In the above article[1], the references in the following paragraph should be corrected as shown below. This excerpt appears in the first column on page 4 of the above article.

Remote retrieval of near-surface chlorophyll-a (Chla) concentration from small inland waters adopts two different approaches: development of local models using in situ Chla measurements in target waterbodies or application of globally trained models with no need of local in situ measurements. While the abundance and diversity of small lakes undermine the former approach, application of global models often biases estimations due to uncertainties of atmospheric correction. Here, we introduce a regional approach wherein local in situ data from select waterbodies is used to retrieve Chla in nearby lakes. We trained semi-empirical (SE) and machine-learning (ML) algorithms, namely 2band, LMDN, and SVR, over four ice-free seasons (2017–2020) using 193 pairs of near-surface Chla in Buffalo Pound Lake, Canada, and near-coincident satellite-derived remote-sensing reflectance from Sentinel-2. Ability of locally trained models and a globally trained mixture density network (MDN) to retrieve Chla for unseen data$(\mathrm{N}=50)$was evaluated for six other catchment lakes. Regional approaches based on locally trained ML models (SVR, LMDN) unbiasedly estimated Chla in test sites (overall error$\approx 60\%$) and outperformed locally trained SE models and globally trained ML models (reduced error by 20%, bias by 50%). These findings suggest that ML-based regional models are capable of expanding regional$Chla$retrieval from optically-similar waterbodies.

Lake ice thickness (LIT), a thematic product of Lakes as an Essential Climate Variable (ECV), is sensitive to changes in air temperature and on-ice snow mass. Here, a novel and efficient analytic method (retracking approach) is presented for the estimation of LIT from Ku-band (13.6 GHz) radar altimetry data. The new retracker, referred to as LRM_LIT, is based on the physical modeling of the conventional radar echoes (also called Low Resolution Mode or LRM) over ice-covered lakes that show a characteristic step-like feature in their leading edge attributed to the reflection of radar waves at the snow-ice and ice-water interfaces. The method is applied to Jason-2 and Jason-3 data acquired over Great Slave Lake, Canada, over three ice seasons (2013-2016). As expected, the agreement between the Jason-2 and Jason-3 LIT estimates over their overlapping period (2016 ice season) is excellent with a mean bias error of 0.013 m and root mean square error (RMSE) of 0.024 m. LIT estimates from LRM_LIT are in good agreement with simulations from a thermodynamic lake ice model and in situ measurements with RMSE values of the order of few centimeters for the three winter seasons. The retracker also provides a robust way to assess the accuracy of LIT estimates which is in the order of 0.10 m when the ice cover is well established and prior to melt onset. In addition, LRM_LIT captures the seasonal transitions during the freeze-up and breakup periods and ice growth over different winter seasons, making it a promising method for monitoring inter-annual variability and trends in LIT from past and current conventional radar altimetry missions.

Recent investigations using polarimetric decomposition and numerical models have helped to improve understanding of how radar signals interact with lake ice. However, further research is needed on how radar signals are impacted by varying lake ice properties. Radiative transfer models provide one method of improving this understanding. These are the first published experiments using the Snow Microwave Radiative Transfer (SMRT) model to investigate the response of different imaging SAR frequencies (L, C, and X-band) at HH and VV polarizations using various incidence angles (20°, 30°, and 40°) to changes in ice thickness, porosity, bubble radius, and ice-water interface roughness. This is also the first use of SMRT in combination with a thermodynamic lake ice model. Experiments were for a lake with tubular bubbles and one without tubular bubbles under difference scenarios. Analysis of the backscatter response to different properties indicate that increasing ice thickness and layer porosity have little impact on backscatter from lake ice. X-band backscatter shows increased response to surface ice layer bubble radius; however, this was limited for other frequencies except at shallower incidence angles (40°). All three frequencies display the largest response to increasing RMS height at the ice-water interface, which supports surface scattering at the ice-water interface as being the dominant scattering mechanism. These results demonstrate that SMRT is a valuable tool for understanding the response of SAR data to changes in freshwater lake ice properties and could be used in the development of inversion models.

Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large ($N =905$) database of colocatedin situradiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ($R_{\mathrm {rs}}$) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of$R_{\mathrm {rs}}$spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between$\sim 73$% and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.

Chlorophyll-a concentration (chla) is a useful indicator of harmful algal blooms in early warning systems that use remote sensing data as input. However, its retrieval is challenging in small waterbodies due to the lack of high spatial-resolution water-color sensors and the substantial optical interference of other water constituents. Here, we demonstrate the potential of support vector machines and Sentinel-2 images to retrieve chla in a shallow eutrophic lake (Buffalo Pound Lake, Saskatchewan, Canada). Following validation against in-situ chla measurements over three open water seasons (2017–2019), our proposed method based on Support Vector Regression (SVR) outperforms the most common semi-empirical models, i.e., locally-tuned indices (normalized difference chlorophyll index, 2band, and 3band), as well as the state-of-the-art global empirical model (Mixture Density Network). The superiority of SVR is shown in terms of overall and stratified accuracy, as well as spatial validity. We argue that for small waterbodies where numerous matched pairs of in-situ chla and reflectance are not available, SVR might retrieve chla more accurately than locally-tuned indices and global empirical models.

Using a coastal Global Navigation Satellite System (GNSS) station, one can measure sea surface height (SSH) using GNSS Interferometric Reflectometry (GNSS-IR) technique as it gives vertical distance between the antenna and the reflective surface in a height reference frame. In this study, Signal-to-Noise Ratio (SNR) data of a GPS station located in Honolulu, Hawaii, were observed over a one-month period to estimate the amplitude of the dominant tidal constituents. SSH values acquired from GNSS-IR are then compared to traditional tidal observations obtained from a nearby tide-gauge station. Obtaining a Root Mean Square Error (RMSE) of 11.5 cm and a correlation of 0.87 between SSH values obtained from GNSS-IR and tide-gauge, we estimated the amplitudes of dominant tidal constituents (M2, S2, K1, P1, 01) with maximum and minimum amplitude differences of 2.38 and 0.28 cm, respectively. Next, a set of control points around the GNSS station were chosen and the FES2012 global tide model was used to calculate the amplitudes of those constituents one more time. The maximum and minimum differences in amplitude obtained from the tide-gauge station and the FES2012 model were 3.65 and 0.31 cm, respectively. This study suggests that the GNSS-IR technique can be used as a tool for estimating tidal constituents over a short period of time in the order of one month.

Algorithms for the generation of a bedfast/floating lake ice product from Sentinel-1A/B synthetic aperture radar (SAR) data were implemented, cross-compared, and validated for various permafrost regions (Alaska, Canada and Russia). The algorithms consisted of: 1) thresholding; 2) Iteration Region Growing with Semantics (IRGS); and 3) K-means. The thresholding algorithm (92.4%) was found to perform slightly better on average than the IRGS algorithm (90.1%), and to outperform K-means (85.3%). The thresholding algorithm was therefore selected for implementation of a processing chain to generate a novel bedfast/floating lake ice product. Using a time series of Sentinel-1 SAR data, the new map product shows the day of year (DOY) when the ice becomes bedfast or remains afloat for individual lake sections.

This work describes a pilot study in southern Ontario, Canada evaluating the use of the `Headwall Nano-Hyperspec' hyperspectral imager onboard a Remotely Piloted Aircraft System (RPAS). Hyperspectral imagers are extremely useful for monitoring vegetation health and water quality, among other environmental parameters. However, guidelines on the use of this specific instrument for these applications are not yet available. As such, recommended operational settings and calibration procedures are presented here, based on nearly 50 flight campaigns over water bodies and vineyards. Using these procedures, spectral reflectance was successfully captured using an RPAS.

Canada has vast water resources that span an enormous range in geography, climate, and ecosystems [1]. Water supply and water quality are the two critical issues relevant to water resources, not only in Canada but globally in a warming climate. The water microsatellite mission described here aims to better prepare end users to respond to the emerging spectrum of water futures issues by revolutionizing remote sensing of water quality and quantity parameters, and permitting unprecedented interconnection and data gathering from Canadian environmental monitoring networks.

A winter time series of ground-based (X- and Ku-bands) scatterometer and spaceborne synthetic aperture radar (SAR) (C-band) fully polarimetric observations coincident with in situ snow and ice measurements are used to identify the dominant scattering mechanism in bubbled freshwater lake ice in the Hudson Bay Lowlands near Churchill, Manitoba. Scatterometer observations identify two physical sources of backscatter from the ice cover: the snow-ice and ice-water interfaces. Backscatter time series at all frequencies show increases from the ice-water interface prior to the inclusion of tubular bubbles in the ice column based on in situ observations, indicating scattering mechanisms independent of double-bounce scatter. The co-polarized phase difference of interactions at the ice-water interface from both scatterometer and SAR observations is centered at 0° during the time series, also indicating a scattering regime other than double bounce. A Yamaguchi three-component decomposition of the RADARSAT-2 C-band time series is presented, which suggests the dominant scattering mechanism to be single-bounce off the ice-water interface with appreciable surface roughness or preferentially oriented facets, regardless of the presence, absence, or density of tubular bubble inclusions. This paper builds on newly established evidence of single-bounce scattering mechanism for freshwater lake ice and is the first to present a winter time series of ground-based and spaceborne fully polarimetric active microwave observations with polarimetric decompositions for bubbled freshwater lake ice.

Observations of ice thickness are limited in high latitude regions, at a time when they are increasingly being requested by operational ice centers. This study aims to improve the retrieval of lake ice thickness using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Aqua (P.M.) and Terra (A.M.) satellites. The accuracy of ice thickness retrievals based on MODIS lake ice surface temperature (LIST) is investigated using a commonly used heat balance equation and the retrieved ice thicknesses are compared to in situ measurements from the Canadian Ice Service. The accuracy of ice thickness estimates is improved when using snow depth from the 1-D thermodynamic lake ice model Canadian Lake Ice Model (CLIMo) rather than an empirical relationship between snow depth and ice thickness utilized in the recent investigations. Taking into account all data over the study period (2002-2014), the mean bias error and the root-mean-square error are reduced from -0.42 to 0.07 m and 0.58 to 0.17 m, respectively, with the novel approach proposed herein. However, this approach is limited to ice thickness estimations of less than ca. 1.7 m.

Snow is a very important component of the climate system. It can influence the energy budget of the atmosphere and hydrological system significantly. The main goal of this paper is to use remote sensing and geographical information system techniques to analyze the spatial and temporal variations in regional scale and to find the relations between meteorological parameters and the snow albedo for the future modeling in snow albedo study. The results revealed spatial and temporal variation throughout different months during the winter season. In addition, the Pearson correlation coefficient analysis showed partial correlation between snow albedo and meteorological variables, which can be used to model snow albedo in some hydrological studies.

Polarimetric synthetic aperture radar satellite and ground-based Ku- and X-band scatterometer measurements are used to explore the scattering mechanism for ice in shallow Arctic lakes, wherein strong radiometric responses are seen for floating ice, and low returns are evident where the ice has grounded. Scatterometer measurements confirm that high backscatter is from the ice/water interface, whereas polarimetric decomposition suggests that the dominant scattering mechanism from that interface is single bounce. Using Fresnel equations, a simple model for surface bounce from the ice/water interface is proposed, and its predictions are supported by experimental parameters such as co-pol phase difference, co-pol ratio, and the results of rigorous numerical modeling. Despite early research suggesting double-bounce scattering from columnar air bubbles and the ice/water interface as the dominant scattering mechanism in shallow lakes, this paper strongly supports a single-bounce model.

Winter season backscatter (σ°) evolution of snow covered lake ice was observed by ground-based University of Waterloo X-(9.6 GHz) and Ku-band (17.2 GHz) scatterometers (UW-SCAT) during the winter of 2010-11. The UW-SCAT post-processing procedure allowed for the observation of σ° at the surface (snow/ice interface, ice types) and the ice volume. Observations indicated that: (1) σ° associated with the development of tubular bubbles within the ice volume causes double-bounce of the signal and high returns at X- and Ku-bands; (2) ice types at the surface (grey ice) composed of high density spherical micro-bubbles result in σ° increases at both X- and Ku-bands; and (3) the removal of snow overlying ice results in a drop in Ku-band σ° up to 5.5 dB, exhibiting sensitivity to snow water equivalent.

Lakes offer several economical, ecological, natural, and social benefits, the quality of which depend directly on their water quality. Conventional methodologies of assessing lakes' water quality are expensive and limited. Remote sensing introduces new technologies for effective, accurate and more efficient water quality measurements. This paper utilizes MERIS and in-situ data to assess the water quality on Lake Simcoe, Canada, by specifically looking at Secchi Disk depth. A Linear Mixed Effect model was used as the regression method. There was a significant positive correlation between measured and predicted Log10(SDD) (R2=0.78). Quality of water in Lake Simcoe has significantly deteriorated over the course of the past decade, with severe environmental and social impacts. Temporally, the highest and lowest average values for SDD were found in summer (1.13 m–20.03 m) and fall (0.78 m–28.67 m). Spatially, the SDD average value in the time period of study (2002–2011) was high in the northern east region, and low in the southern part (0.94 m–21.64 m).

The CoReH2O mission design is mature; there are two viable technical configurations, and the breadboards of key hardware components, such as the dual-polarized antenna array feeds and high power amplifiers have been built and tested. The baseline retrieval algorithm has been intensively tested using simulated and experimental data. The tests confirm that the threshold performance for SWE products can be met under a wide range of snow conditions. Data from further field campaigns by the airborne SnowSAR acquired Austria, Canada, and Alaska from November 2012 through April 2013 are being analysed to investigate retrieval performance over alpine, glacier and tundra terrains.

The University of Waterloo scatterometer, which is a system developed for observation of snow and ice properties, is described. The system is composed of two frequency-modulated continuous-wave radars operating at center frequencies of 17.2 and 9.6 GHz. A field-deployable platform allows a rapid setup and observation at remote sites under harsh environmental conditions. A two-axis positioning system moves the radar beam across a user-programmable range of azimuth (±180°) and elevation angles (15°-105°). Typical azimuth scans of 60° angular width generate between 21 and 586 independent samples, depending on the wavelength and the elevation angle. The backscatter response of terrestrial snow in the Canadian Subarctic is demonstrated with two experiments conducted in Churchill, MB, Canada, between 2009 and 2011.

Retrieval of snow volume properties at high spatial resolutions has become a priority in hydrological and climatological research. Recent studies have identified the 8 to 18 GHz range as particularly sensitive to snow water equivalent. In this study we present snow target observations using a dual frequency (17.2 GHz and 9.6 GHz) scatterometer system. The observations were collected in a unique tundra environment providing a novel dataset for comparison with in situ snow survey data. Moreover, the scatterometer was sled mounted allowing observations to be collected over a substantial spatial domain.

Knowledge about the freeze/thaw state of the surface is of major importance for climate modelling, hydrology and numerous other applications. In this study, a freeze/thaw state detection algorithm using the ASCAT scatterometer is compared to Land Surface Temperature (LST) from MODIS as well as to a product derived from ENVISAT ASAR data. Good agreement with the LST product was found over the study area in Northern Siberia with disagreement below 22% for all 8-day periods of 2007. SAR derived surface status can, if sufficient sampling is available, provide similar results as with ASCAT but even with higher spatial detail.

The COld REgions Hydrology High-resolution Observatory (CoReH2O) satellite mission was selected for detailed scientific and technical studies within the Earth Explorer Programme of ESA. The sensor is a dual frequency SAR, operating at 17.2 GHz and 9.6 GHz, VV and VH polarizations The mission will deliver spatially distributed snow and ice observations to improve the representation of the croysphere in hydrological and climate models. Primary parameters are the extent and water equivalent (SWE) of the snow pack and snow accumulation on glaciers. Scientific preparations of the mission include the development and testing of algorithms for retrieval of snow parameters, studies on synergy of CoReH2O-type snow products with passive microwave measurements, the assimilation of satellite snow data in process models, and field experiments. Performance of retrievals for snow extent and SWE was tested with simulated and experimental data, including Ku- and X-band SAR images of the airborne SnowSAR system.

The COld REgions Hydrology High-resolution Observatory (CoReH2O) satellite mission has been selected for detailed scientific and technical studies within the ESA Living Planet Programme. The mission addresses the need for distributed snow and ice observations to improve the representation of the cryosphere in climate models and the prediction of the water cycle. The sensor is a dual frequency SAR, operating at 17.2 GHz and 9.6 GHz, VV and VH polarizations. This configuration enables the decomposition of the scattering signal for retrieving snow mass (SWE) and other physical properties of snow and ice. A major task for mission preparation is the development and testing of algorithms for SWE retrieval. The baseline algorithm applies a constrained minimization approach, matching forward computed and measured backscatter by iterating for SWE and effective grain size of the snow volume. Experimental campaigns on Kuand X-band backscatter of snow deliver important information for retrieval development and validation.

On the Tibetan Plateau, very little research has focused on lake ice-cover change; only a small number of published papers on Qinghai Lake ice observations. The main goal of this study is to investigate the change in lake ice phenology at Nam Co on the Tibetan Plateau using MODIS and AMSR-E data (monitoring the date of freeze onset, the formation of stable ice cover, first appearance of water, and the complete disappearance of ice) during the period 2000-2009.

The sensitivity of brightness temperature (TB) at 6.9, 10.7, and 18.7 GHz from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) observations is investigated over five winter seasons (2002-2007) on Great Bear Lake and Great Slave Lake, Northwest Territories, Canada. The TBmeasurements are compared to ice thicknesses obtained with a previously validated thermodynamic lake ice model. Lake ice thickness is found to explain much of the increase of TBat 10.7 and 18.7 GHz. TBacquired at 18.7 GHz (V-pol) and 10.7 GHz (H-pol) shows the strongest relation with simulated lake ice thickness over the period of study (R2> 0.90). A comparison of the seasonal evolution of TBfor a cold winter (2003-2004) and a warm winter (2005-2006) reveals that the relationship between TBand ice growth is stronger in the cold winter (2003-2004). Overall, this letter shows the high sensitivity of TBto ice growth and, thus, the potential of AMSR-E mid-frequency channels to estimate ice thickness on large northern lakes.

Snow is a critical component of the global water cycle and climate system, and a major source of water supply in many parts of the world. There is a lack of spatially distributed information on the accumulation of snow on land surfaces, glaciers, lake ice, and sea ice. Satellite missions for systematic and global snow observations will be essential to improve the representation of the cryosphere in climate models and to advance the knowledge and prediction of the water cycle variability and changes that depend on snow and ice resources. This paper describes the scientific drivers and technical approach of the proposed Cold Regions Hydrology High-Resolution Observatory (CoReH2O) satellite mission for snow and cold land processes. The sensor is a synthetic aperture radar operating at 17.2 and 9.6 GHz, VV and VH polarizations. The dual-frequency and dual-polarization design enables the decomposition of the scattering signal for retrieving snow mass and other physical properties of snow and ice.

The COld REgions Hydrology High-resolution Observatory (CoRe-H2O) satellite mission has been selected for scientific and technical studies within the ESA Earth Explorer Programme. The mission addresses the need for spatially detailed snow and ice observations in order to improve the representation of the cryosphere in climate models and to improve the knowledge and prediction of water cycle variability and changes. CoRe-H2O will observe the extent, water equivalent and melting state of the snow cover, accumulation and diagenetic facies of glaciers, and properties of sea ice and lake ice. The sensor is a dual frequency SAR, operating at 17 GHz and 9.6 GHz, VV and VH polarizations. This configuration enables the decomposition of the scattering signal for retrieving physical properties of snow and ice. Scientific preparation activities include experimental field campaigns, improvement of radar backscatter models, and the development of inversion algorithms.

We present a new approach for the detection and mapping of lichen in a heterogeneous habitat of northern Quebec using Landsat imagery. Results from the enhancement-classification method (ECM) and from spectral mixture analysis (SMA) were compared and evaluated for their potential to be applied over large territories. ECM is based on guided unsupervised classification of enhanced satellite images, and provided an overall accuracy of 74.5% and a good discrimination between lichen and non-lichen habitat. However, ECM discrimination between lichen habitats was more problematic. SMA is based is based on the fact that each pixel contains different surface features, each of which contributes to the overall pixel-level signal received by a remote sensor. SMA quantifies the spectral contributions from individual scene components to retrieve their sub-pixel scale proportions. Image SMA fractions showed a good agreement with field validated fractions. Tho two methods were efficient for mapping lichen. However, SMA provided new information not available by classification methods and therefore is recommended for further application to larger areas and spatio-temporal studies.