A novel cross-satellite based assessment of the spatio-temporal development of a cyanobacterial harmful algal bloom

Page, Benjamin P.; Kumar, Abhishek; Mishra, Deepak R.

doi:10.1016/j.jag.2017.11.003

Cited by 55 publications

(38 citation statements)

References 33 publications

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“…Atmospheric correction is an important process for the remote sensing of water quality as water-leaving radiance constitutes a small fraction of the total energy measured by the sensor, with the main contribution coming from the atmosphere [41]. Studies have found that image-based [42][43][44], site-specific [41,45,46], and radiative transfer model [47][48][49] atmospheric correction methods can provide adequate retrievals of surface reflectance for water quality mapping. The Landsat data used in this study have been atmospherically corrected using the Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) surface reflectance processing system.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Future work on this system will focus on utilizing additional sensors, such as Sentinel-2, to improve temporal resolution of satellite observations. Sentinel-2 has been shown to provide high quality estimates of suspended sediments and other water quality parameters [70] and data from Sentinel-2' has already been successfully integrated with Landsat for monitoring water quality [46]. Furthermore, although remote sensing relies on optically active water constituents, recent studies have explored the utility of remote sensing technologies for optically inactive water quality parameters.…”

Section: Implications and Future Workmentioning

confidence: 99%

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Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing

et al. 2018

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Reservoir construction and land use change are altering sediment transport within river systems at a global scale. Changes in sediment transport can impact river morphology, aquatic ecosystems, and ultimately the growth and retreat of delta environments. The Lower Mekong Basin is crucial to five neighboring countries for transportation, energy production, sustainable water supply, and food production. In response, countries have coordinated to develop programs for regional scale water quality monitoring that including surface sediment concentrations (SSSC); however, these programs are based on a limited number of point measurements and due to resource limitations, cannot provide comprehensive insights into sediment transport across all strategic locations within the Lower Mekong Basin. To augment in situ SSSC data from the current monitoring program, we developed an empirical model to estimate SSSC across the Lower Mekong Basin from Landsat observations. Model validation revealed that remotely sensed SSSC estimates captured the spatial and temporal dynamics in a range of aquatic environments (main stem of Mekong river, tributary systems, Mekong Floodplain, and reservoirs) while, on average, slightly underestimating SSSC by about 2 mg•L −1 across all settings. The operational SSSC model was developed and implemented using Google Earth Engine and Google App Engine was used to host an online application that allows users, without any knowledge of remote sensing, to access SSSC data across the region. Expanded access to

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Section: Data Preprocessingmentioning

confidence: 99%

Section: Implications and Future Workmentioning

confidence: 99%

Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing

et al. 2018

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“…FOSI can also be used to map water during floods in ideal cloud-free imaging conditions (Olthof, 2017). Other useful applications include the tracking of water quality, such as the mapping of algae blooms and chlorophyll in lakes (Brezonik et al, 2005) and oceans (Hu, 2009), cyanobacteria (Page et al, 2018), turbidity in small lakes (Lacaux et al, 2007), and the bathymetry of shallow clear waters (Knudby et al, 2016).…”

Section: Watermentioning

confidence: 99%

A Review of Free Optical Satellite Imagery for Watershed-Scale Landscape Analysis

Bevington

Gleason²,

Giroux-Bougard³

et al. 2018

JWSM

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Tyler de Jong is a researcher based in Whitehorse, Yukon with Global Water Futures at McMaster University. Email: tyler.dejong@mcmaster.caWatershed-scale landscape analysis includes many disciplines, including ecological, hydrological, and geographical sciences. e recent proliferation of free optical satellite imagery (FOSI) has changed the possibilities for the monitoring of environmental change at local and global scales. Many reviews exist for discipline-specific remote sensing applications; however, this article seeks to highlight the rapidly growing archive of FOSI and applied tools that can be used by all levels of users. Herein, ten techniques and eight applications of FOSI are reviewed, along with the specifications and limitations of various sources of FOSI. Although this review focuses on Western Canada, the democratization of FOSI is globally relevant, and the objective is to explain basic concepts via figures and reference materials to help summarize this rapidly changing field. KEYWORDS free optical satellite imagery; remote sensing; watershed science; environmental monitoring; open dataRemote sensing is the science of obtaining information about an object from a distance. is information can be collected from terrestrial, aquatic, aerial, or satellite platforms. Optical imaging sensors aboard these platforms record image data consisting of emitted or reflected electromagnetic energy. Since 2008, the availability of free optical satellite imagery (FOSI) has grown exponentially since the United States Geological Survey (USGS) first opened its image archives (Woodcock et al., 2008). As such, the Earth is currently being imaged many times per day at a variety of spatial resolutions (Malenovský et al., 2012;Roy et al., 2014). e availability of FOSI has enabled the development of free online visualization tools (see Appendix A), free open source remote sensing soware (see Appendix B), and free analysis-ready remote sensing products (see Appendix C). e abundance of FOSI has empowered natural resource managers, researchers, and others to increase, and ultimately improve, the monitoring and quantification of watershed-scale environmental change.Review articles have addressed vegetation mapping (Xie et al., 2008), land cover classification (Phiri & Morgenroth, 2017), forest inventories , and large area mapping techniques (Gómez et al., 2016; Hansen & Loveland, 2012 IntroductionAbstract of: 1) fundamental concepts; 2) common datasets; 3) data portals; 4) processing techniques; and 5) applied examples of watershed-scale remote sensing. e objective is to provide readers new to satellite remote sensing with a comprehensive reference guide explained in plain language, and to provide advanced users with a helpful compilation of reference materials.is section provides a synthesis of fundamental concepts in Earth observation (EO) remote sensing science, with a focus on passive optical sensors that measure and record energy emitted from external sources (typically the sun). Earth observation satellites have ne...

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“…A metodologia de recuperação e mapeamento da concentração de clorofila-a envolveu o estudo da reflectância da superfície da água nas imagens Sentinel-2A e a respectiva correlação com dados in situ de clorofila-a, coletados em pontos de monitoramento, durante os anos de 2016 e 2017. Os resultados gerados pelas equações Chl-1, Ha et al, (2017), Chl-2, Page et al, (2018), e Chl-3, Kuhn et al, (2019), mostram a necessidade de uma calibração dos modelos utilizados aos componentes das águas do rio Poti. No entanto, o algoritmo empírico Chl-2 mostra que uma correlação foi estabelecida para identificar a variação espaço-temporal das concentrações de clorofila-a ao longo do rio Poti de maneira ampla e não mais pontual.…”

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Retrieval and mapping of chlorophyll-a concentration from Sentinel-2 images in an urban river in the semiarid region of Brazil

Pereira¹,

Lopes²,

Espíndola³

et al. 2020

Rev. ambiente água

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Recently, the Poti river mouth region has experienced environmental impacts that resulted in a change of landscape in its dry season, highlighting the eutrophication and proliferation of phytoplankton, algae, cyanobacteria and aquatic plants. Considering the aspects related to water-quality monitoring in the semiarid region of Brazil from remote sensing, this study aimed to evaluate the performance of Sentinel-2A satellite data in the retrieval of chlorophyll-a concentration in Poti River in Teresina, Piaui, Brazil. The chlorophyll-a concentration retrieval and mapping methodology involved the study of the water surface reflectance in Sentinel-2A images and their correlation with the chlorophyll-a data collected in situ during the years 2016 and 2017. The results generated by the Chl-1, Ha et al. (2017), Chl-2, Page et al. (2018), and Chl-3, Kuhn et al. (2019) equations show the need for calibrating the algorithms used for the Poti River water components. However, the empirical algorithm Chl-2 shows a correlation has been established to identify the spatiotemporal variation of chlorophyll-a concentration along the Poti River broadly and not punctually. The spatial distribution of this pigment in maps derived from Sentinel-2A is consistent with the pattern of occurrence determined by the in situ data. Therefore, the MSI sensor proved to be a tool suitable for the retrieval and monitoring of chlorophyll-a concentration along the Poti River.

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A novel cross-satellite based assessment of the spatio-temporal development of a cyanobacterial harmful algal bloom

Cited by 55 publications

References 33 publications

Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing

Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing

A Review of Free Optical Satellite Imagery for Watershed-Scale Landscape Analysis

Retrieval and mapping of chlorophyll-a concentration from Sentinel-2 images in an urban river in the semiarid region of Brazil

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