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

The performance and potentials of the CryoSat-2 SAR and SARIn modes for lake level estimation

Over the last few decades, satellite altimetry has proven to be valuable for monitoring lake levels. With the new generation of altimetry missions, CryoSat-2 and Sentinel-3, which operate in Synthetic Aperture Radar (SAR) and SAR Interferometric (SARIn) modes, the footprint size is reduced to approximately 300 m in the along-track direction. Here, the performance of these new modes is investigated in terms of uncertainty of the estimated water level from CryoSat-2 data and the agreement with in situ data. The data quality is compared to conventional low resolution mode (LRM) altimetry products from Envisat, and the performance as a function of the lake area is tested. Based on a sample of 145 lakes with areas ranging from a few to several thousand km 2 , the CryoSat-2 results show an overall superior performance. For lakes with an area below 100 km 2 , the uncertainty of the lake levels is only half of that of the Envisat results. Generally, the CryoSat-2 lake levels also show a better agreement with the in situ data. The lower uncertainty of the CryoSat-2 results entails a more detailed description of water level variations

Evaluation of Machine Learning Algorithms for the Classification of Lake Ice and Open Water from Sentinel-3 SAR Altimetry Waveforms

Lakes cover a significant fraction of the landscape in many northern countries. They play a key role in regulating weather and climate and also have a significant impact on northern communities since the presence (or absence), extent and thickness of lake ice affect transportation (ice roads), food availability, recreational activities, and tourism in wintertime. The drastic decline in in-situ observations of lake ice phenology (i.e., freeze-up and break-up dates and ice cover duration) and lake ice thickness globally over the last three decades make remote sensing technology a viable means for monitoring lake ice conditions. Although satellite radar altimetry has been used in various cryospheric and hydrological studies, little work has been conducted on lake ice compared to, for example, sea ice and the estimation of lake water levels. This study was carried out using Sentinel-3A/B SAR altimetry data acquired over three ice seasons (2018-2019, 2019-2020 and 2020-2021) at 11 large lakes across the Northern Hemisphere. We explored the information provided by radar waveforms to discriminate between open water, first (young) ice, growing ice and melting ice using machine learning models. To characterize the waveforms, seven waveform parameters were derived: Leading Edge Width (LEW), Offset Center of Gravity (OCOG) Width, Pulse Peakiness (PP), backscatter coefficient (Sigma0), late tail to peak power (LTTP), early tail to peak power (ETTP) and the maximum value of the echo power. Four machine learning algorithms including Random Forest (RF), Gradient Boosting Trees (GBT), K Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifiers were tested to assess their capability in classifying the lake surfaces across all years. Manual class labelling based on Sentinel-3 Synthetic Aperture Radar Altimeter (SRAL) waveforms and complementary satellite data (Sentinel-1 imaging SAR data, Sentinel-2 Multispectral Instrument (MSI) Level 1C data, and MODIS Aqua/Terra data) was performed to create training and test samples for the classifiers. Accuracies greater than 95% were achieved across all classifiers using a 4-parameter combination (Sigma0, PP, OCOG Width, and LEW). Amongst all waveform parameters, Sigma0, OCOG width and PP were found to be the most important parameters for discriminating between lake ice and open water. Despite showing comparable classification performances in the overall classification, RF and KNN are found to be a better fit for global lake ice mapping as both are less sensitive to their internal hyperparameters and have faster processing speeds. Additionally, consistent results (>93.7% accuracy in all classifiers) achieved on the accuracy assessment carried out for each lake revealed the strength of the classifiers for spatial transferability. Implementation of RF and KNN could be valuable in a pre-or post-processing step for identifying lake surface conditions under which the retrieval of water level and ice thickness may be limited or not possible and, therefore, inform algorithms currently used for the generation of operational or research products. While the research focused on 11 of the largest lakes of the Northern Hemisphere, the classification approach has potential for application on smaller lakes too since SAR mode data (~300 m along-track resolution) is used in the study

Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment

The availability of data is a major challenge for hydrological modelling in large parts of the world. Remote sensing data can be exploited to improve models of ungauged or poorly gauged catchments. In this study we combine three datasets for calibration of a rainfall-runoff model of the poorly gauged Okavango catchment in Southern Africa: (i) surface soil moisture (SSM) estimates derived from radar measurements onboard the Envisat satellite; (ii) radar altimetry measurements by Envisat providing river stages in the tributaries of the Okavango catchment, down to a minimum river width of about one hundred meters; and (iii) temporal changes of the Earth's gravity field recorded by the Gravity Recovery and Climate Experiment (GRACE) caused by total water storage changes in the catchment. The SSM data are shown to be helpful in identifying periods with over-respectively underestimation of the precipitation input. The accuracy of the radar altimetry data is validated on gauged subbasins of the catchment and altimetry data of an ungauged subbasin is used for model calibration. The radar altimetry data are important to condition model parameters related to channel morphology such as Manning's roughness. GRACE data are used to validate the model and to condition model parameters related to various storage compartments in the hydrological model (e.g. soil, groundwater, bank storage etc.). As precipitation input the FEWS-Net RFE, TRMM 3B42 and ECMWF ERA-Interim datasets are considered and compared

Comparison of sea-ice freeboard distributions from aircraft data and cryosat-2

The only remote sensing technique capable of obtain- ing sea-ice thickness on basin-scale are satellite altime- ter missions, such as the 2010 launched CryoSat-2. It is equipped with a Ku-Band radar altimeter, which mea- sures the height of the ice surface above the sea level. This method requires highly accurate range measure- ments. During the CryoSat Validation Experiment (Cry- oVEx) 2011 in the Lincoln Sea, Cryosat-2 underpasses were accomplished with two aircraft, which carried an airborne laser-scanner, a radar altimeter and an electro- magnetic induction device for direct sea-ice thickness re- trieval. Both aircraft flew in close formation at the same time of a CryoSat-2 overpass. This is a study about the comparison of the sea-ice freeboard and thickness dis- tribution of airborne validation and CryoSat-2 measure- ments within the multi-year sea-ice region of the Lincoln Sea in spring, with respect to the penetration of the Ku- Band signal into the snow

Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources