55 publications

(44 citation statements)

References 59 publications

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“…The RMSE between in situ TSM and GOCI-retrieved TSM was 9.11 g¨m´3, which is reasonably good considering the comparison was not exactly synchronized. The GOCI results retrieved from this algorithm also agreed with other studies in the YRE and Yellow Sea based on in situ data and results retrieved from other ocean colour satellites [22,[41][42][43]. .…”

Section: Quantitative Retrieval Algorithm Of Tsmsupporting

confidence: 86%

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Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

et al. 2016

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Abstract:Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant natural variations. The understanding of such variations is of great significance in coastal waters. The aim of this study is to investigate and assess the diurnal and seasonal variations of surface TSM distribution and its mechanisms in coastal waters based on Geostationary Ocean Color Imager (GOCI) data. As a case study, dynamic variations of TSM in the macro-tidal Yalu River estuary (YRE) of China were analysed. With regard to diurnal variability, there were usually two peaks of TSM in a tidal cycle corresponding to the maximum flood and ebb current. Tidal action appears to play a vital role in diurnal variations of TSM. Both the processes of tidal re-suspension and advection could be identified; however, the diurnal variation of TSM was mainly affected by a re-suspension process. In addition, spring-neap tides can affect the magnitude of TSM diurnal variations in the YRE. The GOCI-retrieved TSM results clearly showed the seasonal variability of surface TSM in this area, with the highest level occurring in winter and the lowest in summer. Moreover, although river discharge to the YRE was much greater in the wet season than the dry season, TSM concentrations were significantly higher in the dry season. Wind waves were considered to be the main factor affecting TSM seasonal variation in the YRE.

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Section: Quantitative Retrieval Algorithm Of Tsmsupporting

confidence: 86%

“…The GOCI results retrieved from this algorithm also agreed with other studies in the YRE and Yellow Sea based on in situ data and results retrieved from other ocean colour satellites [22,[41][42][43]. "HW" and "LW" represent for high slack water and low slack water, respectively.…”

Section: Quantitative Retrieval Algorithm Of Tsmsupporting

confidence: 85%

Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

et al. 2016

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“…It is reported that the environment of the Yangtze River estuary is getting worse due to the rapid developments and urbanization in the surrounding industrial areas (Chen et al, 2015a;Hsu and Lin., 2010). As a result, there are more and more studies focusing on this region due to its important ecological and economic role (Chen et al, 2015a;Feng et al, 2014;Shen et al, 2010). Water bodies of the study areas were extracted based on a method developed by Jiang et al (2014).…”

Section: Study Areasmentioning

confidence: 99%

“…1d, triangles, Table 1). The spectra were measured based on above-water spectrum measurement method that could effectively avoid the influence of sun and sky glint, water vapor, aerosols, and vessel shadow (Tang et al, 2004a), which was widely applied to the water bodies such as estuaries and coasts of China (Chen et al, 2015a;Feng et al, 2014;Zhang et al, 2014). It should be noted that the distance from some samples (Fig.…”

Section: In Situ and Satellite Datamentioning

confidence: 99%

“…The topics of TSS concentration monitoring and spatial and temporal variation assessment have been paid great attention, and the associated research work has been conducted frequently by a variety of scholars, government branches and society communities (Caballero et al, 2014;Giardino et al, 2015;Liu et al, 2003;Lu et al, 2012;Montanher et al, 2014;Nechad et al, 2010;Olmanson et al, 2013;Pozdnyakov et al, 2005;Rao et al, 2009;Shen et al, 2008;Tang et al, 2004b;Zhang et al, 2007). Many methods can be used to estimate TSS concentrations of water bodies, including hydrologicalsite monitoring, in situ investigation, physical models, numerical simulation, remote sensing and so on (Chen et al, 2015a). Retrieving TSS concentrations from remote sensing data has unique advantages due to the wide spatial coverage and periodic revisit, such as the Land Observation Satellite (Landsat), the Earth-Observing One satellite (EO-1), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Medium Resolution Imaging Spectrometer (MERIS), the Geostationary Ocean Color Imager (GOCI), the Sea Viewing Wide Field of View Sensor (SeaWiFS), Systeme Probatoire d'Observation dela Tarre (SPOT) and the Environment and Disaster Monitoring and Forecasting Small Satellite Constellation (HJ).…”

Section: Introductionmentioning

confidence: 99%

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A Landsat-based model for retrieving total suspended solids concentration of estuaries and coasts in China

Wang

Chen²,

Li³

et al. 2017

Geosci. Model Dev.

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Abstract. Retrieving total suspended solids (TSS) concentration accurately is essential for sustainable management of estuaries and coasts, which plays a key role in the interaction between hydrosphere, pedosphere and atmosphere. Although many TSS retrieval models have been published, the general inversion method that is applicable to different field conditions is still under research. In order to obtain a TSS remote sensing model that is suitable for estimating TSS concentrations with wide range in estuaries and coasts by Landsat imagery, after reviewing a number of Landsatbased TSS retrieval models and improving a comparatively better one among them, this study developed a quadratic model using the ratio of logarithmic transformation of red band and near-infrared band and logarithmic transformation of TSS concentration (QRLTSS) based on 119 in situ samples collected in 2006-2013 from five regions of China. It was found that the QRLTSS model works well and shows a satisfactory performance. The QRLTSS model based on Landsat TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus) and OLI (Operational Land Imager) sensors explained about 72 % of the TSS concentration variation (TSS: 4.3-577.2 mg L −1 , N = 84, P value < 0.001) and had an acceptable validation accuracy (TSS: 4.5-474 mg L −1 , root mean squared error (RMSE) ≤ 25 mg L −1 , N = 35). In addition, a threshold method of red-band reflectance (OLI: 0.032, ETM+ and TM: 0.031) was proposed to solve the two-valued issue of the QRLTSS model and to retrieve TSS concentration from Landsat imagery. After a 6S modelbased atmospheric correction of Landsat OLI and ETM+ imagery, the TSS concentrations of three regions (Moyangjiang River estuary, Pearl River estuary and Hanjiang River estuary) in Guangdong Province in China were mapped by the QRLTSS model. The results indicated that TSS concentrations in the three estuaries showed large variation ranging from 0.295 to 370.4 mg L −1 . Meanwhile we found that TSS concentrations retrieved from Landsat imagery showed good validation accuracies with the synchronous water samples (TSS: 7-160 mg L −1 , RMSE: 11.06 mg L −1 , N = 22). The further validation from EO-1 Hyperion imagery also showed good performance (in situ synchronous measurement of TSS: 106-220.7 mg L −1 , RMSE: 26.66 mg L −1 , N = 13) of the QRLTSS model for the area of high TSS concentrations in the Lingding Bay of the Pearl River estuary. Evidently, the QRLTSS model is potentially applied to simulate high-dynamic TSS concentrations of other estuaries and coasts by Landsat imagery, improving the understanding of the spatial and temporal variation of TSS concentrations on regional and global scales. Furthermore, the QRLTSS model can be optimized to establish a regional or unified TSS retrieval model of estuaries and coasts in the world for different satellite sensors with medium-and high-resolution similar to Landsat TM, ETM+ and OLI sensors or with similarPublished by Copernicus Publications on behalf of the European Geosciences Union.

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Appraisal of the Variation of TSS and Turbidity on Fish Production in Mahanadi Delta Region Post Fani and Phailin Cyclones

Karmakar

Roy

2022

Anthropogeomorphology

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Estimation of total suspended matter concentration from MODIS data using a neural network model in the China eastern coastal zone

Cited by 55 publications

References 59 publications

Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

A Landsat-based model for retrieving total suspended solids concentration of estuaries and coasts in China

Appraisal of the Variation of TSS and Turbidity on Fish Production in Mahanadi Delta Region Post Fani and Phailin Cyclones

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