A Simple Red Tide Monitoring Method using Sentinel-2 Data for Sustainable Management of Brackish Lake Koyama-ike, Japan

Sakuno, Yuji; Maeda, Akiko; Mori, Akira; Ono, Shuji; Ito, Akihiro

doi:10.3390/w11051044

Cited by 16 publications

(7 citation statements)

References 23 publications

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“…The prominent reflectance peak located in the 700–720 nm region shifts towards longer wavelengths as phytoplankton concentrations increase; thus, these bands are suggested as the best option for HABs mapping ( Toming et al, 2016 ). Sakuno et al (2019) already indicated the potential of the 704 nm red-edge band of S2 for red tide monitoring in Japan. The sensitivity of NDCI to chl-a concentration in turbid productive waters has been demonstrated, as well as its potential application to several platforms and diverse environments, producing minimal uncertainty ( Mishra and Mishra, 2012 ).…”

Section: Discussionmentioning

confidence: 98%

Using Copernicus Sentinel-2 and Sentinel-3 data to monitor harmful algal blooms in Southern Chile during the COVID-19 lockdown

Rodriguez-Benito¹,

Navarro

Caballero

2020

Marine Pollution Bulletin

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During the southern summer of 2020, large phytoplankton blooms were detected using satellite technology in Chile (western Patagonia), where intensive salmonid aquaculture is carried out. Some harvesting sites recorded massive fish mortalities, which were associated with the presence of the dinoflagellate species Cochlodinium sp. The bloom included other phytoplankton species, as Lepidodinium chlorophorum, which persistently changed the colour of the ocean to green. These blooms coincided with the government-managed emergency lockdown due to the COVID-19 pandemic. Local in situ sampling was slowed down. However, imagery from the Copernicus programme allowed operational monitoring. This study shows the benefits of both Sentinel-3 and Sentinel-2 satellites in terms of their spectral, spatial and temporal capabilities for improved algal bloom monitoring. These novel tools, which can foster optimal decision-making, are available for delivering early alerts in situations of natural catastrophes and blockages, such as those occurred during the global COVID-19 lockdown.

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Section: Discussionmentioning

confidence: 98%

Using Copernicus Sentinel-2 and Sentinel-3 data to monitor harmful algal blooms in Southern Chile during the COVID-19 lockdown

Rodriguez-Benito¹,

Navarro

Caballero

2020

Marine Pollution Bulletin

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“…The prominent reflectance peak located in the 700-720 nm region shifts towards longer wavelengths as phytoplankton concentrations increase; thus, the 704 nm band is suggested as the best option for HABs mapping 19 . Sakuno et al already indicated the potential of the 704 nm red-edge band of S2 for red tide monitoring in Japan 21 . The sensitivity of NDCI to chl-a concentration in turbid productive waters has been demonstrated, as well as its potential application to several platforms and diverse environments producing minimal uncertainty 31 .…”

Section: Discussionmentioning

confidence: 99%

“…As an alternative to ocean colour sensors, land-focused sensors, which have higher spatial resolution, can be applied. The latest generation of satellites such as the twin Sentinel-2A/B (S2) satellites of the European Union's Copernicus earth observation programme, carry the MSI (MultiSpectral Instrument) sensor, whose bands have valuable applications for phytoplankton estimation [17][18][19] and algal bloom detection 20,21 . The mission was initially designed for the evaluation of urban planning and terrestrial ecosystems, but the incorporation of bands in the red-edge spectrum (704, 740, and 783 nm; see Supplementary Table S1), its radiometric quality (12 bits), its high-revisit frequency (5 days at the equator), and its high spatial resolution (10 m) have proven its utility for coastal and inland waters examination 18,19,22 .…”

mentioning

confidence: 99%

New capabilities of Sentinel-2A/B satellites combined with in situ data for monitoring small harmful algal blooms in complex coastal waters

Caballero

Fernández

Escalante

et al. 2020

Sci Rep

117

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The increased frequency of harmful algal blooms (HABs) is a major environmental concern worldwide, resulting not only in increased treatment costs for drinking water but also in impacts on tourism, commercial fishing and aquaculture and risks to human and animal health. Traditional strategies with ship-based approaches based on field sampling and laboratory analysis have been adopted to assess HABs. However, these methods are labour intensive and costly and do not provide synoptic views of the bloom conditions. Here, we show that the Sentinel-2 twin satellite mission of the Copernicus programme, in combination with in situ data, is a powerful tool that can offer valuable spatiotemporal information about a bloom of the dinoflagellate Lingulodinium polyedra that occurred on the SW Iberian Peninsula. Using the robust ACOLITE atmospheric correction processor combined with the normalized difference chlorophyll index (NDCI), the enhanced mapping of small blooms can be performed at a 10 m spatial resolution, revealing surface patches and a heterogeneous distribution. This research also demonstrates the improved capabilities of Sentinel-2 compared to those of Landsat-8 and Sentinel-3 for continuous monitoring. The Sentinel-3 and Sentinel-2 missions provide ecosystem observations that allow the environmental community and water managers to evaluate changes in water quality and bloom distribution and that facilitate field-based measurements. Therefore, the value added by the Copernicus products in terms of frequency and synoptic observations is of paramount importance for ecological and management purposes at regional and national scales.

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“…The footprint of many of the remote-sensing sensors cover large areas with high temporal resolution; thus, they can potentially capture the spatial and temporal variabilities of HABs, as evidenced by the extensive literature describing the detection, monitoring, and forecasting of HABs using remote sensing-based techniques and sensors [9]. Investigations utilizing moderate-resolution imaging spectroradiometers (MODIS-Aqua and MODIS-Terra), SeaWiFS, MERIS, Sentinel-2, and unmanned aerial vehicles have contributed the most to these studies [9,[37][38][39][40][41][42]. The more recent and advanced satellites (e.g., Sentinel-3, launched in February 2016) provide added valuable resources for ocean color products, yet their recent deployment and, hence, their short record of historical data compared to earlier operational satellites (e.g., MODIS: 1999-present) puts them on the waiting list for future machine learning-based forecasting projects.…”

Section: Introductionmentioning

confidence: 99%

A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom

et al. 2021

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In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena in Florida’s coastal areas. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, we developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models the K. brevis abundance is used as the target, and 10 level-02 ocean color products extracted from daily archival MODIS satellite data are used as controlling factors. The adopted approach addresses two main shortcomings of earlier models: (1) the paucity of satellite data due to cloudy scenes and (2) the lag time between the period at which a variable reaches its highest correlation with the target and the time the bloom occurs. Eleven spatio-temporal models were generated, each from 3 consecutive day satellite datasets, with a forecasting span from 1 to 11 days. The 3-day models addressed the potential variations in lag time for some of the temporal variables. One or more of the generated 11 models could be used to predict HAB occurrences depending on availability of the cloud-free consecutive days. Findings indicate that XGBoost outperformed the other methods, and the forecasting models of 5–9 days achieved the best results. The most reliable model can forecast eight days ahead of time with balanced overall accuracy, Kappa coefficient, F-Score, and AUC of 96%, 0.93, 0.97, and 0.98 respectively. The euphotic depth, sea surface temperature, and chlorophyll-a are always among the most significant controlling factors. The proposed models could potentially be used to develop an “early warning system” for HABs in southwest Florida.

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A Simple Red Tide Monitoring Method using Sentinel-2 Data for Sustainable Management of Brackish Lake Koyama-ike, Japan

Cited by 16 publications

References 23 publications

Using Copernicus Sentinel-2 and Sentinel-3 data to monitor harmful algal blooms in Southern Chile during the COVID-19 lockdown

Using Copernicus Sentinel-2 and Sentinel-3 data to monitor harmful algal blooms in Southern Chile during the COVID-19 lockdown

New capabilities of Sentinel-2A/B satellites combined with in situ data for monitoring small harmful algal blooms in complex coastal waters

A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom

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