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2. Estimation of the Biogeochemical and Physical Properties of Lakes Based on Remote Sensing and Artificial Intelligence Applications.

Author

Toming, Kaire, Liu, Hui, Soomets, Tuuli, Uuemaa, Evelyn, Nõges, Tiina, and Kutser, Tiit

Subjects
REMOTE sensing, ARTIFICIAL intelligence, OPTICAL remote sensing, WATER quality, BIOCHEMICAL oxygen demand, FECAL contamination
Abstract

Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO4), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O2), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Primaarproduktsiooni hindamine kaugseire abil

Author

Soomets, Tuuli and Kutser, Tiit

Subjects
radioaktiivse süsiniku ( 14C) meetod, fluorestsentsi kineetikameetod, kaugseire, primaarproduktsioon, hapniku stabiilsete isotoopide (16O, 17O ja 18O) meetod, fotosüntees
Abstract

Deliverable 2.2.2. Estimating primary production from remote sensing., Kogu ökosüsteemi produktiivsuse alus on primaarproduktsioon ehk esmasproduktsioon (PP, primary production). See on autotroofide poolt fotosünteesiprotsessis toodetud orgaaniline aine (seotud energia hulk moodustatud anorgaanilisest süsinikust ja mineraalainetest). Veekogudes on esmasprodutsentideks põhiliselt mikroorganismid - vetikad ja tsüanobakterid (sinivetikad). PP mõjutavad kõige rohkem valgus, toiteainete hulk ja temperatuur. Muudest mõjuritest on olulised vee segunemine ning vaenlaste (zooplanktoni) valikuline toitumine. Vee liikumisest tingitult võivad vetikad kanduda sügavale, kus neil pole võimalik kasvada. PP hindamiseks on mitu võimalust. Veekogudes kasutatakse neist sagedamini nelja: hapniku- (O2–) meetod, radioaktiivse süsiniku ( 14C) meetod, hapniku stabiilsete isotoopide (16O, 17O ja 18O) meetod ning fluorestsentsi kineetikameetod. Kaks esimest meetodit põhinevad ainevahetuse otsesel mõõtmisel. Kõige täpsemaks loetakse 14C meetodi (mida kasutati ka antud töös), kuna selle meetodi suur tundlikkus võimaldab kasutada lühikest ekspositsiooniaega ja teha mõõtmisi ka väheproduktiivsetes vetes. Tulemus ei sõltu lämmastiku vormist, kuid sõltub ekspositsiooni pikkusest. Mõõdetud PP ühikuks on kas milligrammi süsinikku kuupmeetris (mg C m-3 ) või milligrammi süsinikku ruutmeetril (mg C m-2 ). Viimane näitab, kui palju toodetakse orgaanilist ainet terves veesambas ja on nimetatud integraalseks primaarproduktsiooniks PPint. Kuna kõik eelmainitud meetodid on väga komplekssed, keerulised, ajamahukad ning kulukad teostada, siis PP hindamine kaugseire abil võiks pakkuda tõhusat lahendust, seda eriti PP hindamisel üle suuremate regioonide. Kaugseire annab infot kogu veekogu kohta, vastupidiselt in-situ mõõtmistele, mis annab infot vaid ühe ruumipunkti kohta.

Published
2021
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15. Remote Sensing of Black Lakes and Using 810 nm Reflectance Peak for Retrieving Water Quality Parameters of Optically Complex Waters

Author

Kutser, Tiit, Paavel, Birgot, Verpoorter, Charles, Ligi, Martin, Soomets, Tuuli, Toming, Kaire, Casal, Gema, Kutser, Tiit, Paavel, Birgot, Verpoorter, Charles, Ligi, Martin, Soomets, Tuuli, Toming, Kaire, and Casal, Gema

Abstract

Many lakes in boreal and arctic regions have high concentrations of CDOM (coloured dissolved organic matter). Remote sensing of such lakes is complicated due to very low water leaving signals. There are extreme (black) lakes where the water reflectance values are negligible in almost entire visible part of spectrum (400-700 nm) due to the absorption by CDOM. In these lakes, the only water-leaving signal detectable by remote sensing sensors occurs as two peaksnear 710 nm and 810 nm. The first peak has been widely used in remote sensing of eutrophic waters for more than two decades. We show on the example of field radiometry data collected in Estonian and Swedish lakes that the height of the 810 nm peak can also be used in retrieving water constituents from remote sensing data. This is important especially in black lakes where the height of the 710 nm peak is still affected by CDOM. We have shown that the 810 nm peak can be used also in remote sensing of a wide variety of lakes. The 810 nm peak is caused by combined effect of slight decrease in absorption by water molecules and backscattering from particulate material in the water. Phytoplankton was the dominant particulate material in most of the studied lakes. Therefore, the height of the 810 peak was in good correlation with all proxies of phytoplankton biomasschlorophyll-a (R-2 = 0.77), total suspended matter (R-2 = 0.70), and suspended particulate organic matter (R-2 = 0.68). There was no correlation between the peak height and the suspended particulate inorganic matter. Satellite sensors with sufficient spatial and radiometric resolution for mapping lake water quality (Landsat 8 OLI and Sentinel-2 MSI) were launched recently. In order to test whether these satellites can capture the 810 nm peak we simulated the spectral performance of these two satellites from field radiometry data. Actual satellite imagery from a black lake was also used to study whether these sensors can detect the peak despite their band conf

Published
2016
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17. Testing the performance of empirical remote sensing algorithms in the Baltic Sea waters with modelled and in situreflectance data

Author

Ligi, Martin, Kutser, Tiit, Kallio, Kari, Attila, Jenni, Koponen, Sampsa, Paavel, Birgot, Soomets, Tuuli, and Reinart, Anu

Abstract

Remote sensing studies published up to now show that the performance of empirical (band-ratio type) algorithms in different parts of the Baltic Sea is highly variable. Best performing algorithms are different in the different regions of the Baltic Sea. Moreover, there is indication that the algorithms have to be seasonal as the optical properties of phytoplankton assemblages dominating in spring and summer are different. We modelled 15,600 reflectance spectra using HydroLight radiative transfer model to test 58 previously published empirical algorithms. 7200 of the spectra were modelled using specific inherent optical properties (SIOPs) of the open parts of the Baltic Sea in summer and 8400 with SIOPs of spring season. Concentration range of chlorophyll-a, coloured dissolved organic matter (CDOM) and suspended matter used in the model simulations were based on the actually measured values available in literature. For each optically active constituent we added one concentration below actually measured minimum and one concentration above the actually measured maximum value in order to test the performance of the algorithms in wider range. 77 in situreflectance spectra from rocky (Sweden) and sandy (Estonia, Latvia) coastal areas were used to evaluate the performance of the algorithms also in coastal waters. Seasonal differences in the algorithm performance were confirmed but we found also algorithms that can be used in both spring and summer conditions. The algorithms that use bands available on OLCI, launched in February 2016, are highlighted as this sensor will be available for Baltic Sea monitoring for coming decades.

Published
2017
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18. Comparison of Lake Optical Water Types Derived from Sentinel-2 and Sentinel-3.

Author

Soomets, Tuuli, Uudeberg, Kristi, Jakovels, Dainis, Zagars, Matiss, Reinart, Anu, Brauns, Agris, and Kutser, Tiit

Subjects
*DISSOLVED organic matter, *WATER quality, *DRINKING water, *WATER supply, *TERRITORIAL waters, *LAKE management
Abstract

Inland waters play a critical role in our drinking water supply. Additionally, they are important providers of food and recreation possibilities. Inland waters are known to be optically complex and more diverse than marine or ocean waters. The optical properties of natural waters are influenced by three different and independent sources: phytoplankton, suspended matter, and colored dissolved organic matter. Thus, the remote sensing of these waters is more challenging. Different types of waters need different approaches to obtain correct water quality products; therefore, the first step in remote sensing of lakes should be the classification of the water types. The classification of optical water types (OWTs) is based on the differences in the reflectance spectra of the lake water. This classification groups lake and coastal waters into five optical classes: Clear, Moderate, Turbid, Very Turbid, and Brown. We studied the OWTs in three different Latvian lakes: Burtnieks, Lubans, and Razna, and in a large Estonian lake, Lake Võrtsjärv. The primary goal of this study was a comparison of two different Copernicus optical instrument data for optical classification in lakes: Ocean and Land Color Instrument (OLCI) on Sentinel-3 and Multispectral Instrument (MSI) on Sentinel-2. We found that both satellite OWT classifications in lakes were comparable (R2 = 0.74). We were also able to study the spatial and temporal changes in the OWTs of the study lakes during 2017. The comparison between two satellites was carried out to understand if the classification of the OWTs with both satellites is compatible. Our results could give us not only a better overview of the changes in the lake water by studying the temporal and spatial variability of the OWTs, but also possibly better retrieval of Level 2 satellite products when using OWT guided approach. [ABSTRACT FROM AUTHOR]

Published
2019
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19. Spatial and temporal changes of primary production in a deep peri-alpine lake

Author

Soomets, Tuuli, Kutser, Tiit, Wüest, Alfred Johny, and Bouffard, Damien

Subjects
MERIS, Earth observation, Primary production, Bio-optics, Lake Geneva, Remote sensing
Abstract

Lake productivity is fundamental to biogeochemical budgets as well as estimating ecological state and predicting future development. Combining modelling with Earth Observation data facilitates a new perspective for studying lake primary production. In this study, primary production was modelled in the large Lake Geneva using the MEdium Resolution Imaging Spectrometer (MERIS) image archive for 2002–2012. We used a semi-empirical model that estimates primary production as a function of photosynthetically absorbed radiation and quantum yield of carbon fixation. The necessary input parameters of the model—concentration of chlorophyll a, downwelling irradiance, and the diffuse attenuation coefficient—were obtained from MERIS products. The primary production maps allow us to study decennial temporal (with daily frequency) and spatial changes in this lake that a single sample point cannot provide. Modelled estimates agreed with in situ results (R2 = 0.68) and showed a decreasing trend (∼27%) in production in Lake Geneva for the selected decade. Yet, in situ monitoring measurements missed the general increase of productivity near the incoming Rhône River. We show that the temporal and spatial resolution provided by satellite observations allows estimates of primary production at the basin-scale. The phytoplankton annual primary production was estimated as ∼302 (SD 20) g C m−2 yr−1 for Lake Geneva for 2003 to 2011. This study demonstrates that maps of primary production can be obtained even with reduced resolution (1200 m) MERIS data and relatively simple methods, and thereby calls for deeper integration of remote sensing products into conventional in situ observation approaches.

20. Kaugseire kasutamise võimekusest klorofüll a kontsentratsioonide hindamisel erinevates Eesti järvedes

Author

Pettai, Kerli, Tuvikene, Lea, and Soomets, Tuuli

Subjects
Eesti, kaugseire, magistritööd, järved, klorofüll
Abstract

Kaugseirega on Eestis eelnevalt uuritud vaid Peipsit ja Võrtsjärve. Ülejäänud järvi ei ole varem kaugseire abil uuritud, kuna siseveekogude seireks pole varasemate satelliitide sensorite spektraalne ja ruumiline lahutus olnud piisav. 2013. aastal alustas missiooni Landsat 8, mille pardal oleva sensori OLI lahutusvõime on 15 x 30 meetrit. Töö eesmärgiks on tuvastada, kas kaugseire abil on võimalik Eesti erinevates järvedes veekvaliteeti hinnata. 2014. ja 2015. aastal koguti pilvevabadel satelliidi ülelennu päevadel veeproovid ning laboris mõõdeti klorofüll a, koguheljum ja värvunud lahustunud orgaaniline aine. 2013. aasta Peipsi kontaktmõõtmiste tulemused saadi Tuuli Soometsa käest. Töös kasutati Landsat 8 OLI sensori L2 andmeid, mis töödeldi vabatarkvara programmis SeaDAS. Antud programmi abil leiti klorofüll a kontsentratsioonid, mida võrreldi kontaktmõõtmiste käigus saadud andmetega. Lisaks Peipsile ja Võrtsjärvele uuriti ka Kokora Mustjärve, Verevi järve, Harku järve ja Äntu Sinijärve. Saadud OLI klorofüll a kontsentratsioonid sobisid in situ andmetega, olgugi, et kahe aasta jooksul oli vaid üksikuid sobilikke mõõtmispäevi. Harku järve ja Äntu Sinijärve kohta tulemused puuduvad, kuna satelliit neid ei tuvastanud. Tulemused näitavad, et kaugseire andmed sobivad hästi tavamonitooringut täiendama, andes võimaluse kontaktmõõtmiste käigus märkamata jäänud vetikaõitsenguid tuvastada. Lake Võrtsjärv and Lake Peipus, the two biggest lakes in Estonia, are the only Estonian lakes that have been studied using remote sensing because the spectral and spatial resolution of previous satellite sensors has been insufficient for examining smaller lakes. In 2013 Landsat 8 started its mission with the Operational Land Imager (OLI) sensor on board, the spatial resolution of which is 15 x 30 meters. The goal of this thesis is to find out whether it is possible to determine the quality of water in different Estonian lakes using remote sensing. In 2014 and 2015 water samples were collected on cloudless satellite flyover days and Chlorophyll a, TSS and CDOM was measured in the laboratory. The in situ results for Lake Peipus in the year 2013 were obtained from Tuuli Soomets. In the research paper the raw data from the Landsat 8 sensor was processed into L2 data in SeaDAS freeware software. This software was used to determine the concentration of chlorophyll a which was then compared to the corresponding results obtained in situ. In addition to Lake Peipus and Lake Võrtsjärv Kokora Mustjärv, Lake Verevi, Lake Harku and Äntu Sinijärv were also tested. The obtained OLI concentration of chlorophyll a matched with the data obtained in situ, despite there having been only a few suitable test days during the 2 years. There is no data on Lake Harku and Äntu Sinijärv as they were not identified by the satellite. The results indicate that the data obtained by remote sensing can successfully be used to supplement conventional monitoring as it provides the opportunity to identify algal bloom that may have gone unnoticed during in situ tests.

Published
2016

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