A Semi-analytical Model for Remote Sensing Retrieval of Suspended Sediment Concentration in the Gulf of Bohai, China

published_or_final_versio

Comparison of machine learning algorithms for retrieval of water quality indicators in case-II waters: a case study of Hong Kong

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters

Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters

Following more than two decades of research and developments made possible through various proof-of-concept hyperspectral remote sensing missions, it has been anticipated that hyperspectral imaging would enhance the accuracy of remotely sensed in-water products. This study investigates such expected improvements and demonstrates the utility of hyperspectral radiometric measurements for the retrieval of near-surface phytoplankton properties1, i.e., phytoplankton absorption spectra (aph) and biomass evaluated through examining the concentration of chlorophyll-a (Chla). Using hyperspectral data (409–800 nm at ~5 nm resolution) and a class of neural networks known as Mixture Density Networks (MDN) (Pahlevan et al., 2020), we show that the median error in aph retrievals is reduced two-to-three times (N = 722) compared to that from heritage ocean color algorithms. The median error associated with our aph retrieval across all the visible bands varies between 20 and 30%. Similarly, Chla retrievals exhibit significant improvements (i.e., more than two times; N = 1902), with respect to existing algorithms that rely on select spectral bands. Using an independent matchup dataset acquired near-concurrently with the acquisition of the Hyperspectral Imager for the Coastal Ocean (HICO) images, the models are found to perform well, but at reduced levels due to uncertainties in the atmospheric correction. The mapped spatial distribution of Chla maps and aph spectra for selected HICO swaths further solidify MDNs as promising machine-learning models that have the potential to generate highly accurate aquatic remote sensing products in inland and coastal waters. For aph retrieval to improve further, two immediate research avenues are recommended: a) the network architecture requires additional optimization to enable a simultaneous retrieval of multiple in-water parameters (e.g., aph, Chla, absorption by colored dissolved organic matter), and b) the training dataset should be extended to enhance model generalizability. This feasibility analysis using MDNs provides strong evidence that high-quality, global hyperspectral data will open new pathways toward a better understanding of biodiversity in aquatic ecosystems

Lähi-kaugseire meetodite arendamine veekogude seisundi hindamiseks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Veekogude kvaliteedi hindamine on inimkonnale oluline olnud juba tuhandeid aastaid ja viimastel aastakümnetel on rohkem tähelepanu hakatud pöörama ka veekogude ökoloogilisele seisundile. Euroopas on veekogude kvaliteedi hindamise aluseks kaks dokumenti: Euroopa Liidu Vee Raamdirektiiv ja Euroopa Liidu Merestrateegia Raamdirektiiv. Mõlemad dokumendid sätestavad, et aastaks 2020 tuleb Euroopa Liidu veekogudes saavutada „hea“ seisund. Nende eesmärkide täitmiseks tuleb regulaarselt veekogude seisundit seirata. Kuivõrd kõikidelt veekogudelt veeproovide võtmine ja laboris analüüsimine ei ole võimalik (liigne raha ja tööjõukulu) ning lisaks ei anna sellised proovid ülevaadet veekogu seisundi parameetrite ruumilise jaotuse kohta tuleb appi võtta optilised instrumendid. Lisaks välitöödel kasutatavale optikale on Copernicus programmi raames järgnevatel aastakümnetel kättesaadav ka mitu erinevat satelliiditulemit. Nende tulemite kasutamiseks peab aga pidevalt nende täpsust hindama ja leidma täpsemaid arvutusmeetmeid, mis sobiksid konkreetsete parameetrite hindamiseks. Töö käigus tõestati, et vee optilised omadused, nagu neeldumine ja hajumine, varieeruvad Läänemere rannikuosas rohkem, kui on variatsioon ranniku ja mere keskosa vahel. Lisaks absoluutväärtuste erinevusele tuvastati ka spektraalse kuju muutusi eri piirkondade vahel. Tõestati, et elektromagnetkiirguse lähisinfrapuna piirkonda saab rakendada veekogude seires (tavaliselt eeldatakse, et selles spektripiirkonnas on veest tulev signaal null) ja eriti on see kasulik ohtralt lahustunud orgaanikat sisaldavate järvede seires. Testiti ja pakuti välja sobivaid kaugseire algoritme Läänemere vee kvaliteedi parameetrite hindamiseks. Analüüsiti erinevate spektromeetrite tulemuste varieeruvust ja leiti, et mõõtmisprotokolli korrektsel jälgimisel on erinevate sensorite tulemused küll erinevad, ent seire teostamiseks piisavalt sarnased. Lõpetuseks uuriti, millised on erinevate käsispektromeetrite potentsiaalsed rakendused.Knowing the quality of different waterbodies has been essential for human kind for thousands of years. There are two main European Union’s documents guiding the status assessment of water bodies: Water Framework Directive and Marine Strategy Framework Directive. Both of these documents state that all waterbodies in the European Union have to achieve “good” status by the year 2020. In order to fulfil this requirement, water bodies have to be monitored in regular bases. It is impossible to collect laboratory samples from every waterbody as it would be too expensive and would require many workers and still wouldn’t provide information about the spatial distribution of water quality parameters within each waterbody. Optical instruments can provide data fast and over larger areas and therefore have to be included in the monitoring programs. In addition to devices used at the in situ measurements are several satellite products that are available through Copernicus program for the coming decades. These products must, however, be constantly validated with in situ measurements. Additionally, new calculation methods have to be developed to improve the results precision. During this thesis, the variability of optical properties (like absorption and scattering) was assessed in the Baltic Sea. It was studied how much this variability influences the reflectance signal that reaches water remote sensing instruments. The performance of different set-ups and protocols of field spectrometers to collect reflectance data was assessed. The possibility to use near-infrared part of the spectrum in water remote sensing was investigated. In extreme absorbing lakes this is the only part of radiation providing us information about the water properties, but it proved to be useful also in other waterbodies. The performance of many remote sensing algorithms in retrieving water quality parameters in the Baltic Sea was tested. The possible applications for hand-held spectrometers were investigated

Optical constituent concentrations and uncertainties obtained for Case 1 and 2 waters from a spectral deconvolution model applied to in situ IOPs and radiometry

A spectral deconvolution model (SDM) for inversion of light absorption, a(λ) and backscattering, bb(λ), to estimate concentrations of chlorophyll (CHL), coloured dissolved organic material (CDOM) and non-biogenic mineral suspended solids (MSS) in offshore and shelf waters is presented. This approach exploits the spectral information embedded in the ratio bb(λ)/a(λ), without the need to know each parameter separately. The model has been applied to in situ inherent optical properties (IOPs), a(λ) and bb(λ), and to in situ remote sensing reflectance, rrs(λ). CHL, MSS and CDOM estimates are provided by propagating uncertainties in input IOPs and material-specific IOPs using a bootstrapping approach. Application of the SDM to a data set collected in the Ligurian Sea provides Mean Average Errors (MAE) of < 0.7 mg m-3 for CHL, < 0.02 m-1 for CDOM and < 0.2 g m-3 for MSS. The SDM is found to perform as well as, or in some cases better than, single parameter algorithms and other semi-analytical algorithms (SAA) for each parameter for the Ligurian Sea data set. The SDM CHL product is tested using the NOMAD, Case 1 dominated, global data set and found to perform consistently with the QAA algorithm (Lee et al. 2002) but with slightly poorer performance than standard OCx algorithms. However, the additional estimates of CDOM and MSS provided by the SDM suggest that the approach may be particularly useful for Case 2 waters. Successful retrieval of constituent concentrations with uncertainties suggests good potential to adapt this technique for satellite remote sensing

Future Retrievals of Water Column Bio-Optical Properties using the Hyperspectral Infrared Imager (HyspIRI)

Interpretation of remote sensing reflectance from coastal waters at different wavelengths of light yields valuable information about water column constituents, which in turn, gives information on a variety of processes occurring in coastal waters, such as primary production, biogeochemical cycles, sediment transport, coastal erosion, and harmful algal blooms. The Hyperspectral Infrared Imager (HyspIRI) is well suited to produce global, seasonal maps and specialized observations of coastal ecosystems and to improve our understanding of how phytoplankton communities are spatially distributed and structured, and how they function in coastal and inland waters. This paper draws from previously published studies on high-resolution, hyperspectral remote sensing of coastal and inland waters and provides an overview of how the HyspIRI mission could enable the retrieval of new aquatic biophysical products or improve the retrieval accuracy of existing satellite-derived products (e.g., inherent optical properties, phytoplankton functional types, pigment composition, chlorophyll-a concentration, etc.). The intent of this paper is to introduce the development of the HyspIRI mission to the coastal and inland remote sensing community and to provide information regarding several potential data products that were not originally part of the HyspIRI mission objectives but could be applicable to research related to coastal and inland waters. Further work toward quantitatively determining the extent and quality of these products, given the instrument and mission characteristics, is recommended

Estimation of Phytoplankton Chlorophyll-a Concentration in the Western Basin of Lake Erie Using Sentinel-2 and Sentinel-3 Data

Worldwide phenomena called algae bloom has been recently a serious matter for inland water bodies. Temporal and spatial variability of the bloom makes it di cult to use in-situ monitoring of the lakes. This study aimed to evaluate the potential of Sentinel-3 Ocean and Land Colour Instrument (OLCI) and Sentinel-2 Multispectral Instrument (MSI) data for monitoring algal blooms in Lake Erie. Chlorophyll-a (Chl-a) related products were tested using NOAA-Great Lakes Chl-a monitoring data over summer 2016 and 2017. Thematic water processor, fluorescence line height/maximum chlorophyll index (MCI) and S2 MCI, plug-in SNAP were assessed for their ability to estimate Chl-a concentration. We processed both Top of the Atmosphere (TOA) reflectance and radiance data. Results show that while FLH algorithms are limited to lakes with Chl-a < 8 mg m-3, MCI has the potential to be used effectively to monitor Chl-a concentration over eutrophic lakes. Sentinel-3 MCI is suggested for Chl-a > 20 mg m-3 and Sentinel-2 MCI for Chla > 8 mg m-3. The different Chl-a range limitation for the MCI products can be due to the different location of the maximum peak bands, 705 and 709 for MSI and OLCI sensors respectively. TOA radiances showed a signi cantly better correlation with in situ data compared to TOA reflectances which may be related to the poor pixel identi cation during the process of pixel flagging affected by the complexity of Case-2 water. Our fi nding suggests that Sentinel-2 MCI achieves better performance for Chl-a retrieval (R2 = 0.90). However, the FLH algorithms outperformed showing negative reflectance due to the shift of reflectance peak to longer wavelengths along with increasing Chl-a values. Although the algorithms show moderate performance for estimating Chl-a concentration; this study demonstrated that the new satellite sensors, OLCI and MSI, can play a signi ficant role in the monitoring of algae blooms for Lake Erie

Research trends in the use of remote sensing for inland water quality science: Moving towards multidisciplinary applications

Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10-15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists

Estimating the water quality condition of river and lake water in the Midwestern United States from its spectral characteristics

This study focuses on developing/calibrating remote sensing algorithms for water quality retrieval in Midwestern rivers and lakes. In the first part of this study, the spectral measurements collected using a hand-held spectrometer as well as water quality observations for the Wabash River and its tributary the Tippecanoe River in Indiana were used to develop empirical models for the retrieval of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using a subset of the field measurements with the rest for model validation. Spectral characteristics indicative of waters dominated by different inherent optical properties (IOPs) were identified and used as the basis of selecting bands for empirical model development. The second part of this study focuses on the calibration of an existing bio-geo-optical model for studying the spatial variability of chl, non-algal particles (NAP), and colored dissolved organic matter (CDOM) in episodic St. Joseph River plumes in southern Lake Michigan. One set of EO-1 Hyperion imagery and one set of boat-based spectrometer measurements were successfully acquired to capture episodic plume events. Coincident water quality measurements were also collected during these plume events. A database of inherent optical properties (IOPs) measurements and spectral signatures was generated and used to calibrate a bio-geo-optical model. Finally, a comprehensive spectral-biogeochemical database was developed for the Wabash River and its tributaries in Indiana by conducting field sampling of the rivers using a boat platform over different hydrologic conditions during summer 2014. In addition to the various spectral measurements taken by a handheld field spectrometer, this database includes corresponding in situ measurements of water quality parameters (chl, NAP, and CDOM), nutrients (TN, TP, dissolved organic carbon (DOC)), water-column IOPs, water depths, substrate types and bottom reflectance spectra. The temporal variability of water quality parameters and nutrients in the rivers was analyzed and studied. A look-up table (LUT) based spectrum matching methodology was applied to the collected observations in the database to simplify the retrieval of water quality parameters and make the data accessible to a wider range of end users. It was found that the ratio of the reflectance peak at the red edge (704 nm) with the local minimum caused by chlorophyll absorption at 677 nm was a strong predictor of chl concentrations (coefficient of determination ( R2) = 0.95). The reflectance peak at 704 nm was also a good predictor for TSS estimation (R2 = 0.75). In addition, we also found that reflectance within the NIR wavelengths (700–890 nm) all showed strong correlation (0.85–0.91) with TSS concentrations and generated robust models. Field measured concentrations of NAP and CDOM at 67% of the sampled sites in the St Joseph River plume fall within one standard deviation of the retrieved means using the spectrometer measurements and the calibrated bio-geo-optical model. The percentage of sites within one standard deviation (88%) is higher for the estimation of chl concentrations. Despite the dynamic nature of the observed plume and the time lag during field sampling, 77% of sampled sites were found to have field measured chl and NAP concentrations falling within one standard deviation of the Hyperion derived values. The spatial maps of water quality parameters generated from the Hyperion image provided a synoptic view of water quality conditions. Analysis highlights that concentrations of NAP, chl, and CDOM were more than three times higher in conjunction with river outflow and inside the river plumes than in ambient water. It is concluded that the storm-initiated plume is a significant source of sediments, carbon and chl to Lake Michigan. The temporal variability of water quality parameters and nutrients in the Wabash River was closely associated with hydrologic conditions, while no significant correlations existed between these parameters and streamflow for the Tippecanoe River, probably due to the two upstream reservoirs. The poor relationship between CDOM and DOC indicates that most DOC in the rivers was from human sources such as wastewater. It was also found that the source of water (surface runoff or combined sewer overflows (CSO)) to a river, water temperature, and nutrients are important factors controlling instream concentrations of phytoplankton. The LUT retrieved chl and NAP concentrations were in good agreement with field measurements with slopes close to 1.0. The average estimation errors for NAP and chl were within 4.1% and 37.7%, respectively, of independently obtained lab measurements. The CDOM levels were not well estimated and the LUT retrievals for CDOM showed large variability, probably due to the small data range collected in this study and the insensitivity of remote sensing reflectance, Rrs, to CDOM change. (Abstract shortened by ProQuest.