Teadusuuringutest rakendusteni–optiliselt keerukate vete seire satelliitsensori MERIS/ENVISAT abil

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Järved ja rannikuveed pakuvad olulisi ökosüsteemi teenuseid. Tagamaks veekeskkondade seire ja ökoloogilise seisundi hindamise on Euroopa Liidus loodud mitmeid direktiive ja regionaalseid konventsioone. Kuna vee kvaliteet võib olla muutlik nii sesoonselt kui ruumiliselt võimaldab kaugseire efektiivset seire meetodit, mille abil saab hinnata vee kvaliteedi hetkeolukorda, muutusi võrreldes varasema seisundiga ning seda ka veekogudes, mis ei ole kaetud tavaseireprogrammide raames. Käesolevas töös uuriti esimese spetsiaalselt optilistelt keerukate vete seireks loodud satelliitsensori MERIS/ENVISAT andmete kasutamisvõimalusi viie Põhja Euroopa järve ja kahe Läänemere rannikuala bio-optiliste andmete alusel. Olemasolevate MERIS standardalgoritmide õigsuse hinnang näitas, et need ei anna täpseid tulemusi veekogudes, kus on kõrge lahustunud orgaanilise aine ja klorofüll a hulk. Fütoplanktoni parameetrite (klorofüll a, sinivetikate biomass, fütoplanktoni biomass) hindamiseks kasutati punases ja lähisinfrapunases spektriosas töötavat spektraalset indeksit, mis kalibreeriti kohalikesse oludesse. Kuna indeks on rakendatav MERIS L1b andmetele, lubab see kvantitatiivselt hinnata vee kvaliteedi parameetreid sinivetika õitsengute korral, mille puhul MERIS standardalgoritmid ei tööta. Hindamaks kaugseire andmetest veealust valgusvälja, millest sõltub veealuste organismide elutegevus, loodi kaalufunktsioonidel põhinev kombineeritud kanalisuhte algoritm, mis selgemate vete puhul kasutab kanalite 490/709 suhet ning sogasemate puhul 560/70 ning hindab edukalt valguse difuusset nõrgenemiskoefitsienti, Kd(490), satelliidiandmetest. Secchi sügavuse hindamiseks andis parimaid tulemusi algoritm, mis võttis pikselhaaval sisendiks satellidiandmetest arvutatud diffusse ja summaarse nõrgenemiskoefitsiendi ning peegeldusteguri väärtused üle nähtava laineala. Töös arendatud algoritmid rakendati MERIS arhiivi 2002–2011 andmetele hindamaks erinevate järvede ökoloogilist seisundit nii nagu on nõutud EL veepoliitika raamdirektiivi poolt. Tulemused näitasid, et kaugseire andmeid saab kasutada täiendava infoallikana ökoloogilise seisundi hindamisel. Väljatöötatud algoritmid ja rakendused on kohandatavad 2016. aasta veebruaris tööd alustanud Sentinel-3/OLCI andmetele, mille abil on optiliselt keerukate vete seire kosmosest võimalik vähemalt aastani 2029.Lakes and coastal areas provide a wide range of essential ecosystem services. Various directives and regional conventions have been established to ensure the monitoring and assessment of the ecological status of the aquatic ecosystems. Since water quality can have rapid changes in temporal and spatial scale, remote sensing can provide a cost-effective approach to assess the current and derive historic water quality information also for waterbodies that have not been part of conventional monitoring programmes. This thesis presents research about applications for MERIS/ENVISAT data in order to monitor optically-complex aquatic environment such as inland and coastal waters on the basis of bio-optical data from five North European lakes and two Baltic Sea coastal sites. The validation of MERIS standard water quality products indicated their unsuitability for waters with high amounts of chlorophyll a and humic substances. To map the phytoplankton parameters (CHL, cyanobacterial biomass, phytoplankton biomass) a spectral index which operates on red and near-infrared bands was used and calibrated to local conditions. So, this index allows derivation of the water quality parameters quantitatively in case of highly scattering cyanobacterial blooms, which is not possible with standard algorithms. To estimate underwater light field via transparency, an empirical combined band ratio algorithm was developed which switches from various band ratios based on the water transparency and determines the diffuse attenuation coefficient Kd(490) with high accuracy. Additionally, Secchi depth can be also estimated reliably via satellite derived inputs of diffuse and total beam attenuation coefficients and reflectance over visible wavelengths. The developed algorithms were applied on the MERIS archive from 2002 to 2011 to estimate the ecological status in lakes as required by the EU Water Framework Directive which showed that remote sensing products could be used as an additional source of information for assessment and reporting purposes. Despite the study in this thesis is based on the MERIS/ENVISAT data, the developed algorithms and methods can be applied on new Sentinel3/OLCI data that will provide EO data over optically complex waters at least until 2029

Keskkonnakaugseire

BeSt programmi toetusel loodud e-kursuse "Keskkonnakaugseire" õppematerjalid

A chlorophyll-a algorithm for Landsat-8 based on mixture density networks

Material suplementario disponible en:Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDNA). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDNA model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDNA model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDNB) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρs ). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDNB models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing

Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N \u3e 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (Rrs), (b) retrieval of particulate backscattering (bbp) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from bbp via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m3], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to \u3e100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to \u3c20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads

ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters

Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA – ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (̂ρw). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. This volume of data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errors in ̂ρw(560) and ̂ρw(664) were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15–30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concentration of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20–30% uncertainties in ̂ρw(490 ≤ λ ≤ 743 nm) yielded 25–70% uncertainties in derived Chla and TSS products for topperforming AC processors. We summarize our results using performance matrices guiding the satellite user community through the OWT-specific relative performance of AC processors. Our analysis stresses the need for better representation of aerosols, particularly absorbing ones, and improvements in corrections for sky- (or sun-) glint and adjacency effects, in order to achieve higher quality downstream products in freshwater and coastal ecosystems

Satellite-assisted monitoring of water quality to support the implementation of the Water Framework Directive

The EU Water Framework Directive1 (WFD) is an ambitious legislation framework to achieve good ecological and chemical status for all surface waters and good quantitative and chemical status for groundwater by 2027. A total of 111,062 surface waterbodies are presently reported on under the Directive, 46% of which are actively monitored for ecological status. Of these waterbodies 80% are rivers, 16% are lakes, and 4% are coastal and transitional waters. In the last assessment, 4% (4,442) of waterbodies still had unknown ecological status, while in 23% monitoring did not include in situ water sampling to support ecological status assessment2. For individual (mainly biological) assessment criteria the proportion of waterbodies without observation data is much larger; the full scope of monitoring under the WFD is therefore still far from being realised. At the same time, 60% of surface waters did not achieve ‘good’ status in the second river basin management plan and waterbodies in Europe are considered to be at high risk of having poor water quality based on combined microbial, physical and physicochemical indicators3

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring

Retrieval of Chlorophyll a from Sentinel-2 MSI Data for the European Union Water Framework Directive Reporting Purposes

The European Parliament and The Council of the European Union have established the Water Framework Directive (2000/60/EC) for all European Union member states to achieve, at least, “good” ecological status of all water bodies larger than 50 hectares in Europe. The MultiSpectral Instrument onboard European Space Agency satellite Sentinel-2 has suitable 10, 20, 60 m spatial resolution to monitor most of the Estonian lakes as required by the Water Framework Directive. The study aims to analyze the suitability of Sentinel-2 MultiSpectral Instrument data to monitor water quality in inland waters. This consists of testing various atmospheric correction processors to remove the influence of atmosphere and comparing and developing chlorophyll a algorithms to estimate the ecological status of water in Estonian lakes. This study shows that the Sentinel-2 MultiSpectral Instrument is suitable for estimating chlorophyll a in water bodies and tracking the spatial and temporal dynamics in the lakes. However, atmospheric corrections are sensitive to surrounding land and often fail in narrow and small lakes. Due to that, deriving satellite-based chlorophyll a is not possible in every case, but initial results show the Sentinel-2 MultiSpectral Instrument could still provide complementary information to in situ data to support Water Framework Directive monitoring requirements

Detecting cyanobacterial blooms in large North European lakes using the Maximum Chlorophyll Index

The Maximum Chlorophyll Index (MCI), developed for the MERIS sensor processing scheme, is used to investigate the seasonaldynamics, spatial distribution, and coverage of cyanobacterial blooms over Lake Peipsi (Estonia/Russia) and Lake Võrtsjärv(Estonia). In these optically complex waters, the amounts of suspended matter and dissolved organic matter vary greatly andindependently of the phytoplankton biomass. We demonstrate that MCI is a useful, new tool for detecting and estimating cyanobacterialbiomass (R2 = 0.73), phytoplankton biomass (R2 = 0.70) and chlorophyll a concentration (R2 = 0.64). The MCI-derivedresults are consistent with known patterns of phytoplankton dynamics in these lakes, whose optical properties are in the same range as in many coastalregions of the Baltic Sea