Remote sensing of lake ice phenology across a range of lakes sizes, ME, USA

Remote sensing of ice phenology for small lakes is hindered by a lack of satellite observations with both high temporal and spatial resolutions. By mergingmulti-source satellite data over individual lakes, we present a new algorithm that successfully estimates ice freeze and thaw timing for lakes with surface areas as small as 0.13 km2 and obtains consistent results across a range of lake sizes. We have developed an approach for classifying ice pixels based on the red reflectance band of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, with a threshold calibrated against ice fraction from Landsat Fmask over each lake. Using a filter derived from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) surface air temperature product, we removed outliers in the time series of lake ice fraction. The time series of lake ice fraction was then applied to identify lake ice breakup and freezeup dates. Validation results from over 296 lakes in Maine indicate that the satellite-based lake ice timing detection algorithm perform well, with mean absolute error (MAE) of 5.54 days for breakup dates and 7.31 days for freezeup dates. This algorithm can be applied to lakes worldwide, including the nearly two million lakes with surface area between 0.1 and 1 km2

A lake ice phenology dataset for the Northern Hemisphere based on passive microwave remote sensing

Lake ice phenology (LIP) is an essential indicator of climate change and helps with understanding of the regional characteristics of climate change impacts. Ground observation records and remote sensing retrieval products of lake ice phenology are abundant for Europe, North America, and the Tibetan Plateau, but there is a lack of data for inner Eurasia. In this work, enhanced-resolution passive microwave satellite data (PMW) were used to investigate the Northern Hemisphere Lake Ice Phenology (PMW LIP). The Freeze Onset (FO), Complete Ice Cover (CIC), Melt Onset (MO), and Complete Ice Free (CIF) dates were derived for 753 lakes, including 409 lakes for which ice phenology retrievals were available for the period 1978 to 2020 and 344 lakes for which these were available for 2002 to 2020. Verification of the PMW LIP using ground records gave correlation coefficients of 0.93 and 0.84 for CIC and CIF, respectively, and the corresponding values of the RMSE were 11.84 and 10.07 days. The lake ice phenology in this dataset was significantly correlated (P < 0.001) with that obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data–the average correlation coefficient was 0.90 and the average RMSE was 7.87 days. The minimum RMSE was 4.39 days for CIF. The PMW is not affected by the weather or the amount of sunlight and thus provides more reliable data about the freezing and thawing process information than MODIS observations. The PMW LIP dataset provides the basic freeze–thaw data that is required for research into lake ice and the impact of climate change in the cold regions of the Northern Hemisphere. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00081.Peer reviewe

Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery

Depleting lake ice is a climate change indicator, just like sea-level rise or glacial retreat. Monitoring Lake Ice Phenology (LIP) is useful because long-term freezing and thawing patterns serve as sentinels to understand regional and global climate change. We report a study for the Oberengadin region of Switzerland, where several small- and medium-sized mountain lakes are located. We observe the LIP events, such as freeze-up, break-up and ice cover duration, across two decades (2000–2020) from optical satellite images. We analyse the time series of MODIS imagery by estimating spatially resolved maps of lake ice for these Alpine lakes with supervised machine learning. To train the classifier we rely on reference data annotated manually based on webcam images. From the ice maps, we derive long-term LIP trends. Since the webcam data are only available for two winters, we cross-check our results against the operational MODIS and VIIRS snow products. We find a change in complete freeze duration of −0.76 and −0.89 days per annum for lakes Sils and Silvaplana, respectively. Furthermore, we observe plausible correlations of the LIP trends with climate data measured at nearby meteorological stations. We notice that mean winter air temperature has a negative correlation with the freeze duration and break-up events and a positive correlation with the freeze-up events. Additionally, we observe a strong negative correlation of sunshine during the winter months with the freeze duration and break-up events

Remote sensing-based statistical approach for defining drained lake basins in a continuous Permafrost region, North Slope of Alaska

Lake formation and drainage are pervasive phenomena in permafrost regions. Drained lake basins (DLBs) are often the most common landforms in lowland permafrost regions in the Arctic (50% to 75% of the landscape). However, detailed assessments of DLB distribution and abundance are limited. In this study, we present a novel and scalable remote sensing-based approach to identifying DLBs in lowland permafrost regions, using the North Slope of Alaska as a case study. We validated this first North Slope-wide DLB data product against several previously published sub-regional scale datasets and manually classified points. The study area covered \u3e71,000 km2, including a \u3e39,000 km2 area not previously covered in existing DLB datasets. Our approach used Landsat-8 multispectral imagery and ArcticDEM data to derive a pixel-by-pixel statistical assessment of likelihood of DLB occurrence in sub-regions with different permafrost and periglacial landscape conditions, as well as to quantify aerial coverage of DLBs on the North Slope of Alaska. The results were consistent with previously published regional DLB datasets (up to 87% agreement) and showed high agreement with manually classified random points (64.4–95.5% for DLB and 83.2– 95.4% for non-DLB areas). Validation of the remote sensing-based statistical approach on the North Slope of Alaska indicated that it may be possible to extend this methodology to conduct a comprehensive assessment of DLBs in pan-Arctic lowland permafrost regions. Better resolution of the spatial distribution of DLBs in lowland permafrost regions is important for quantitative studies on landscape diversity, wildlife habitat, permafrost, hydrology, geotechnical conditions, and high-lat-itude carbon cycling

Evaluation of Machine Learning Algorithms for Lake Ice Classification from Optical Remote Sensing Data

The topic of lake ice cover mapping from satellite remote sensing data has gained interest in recent years since it allows the extent of lake ice and the dynamics of ice phenology over large areas to be monitored. Mapping lake ice extent can record the loss of the perennial ice cover for lakes located in the High Arctic. Moreover, ice phenology dates, retrieved from lake ice maps, are useful for assessing long-term trends and variability in climate, particularly due to their sensitivity to changes in near-surface air temperature. However, existing knowledge-driven (threshold-based) retrieval algorithms for lake ice-water classification that use top-of-the-atmosphere (TOA) reflectance products do not perform well under the condition of large solar zenith angles, resulting in low TOA reflectance. Machine learning (ML) techniques have received considerable attention in the remote sensing field for the past several decades, but they have not yet been applied in lake ice classification from optical remote sensing imagery. Therefore, this research has evaluated the capability of ML classifiers to enhance lake ice mapping using multispectral optical remote sensing data (MODIS L1B (TOA) product). Chapter 3, the main manuscript of this thesis, presents an investigation of four ML classifiers (i.e. multinomial logistic regression, MLR; support vector machine, SVM; random forest, RF; gradient boosting trees, GBT) in lake ice classification. Results are reported using 17 lakes located in the Northern Hemisphere, which represent different characteristics regarding area, altitude, freezing frequency, and ice cover duration. According to the overall accuracy assessment using a random k-fold cross-validation (k = 100), all ML classifiers were able to produce classification accuracies above 94%, and RF and GBT provided above 98% classification accuracies. Moreover, the RF and GBT algorithms provided a more visually accurate depiction of lake ice cover under challenging conditions (i.e., high solar zenith angles, black ice, and thin cloud cover). The two tree-based classifiers were found to provide the most robust spatial transferability over the 17 lakes and performed consistently well across three ice seasons, better than the other classifiers. Moreover, RF was insensitive to the choice of the hyperparameters compared to the other three classifiers. The results demonstrate that RF and GBT provide a great potential to map accurately lake ice cover globally over a long time-series. Additionally, a case study applying a convolution neural network (CNN) model for ice classification in Great Slave Lake, Canada is presented in Appendix A. Eighteen images acquired during the the ice season of 2009-2010 were used in this study. The proposed CNN produced a 98.03% accuracy with the testing dataset; however, the accuracy dropped to 90.13% using an independent (out-of-sample) validation dataset. Results show the powerful learning performance of the proposed CNN with the testing data accuracy obtained. At the same time, the accuracy reduction of the validation dataset indicates the overfitting behavior of the proposed model. A follow-up investigation would be needed to improve its performance. This thesis investigated the capability of ML algorithms (both pixel-based and spatial-based) in lake ice classification from the MODIS L1B product. Overall, ML techniques showed promising performances for lake ice cover mapping from the optical remote sensing data. The tree-based classifiers (pixel-based) exhibited the potential to produce accurate lake ice classification at a large-scale over long time-series. In addition, more work would be of benefit for improving the application of CNN in lake ice cover mapping from optical remote sensing imagery

A Machine Learning Approach to Classify Open Water and Ice Cover on Slave River Delta

Seasonal temperature trend and ice phenology in Great Slave lake (GSL), are strongly influenced by warmer inflow from Slave river. The Slave river flows to GSL through Slave river delta (SRD), bringing a rise in temperature that triggers the ice break-up process of the lake. Slave river discharge is subject to multiple stressors including climate warming and upstream water activities, which in turn, directly affects the GSL break-up process. Consequently, monitoring the break-up process at SRD, where the river connects to the lake, serves as an indicator to better understand the cascading effects on GSL ice break-up. This research aims to develop random forest (RF) models to monitor the SRD ice break-up processes, using a combination of satellite images with optical sensors at high spatial resolution, including Landsat-5, Landsat-8, Sentinel-2a, and Sentinel-2b. The RF models were trained using manually selected training pixels to classify ice, open water, and cloud within the SRD. The break-up start period is defined by minimum and maximum thresholds of 60% and 90% on ice fraction, which are a trade-off between maximizing the available images and not including images that are taken after the break-up start. The results show high variability in the rate of break-up within delta using images in recent years with better temporal resolution. Furthermore, a statistically significant trend is observed from 1984 to 2023 using the Mann-Kendall test, with a p-value of 0.05. This study is of great significance to northern and high latitude communities who rely on lake ice for activities such as transportation, and sustenance. Moreover, the break-up of the delta plays a pivotal role in supplying nutrients and sediments, and also in the occurrence of spring flooding. Therefore, the outcomes of this study can be leveraged to shape effective water resource management policies based on the regional characteristics of climate and hydrological patterns

The response of mountain lakes to environmental change

Lakes act as sentinels of environmental change by incorporating forcing across scales: climate scales, catchment scales, and within-lake scales. To fully understand the changes that lake ecosystems undergo, we must explore past changes and present trends, both on a fine scale – in individual lake systems – and on a macroscale –across broad geographic regions. Mountain lakes are useful as study systems because they are often remote and generally free of direct human influence. However, external impacts still affect mountain lake ecosystems, both through input of exogenous material and air temperature warming that influences the formation and breakup of lake ice. In this work, we use a combination of sediment records, intensive sampling, and remote sensing to understand the effects of climate change on mountain lake ecosystems. We refine our understanding of winter mountain lake hydrology through three studies that address: 1. Whether aeolian dust records in mountain lake cores capture deposition rate changes of exogenous dust input 2. Whether North American mountain lake ice cover period is changing 3. How mountain lake ecohydrology responds to shifts in ice cover timing (i.e., ice phenology) We found heterogeneity in mountain lake responses across scales in each of our studies. In the case of exogenous dust deposition to lakes, sediment cores revealed that dust can be an important source of nutrients to lakes; however, sediment records do not reveal changing rates of deposition between the distant and recent past. Apparent changes are rather an artifact of timescale dependence. When taking a continental-scale view of ice phenology, using a remote sensing dataset of 1,629 lakes, we find that ice phenology patterns do not readily cohere with ice phenology patterns from single lakes or lakes within a similar geographic region. Instead, lake ice phenology shows heterogeneous responses in different geographic regions (e.g., between the Sierra Nevada and the Rocky Mountains), hinting at potential resiliency to climate forcing in different regions of North America. Lastly, using high-frequency time series of dissolved oxygen concentration across morphologically distinct lakes, I found that lakes experiencing similar winter conditions showed heterogeneous oxygen dynamics along a depth gradient. Shallow lakes respond to winter ice cover conditions by depleting oxygen more quickly than deep lakes. I additionally explore the effects of sediment organic matter and winter meteorological dynamics. I anticipate that these results will be useful for understanding linkages between broader climate forcing. As air temperatures increase, heterogeneous landscape factors may confound the anticipated physical, chemical, and ecological lake responses, leading to questions about how lakes may show variations in timing or resiliency in response to climate change in the future

Teledetecció de la coberta de gel estacional dels estanys d'alta muntanya d'Aigüestortes. Teoria i nous mètodes per a estanys de mida petita en la fase de congelació

La fenologia del gel dels llacs pirinencs està poc estudiada tot i ser un bon indicador que milloraria la comprensió que tenim del canvi climàtic i dels seus impactes en aquests ecosistemes tan sensibles. Així doncs, en aquest treball, s'exploren les possibilitats de la teledetecció per a estanys d'alta muntanya de mida petita, entre les 5 i les 25ha en l'àmbit del Parc Nacional d'Aigüestortes i Estany de Sant Maurici, i al llarg de 22 temporades d'hivern, des de l'any 2000 fins al 2022, a la recerca de les dates de congelació completa. Al mateix temps, s'analitzen les problemàtiques dels píxels mixtes, noves tècniques de recollida de dades in situ, i un algorisme de detecció automàticaLa fenología del hielo de los lagos pirenaicos está poco estudiada a pesar de ser un buen indicador que mejoraría la comprensión del cambio climático y sus impactos en estos ecosistemas tan sensibles. Así pues, en este trabajo, se exploran las posibilidades de la teledetección para lagos de alta montaña de pequeño tamaño, entre las 5 y las 25 ha en el ámbito del Parque Nacional de Aigüestortes i Estany de Sant Maurici, y durante 22 temporadas de invierno, desde el año 2000 hasta el 2022, en busca de las fechas de congelación completa. Al mismo tiempo, se analizan las problemáticas de los píxeles mixtos, nuevas técnicas de recogida de datos in situ y un algoritmo de detección automática.The ice phenology of the Pyrenean lakes has been little studied despite being a good indicator that would improve the understanding of climate change and its impacts on these highly sensitive ecosystems. Thus, in this work, the possibilities of remote sensing for small high mountain lakes are explored, between 5 and 25ha around the Aigüestortes i Estany de Sant Maurici National Park, and for 22 winter seasons, from the year 2000 to 2022, looking for the freeze up ending dates. At the same time, the problems of mixed pixels, new data collection techniques in situ and an automatic detection algorithm are analyzed

Amélioration des estimations hydrométriques dérivées des données altimétriques satellitaires acquises sur des étendues d’eau continentales soumises à l’englacement

Les eaux douces continentales constituent l’une des composantes principales du cycle de l’eau. Elles assurent sa continuité à travers des échanges de flux d’eau et d’énergie avec ses différentes composantes. De nombreux plans d’eau douce (lacs, rivières, réservoirs, etc.) se retrouvent dans les régions situées dans les hautes latitudes nord, où la cryosphère est dominante. L’une des particularités de ces plans d’eau est la congélation partielle ou complète pendant les saisons froides. De plus, ils ont une grande sensibilité aux changements climatiques. En effet, les variations spatio-temporelles du climat aux échelles régionales et locales affectent grandement l’hydrologie de ces plans d’eau en termes de niveau d’eau et de débit. D’où l’intérêt de disposer d’outils simples et efficaces pour surveiller et gérer ces ressources. L’inaccessibilité aux plans d’eau isolés et l’effet de la glace sur la qualité des mesures des niveaux d’eau à l’échelle des stations limnimétriques rendent la surveillance de la variation des niveaux d’eau difficiles. Compte tenu de sa couverture spatio-temporelle, de sa période de répétitivité, et des bandes de fréquence utilisées, l’altimétrie radar par satellite pourrait être une meilleure alternative pour surmonter les limites liées aux mesures in situ. Cependant, la présence de cibles hétérogènes, comme les couverts de glace, présente un défi majeur pour exploiter les données des niveaux d’eau dérivées de la technologie par satellite altimétrique au-dessus des plans d’eau couverts de glace. Cette étude a pour ultime objectif d’améliorer les estimations des niveaux d’eau dérivées de l’altimétrie radar par satellite sur des étendues d’eau continentales couvertes de glace. L’étude s’applique à étudier le potentiel de deux satellites altimétriques, Jason-2 et SARAL/Altika, possédant des caractéristiques technologiques différentes, à suivre les variations des niveaux d’eau des étendues d’eau soumises à l’englacement sur le territoire canadien. Le premier objectif spécifique de cette étude concerne l’analyse de la capacité des algorithmes de retraitements utilisés par les missions Jason-2 et SARAL/Altika à estimer les niveaux d’eau sur vingt étendues d’eau couvertes de glace au Canada. Cette analyse est effectuée sur les produits dérivés des algorithmes de retraitement et sur les mesures in situ pendant deux périodes : la période de recouvrement des satellites Jason-2 et SARAL/Altika, comprise entre 2008 et 2016, et les périodes des variations saisonnières de l’état de surface. Les résultats montrent que pour Jason-2, c’est l’algorithme de seuillage ICE-1 qui fournit les meilleures estimations de niveau d’eau, avec des erreurs RMSE non biaisées (unRMSE) ≤ 0,3 m et des r ≥ 0,8 pour 90 % des étendues d’eau. Pour ce qui est de SARAL/Altika, la majorité des algorithmes de retraitement utilisés donnent des résultats très comparables aux observations in situ, démontrant les bonnes performances de la technologie SARAL. Cependant, les algorithmes de retraitement utilisés par les deux satellites Jason-2 et SARAL/Altika fournissent des précisions faibles pendant les périodes marquées par le mélange eau-glace, c’est-à-dire les périodes de gel et de dégel. Le deuxième objectif spécifique est d’améliorer les estimations des niveaux d’eau issues du satellite Jason-2 pendant les périodes de gel et de dégel. Une approche de détection automatique est proposée afin de discriminer les points de mesure de l’eau libre, de la glace pure et de la glace partielle sur quatre plans d’eau couverts de glace : le Grand Lac des Esclaves, le lac Athabasca, le lac Winnipeg, et le lac des Bois. Cette approche se base sur l’intégration des données actives et passives du satellite Jason-2 dans un processus de clustering afin de définir les clusters correspondant à chaque état de surface. L’application du seuil de détection du cluster de l’eau libre a permis d'améliorer la qualité des mesures de niveau d'eau pendant les périodes de gel et de dégel. Les résultats montrent que le coefficient de corrélation r est amélioré d’environ 0,8 à plus de 0,9 avec des biais inférieurs à 20 cm. Le troisième objectif spécifique évalue le potentiel de l’approche de détection automatique des points de mesures développé dans l’objectif 2, avec les données du satellite SARAL/Altika. Dans cette partie, les données actives et passives dérivées du satellite SARAL/Altika ont été exploitées pour concevoir les seuils de discrimination de chaque état de surface (eau libre, glace pure, glace partielle de gel et dégel) sur les mêmes quatre plans d’eau étudiés. L’application du seuil de l’eau libre offre une amélioration de la qualité des mesures de niveau de l’eau avec une amélioration des corrélations r d’environ 0,8 à plus de 0,92 avec des biais inférieurs à 10 cm. Le quatrième objectif spécifique met en place une approche de classification des formes d’onde selon la nature et l’état de surface pendant les périodes de gel et de dégel pour les satellites altimétriques Jason-2 et SARAL/Altika. Le site d’étude considéré pour le développement de cette approche est le Grand Lac des Esclaves. Un processus de classification non supervisée basé sur les paramètres des formes d’onde et les résultats des interprétations des données altimétriques et radiométriques sur l’état de surface a été utilisé avant de développer l’approche de classification supervisée des formes d’onde pour Jason-2 et SARAL/Altika, nommée le modèle entrainé de classification - Classification Trained Model (CTM). Les modèles supervisés du K-plus proche voisin (KNN, K-Nearest Neighbour) et de machine à vecteurs de support (SVM, Support Vector Machine) ont été évalués pour cette conception. Le modèle basé sur l’approche SVM a produit les meilleurs résultats, présentant une précision globale (Overall Accuracy) de l’ordre de 92 % avec Jason-2 et de 98 % avec SARAL/Altika. Ce modèle développé est utilisé pour classifier l’ensemble des formes d’onde en fonction de l’état de surface du plan d’eau étudié. Les résultats ont été superposés à des produits Moderate Resolution Imaging Spectroradiometer (MODIS) pour une évaluation qualitative et semi-quantitative.Abstract : The continental freshwater is one of the main components of the water cycle. These resouces ensure its continuity through the exchange of water and energy fluxes with the different components of the water cycle. Most of the continental water bodies (lakes, rivers, reservoirs, etc.) are in the northern high latitudes, dominated by the cryosphere. These water bodies froze completely or partly during cold seasons. In addition, they have a high sensitivity to climate change. Climate variations at the local and global scales may affect the hydrological regime (water level and flow) of these water bodies. Hence the interest in having a simple and efficient tools to monitor changes of these resources. The gauging stations could not provide good measurements of water level due to the limited accessibility of isolated water bodies, and the potential contamination of measured data by ice. Satellite radar altimetry appears as a good alternative to overcome these limitations given its spatiotemporal coverage, its ground track repetitivity period, and the frequency bands used. However, the presence of heterogeneous targets within the altimeter footprint, such as ice cover, remains a major challenge to estimate water levels over ice-covered water bodies. The aim of this study is to improve the estimations of water levels obtained from spatial radar altimetry over ice-covered water bodies. This study investigates the potential of the two satellites altimetry Jason-2 and SARAL/Altika with different characteristics to monitor water-level changes over ice-covered water bodies in the Canadian territory. The first objective of this study is to analyze the potential of Jason-2 and SARAL/Altika retracking algorithms to retrieve water levels from altimeter measurements acquired over 20 ice-covered water bodies across Canada. In this analysis, products derived from retracking algorithms were compared with in situ measurements during two periods: (1) the time series considered for each satellite (2008–2016 for Jason-2, and 2013–2016 for SARAL/Altika); and (2) the freeze-thaw periods included in each time series. The results showed that retracking ICE-1 (used with Jason-2 data) provided better water level accuracy for 90% of the studied water bodies (r ≥ 0.8, unbiased RMSE ≤ 0.3 m). All the retracking algorithms used by SARAL/Altika provided results that are comparable to in situ observations, thus denoting the good performance of the SARAL technology. However, all retracking algorithms used by Jason-2 and SARAL/Altika provide low accuracy during freeze-up and thaw periods. The second objective attempts to improve the measurements of water levels obtained by Jason-2 data during freeze and thaw periods. Here, an automatic approach is proposed to identify the Jason-2 altimetry measurements corresponding to open water, ice, and transition (water ice) over four Canadian lakes: Great Slave Lake, Lake Athabasca, Lake Winnipeg, and Lake of the Woods. This approach is based on the integration of backscatter coefficients and peakiness at Ku-band and brightness temperature observations obtained from Jason-2 data in a clustering process to define the clusters and threshold of each surface state. The use of open water threshold improves the quality of water-level estimation over the four lakes during freeze-up and thaw periods. The results show that the coefficient of correlation (r) increased in average from about 0.8 without the use of the thresholds to more than 0.90, with unbiased RMSE errors less than 20 cm. The third objective evaluates the efficiency of the automatic approach proposed in the second objective, with SARAL/Altika data. In this section, active and passive observations derived from SARAL/Altika data were used to design the thresholds of each state surface (open water, pure ice, ice freeze-up, and ice break-up) over the same four studied water bodies. The application of open water threshold improved the quality of water levels measurements from r ~ 0.8 to r more than 0.92 with unbiased RMSE less than 10 cm. The fourth objective proposes a new approach for classifying waveforms data derived from Jason-2 and SARAL/Altika satellite missions during freeze-up and thaw periods based on the surface state over ice-covered water bodies. The considered study area for the development of this approach is Great Slave Lake. An unsupervised classification process based on waveform parameters and the results of interpretations of active and passive data was used before developing the supervised classification approach for Jason-2 and SARAL/Altika, named Classification Trained Model (CTM). K-nearest neighbor (KNN) and support vector machine (SVM) models were evaluated for this concept. The SVM-based model provided the best results (accuracy of 92% with Jason-2, and 98% with SARAL/Altika). It was used to classify all waveforms of the studied water body. Results were superimposed to MODIS products for qualitative visual and semi-quantitative assessments