Applications of Landsat‐5 TM imagery in assessing and mapping water quality in Reelfoot Lake, Tennessee

Wang, F.; Han, Lijuan; Kung, Hsiang‐te; Arsdale, Roy B. Van

doi:10.1080/01431160500191704

Cited by 93 publications

(56 citation statements)

References 15 publications

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“…The C2RCC atmospheric correction does not provide realistic reflectance in cyanobacterial bloom situations as is seen in Figure 6. The top of atmosphere spectra have significant signal at 709 nm band (critical for the retrieval of chlorophyll a [42]) and further in NIR part of spectrum (necessary to separate surface scum from dense subsurface bloom). All this useful information is removed by the C2RCC.…”

Section: Discussionmentioning

confidence: 99%

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

et al. 2017

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Abstract:The launch of Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A in 2016 is the beginning of a new era in long time, continuous, high frequency water quality monitoring of coastal waters. Therefore, there is a strong need to validate the OLCI products to be sure that the technical capabilities provided will be used in the best possible way in water quality monitoring and research. The Baltic Sea is an optically complex waterbody where many ocean colour products, performing well in other waterbodies, fail. We tested the performance of standard Case-2 Regional/Coast Colour (C2RCC) processing chain in retrieving water reflectance, inherent optical properties (IOPs), and water quality parameters such as chlorophyll a, total suspended matter (TSM) and coloured dissolved organic matter (CDOM) in the Baltic Sea. The reflectance spectra produced by the C2RCC are realistic in both shape and magnitude. However, the IOPs, and consequently the water quality parameters estimated by the C2RCC, did not have correlation with in situ data. On the other hand, some tested empirical remote sensing algorithms performed well in retrieving chlorophyll a, TSM, CDOM and Secchi depth from the reflectance produced by the C2RCC. This suggests that the atmospheric correction part of the processor performs relatively well while IOP retrieval part of the neural network needs extensive training with actual IOP data before it can produce reasonable estimates for the Baltic Sea.

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Section: Discussionmentioning

confidence: 99%

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

et al. 2017

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“…In many cases, a linear regression of SSC with a single "Red" band or with a NIR/Red ratio shows high correlations, especially for highly turbid waters (e.g., [48,49,[61][62][63][64][65]). Other methods use log-linear (e.g., [66]), exponential (e.g., [67]), or linear regressions over multiple spectral bands [68,69]. However, the regression coefficients retrieved through in-situ calibration are typically site-specific and are not generally applicable to other datasets.…”

Section: Empirical Retrieval Modelsmentioning

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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“…Goddijn-Murphy et al (2009) observam que o SR oferece diversas técnicas para o monitoramento de recursos hídricos em diferentes áreas e escalas de tempo. Além disso, existe também uma variabilidade de parâmetros a serem monitorados por SR: transparência secchi (KLOIBER; BREZONIK; BAUER, 2002), clorofila-a (CHEN et al, 2011;GITELSON et al, 2008;KLOIBER;BREZONIK;BAUER, 2002;ZHANG et al, 2011), carbono orgânico particulado (HADJIMITSIS & CLAYTON, 2011), sólidos suspensos totais (ARTIGAS et al, 2008;WANG et al, 2006), turbidez (WANG et al, 2006), entre outros.…”

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Geotecnologias na gestão de reservatórios: uma revisão e uma proposta de integração

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et al. 2017

Eng. Sanit. Ambient.

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Applications of Landsat‐5 TM imagery in assessing and mapping water quality in Reelfoot Lake, Tennessee

Cited by 93 publications

References 15 publications

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

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

Geotecnologias na gestão de reservatórios: uma revisão e uma proposta de integração

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