Atmospheric correction of satellite optical imagery over inland waters is a key remaining challenge in aquatic remote sensing. This is due to numerous confounding factors such as the complexity of water optical properties, the surface glint, the heterogeneous nature of atmospheric aerosols, and the proximity of bright land surfaces. This combination of factors makes it difficult to retrieve accurate information about the system observed. Moreover, the impact of radiance coming from adjacent land (adjacency effects) in complex geometries further adds to this challenge, especially for small lakes. In this study, ten atmospheric correction algorithms were evaluated for high-resolution multispectral imagery of Landsat-8 Operational Land Imager and Sentinel-2 MultiSpectral Instrument using in situ optical measurements from ~300 lakes across Canada. The results of the validation show that the performance of the algorithms varies by spectral band and evaluation metrics. The dark spectrum fitting algorithm had the best performance in terms of similarity angle (spectral shape), while the neural network-based models showed the lowest errors and bias per band. However, none of the tested atmospheric correction algorithms meet a 30% retrieval accuracy target across all the visible bands, likely due to uncorrected adjacency effects. To quantify this process, three-dimensional radiative transfer simulations were performed and compared to satellite observations. These simulations show that up to 60% of the top of atmosphere reflectance in the near-infrared bands over the lake was from the adjacent lands covered with green vegetation. The significance of these adjacency effects on atmospheric correction has been analyzed qualitatively, and potential efforts to improve the atmospheric correction algorithms are discussed.

Ice melting in the Arctic Ocean exposes the surface water to more radiative energy with poorly understood effects on photo-biogeochemical processes and heat deposition in the upper ocean. In August 2009, we documented the vertical variability of light absorbing components at 37 stations located in the southeastern Beaufort Sea including both Mackenzie River-influenced waters and polar mixed layer waters. We found that melting multiyear ice released significant amount of non-algal particulates (NAP) near the sea surface relative to subsurface waters. NAP absorption coefficients at 440 nm (a_NAP(440)) immediately below the sea surface were on average 3-fold (up to 10-fold) higher compared to subsurface values measured at 2–3 m depth. The impact of this unusual feature on the light transmission and remote sensing reflectance (R_rs) was further examined using a radiative transfer model. A 10-fold particle enrichment homogeneously distributed in the first meter of the water column slightly reduced photosynthetically available and usable radiation (PAR and PUR) by ∼6 and ∼8%, respectively, relative to a fully homogenous water column with low particle concentration. In terms of R_rs, the particle enrichment significantly flattered the spectrum by reducing the R_rs by up to 20% in the blue-green spectral region (400–550 nm). These results highlight the impact of meltwater on the concentration of particles at sea surface, and the need for considering non-uniform vertical distribution of particles in such systems when interpreting remotely sensed ocean color. Spectral slope of a_NAP spectra calculated in the UV (ultraviolet) domain decreased with depth suggesting that this parameter is sensitive to detritus composition and/or diagenesis state (e.g., POM (particulate organic matter) photobleaching).

In most coastal waters, riverine inputs of suspended particulate matter (SPM) and colored dissolved organic matter (CDOM) are the primary optically active constituents. Moderate- and high-resolution satellite optical sensors, such as the Operational Land Imager (OLI) on Landsat-8 and the MultiSpectral Instrument (MSI) on Sentinel-2, offer a synoptic view at high spatial resolution (10–30 m) with weekly revisits allowing the study of coastal dynamics (e.g., river plumes and sediment re-suspension events). Accurate estimations of CDOM and SPM from space require regionally tuned bio-optical algorithms. Using an in situ dataset of CDOM, SPM, and optical properties (both apparent and inherent) from various field campaigns carried out in the coastal waters of the estuary and Gulf of St. Lawrence (EGSL) and eastern James Bay (JB) (N = 347), we developed regional algorithms for OLI and MSI sensors. We found that CDOM absorption at 440 nm [a_g (440)] can be retrieved using the red-to-green band ratio for both EGSL and JB. In contrast, the SPM algorithm required regional adjustments due to significant differences in mass-specific inherent optical properties. Finally, the application of regional algorithms to satellite images from OLI and MSI indicated that the atmospheric correction (AC) algorithm C2RCC gives the most accurate remote-sensing reflectance (R_rs) absolute values. However, the ACOLITE algorithm gives the best results for CDOM estimation (almost null bias; median symmetric accuracy of 45% and R² of 0.78) as it preserved the R_rs spectral shape, while tending to yield positively bias SPM (88%). We conclude that the choice of the algorithm depends on the parameter of interest.

Climate change has affected the Arctic Ocean (AO) and its marginal seas significantly. The reduction of sea ice in the Arctic region has altered the magnitude of photosynthetically available radiation (PAR) entering the water column, impacting primary productivity. Increasing cloudiness in the atmosphere and rising turbidity in the coastal waters of the Arctic region are considered as the major factors that counteract the effect of reduced sea ice on underwater PAR. Additionally, extreme solar zenith angles and sea-ice cover in the AO increase the complexity of retrieving PAR. In this study, a PAR algorithm based on radiative transfer in the atmosphere and satellite observations is implemented to evaluate the effect of these factors on PAR in the coastal AO. To improve the performance of the algorithm, a flag is defined to identify pixels containing open-water, sea-ice or cloud. The use of flag enabled selective application of algorithms to compute the input parameters for the PAR algorithm. The PAR algorithm is validated using in situ measurements from various coastal sites in the Arctic and sub-Arctic seas. The algorithm estimated daily integrated PAR above the sea surface with an uncertainty of 19% in summer. The uncertainty increased to 24% when the algorithm was applied year-round. The PAR values at the seafloor were estimated with an uncertainty of 76%, with 36% of the samples under sea ice and/or cloud cover. The robust performance of the PAR algorithm in the pan-Arctic region throughout the year will help to effectively study the temporal and spatial variability of PAR in the Arctic coastal waters. The calculated PAR data are used to quantify the changing trend in PAR at the seafloor in the coastal AO with depth < 100 m using MODIS-Aqua data from 2003 to 2020. The general trends calculated using the pixels with average PAR > 0.415 mol m${}^{-2}$ day${}^{-1}$ at the seafloor during summer indicate that the annual average of PAR entering the water column in the coastal AO between 2003 and 2020 increased by 23%. Concurrently, due to increased turbidity, the attenuation in the water column increased by 22%. The surge in incident PAR in the water column due to retreating sea ice first led to increased PAR observed at the seafloor (∼12% between 2003 and 2014). However, in the last decade, the rapid increase in light attenuation of the water column has restricted the increase in average annual PAR reaching the bottom in the coastal AO.