Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern Brazil

Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates

Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers

The nearshore coastal ocean is one of the most dynamic and biologically productive regions on our planet, supporting a wide range of ecosystem services. It is also one of the most vulnerable regions, increasingly exposed to anthropogenic pressure. In the context of climate change, monitoring changes in nearshore coastal waters requires systematic and sustained observations of key essential climate variables (ECV), one of which is sea surface temperature (SST). As temperature influences physical, chemical and biological processes within coastal systems, accurate monitoring is crucial for detecting change. SST is an ECV that can be measured systematically from satellites. Yet, owing to a lack of adequate in situ data, the accuracy and precision of satellite SST at the coastline are not well known. In a prior study, we attempted to address this by taking advantage of in situ SST measurements collected by a group of surfers. Here, we make use of a three year time-series (2014–2017) of in situ water temperature measurements collected using a temperature logger (recording every 30 min) deployed within a kelp forest (∼3 m below chart datum) at a subtidal rocky reef site near Plymouth, UK. We compared the temperature measurements with three other independent in situ SST datasets in the region, from two autonomous buoys located ∼7 km and ∼33 km from the coastline, and from a group of surfers at two beaches near the kelp site. The three datasets showed good agreement, with discrepancies consistent with the spatial separation of the sites. The in situ SST measurements collected from the kelp site and the two autonomous buoys were matched with operational Advanced Very High Resolution Radiometer (AVHRR) EO SST passes, all within 1 h of the in situ data. By extracting data from the closest satellite pixel to the three sites, we observed a significant reduction in the performance of AVHRR at retrieving SST at the coastline, with root mean square differences at the kelp site over twice that observed at the two offshore buoys. Comparing the in situ water temperature data with pixels surrounding the kelp site revealed the performance of the satellite data improves when moving two to three pixels offshore and that this improvement was better when using an SST algorithm that treats each pixel independently in the retrieval process. At the three sites, we related differences between satellite and in situ SST data with a suite of atmospheric variables, collected from a nearby atmospheric observatory, and a high temporal resolution land surface temperature (LST) dataset. We found that differences between satellite and in situ SST at the coastline (kelp site) were well correlated with LST and solar zenith angle; implying contamination of the pixel by land is the principal cause of these larger differences at the coastline, as opposed to issues with atmospheric correction. This contamination could be either from land directly within the pixel, potentially impacted by errors in geo-location, or possibly through thermal adjacency effects. Our results demonstrate the value of using benthic temperature loggers for evaluating satellite SST data in coastal regions, and highlight issues with retrievals at the coastline that may inform future improvements in operational products

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 coastal lagoon tidal flooding and repletion with multidate ASTER thermal imagery

Coastal lagoons mix inflowing freshwater and tidal marine waters in complex spatial patterns. This project sought to detect and measure temperature and spatial variability of flood tides for a constricted coastal lagoon using multitemporal remote sensing. Advanced Spaceborne Thermal Emission Radiometer (ASTER) thermal infrared data provided estimates of surface temperature for delineation of repletion zones in portions of Chincoteague Bay, Virginia. ASTER high spatial resolution sea-surface temperature imagery in conjunction with in situ observations and tidal predictions helped determine the optimal seasonal data for analyses. The selected time series ASTER satellite data sets were analyzed at different tidal phases and seasons in 2004–2006. Skin surface temperatures of ocean and estuarine waters were differentiated by flood tidal penetration and ebb flows. Spatially variable tidal flood penetration was evaluated using discrete seed-pixel area analysis and time series Principal Components Analysis. Results from these techniques provide spatial extent and variability dynamics of tidal repletion, flushing, and mixing, important factors in eutrophication assessment, water quality and resource monitoring, and application of hydrodynamic modeling for coastal estuary science and management

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelf-sea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea continuum including the Danube-Black Sea is considerable

Development of Satellite-Assisted Forecasting System for Oyster Norovirus Outbreaks

Norovirus outbreaks can cause the closure of oyster harvesting waters and acute gastroenteritis in humans associated with consumption of contaminated raw oysters. The overall goal of this study was to develop a satellite-assisted forecasting system for oyster norovirus outbreaks. The forecasting system is comprised of three components: (1) satellite algorithms for retrieval of environmental variables, including salinity, temperature, and gage height, (2) an Artificial Neural Network (ANN) based model, called NORF model, for predicting relative risk levels of oyster norovirus outbreaks, and (3) a mapping method for visualizing spatial distributions of norovirus outbreak risks in oyster harvest areas along Louisiana coast. The new satellite algorithms, characterized with linear correlation coefficient ranging from 0.7898 to 0.9076, make it possible to produce spatially distributed daily data with a high resolution (1 kilometer) for salinity, temperature, and gage height in coastal waters. Findings from this study suggest that oyster norovirus outbreaks are predictable, and in Louisiana oyster harvest areas, the NORF model predicted historical outbreaks from 1994 - 2014 without any confirmed false positive or false negative predictions when the estimated relative risk level was \u3e 0.6, while no outbreak occurred when the risk level was \u3c 0.5. However, more outbreak data are needed to confirm the threshold for norovirus outbreaks. Gage height and temperature were the most important environmental predictors of oyster norovirus outbreaks while wind, rainfall, and salinity also predicted norovirus outbreaks. The ability to predict oyster norovirus outbreaks at their onset makes it possible to prevent or at least reduce the risk of norovirus outbreaks by closing potentially affected oyster beds. By combining the NORF model with the remote sensing algorithms created in this dissertation, it is possible to map oyster norovirus outbreak risks in all oyster growing waters and particularly in the areas without direct measurements of relevant environmental variables, greatly expanding the coverage and enhancing the effectiveness of oyster monitoring programs. The hot spot (risk) maps, constructed using the methods developed in this dissertation, make it possible for oyster monitoring programs to manage oyster harvest waters more efficiently by focusing on hot spot areas with limited resources

Retrieval Of Environmental Parameters Over Water Areas Using MODIS Data

In this study, new and simple algorithms to retrieve cirrus cloud, sediment and aerosol optical depth (AOD) from Moderate Resolution Radiospectrometer (MODIS) imagery is suggested and demonstrated. Techniques used in this thesis are totally new and never been used before by others researchers. The presences of cirrus cloud have been detected utilizing gradient technique algorithm. The algorithm is based on the gradient connecting the 1.38 and 1.24 μm lines of the log– log graph of apparent reflectance against the MODIS wavelength. This algorithm has been tested over the Yellow Sea, Gulf of Martaban, Mediterranean Sea and Atlantic Ocean. The result of this algorithm is then compared with the band ratio technique and the accuracy was determined with percentage difference below 10%. The presence of sediment reflectance in the MODIS imagery not only saturate the ocean color channels, but also enhance the reflectance of near infrared channels used in the atmospheric correction algorithm. This leads to the overestimation of the aerosol contributions and underestimation of the derived water-leaving reflectance in the visible channels. In this study, a new and simple method to detect and mask the presence of sediment reflectance in the MODIS imagery is proposed. The algorithm is based on the difference of the gradient of the line connecting the 0.47 and 2.13 μm channels and 0.47 and 0.66 μm channels of a log–log graph of the apparent reflectance values against their MODIS wavelengths. A sample results over Yellow Sea, Gulf of Martaban and Arabian Sea was demonstrated. The accuracy of the algorithm was tested and determined

Optical remote sensing of water quality parameters retrieval in the Barents Sea

This thesis addresses various aspects of monitoring water quality indicators (WQIs) using optical remote sensing technologies. The dynamic nature of aquatic systems necessitate frequent monitoring at high spatial resolution. Machine learning (ML)-based algorithms are becoming increasingly common for these applications. ML algorithms are required to be trained by a significant amount of training data, and their accuracy depends on the performance of the atmospheric correction (AC) algorithm being used for correcting atmospheric effects. AC over open oceanic waters generally performs reasonably well; however, limitations still exist over inland and coastal waters. AC becomes more challenging in the high north waters, such as the Barents Sea, due to the unique in-water optical properties at high latitudes, long ray pathways, as well as the scattering of light from neighboring sea ice into the sensors’ field of view adjacent to ice-infested waters. To address these challenges, we evaluated the performances of state-of-the-art AC algorithms applied to the high-resolution satellite sensors Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI), both for high-north (Paper II) and for global inland and coastal waters (Paper III). Using atmospherically corrected remote sensing reflectance (Rrs ) products, estimated after applying the top performing AC algorithm, we present a new bandpass adjustment (BA) method for spectral harmonization of Rrs products from OLI and MSI. This harmonization will enable an increased number of ocean color (OC) observations and, hence, a larger amount of training data. The BA model is based on neural networks (NNs), which perform a pixel-by-pixel transformation of MSI-derived Rrs to that of OLI equivalent for their common bands. In addition, to accurately retrieve concentrations of Chlorophyll-a (Chl-a) and Color Dissolved Organic Matter (CDOM) from remotely sensed data, we propose in the thesis (Paper 1) an NN-based WQI retrieval model dubbed Ocean Color Net (OCN). Our results indicate that Rrs retrieved via the Acolite Dark Spectrum Fitting (DSF) method is in best agreement with in-situ Rrs observations in the Barents Sea compared to the other methods. The median absolute percentage difference (MAPD) in the blue-green bands ranges from 9% to 25%. In the case of inland and coastal waters (globally), we found that OC-SMART is the top performer, with MAPD Rrs products for varying optical regimes than previously presented methods. Additionally, to improve the analysis of remote sensing spectral data, we introduce a new spatial window-based match-up data set creation method which increases the training data set and allows for better tuning of regression models. Based on comparisons with in-water measured Chl-a profiles in the Barents Sea, our analysis indicates that the MSI-derived Rrs products are more sensitive to the depth-integrated Chl-a contents than near-surface Chl-a values (Paper I). In the case of inland and coastal waters, our study shows that using combined OLI and BA MSI-derived Rrs match-ups results in considerable improvement in the retrieval of WQIs (Paper III). The obtained results for the datasets used in this thesis illustrates that the proposed OCN algorithm shows better performance in retrieving WQIs than other semi-empirical algorithms such as the band ratio-based algorithm, the ML-based Gaussian Process Regression (GPR), as well as the globally trained Case-2 Regional/Coast Colour (C2RCC) processing chain model C2RCC-networks, and OC-SMART. The work in this thesis contributes to ongoing research in developing new methods for merging data products from multiple OC missions for increased coverage and the number of optical observations. The developed algorithms are validated in various environmental and aquatic conditions and have the potential to contribute to accurate and consistent retrievals of in-water constituents from high-resolution satellite sensors

Improving Chlorophyll-a Estimation from Sentinel-2 (MSI) in the Barents Sea using Machine Learning

This article addresses methodologies for remote sensing of ocean Chlorophyll-a (Chl-a), with emphasis on the Barents Sea. We aim at improving the monitoring capacity by integrating in situ Chl-a observations and optical remote sensing to locally train machine learning (ML) models. For this purpose, in situ measurements of Chl-a ranging from 0.014–10.81 mg/m 3 , collected for the years 2016–2018, were used to train and validate models. To accurately estimate Chl-a, we propose to use additional information on pigment content within the productive column by matching the depth-integrated Chl-a concentrations with the satellite data. Using the optical images captured by the multispectral imager instrument on Sentinel-2 and the in situ measurements, a new spatial window-based match-up dataset creation method is proposed to increase the number of match-ups and hence improve the training of the ML models. The match-ups are then filtered to eliminate erroneous samples based on the spectral distribution of the remotely sensed reflectance. In addition, we design and implement a neural network model dubbed as the ocean color net (OCN), that has performed better than existing ML-based techniques, including the Gaussian process Regression (GPR), regionally tuned empirical techniques, including the ocean color (OC3) algorithm and the spectral band ratios, as well as the globally trained Case-2 regional/coast colour (C2RCC) processing chain model C2RCC-networks. The proposed OCN model achieved reduced mean absolute error compared to the GPR by 5.2%, C2RCC by 51.7%, OC3 by 22.6%, and spectral band ratios by 29%. Moreover, the proposed spatial window and depth-integrated match-up creation techniques improved the performance of the proposed OCN by 57%, GPR by 41.9%, OC3 by 5.3%, and spectral band ratio method by 24% in terms of RMSE compared to the conventional match-up selection approach