Unleashing the power of remote sensing data in aquatic research: Guidelines for optimal utilization

IF 5.1 2区 地球科学 Q1 LIMNOLOGY Limnology and Oceanography Letters Pub Date : 2024-08-07 DOI:10.1002/lol2.10427
Igor Ogashawara, Sabine Wollrab, Stella A. Berger, Christine Kiel, Andreas Jechow, Alexis L. N. Guislain, Peter Gege, Thomas Ruhtz, Martin Hieronymi, Thomas Schneider, Gunnar Lischeid, Gabriel A. Singer, Franz Hölker, Hans-Peter Grossart, Jens C. Nejstgaard
{"title":"Unleashing the power of remote sensing data in aquatic research: Guidelines for optimal utilization","authors":"Igor Ogashawara,&nbsp;Sabine Wollrab,&nbsp;Stella A. Berger,&nbsp;Christine Kiel,&nbsp;Andreas Jechow,&nbsp;Alexis L. N. Guislain,&nbsp;Peter Gege,&nbsp;Thomas Ruhtz,&nbsp;Martin Hieronymi,&nbsp;Thomas Schneider,&nbsp;Gunnar Lischeid,&nbsp;Gabriel A. Singer,&nbsp;Franz Hölker,&nbsp;Hans-Peter Grossart,&nbsp;Jens C. Nejstgaard","doi":"10.1002/lol2.10427","DOIUrl":null,"url":null,"abstract":"<p>The use of satellite remote sensing for monitoring water quality in inland water systems has been growing in the last decades especially due to the development of new orbital sensors (Kutser et al. <span>2020</span>; Ogashawara <span>2021</span>). Earth observations provide new angles for limnology, such as a universal perspective of multiple aquatic ecosystems simultaneously, regional to global coverage, the potential to acquire time series of data and its valuable input to predictive models. Additionally, it allows the retrieval of several parameters across the surfaces of an increasing number of smaller lakes, providing not only the surface area and elevation, but also surface biogeochemical data. The exponential growth of studies using this technology highlights that the improved computing resources, increased amount of satellite imagery, and development of operational remote sensing algorithms to understand complex inland water systems is now a reality (Topp et al. <span>2020</span>).</p><p>With the increasing access to satellite data, several organizations are developing remote sensing-based products for water quality. These products are currently distributed by national and international agencies (i.e., European Space Agency [ESA], US Geological Survey [USGS]), international programs (i.e., Copernicus Marine, Copernicus Land, and Copernicus Climate Change), academic research (i.e., Minnesota Lake Browser, https://lakes.rs.umn.edu/), and private industry (i.e., CyanoLakes, https://www.cyanolakes.com/; CyanoAlert, https://cyanoalert.com/). Typically, the data behind these products have undergone substantial processing including atmospheric correction, identification of quality issues, and bio-geo-optical algorithms to derive the desired bio-geophysical variables. Figure 1 exemplifies the main procedures for generating a quality controlled remote sensing-based water quality product (inland, coastal, and marine). Procedures are divided into five types: (1) the initial data needed (the Level 1 satellite imagery, the in situ radiometric data, the in situ bio-geo-optical properties [especially inherent optical properties] and in situ water quality curated data); (2) the remote sensing processes (atmospheric correction and bio-geo-optical modeling), 3) the validation processes (of the remote sensing processes using in situ collected data); (4) the remote sensing-based products such as the atmospheric and glint correction imagery; and (5) the water quality products which are produced by applying the selected bio-geo-optical algorithms (locally and seasonally adapted to the dominating water constituents and validated with in situ water quality data) to the atmospherically corrected image. Finally, the remote sensing-based product needs to pass a quality assurance and quality control (QA/QC) to generate a final curated product.</p><p>As presented in Fig. 1, obtaining remote sensing-based water quality products is intricate, particularly for inland waters where optical properties are highly variable due to the naturally wide fluctuation in optically active constituents (OACs; i.e., phytoplankton pigments, colored dissolved organic matter [CDOM] and sediment) in the water column (Ogashawara et al. <span>2017</span>). To illustrate this complexity, algal blooms can manifest in brown waters rich in CDOM and mobilized sediments that induce turbidity (Lebret et al. <span>2018</span>). Due to this optical complexity, many remote sensing-based ocean color products mask out turbid waters, resulting in the exclusion of numerous freshwater systems. To promote the utilization of remote sensing technology and to enhance the understanding of the tradeoffs using remote sensing data, this letter addresses (i) the primary issues leading to problems in interpreting remote sensing data; (ii) the consequences of the misinterpretation; and (iii) suggests strategies for the utilization of remote sensing data, along with approaches to contribute to the reliable calibration and validation of remote sensing-based water quality products.</p><p>The selection of the remote sensing product is one of the primary considerations for limnological studies. Remote sensing-based products are designed for open ocean (ocean color products), coastal or inland waters, and it is crucial to discern the differences among them before making a choice. These differences arise from the light availability within the water column, where, in a first approximation: (1) open ocean waters predominantly absorb the red part of visible light, (2) coastal waters and clear inland waters absorb both blue and red light, and (3) turbid inland waters strongly absorb from short wavelengths to the red part of visible light (Kirk <span>2011</span>). Understanding these variations in the interaction between light and water facilitates the decision for the appropriate spectral region to be used during remote sensing data processing for atmospheric correction and bio-geo-optical modeling.</p><p>One example highlighting the importance of selecting the appropriate spectral region is the computation of chlorophyll <i>a</i> (Chl <i>a</i>) concentration from satellite data. Processing algorithms developed for the open ocean rely on the blue and green spectral band ratio due to Chl <i>a</i> absorption around 440 nm and the very low CDOM background signal (O'Reilly and Werdell <span>2019</span>). In contrast, coastal water products utilize the entire spectrum with a Neural Network approach (Brockmann et al. <span>2016</span>), while inland water remote sensing products so far typically base calculations on the ratio of aquatic reflectance at 665 nm (red peak of Chl <i>a</i> absorption) and the red-edge around 700 nm (scattering of algal cells, Gitelson <span>1992</span>). Given the low Chl <i>a</i> concentration in the open ocean, spectral bands within the red range are often dominated by water absorption and become unsuitable for Chl <i>a</i> retrieval. In inland waters (where CDOM is usually present), blue spectral bands are usually dominated by CDOM absorption, masking Chl <i>a</i> absorption at 440 nm, thus favoring the use of Chl <i>a</i> absorption at 665 nm. As a comparison, in situ Chl <i>a</i> sensors have recently been developed that use red light excitation rather than the traditional blue light excitation, in response to these optical challenges typical for coastal and inland waters. Additionally, it is crucial to highlight that open ocean, coastal, and inland water Chl <i>a</i> remote sensing products have been optimized for different concentration ranges, a factor that should be considered before using the data. Due to the intricate relationships between different water types and light, understanding the remote sensing data processing approaches in a remote sensing-based water quality product is essential for understanding the advantages and disadvantages of each product.</p><p>Figure 2A presents examples of typical aquatic reflectance spectra (remote sensing reflectance) from different aquatic environments which visually highlights the contrast interactions between light and water. To showcase the importance of selecting the most suitable approach for estimating Chl <i>a</i> concentration Fig. 2BD,F presents three remote sensing-based Chl <i>a</i> products from the Sentinel 2 MultiSpectral Instrument (MSI) over lakes located in the Mecklenburg–Brandenburg Lake District in northeastern Germany (Ogashawara et al. <span>2021</span>). We selected traditional remote sensing approaches for (i) open ocean (Fig. 2B), (ii) inland waters, and (iii) coastal waters (Fig. 2F). The visual differences among these three different remote sensing-based products for the Sentinel 2 MSI image (Scene ID: GS2A_20190726T102031_021369_N02.08) are further supported by scatter plots of the respective remote sensing estimated Chl <i>a</i> concentration and a water sample-based laboratory measurement of Chl <i>a</i> concentration using high-performance liquid chromatography (HPLC) done on the same day (Fig. 2C,E,G, respectively). In these examples, it was observed that the open ocean approach (Fig. 2C) underestimates the Chl <i>a</i> concentrations, the inland water approach (Fig. 2E) underestimates the Chl <i>a</i> for more eutrophic waters and the coastal approach (Fig. 2G) showed an underestimation for all Chl <i>a</i> concentrations. These results agree with the previous paragraph that when applying an open ocean approach in lakes the results may be strongly underestimating the true concentration of Chl <i>a</i>, especially in turbid waters, as the use of the blue and green regions of the visible spectrum are heavily affected by CDOM. It also highlights the importance of using in situ data to validate the selected satellite product—as the validation process is essential for the QA/QC (see Fig. 1).</p><p>A major challenge for remote sensing data processing in inland waters (Fig. 1) is the atmospheric correction (Pahlevan et al. <span>2021</span>). Atmospheric correction is the process of removing the optical effects of the atmosphere in the view field of a satellite or airborne sensor observing a target on the Earth's surface. A part of the atmospheric correction, is the glint correction which removes both the measured signal from light that is specularly reflected at the water surface from the sun, as well as reflected from the sky toward the sensor. Approximately, 90% of the total signal measured by a satellite stem from the atmosphere (IOCCG <span>2010</span>), and the intensity of the glint can be higher than the intensity of the water leaving radiance, depending on the brightness of water, solar azimuth angle and on wavelength. Therefore, the accuracy requirements of the correction methods are much higher over water than over land. Figure 3 presents average reflectance spectra of a eutrophic lake for a Sentinel 2 MSI image without atmospheric correction (top-of-atmosphere reflectance—<i>R</i><sub>TOA</sub>), with a land based atmospheric correction (surface reflectance—SR) and using an aquatic atmospheric correction for the computation of the Remote Sensing Reflectance (<i>R</i><sub>rs</sub>). A recent study performed a similar comparison for the Landsat SR products and showed that the use of SR products for the green and red spectral bands had uncertainties close to 30%, whereas the uncertainties in the blue and coastal-aerosol bands ranged from 48% to 110% when compared to in situ <i>R</i><sub>rs</sub> (Maciel et al. <span>2023</span>). These results highlight the importance of having an aquatic atmospheric correction and to carefully evaluate the tradeoffs of the use of SR in limnological studies.</p><p>Considering that there is no universally acceptable inland water atmospheric correction processor, limnological studies need to first validate different atmospheric correction processors as highlighted in Fig. 1. This validation of the atmospheric correction is crucial to make sure that the remote sensing data used as input for the studies using machine learning and artificial intelligence approaches (in which data quality is absolutely critical) are not largely biased. However, it becomes challenging because it requires in situ radiometric data to perform this validation. This type of data is still not commonly used by most scientists not specialized in remote sensing, despite that it is crucial to develop and calibrate the water atmospheric correction processors for inland and coastal waters.</p><p><i>How to choose the right remote sensing-based water quality product?</i> Before incorporation of remote sensing data in aquatic research, it is important to look for the Algorithm Theoretical Basis Document (ATBD) of the remote sensing-based product and the proper reference of the product to precisely understand its development and limitations. Another recommendation is to use remote sensing-based products, which have been standardized and quality controlled by a reputable organization, such as the Committee on Earth Observation Satellites (CEOS) that recently created a minimum set of requirements for different remote sensing-based products (CEOS <span>2021</span>). With this verification of quality by CEOS, it will be easier to identify if the retrieved information is trustful or not. Finally, a simple recommendation is to always use a remote sensing-based product developed for the specific type of water under investigation: open ocean, coastal or inland waters. While ocean color products (made for open ocean) are easy to find for inland waters, inland water global products are still scarce due to the optical complexity of these aquatic environments. Nevertheless, some products were developed for global inland waters based on a blended algorithm approach which first classifies the aquatic system by its optical similarities (optical water typology) and then estimates other parameters. Some examples of these products are the Copernicus Land Lakes Water Quality product (https://land.copernicus.eu/global/products/lwq) and the European Space Agency Lakes Climate Change Initiative (https://climate.esa.int/en/projects/lakes/). While these initiatives are based on lakes, they also include reservoirs, however, these are global products and may not be optimized for a specific study site. Additionally the US Geological Survey (USGS) has a provisional product of aquatic reflectance which is produced after running an aquatic atmospheric correction (https://www.usgs.gov/landsat-missions/landsat-provisional-aquatic-reflectance), however it is still not fully validated for inland waters and it is still in provisional phase.</p><p><i>How to choose the right remote sensing processes?</i> To help with the selection of the best approach, Neil et al. (<span>2019</span>) proposed a tree scheme to simply identify the best bio-geo-optical algorithm to use for Chl <i>a</i> concentration estimation based on the trophic state of the aquatic system where: the open ocean approach should be used for oligotrophic waters, the inland water approach should be used for mesotrophic and eutrophic waters and a quasi-analytical approach should be used for hypertrophic waters. This decision tree is very helpful for an initial selection of the remote sensing data processing approach; however, there are aquatic systems which are not covered, for example, aquatic systems with very high CDOM concentration (polyhumic waters). Similarly, Pahlevan et al. (<span>2021</span>) tested different atmospheric corrections processors and provided a ranking per optical water type which can facilitate the selection of the atmospheric correction approach.</p><p><i>How to improve remote sensing-based water quality products for my study site?</i> To improve these products for a regional level, it is useful to follow the indicated processing chain of Fig. 1. This will require in situ radiometric data, thus there is an urge for the collection of this type of data. However, matching data with satellite passages is a big challenge. From the total 12,000 worldwide <i>R</i><sub>rs</sub> spectra compiled by Maciel et al. (<span>2023</span>) just a small part (<i>N</i> = 1100) had match-ups with satellite data. This fact highlights the need to align field sampling with satellite passages on cloud free days, which can be difficult for some parts of the world where cloud cover is unpredictable. In these areas, the deployment of sensors could be an alternative for the acquisition of in situ radiometric, optical properties and water quality data. Ideally, such deployed systems should be equipped with autonomous in situ systems for all required parameters, and they need to be deployed in carefully selected aquatic reference systems which would cover a gradient of organic matter, different trophic levels, and different catchments. This would allow to acquire match-up data for calibration and validation that can be extended to optically similar waters. A well-validated atmospheric correction can strengthen the accuracy of water quality products, which depend on your choice of the bio-geo-optical model. Regarding the existing water quality monitoring programs, the data collection of the absorption coefficient of CDOM (<i>a</i><sub>CDOM</sub>), the concentration of total suspended solids (TSS) and the concentration of phytoplankton pigments should be emphasized as essential variables.</p><p><i>How to use remote sensing data without</i> in situ <i>radiometric data to validate the atmospheric correction?</i> Considering that in situ radiometric data is still not a common measurement for many scientists working in inland and coastal waters, it is important to highlight the existence of aquatic reflectance products such as: the Copernicus Land Lakes Water Quality product, the European Space Agency Lakes Climate Change Initiative and the USGS provisional product of aquatic reflectance. These products could be carefully used for limnological studies—including machine learning and artificial intelligence of big data analysis. Another alternative is the use of different atmospheric correction approaches based on the optical water type of your system (as in Pahlevan et al. <span>2021</span>) and to use the existing in situ water quality data to validate the estimation from satellite data coming from different atmospheric correction processors. This acknowledges the importance of having an atmospheric correction targeting inland waters and can be used to calculate the uncertainties of this process.</p><p><i>How to best align scientists working in inland and coastal waters, with remote sensing scientists?</i> Fortunately, inland water remote sensing is rapidly developing as a new discipline and several initiatives have been launched recently to disseminate remote sensing applications and products better. International networks such as the Group of Earth Observation (GEO) AquaWatch, the International Water Association (IWA) and the World Water Quality Alliance (WWQA) have been offering free webinars to inform the inland water research community on the current state-of-the-art of inland water remote sensing. With the global reach of these networks helping to disseminate the knowledge of remote sensing to non-remote sensing experts. Another network is the Global Lake Ecological Observatory Network (GLEON) which started in the United States and has been expanding worldwide and currently hosts a working group on Aquatic Remote Sensing which was created to establish the relationship between aquatic ecologists and remote sensing experts. These initiatives are complemented by online training which are available to anyone in the world such as the courses offered by the National Aeronautics and Space Administration (NASA) program on Applied Remote Sensing Training (ARSET).</p><p>The continuous growth and acceptance of remote sensing technology in limnology coupled with the standardization of satellite-based water quality products and the increase in data collection for calibration and validation offers the unique opportunity of operational use of such technology for reliable inland water monitoring. This will be achieved when aquatic sciences and remote sensing communities will join forces for the calibration and validation of the remote sensing-based water quality products with in situ radiometric and biogeochemical data. This will enable users to put results into adequate context and to understand the tradeoffs of the use of remote sensing data in the future. More synergies between these communities are needed to harmonize products, offer training materials and guides for the best use of remotely sensed data, as well as re-evaluate previously published material based on the newer approaches outlined above. Such synergies will effectively help to overcome methodological limitations and improve our ability to accurately monitor our rapidly changing inland waters.</p><p>The authors have declared no conflict of interest.</p>","PeriodicalId":18128,"journal":{"name":"Limnology and Oceanography Letters","volume":"9 6","pages":"667-673"},"PeriodicalIF":5.1000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lol2.10427","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Limnology and Oceanography Letters","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/lol2.10427","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"LIMNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The use of satellite remote sensing for monitoring water quality in inland water systems has been growing in the last decades especially due to the development of new orbital sensors (Kutser et al. 2020; Ogashawara 2021). Earth observations provide new angles for limnology, such as a universal perspective of multiple aquatic ecosystems simultaneously, regional to global coverage, the potential to acquire time series of data and its valuable input to predictive models. Additionally, it allows the retrieval of several parameters across the surfaces of an increasing number of smaller lakes, providing not only the surface area and elevation, but also surface biogeochemical data. The exponential growth of studies using this technology highlights that the improved computing resources, increased amount of satellite imagery, and development of operational remote sensing algorithms to understand complex inland water systems is now a reality (Topp et al. 2020).

With the increasing access to satellite data, several organizations are developing remote sensing-based products for water quality. These products are currently distributed by national and international agencies (i.e., European Space Agency [ESA], US Geological Survey [USGS]), international programs (i.e., Copernicus Marine, Copernicus Land, and Copernicus Climate Change), academic research (i.e., Minnesota Lake Browser, https://lakes.rs.umn.edu/), and private industry (i.e., CyanoLakes, https://www.cyanolakes.com/; CyanoAlert, https://cyanoalert.com/). Typically, the data behind these products have undergone substantial processing including atmospheric correction, identification of quality issues, and bio-geo-optical algorithms to derive the desired bio-geophysical variables. Figure 1 exemplifies the main procedures for generating a quality controlled remote sensing-based water quality product (inland, coastal, and marine). Procedures are divided into five types: (1) the initial data needed (the Level 1 satellite imagery, the in situ radiometric data, the in situ bio-geo-optical properties [especially inherent optical properties] and in situ water quality curated data); (2) the remote sensing processes (atmospheric correction and bio-geo-optical modeling), 3) the validation processes (of the remote sensing processes using in situ collected data); (4) the remote sensing-based products such as the atmospheric and glint correction imagery; and (5) the water quality products which are produced by applying the selected bio-geo-optical algorithms (locally and seasonally adapted to the dominating water constituents and validated with in situ water quality data) to the atmospherically corrected image. Finally, the remote sensing-based product needs to pass a quality assurance and quality control (QA/QC) to generate a final curated product.

As presented in Fig. 1, obtaining remote sensing-based water quality products is intricate, particularly for inland waters where optical properties are highly variable due to the naturally wide fluctuation in optically active constituents (OACs; i.e., phytoplankton pigments, colored dissolved organic matter [CDOM] and sediment) in the water column (Ogashawara et al. 2017). To illustrate this complexity, algal blooms can manifest in brown waters rich in CDOM and mobilized sediments that induce turbidity (Lebret et al. 2018). Due to this optical complexity, many remote sensing-based ocean color products mask out turbid waters, resulting in the exclusion of numerous freshwater systems. To promote the utilization of remote sensing technology and to enhance the understanding of the tradeoffs using remote sensing data, this letter addresses (i) the primary issues leading to problems in interpreting remote sensing data; (ii) the consequences of the misinterpretation; and (iii) suggests strategies for the utilization of remote sensing data, along with approaches to contribute to the reliable calibration and validation of remote sensing-based water quality products.

The selection of the remote sensing product is one of the primary considerations for limnological studies. Remote sensing-based products are designed for open ocean (ocean color products), coastal or inland waters, and it is crucial to discern the differences among them before making a choice. These differences arise from the light availability within the water column, where, in a first approximation: (1) open ocean waters predominantly absorb the red part of visible light, (2) coastal waters and clear inland waters absorb both blue and red light, and (3) turbid inland waters strongly absorb from short wavelengths to the red part of visible light (Kirk 2011). Understanding these variations in the interaction between light and water facilitates the decision for the appropriate spectral region to be used during remote sensing data processing for atmospheric correction and bio-geo-optical modeling.

One example highlighting the importance of selecting the appropriate spectral region is the computation of chlorophyll a (Chl a) concentration from satellite data. Processing algorithms developed for the open ocean rely on the blue and green spectral band ratio due to Chl a absorption around 440 nm and the very low CDOM background signal (O'Reilly and Werdell 2019). In contrast, coastal water products utilize the entire spectrum with a Neural Network approach (Brockmann et al. 2016), while inland water remote sensing products so far typically base calculations on the ratio of aquatic reflectance at 665 nm (red peak of Chl a absorption) and the red-edge around 700 nm (scattering of algal cells, Gitelson 1992). Given the low Chl a concentration in the open ocean, spectral bands within the red range are often dominated by water absorption and become unsuitable for Chl a retrieval. In inland waters (where CDOM is usually present), blue spectral bands are usually dominated by CDOM absorption, masking Chl a absorption at 440 nm, thus favoring the use of Chl a absorption at 665 nm. As a comparison, in situ Chl a sensors have recently been developed that use red light excitation rather than the traditional blue light excitation, in response to these optical challenges typical for coastal and inland waters. Additionally, it is crucial to highlight that open ocean, coastal, and inland water Chl a remote sensing products have been optimized for different concentration ranges, a factor that should be considered before using the data. Due to the intricate relationships between different water types and light, understanding the remote sensing data processing approaches in a remote sensing-based water quality product is essential for understanding the advantages and disadvantages of each product.

Figure 2A presents examples of typical aquatic reflectance spectra (remote sensing reflectance) from different aquatic environments which visually highlights the contrast interactions between light and water. To showcase the importance of selecting the most suitable approach for estimating Chl a concentration Fig. 2BD,F presents three remote sensing-based Chl a products from the Sentinel 2 MultiSpectral Instrument (MSI) over lakes located in the Mecklenburg–Brandenburg Lake District in northeastern Germany (Ogashawara et al. 2021). We selected traditional remote sensing approaches for (i) open ocean (Fig. 2B), (ii) inland waters, and (iii) coastal waters (Fig. 2F). The visual differences among these three different remote sensing-based products for the Sentinel 2 MSI image (Scene ID: GS2A_20190726T102031_021369_N02.08) are further supported by scatter plots of the respective remote sensing estimated Chl a concentration and a water sample-based laboratory measurement of Chl a concentration using high-performance liquid chromatography (HPLC) done on the same day (Fig. 2C,E,G, respectively). In these examples, it was observed that the open ocean approach (Fig. 2C) underestimates the Chl a concentrations, the inland water approach (Fig. 2E) underestimates the Chl a for more eutrophic waters and the coastal approach (Fig. 2G) showed an underestimation for all Chl a concentrations. These results agree with the previous paragraph that when applying an open ocean approach in lakes the results may be strongly underestimating the true concentration of Chl a, especially in turbid waters, as the use of the blue and green regions of the visible spectrum are heavily affected by CDOM. It also highlights the importance of using in situ data to validate the selected satellite product—as the validation process is essential for the QA/QC (see Fig. 1).

A major challenge for remote sensing data processing in inland waters (Fig. 1) is the atmospheric correction (Pahlevan et al. 2021). Atmospheric correction is the process of removing the optical effects of the atmosphere in the view field of a satellite or airborne sensor observing a target on the Earth's surface. A part of the atmospheric correction, is the glint correction which removes both the measured signal from light that is specularly reflected at the water surface from the sun, as well as reflected from the sky toward the sensor. Approximately, 90% of the total signal measured by a satellite stem from the atmosphere (IOCCG 2010), and the intensity of the glint can be higher than the intensity of the water leaving radiance, depending on the brightness of water, solar azimuth angle and on wavelength. Therefore, the accuracy requirements of the correction methods are much higher over water than over land. Figure 3 presents average reflectance spectra of a eutrophic lake for a Sentinel 2 MSI image without atmospheric correction (top-of-atmosphere reflectance—RTOA), with a land based atmospheric correction (surface reflectance—SR) and using an aquatic atmospheric correction for the computation of the Remote Sensing Reflectance (Rrs). A recent study performed a similar comparison for the Landsat SR products and showed that the use of SR products for the green and red spectral bands had uncertainties close to 30%, whereas the uncertainties in the blue and coastal-aerosol bands ranged from 48% to 110% when compared to in situ Rrs (Maciel et al. 2023). These results highlight the importance of having an aquatic atmospheric correction and to carefully evaluate the tradeoffs of the use of SR in limnological studies.

Considering that there is no universally acceptable inland water atmospheric correction processor, limnological studies need to first validate different atmospheric correction processors as highlighted in Fig. 1. This validation of the atmospheric correction is crucial to make sure that the remote sensing data used as input for the studies using machine learning and artificial intelligence approaches (in which data quality is absolutely critical) are not largely biased. However, it becomes challenging because it requires in situ radiometric data to perform this validation. This type of data is still not commonly used by most scientists not specialized in remote sensing, despite that it is crucial to develop and calibrate the water atmospheric correction processors for inland and coastal waters.

How to choose the right remote sensing-based water quality product? Before incorporation of remote sensing data in aquatic research, it is important to look for the Algorithm Theoretical Basis Document (ATBD) of the remote sensing-based product and the proper reference of the product to precisely understand its development and limitations. Another recommendation is to use remote sensing-based products, which have been standardized and quality controlled by a reputable organization, such as the Committee on Earth Observation Satellites (CEOS) that recently created a minimum set of requirements for different remote sensing-based products (CEOS 2021). With this verification of quality by CEOS, it will be easier to identify if the retrieved information is trustful or not. Finally, a simple recommendation is to always use a remote sensing-based product developed for the specific type of water under investigation: open ocean, coastal or inland waters. While ocean color products (made for open ocean) are easy to find for inland waters, inland water global products are still scarce due to the optical complexity of these aquatic environments. Nevertheless, some products were developed for global inland waters based on a blended algorithm approach which first classifies the aquatic system by its optical similarities (optical water typology) and then estimates other parameters. Some examples of these products are the Copernicus Land Lakes Water Quality product (https://land.copernicus.eu/global/products/lwq) and the European Space Agency Lakes Climate Change Initiative (https://climate.esa.int/en/projects/lakes/). While these initiatives are based on lakes, they also include reservoirs, however, these are global products and may not be optimized for a specific study site. Additionally the US Geological Survey (USGS) has a provisional product of aquatic reflectance which is produced after running an aquatic atmospheric correction (https://www.usgs.gov/landsat-missions/landsat-provisional-aquatic-reflectance), however it is still not fully validated for inland waters and it is still in provisional phase.

How to choose the right remote sensing processes? To help with the selection of the best approach, Neil et al. (2019) proposed a tree scheme to simply identify the best bio-geo-optical algorithm to use for Chl a concentration estimation based on the trophic state of the aquatic system where: the open ocean approach should be used for oligotrophic waters, the inland water approach should be used for mesotrophic and eutrophic waters and a quasi-analytical approach should be used for hypertrophic waters. This decision tree is very helpful for an initial selection of the remote sensing data processing approach; however, there are aquatic systems which are not covered, for example, aquatic systems with very high CDOM concentration (polyhumic waters). Similarly, Pahlevan et al. (2021) tested different atmospheric corrections processors and provided a ranking per optical water type which can facilitate the selection of the atmospheric correction approach.

How to improve remote sensing-based water quality products for my study site? To improve these products for a regional level, it is useful to follow the indicated processing chain of Fig. 1. This will require in situ radiometric data, thus there is an urge for the collection of this type of data. However, matching data with satellite passages is a big challenge. From the total 12,000 worldwide Rrs spectra compiled by Maciel et al. (2023) just a small part (N = 1100) had match-ups with satellite data. This fact highlights the need to align field sampling with satellite passages on cloud free days, which can be difficult for some parts of the world where cloud cover is unpredictable. In these areas, the deployment of sensors could be an alternative for the acquisition of in situ radiometric, optical properties and water quality data. Ideally, such deployed systems should be equipped with autonomous in situ systems for all required parameters, and they need to be deployed in carefully selected aquatic reference systems which would cover a gradient of organic matter, different trophic levels, and different catchments. This would allow to acquire match-up data for calibration and validation that can be extended to optically similar waters. A well-validated atmospheric correction can strengthen the accuracy of water quality products, which depend on your choice of the bio-geo-optical model. Regarding the existing water quality monitoring programs, the data collection of the absorption coefficient of CDOM (aCDOM), the concentration of total suspended solids (TSS) and the concentration of phytoplankton pigments should be emphasized as essential variables.

How to use remote sensing data without in situ radiometric data to validate the atmospheric correction? Considering that in situ radiometric data is still not a common measurement for many scientists working in inland and coastal waters, it is important to highlight the existence of aquatic reflectance products such as: the Copernicus Land Lakes Water Quality product, the European Space Agency Lakes Climate Change Initiative and the USGS provisional product of aquatic reflectance. These products could be carefully used for limnological studies—including machine learning and artificial intelligence of big data analysis. Another alternative is the use of different atmospheric correction approaches based on the optical water type of your system (as in Pahlevan et al. 2021) and to use the existing in situ water quality data to validate the estimation from satellite data coming from different atmospheric correction processors. This acknowledges the importance of having an atmospheric correction targeting inland waters and can be used to calculate the uncertainties of this process.

How to best align scientists working in inland and coastal waters, with remote sensing scientists? Fortunately, inland water remote sensing is rapidly developing as a new discipline and several initiatives have been launched recently to disseminate remote sensing applications and products better. International networks such as the Group of Earth Observation (GEO) AquaWatch, the International Water Association (IWA) and the World Water Quality Alliance (WWQA) have been offering free webinars to inform the inland water research community on the current state-of-the-art of inland water remote sensing. With the global reach of these networks helping to disseminate the knowledge of remote sensing to non-remote sensing experts. Another network is the Global Lake Ecological Observatory Network (GLEON) which started in the United States and has been expanding worldwide and currently hosts a working group on Aquatic Remote Sensing which was created to establish the relationship between aquatic ecologists and remote sensing experts. These initiatives are complemented by online training which are available to anyone in the world such as the courses offered by the National Aeronautics and Space Administration (NASA) program on Applied Remote Sensing Training (ARSET).

The continuous growth and acceptance of remote sensing technology in limnology coupled with the standardization of satellite-based water quality products and the increase in data collection for calibration and validation offers the unique opportunity of operational use of such technology for reliable inland water monitoring. This will be achieved when aquatic sciences and remote sensing communities will join forces for the calibration and validation of the remote sensing-based water quality products with in situ radiometric and biogeochemical data. This will enable users to put results into adequate context and to understand the tradeoffs of the use of remote sensing data in the future. More synergies between these communities are needed to harmonize products, offer training materials and guides for the best use of remotely sensed data, as well as re-evaluate previously published material based on the newer approaches outlined above. Such synergies will effectively help to overcome methodological limitations and improve our ability to accurately monitor our rapidly changing inland waters.

The authors have declared no conflict of interest.

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在水产研究中释放遥感数据的力量:最佳利用准则
过去几十年来,卫星遥感技术在监测内陆水系水质方面的应用不断增加,特别是由于新型轨道传感器的发展(Kutser 等,2020 年;Ogashawara,2021 年)。对地观测为湖泊学提供了新的视角,如同时观测多个水生生态系统的普遍视角、区域到全球的覆盖范围、获取时间序列数据的潜力及其对预测模型的宝贵投入。此外,它还可以检索越来越多的小型湖泊表面的多个参数,不仅提供表面积和海拔高度,还提供表面生物地球化学数据。利用该技术进行的研究呈指数级增长,这突出表明计算资源的改善、卫星图像数量的增加,以及用于了解复杂内陆水系的实用遥感算法的开发现已成为现实(Topp 等,2020 年)。这些产品目前由国家和国际机构(即欧洲航天局 [ESA]、美国地质调查局 [USGS])、国际项目(即哥白尼海洋、哥白尼陆地和哥白尼气候变化)、学术研究(即明尼苏达湖泊浏览器,https://lakes.rs.umn.edu/)和私营企业(即 CyanoLakes,https://www.cyanolakes.com/;CyanoAlert,https://cyanoalert.com/)分发。通常情况下,这些产品背后的数据都经过大量处理,包括大气校正、质量问题识别和生物地球光学算法,以得出所需的生物地球物理变量。图 1 举例说明了生成基于质量控制的遥感水质产品(内陆、沿岸和海洋)的主要程序。程序分为五类:(1) 所需的初始数据(1 级卫星图像、原位辐射数据、原位生物地理光学特性[特别是固有 光学特性]和原位水质曲线数据);(2) 遥感过程(大气校正和生物地理光学建模);(3) 验证过程(利 用原位采集的数据对遥感过程进行验证);(4) 基于遥感的产品,如大气校正和闪烁校正图像;以及 (5) 水质产品,通过将选定的生物地球光学算法(根据当地和季节的主要水成 分进行调整,并利用现场水质数据进行验证)应用于大气校正图像而生成。最后,基于遥感的产品需要通过质量保证和质量控制(QA/QC),以生成最终的策划产品。如图 1 所示,获得基于遥感的水质产品非常复杂,尤其是对于内陆水域,由于水体中光学活性成分(OACs;即浮游植物色素、有色溶解有机物 [CDOM] 和沉积物)的自然波动很大,这些水域的光学特性变化很大(Ogashawara 等人,2017 年)。为了说明这种复杂性,藻类大量繁殖可表现为富含 CDOM 的褐色水体和引起浑浊的移动沉积物(Lebret 等,2018 年)。由于这种光学复杂性,许多基于遥感技术的海洋颜色产品会掩盖浑浊水域,导致许多淡水系统被排除在外。为促进对遥感技术的利用,并加强对遥感数据使用权衡的理解,本信讨论了(i)导致遥感数据解释问题的主要问题;(ii)误读的后果;以及(iii)建议利用遥感数据的策略,以及有助于可靠校准和验证基于遥感的水质产品的方法。选择遥感产品是湖泊学研究的首要考虑因素之一。基于遥感的产品是为开阔海域(海 洋颜色产品)、沿岸或内陆水域设计的,在做出选择之前,关键是要弄清它们之间的差 别。这些差异源于水体中光的可用性,近似地讲,(1) 开阔海域主要吸收可见光的红色部分,(2) 沿岸水域和清澈的内陆水域吸收蓝光和红光,(3) 浑浊的内陆水域强烈吸收可见光的短波长到红色部分(Kirk,2011 年)。了解光与水之间相互作用的这些变化,有助于在处理遥感数据时决定使用适当的光谱区域,以进行大气校正和生物地球光学建模。 从卫星数据中计算叶绿素 a(Chl a)浓度就是一个强调选择适当光谱区域重要性的例子。由于叶绿素 a 在 440 纳米附近的吸收和极低的 CDOM 背景信号,为开阔海域开发的处理算法依赖于蓝绿光谱带的比值(O'Reilly 和 Werdell,2019 年)。相比之下,沿岸水域产品采用神经网络方法利用整个光谱(Brockmann 等,2016 年),而迄今为止的内陆水域遥感产品通常基于 665 纳米(Chl a 吸收的红色峰值)和 700 纳米(藻类细胞散射,Gitelson,1992 年)附近的红边水生反射率之比进行计算。由于开阔海域的 Chl a 浓度较低,红色范围内的光谱带通常被水吸收,不适合进行 Chl a 采集。在内陆水域(通常存在 CDOM),蓝色光谱带通常被 CDOM 的吸收所主导,掩盖了 440 纳米波长处的 Chl a 吸收,从而有利于使用 665 纳米波长处的 Chl a 吸收。作为比较,最近开发的原位 Chl a 传感器使用红光激发,而不是传统的蓝光激发,以应对沿海和内陆水域的典型光学挑战。此外,必须强调的是,开阔洋、沿岸和内陆水域的 Chl a 遥感产品已针对不同的浓度范围进行了优化,这是在使用数据前应考虑的一个因素。由于不同类型的水与光之间的关系错综复杂,因此了解基于遥感技术的水质产品中的遥感数据处理方法对于了解每种产品的优缺点至关重要。图 2A 展示了不同水生环境中典型的水生反射光谱(遥感反射率)示例,直观地突出了光与水之间的对比交互作用。图 2BD,F 展示了德国东北部梅克伦堡-勃兰登堡湖区(Ogashawara 等人,2021 年)湖泊上空的哨兵 2 号多光谱仪器(MSI)提供的三种基于遥感的 Chl a 产品,以说明选择最适合的方法估算 Chl a 浓度的重要性。我们选择了传统的遥感方法:(i) 开阔海洋(图 2B);(ii) 内陆水域;(iii) 沿海水域(图 2F)。对哨兵 2 号 MSI 图像(场景 ID:GS2A_20190726T102031_021369_N02.08)的这三种不同的遥感产品之间的视觉差异,可通过各自的遥感估算 Chl a 浓度与实验室在同一天使用高效液相色谱法(HPLC)测量的水样 Chl a 浓度的散点图(分别见图 2C、E、G)得到进一步证实。在这些例子中,可以观察到开阔海域方法(图 2C)低估了 Chl a 浓度,内陆水域方法(图 2E)低估了富营养化程度较高水域的 Chl a 浓度,而沿岸方法(图 2G)则低估了所有 Chl a 浓度。这些结果与前文所述一致,即在湖泊中采用开阔海域方法时,由于可见光谱的蓝色和绿 色区域的使用受到 CDOM 的严重影响,其结果可能会严重低估 Chl a 的真实浓度,特别是在 浑浊水域中。这也凸显了使用原位数据验证所选卫星产品的重要性--因为验证过程对质量保证/质量控制至关重要(见图 1)。内陆水域(图 1)遥感数据处理的一大挑战是大气校正(Pahlevan 等,2021 年)。大气校正是在卫星或机载传感器观测地球表面目标的视场中消除大气光学效应的过程。大气校正的一部分是闪烁校正,它可以去除从太阳镜面反射到水面的光以及从天空反射到传感器的光产生的测量信号。卫星测量到的总信号中约有 90% 来自大气层(IOCCG,2010 年),闪烁光的强度可能会高于水面离开辐射的强度,这取决于水的亮度、太阳方位角和波长。因此,水上校正方法的精度要求远高于陆地。图 3 展示了一个富营养化湖泊的平均反射率光谱,该湖泊的哨兵 2 号 MSI 图像未进行大气校正(大气顶反射率-RTOA),使用了陆基大气校正(表面反射率-SR),并在计算遥感反射率 (Rrs) 时使用了水生大气校正。 最近的一项研究对大地遥感卫星 SR 产品进行了类似的比较,结果表明,与原位 Rrs 相比,在绿色和红色光谱波段使用 SR 产品的不确定性接近 30%,而在蓝色和沿岸气溶胶波段的不确定性则在 48%到 110%之间(Maciel 等,2023 年)。考虑到没有普遍接受的内陆水域大气校正处理器,湖泊研究需要首先验证不同的大气校正处理器,如图 1 所示。大气校正的验证对于确保遥感数据作为机器学习和人工智能方法(数据质量绝对重要)的研究输入数据不会出现严重偏差至关重要。然而,由于需要现场辐射测量数据来进行验证,这就变得非常具有挑战性。尽管这类数据对于开发和校准内陆和沿海水域的水大气校正处理器至关重要,但大多数非遥感专业的科学家仍不常用这类数据。在将遥感数据用于水产研究之前,重要的是要查找基于遥感产品的算法理论基础文件(ATBD)和该产品的适当参考资料,以准确了解其发展和局限性。另一项建议是使用由知名组织(如地球观测卫星委员会(CEOS))进行标准化和质量控制的遥感产品,该组织最近为不同的遥感产品制定了一套最低要求(CEOS 2021)。有了地球观测卫星委员会的质量验证,就更容易确定检索到的信息是否可信。最后,一个简单的建议是,始终使用针对特定调查水域类型开发的遥感产品:公海、沿 海或内陆水域。虽然海洋颜色产品(针对公海)在内陆水域很容易找到,但由于这些水域环境的光学 复杂性,内陆水域的全球产品仍然很少。尽管如此,还是开发了一些基于混合算法方法的全球内陆水域产品,该方法首先根据光学相似性对水生系统进行分类(光学水域类型学),然后估算其他参数。哥白尼陆地湖泊水质产品(https://land.copernicus.eu/global/products/lwq)和欧洲航天局湖泊气候变化倡议(https://climate.esa.int/en/projects/lakes/)就是这些产品的一些例子。虽然这些计划以湖泊为基础,但也包括水库,不过,这些都是全球性产品,可能无法针对特定研究地点进行优化。此外,美国地质调查局(USGS)有一个水生反射率的临时产品,该产品是在运行水生大气校正(https://www.usgs.gov/landsat-missions/landsat-provisional-aquatic-reflectance)后生成的,但它仍未针对内陆水域进行充分验证,目前仍处于临时阶段。如何选择正确的遥感过程?为帮助选择最佳方法,Neil 等人(2019 年)提出了一个树形方案,可根据水生系统的营养状态,简单确定用于 Chl a 浓度估算的最佳生物-地光算法,其中:低营养水域应使用开阔洋方法,中营养和富营养化水域应使用内陆水方法,高营养水域应使用准分析方法。这个决策树对初步选择遥感数据处理方法很有帮助;但是,有些水生系统并没有包括在内,例如,CDOM 浓度非常高的水生系统(多水体)。同样,Pahlevan 等人(2021 年)测试了不同的大气校正处理器,并提供了每种光学水体类型的排名,这有助于选择大气校正方法。要在区域层面改进这些产品,最好遵循图 1 所示的处理链。这就需要现场辐射测量数据,因此有必要收集这类数据。然而,如何将数据与卫星传回的数据相匹配是一个巨大的挑战。在 Maciel 等人(2023 年)编制的全球 12,000 个 Rrs 光谱中,只有一小部分(N = 1100)与卫星数据匹配。 这一事实突出表明,在无云的日子里,实地采样需要与卫星通过的数据保持一致,而这对于世界上云层覆盖不可预测的某些地区来说是很困难的。在这些地区,部署传感器可以作为获取原地辐射测量、光学特性和水质数据的替代方法。理想的情况是,这些部署的系统应配备用于所有所需参数的自主原位系统,并且需要部署在精心挑选的水生参考系统中,这些系统应涵盖有机物梯度、不同营养级和不同集水区。这样就可以获得用于校准和验证的匹配数据,并将其扩展到光学相似的水域。经过充分验证的大气校正可提高水质产品的准确性,而水质产品的准确性取决于生物-地球-光学模型的选择。在现有的水质监测计划中,CDOM 吸收系数(aCDOM)、总悬浮固体(TSS)浓度和浮游植物色素浓度的数据收集应作为基本变量加以重视。考虑到对许多在内陆和沿海水域工作的科学家来说,原位辐射测量数据仍不是常用的测量方法,因此有必要强调水生反射率产品的存在,如哥白尼陆地湖泊水质产品、欧洲航天局湖泊气候变化倡议和美国地质调查局水生反射率临时产品。这些产品可用于湖泊学研究,包括大数据分析的机器学习和人工智能。另一种方法是根据系统的光学水类型使用不同的大气校正方法(如 Pahlevan 等人,2021 年),并使用现有的原位水质数据来验证来自不同大气校正处理器的卫星数据的估算结果。这就承认了针对内陆水域进行大气校正的重要性,并可用于计算这一过程的不确定性。如何使在内陆和沿海水域工作的科学家与遥感科学家保持最佳一致?幸运的是,内陆水域遥感作为一门新学科正在迅速发展,最近已发起了几项倡议,以更好地传播遥感应用和产品。地球观测组织(GEO)AquaWatch、国际水协会(IWA)和世界水质联盟(WWQA)等国际网络一直在提供免费的网络研讨会,向内陆水域研究界介绍当前内陆水域遥感的最新进展。这些网络遍布全球,有助于向非遥感专家传播遥感知识。另一个网络是全球湖泊生态观测站网络(GLEON),该网络始于美国,一直在向全球扩展,目前设有一个水生遥感工作组,该工作组的成立是为了建立水生生态学家与遥感专家之间的关系。这些举措得到了在线培训的补充,世界上任何人都可以参加在线培训,如美国国家航空航天局(NASA)的应用遥感培训(ARSET)计划提供的课程。湖沼学遥感技术的不断发展和被接受,加上基于卫星的水质产品的标准化,以及用于校准和验证的数据收集的增加,为实际使用这种技术进行可靠的内陆水域监测提供了独特的机会。当水产科学界和遥感界联合起来,利用现场辐射测量和生物地球化学数据对基于遥感的水质产品进行校准和验证时,就能实现这一目标。这将使用户能够将结果与实际情况充分结合起来,并了解今后使用遥感数据的利弊得失。这些团体之间需要更多的协同作用,以协调产品,提供培训材料和最佳使用遥感数 据的指南,并根据上述新方法重新评估以前出版的资料。这种协同作用将有效帮助克服方法上的局限性,提高我们准确监测瞬息万变的内陆水域的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.00
自引率
3.80%
发文量
63
审稿时长
25 weeks
期刊介绍: Limnology and Oceanography Letters (LO-Letters) serves as a platform for communicating the latest innovative and trend-setting research in the aquatic sciences. Manuscripts submitted to LO-Letters are expected to present high-impact, cutting-edge results, discoveries, or conceptual developments across all areas of limnology and oceanography, including their integration. Selection criteria for manuscripts include their broad relevance to the field, strong empirical and conceptual foundations, succinct and elegant conclusions, and potential to advance knowledge in aquatic sciences.
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Issue Information Capitalizing on the wealth of chemical data in the accretionary structures of aquatic taxa: Opportunities from across the tree of life The Great Lakes Winter Grab: Limnological data from a multi‐institutional winter sampling campaign on the Laurentian Great Lakes Disentangling effects of droughts and heatwaves on alpine periphyton communities: A mesocosm experiment Snow removal cools a small dystrophic lake
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