Optical types of inland and coastal waters

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in‐water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions

Optiliste veetüüpide põhine lähenemine sise- ja rannikuvee veekvaliteedi hindamiseks

Väitekirja elektrooniline versioon ei sisalda publikatsiooneInimestele on meeldinud ajast aega elada seal, kus maa ja vesi kohtuvad. Mistõttu on järvede, jõgede ja rannikualade lähedal inimtegevuse mõju suurenenud, mis omakorda põhjustab veekogude seisundi muutumist ning loob vajaduse veekogude operatiivseks seireks. Enamasti põhinevad veekogude seireprogrammid veekogudes teostatud punktmõõtmistel. See meetod aga ei suuda kajastada kogu veekogu kiiresti muutuvaid omadusi ja reaalset seisundit. Seetõttu on oluline lisaks punktmõõtmistele rakendada veekeskkonna operatiivse jälgimise meetodeid, millest kaugseire on üks võimsamaid. Kaugseire pakub tõhusaid viise veekvaliteedi ruumiliste ja ajaliste erinevuste jälgimiseks. Euroopa Liidu ja Euroopa Kosmoseagentuuri Copernicus programmi raames loodud Sentinel-2 ja Sentinel-3 seeria satelliitide hea ruumilise, ajalise ja spektraalse lahutusega andmete tasuta kättesaadavus on loonud reaalse võimaluse sise- ja rannikuvete seires operatiivselt kasutada täiendavalt satelliitandmeid. Need andmed võimaldavad jälgida kogu veekogu ajalist ja ruumilist muutlikkust ning seirata ka raskesti ligipääsetavaid veekogusid. Sise- ja rannikuveed on optiliselt keerukad, sest vee optilised omadused on mõjutatud sõltumatult erinevate optiliselt aktiivsete ainete poolt. Seetõttu standardsed kaugseire algoritmid veekvaliteedi hindamiseks neis veekogudes tihti ei tööta. Doktoritöö tulemusena tutvustati optiliste veetüüpide põhist lähenemist sise- ja rannikuvete veekvaliteedi parameetrite hindamiseks kaugseireandmete põhjal. Eelnimetatud meetod võtab arvesse vee optilisi omadusi ega piiritle ennast konkreetse veekoguga, seetõttu on tulemused rakendatavad kõigil sarnaste optiliste omadustega veekogudel üle maailma.Humans have long enjoyed living where land and water meet. At the same time, the impact of human activities close to lakes, rivers, and coastal areas has increased, which has caused the deterioration of water bodies. Therefore, the state of a water body requires constant monitoring to assess the magnitude of the impact of human activity and to respond when needed. Traditional water monitoring programs are mainly based on in situ measurements; however, considering that water bodies are dynamic in nature, this method may not reflect the status of the whole water body. Therefore, in addition to traditional monitoring, it is important to implement methods that allow more operative monitoring of the aquatic environment. Remote sensing offers effective ways to observe spatial and temporal variations in water quality. The free availability of data with high spatial, temporal and spectral resolution from the Sentinel-2 and Sentinel-3 family satellites launched under the European Union and the European Space Agency Copernicus programme has created a real opportunity for satellite data being used operationally for additional water quality monitoring for inland and coastal waters. Such waters are optically complex, as they are independently influenced by different optically significant constituents. Therefore, standard remote sensing algorithms to estimate water quality often fail in these waters. As a result of the thesis, an optical water type guided approach to estimate water quality in inland and coastal waters using remote sensing data was presented. The method considers the optical properties of water but does not limit itself to a particular water body. So, results are applicable to all the water bodies with similar optical properties of water.https://www.ester.ee/record=b534022

Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

Determining the dominant optically active substances in water bodies via classification can improve the accuracy of bio-optical and water quality parameters estimated by remote sensing. This study provides four robust centroid sets from in situ remote sensing reflectance (Rrs (λ)) data presenting typical optical types obtained by plugging different similarity measures into fuzzy c-means (FCM) clustering. Four typical types of waters were studied: (1) highly mixed eutrophic waters, with the proportion of absorption of colored dissolved organic matter (CDOM), phytoplankton, and non-living particulate matter at approximately 20%, 20%, and 60% respectively; (2) CDOM-dominated relatively clear waters, with approximately 45% by proportion of CDOM absorption; (3) nonliving solids-dominated waters, with approximately 88% by proportion of absorption of nonliving particulate matter; and (4) cyanobacteria-composed scum. We also simulated spectra from seven ocean color satellite sensors to assess their classification ability. POLarization and Directionality of the Earth's Reflectances (POLDER), Sentinel-2A, and MEdium Resolution Imaging Spectrometer (MERIS) were found to perform better than the rest. Further, a classification tree for MERIS, in which the characteristics of Rrs (709)/Rrs (681), Rrs (560)/Rrs (709), Rrs (560)/Rrs (620), and Rrs (709)/Rrs (761) are integrated, is also proposed in this paper. The overall accuracy and Kappa coefficient of the proposed classification tree are 76.2% and 0.632, respectively