Suspended particulate matter in Lake Erie derived from MODIS aquatic colour imagery

Binding, Caren; Jerome, John H.; Bukata, R. P.; Booty, William G.

doi:10.1080/01431160903302973

Cited by 64 publications

(39 citation statements)

References 36 publications

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“…2) was calculated in the same way as Zhang et al (2014) and Chen et al (2015b). Water samples (about 1.5 L) were collected within the water depth of 1 m, and TSS concentrations were measured by a weighing method (Binding et al, 2012;Caballero et al, 2014).…”

Section: In Situ and Satellite Datamentioning

confidence: 99%

“…Based on the rigorous theoretical derivation, the semianalytical and analytical models are likely more applicable to different water bodies than the empirical or semiempirical methods (Binding et al, 2012(Binding et al, , 2010Chen et al, 2015b;Giardino et al, 2007;Ma et al, 2010;Sipelgas et al, 2009). However, there are still limitations of the application due to the difficulties of retrieval or inaccuracies on initialization parameters (Binding et al, 2012;Chen et al, 2015b;Ma et al, 2010;Wu et al, 2013). Therefore, the empirical (especially semiempirical) methods are still used to estimate TSS concentration and will continue to be used for a long time due to their simplicity and sufficient accuracies.…”

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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Section: In Situ and Satellite Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

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“…However, it cannot be applied in other areas when optical properties are different from those used for model development. The analytical models relied on serious theory, but their applications are limited because of difficulties or inaccuracies in obtaining model-driving parameters (Sipelgas et al 2009;Binding et al 2010Binding et al , 2012. The semi-analytical model is based on the relationship between the inherent optical properties of water and chlorophyll-a concentration, plus some empirical relationships Gitelson et al 2008;Carder et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal variations of chlorophyll-a concentration from 2009 to 2012 in Poyang Lake, China

et al. 2014

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Water eutrophication in Poyang Lake, the largest freshwater lake in China, has been considered to be an obstacle to aquatic environment protection and regional sustainable development. Chlorophyll-a concentration is one of the most important indices of water eutrophication. This paper builds seasonal chlorophyll-a concentration retrieval models using a semi-analytical model. Quarterly distributions of chlorophyll-a concentration from 2009 to 2012 are explored using multi-spectra data from a moderate-resolution imaging spectroradiometer (MODIS). The correlation coefficient of the retrieval models primarily ranged from 0.6 to 0.9. The results show that the chlorophyll-a concentration in Poyang Lake has significant seasonality characteristics that present low values in the winter and spring, and present relatively high values in the summer and autumn; this report also presents an obvious, increasing trend of inter-annual variability from 2009 to 2012. The spatial distribution of the chlorophyll-a concentration has regional differences that give relatively high values adjacent to the shore in the north area of Poyang Lake, in the flow in river entries, and in the main channel area in the central and south areas of Poyang Lake. The natural hydrology features have a close relationship with the variation in the chlorophyll-a concentration. Intensive human activities are the main driving forces for the increasing chlorophyll-a concentration.

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“…A recent study compared 31 empirically-based methods to retrieve SSC using MODIS reflectance [32]. Binding et al [33] explored the empirical correlation between SSC and the ratios of different MODIS bands in "Red" and near-infrared (NIR) spectrum, so as to infer an optimal spectral band to calibrate a semi-analytical model. However, no direct and systematic comparison of empirical versus semi-analytical methods has been made.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral and Multispectral Retrieval of Suspended Sediment in Shallow Coastal Waters Using Semi-Analytical and Empirical Methods

et al. 2017

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Natural lagoons and estuaries worldwide are experiencing accelerated ecosystem degradation due to increased anthropogenic pressure. As a key driver of coastal zone dynamics, suspended sediment concentration (SSC) is difficult to monitor with adequate spatial and temporal resolutions both in the field and using remote sensing. In particular, the spatial resolutions of currently available remote sensing data generated by satellite sensors designed for ocean color retrieval, such as MODIS (Moderate Resolution Imaging Spectroradiometer) or SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), are too coarse to capture the dimension and geomorphological heterogeneity of most estuaries and lagoons. In the present study, we explore the use of hyperspectral (Hyperion) and multispectral data, i.e., the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and ALOS (Advanced Land Observing Satellite), to estimate SSC through semi-analytical and empirical approaches in the Venice lagoon (Italy). Key parameters of the retrieval models are calibrated and cross-validated by matching the remote sensing estimates of SSC with in situ data from a network of water quality sensors. Our analysis shows that, despite the higher spectral resolution, hyperspectral data provide limited advantages over the use of multispectral data, mainly due to information redundancy and cross-band correlation. Meanwhile, the limited historical archive of hyperspectral data (usually acquired on demand) severely reduces the chance of observing high turbidity events, which are relatively rare but critical in controlling the coastal sediment and geomorphological dynamics. On the contrary, retrievals using available multispectral data can encompass a much wider range of SSC values due to their frequent acquisitions and longer historical archive. For the retrieval methods considered in this study, we find that the semi-analytical method outperforms empirical approaches, when applied to both the hyperspectral and multispectral dataset. Interestingly, the improved performance emerges more clearly when the data used for testing are kept separated from those used in the calibration, suggesting a greater ability of semi-analytical models to "generalize" beyond the specific data set used for model calibration.

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Suspended particulate matter in Lake Erie derived from MODIS aquatic colour imagery

Cited by 64 publications

References 36 publications

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

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

Spatial and temporal variations of chlorophyll-a concentration from 2009 to 2012 in Poyang Lake, China

Hyperspectral and Multispectral Retrieval of Suspended Sediment in Shallow Coastal Waters Using Semi-Analytical and Empirical Methods

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