“…This regionally optimized Chl product showed a high coefficient of determination ( r ² = 0.79) and acceptable errors ( RMSE = 5.4 mg m³; see [1] for more information). The regionally optimized Chl product according to Eq.…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 91%
“… Fig. 3 Histogram of Chl from a Sentinel-2 image of Lake Rotoehu taken on 12 December 2016 [1] . The positions of the median (Q 50 ), lower (Q 25 ) and upper (Q 75 ) quantiles are shown as vertical lines.…”
Section: Experimental Design Materials and Methodsmentioning
Horizontal patchiness of water quality attributes in lakes substantially influences the ability to accurately determine an average condition of a lake from traditional in situ sampling. Monitoring programmes for lake water quality often rely on water samples from one or few locations but the assumption of representativeness is seldomly tested. Satellite observations can support environmental monitoring by detecting horizontal variability of water quality attributes over entire lakes. This article is a co-submission with Lehmann et al. (2021), who present a method to create a regional calibration of a satellite chlorophyll
a
algorithm and a spatial analysis of an image time series to detect recurring patchiness. Our method was developed on 13 lakes in the central North Island of New Zealand and this publication makes available the data used in our analysis and the spatial fields of results. These data are immediately valuable for practitioners operating within the region of interest providing a five year archive of synoptic water quality data and spatial fields to help optimize in situ monitoring efforts. In addition, there is value to the wider scientific community as the study lakes are a useful ‘natural lab’ for the development of aquatic remote sensing methods due to the range of trophic conditions and water colour in a single satellite image scene. Together with decades of in situ water quality records, our data is therefore useful for the development and validation of widely applicable methods of water quality retrieval from satellite data.
“…This regionally optimized Chl product showed a high coefficient of determination ( r ² = 0.79) and acceptable errors ( RMSE = 5.4 mg m³; see [1] for more information). The regionally optimized Chl product according to Eq.…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 91%
“… Fig. 3 Histogram of Chl from a Sentinel-2 image of Lake Rotoehu taken on 12 December 2016 [1] . The positions of the median (Q 50 ), lower (Q 25 ) and upper (Q 75 ) quantiles are shown as vertical lines.…”
Section: Experimental Design Materials and Methodsmentioning
Horizontal patchiness of water quality attributes in lakes substantially influences the ability to accurately determine an average condition of a lake from traditional in situ sampling. Monitoring programmes for lake water quality often rely on water samples from one or few locations but the assumption of representativeness is seldomly tested. Satellite observations can support environmental monitoring by detecting horizontal variability of water quality attributes over entire lakes. This article is a co-submission with Lehmann et al. (2021), who present a method to create a regional calibration of a satellite chlorophyll
a
algorithm and a spatial analysis of an image time series to detect recurring patchiness. Our method was developed on 13 lakes in the central North Island of New Zealand and this publication makes available the data used in our analysis and the spatial fields of results. These data are immediately valuable for practitioners operating within the region of interest providing a five year archive of synoptic water quality data and spatial fields to help optimize in situ monitoring efforts. In addition, there is value to the wider scientific community as the study lakes are a useful ‘natural lab’ for the development of aquatic remote sensing methods due to the range of trophic conditions and water colour in a single satellite image scene. Together with decades of in situ water quality records, our data is therefore useful for the development and validation of widely applicable methods of water quality retrieval from satellite data.
“…Thus, the chl-a anomalies were defined as the residuals from a BRT predicting in situ analog chl-a as a function of four predictor variables-the day of the year, latitude, longitude, and sensor. This approach allowed us to account for patchiness in the chl-a data [42] and bypass the need for gap-filling the time series. This allowed for the calculation of trends over time in chl-a anomalies such that the trends would be less influenced by the seasonal or spatial coverage of the data, the sensor timespan or the interactions among these factors.…”
Anthropogenic activity is leading to widespread changes in lake water quality—a key contributor to socio-ecological health. But, the anthropogenic forces affecting lake water quality (climate change, land use change, and invasive species) are unevenly distributed across lakes, across the seasonal cycle, and across space within lakes, potentially leading to highly variable water quality responses that are poorly documented at the global scale. Here, we used 742 million chlorophyll-a (chl-a) estimates merged over 6 satellite sensors (daily, 1 to 4 km resolution) to quantify water quality changes from 1997 to 2020 in 344 globally-distributed large lakes. Chl-a decreased across 56% of the cumulative total lake area, challenging the putative widespread increase in chl-a that is expected due to human activity. 19% of lakes exhibited both positive and negative chl-a trends (p-value < 0.1) across different locations or times of the year. This spatiotemporal complexity demonstrates the value of moderate resolution mapping of lake chl-a to inform water management decision-making and to determine the local ecological consequences of human activity.
“…Throughout the 22-research papers that was utilised to compile this review article, the search technique is referred to as the many sorts of water bodies are being observed. According to the pie chart percentile, it is obvious that the bulk of the geospatial research was conducted on lakes [27] [28] [29] [29] [30], and inland water bodies [31], [32] [15] [33] with no distinction made between the two types of water bodies. Again, according to this review study, 14 percent of the research articles were focused on the bay [19] [33] [34] , sea [14], [35] [36], and coastal environment [31] [11] [20], whereas marsh (10%) [13][12], reservoir (5%) [37], and estuaries (5%) [19] were found to be at the bottom of the priority list.…”
Water acts as the medium which helps supply seafood and freshwater food for human consumption and drinking water. It is thus imperative that such a precious resource should be well monitored to safeguard human health and survival. Conventionally water quality can be monitored through manual laboratory-based methods which are time-consuming. Ground-based sensors are helping in expediting this task, though it involves the use of multiple sensors at various locations and requires regular maintenance and replacement. Satellite technology provides a good alternative method as it can scan large areas at a relatively low cost. Measurements of parameters such as sea surface temperature, total suspended solids (turbidity), primary food production (chlorophyll A), abnormal movement of aquatic animal, disease occurrence, water oxygen deficiency, growth monitoring of aquatic life and many others have been successfully reported in the literature. With the advent of micro-satellites that can transmit higher resolution images with a finer spectral resolution, greater advancements can be made for the detection of a greater number of critical water quality parameters. This paper reviewed the existing status of the water quality monitoring from space technology and covered published research done in various parts of the globe, including the problems, solutions, algorithm used, the advantages of the study, research region, water bodies, water quality variable, and satellites data.
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