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  "notes": "Ocean monitoring systems are designed for continuous 
            
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monitoring to track their evolution and anticipate environmental 
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monitoring to track their evolution and anticipate environmental 
            
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issues. However, they are often based on IoT systems that offer little 
96
issues. However, they are often based on IoT systems that offer little 
            
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spatial coverage and are hard to maintain. Satellite remote sensing 
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spatial coverage and are hard to maintain. Satellite remote sensing 
            
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offers good geographical coverage but they also face several 
98
offers good geographical coverage but they also face several 
            
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challenges to become a monitoring system. This paper introduces an 
99
challenges to become a monitoring system. This paper introduces an 
            
100
easy-to-use software tool to crawl water-quality data from up to 6 
100
easy-to-use software tool to crawl water-quality data from up to 6 
            
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satellite instruments from the ESA and NASA. Particularly, Chl-a data 
101
satellite instruments from the ESA and NASA. Particularly, Chl-a data 
            
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is deeply analyzed in terms of reliability and data coverage for a 
102
is deeply analyzed in terms of reliability and data coverage for a 
            
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highly anthropised coastal lagoon (Mar Menor, Spain), where serious 
103
highly anthropised coastal lagoon (Mar Menor, Spain), where serious 
            
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socio-environmental issues are happening. Our results show a good 
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socio-environmental issues are happening. Our results show a good 
            
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linear correlation between in situ data and SRS data, reaching values 
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linear correlation between in situ data and SRS data, reaching values 
            
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close to 0.9, and stating the relevance of organic matter inputs from 
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close to 0.9, and stating the relevance of organic matter inputs from 
            
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ephemeral streams in Chl-a concentrations. Moreover, temporal 
107
ephemeral streams in Chl-a concentrations. Moreover, temporal 
            
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granularity is increased from 5 to 1.5 days by combining SRS 
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granularity is increased from 5 to 1.5 days by combining SRS 
            
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sources.",
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sources.",
            
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    "es": "Ocean monitoring systems are designed for continuous 
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    "es": "Ocean monitoring systems are designed for continuous 
            
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monitoring to track their evolution and anticipate environmental 
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monitoring to track their evolution and anticipate environmental 
            
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issues. However, they are often based on IoT systems that offer little 
113
issues. However, they are often based on IoT systems that offer little 
            
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spatial coverage and are hard to maintain. Satellite remote sensing 
114
spatial coverage and are hard to maintain. Satellite remote sensing 
            
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offers good geographical coverage but they also face several 
115
offers good geographical coverage but they also face several 
            
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challenges to become a monitoring system. This paper introduces an 
116
challenges to become a monitoring system. This paper introduces an 
            
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easy-to-use software tool to crawl water-quality data from up to 6 
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easy-to-use software tool to crawl water-quality data from up to 6 
            
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satellite instruments from the ESA and NASA. Particularly, Chl-a data 
118
satellite instruments from the ESA and NASA. Particularly, Chl-a data 
            
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is deeply analyzed in terms of reliability and data coverage for a 
119
is deeply analyzed in terms of reliability and data coverage for a 
            
120
highly anthropised coastal lagoon (Mar Menor, Spain), where serious 
120
highly anthropised coastal lagoon (Mar Menor, Spain), where serious 
            
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socio-environmental issues are happening. Our results show a good 
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socio-environmental issues are happening. Our results show a good 
            
122
linear correlation between in situ data and SRS data, reaching values 
122
linear correlation between in situ data and SRS data, reaching values 
            
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close to 0.9, and stating the relevance of organic matter inputs from 
123
close to 0.9, and stating the relevance of organic matter inputs from 
            
124
ephemeral streams in Chl-a concentrations. Moreover, temporal 
124
ephemeral streams in Chl-a concentrations. Moreover, temporal 
            
125
granularity is increased from 5 to 1.5 days by combining SRS sources."
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granularity is increased from 5 to 1.5 days by combining SRS sources."
            
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