Automated Fractional Snow Cover Monitoring From Near-Surface Remote Sensing In Grassland

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. I developed a mostly semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research, which make use of RGB images only, the use of the monochrome RGB + NIR (near-infrared) channel reduced pixel misclassification and increased accuracy. The results have an average RMSE of 7.67 compared to visual estimates. This is a promising outcome, although not every PhenoCam system has NIR capability

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

Assimilation des données GRACE dans le modèle MESH pour l’amélioration de l'estimation de l'équivalent en eau de la neige

Abstract: Water storage changes over space and time play a major rule in the Earth’s climate system through the exchange of water and energy fluxes among the Earth’s water storage compartments and between atmosphere, continents, and oceans. In many parts of northern-latitude areas spring meltwater controls the availability of freshwater resources. With respect to terrestrial hydrologic process, snow water equivalent (SWE) is the most critical snow characteristic to hydrologists and water resource managers. The first objective of this study examined the spatiotemporal variations of terrestrial water storages and their linkages with SWE variabilities over Canada. Terrestrial water storage anomaly (TWSA) from the Gravity Recovery and Climate Experiment (GRACE), the WaterGAP Global Hydrology Model (WGHM), and the Global Land Data Assimilation System (GLDAS) were employed. SWE anomaly (SWEA) products were provided by the Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR-E), and Canadian Meteorological Centre (CMC). The grid cell (1°×1°) and basin-averaged analyses were applied to find any possible relationship between TWSA and SWEA over the Canadian territory, from December 2002 to March 2011. Results showed that GRACE versus CMC provided the highest percentage of significant positive correlation (62.4% of the 1128 grid cells), with an average significant positive correlation coefficient of 0.5, and a maximum of 0.9. In western Canada, GRACE correlated better with multiple SWE data sets than GLDAS. Yet, over eastern Canada, mainly in the northern Québec area (~ 55ºN), GRACE provided weak or insignificant correlations with all snow products, while GLDAS appeared to be significantly correlated. For the TWSA-SWEA analysis at the basin-averaged scale, significant relationships were observed between TWSA and SWEA for most of the fifteen basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products, compared to satellite-based SWE data. Stronger relationships were found in snow-dominated basins (Rs >= 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in northern Québec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behavior was observed in the western Hudson Bay Basin. In both regions, it was found that the contribution of non-SWE compartments, including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the WGHM simulations and then subtracted from GRACE observations. The GRACE-derived SWEA correlation results showed improved relationships with three SWEA products (CMC, GlobSnow2, AMSR-E). The improvement is particularly important in the sub-basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE-derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). In general, results revealed the importance of SWE changes in association with the terrestrial water storage (TWS) variations. The second objective of this thesis investigates whether integration of remotely sensed terrestrial water storage (TWS) information, which is derived from GRACE, can improve SWE and streamflow simulations within a semi-distributed hydrology land surface model. A data assimilation (DA) framework was developed to combine TWS observations with the MESH (Modélisation Environnementale Communautaire – Surface Hydrology) model using an ensemble Kalman smoother (EnKS). This study examined the incorporation and development of the ensemble-based GRACE data assimilation framework into the MESH modeling framework for the first time. The snow-dominated Liard Basin was selected as a case study. The proposed assimilation methodology reduced bias of monthly SWE simulations at the basin scale by 17.5 % and improved unbiased root-mean-square difference (ubRMSD) by 23 %. At the grid scale, the DA method improved ubRMSD values and correlation coefficients of SWE estimates for 85 % and 97 % of the grid cells, respectively. Effects of GRACE DA on streamflow simulations were evaluated against observations from three river gauges, where it could effectively improve the simulation of high flows during snowmelt season from April to June. The influence of GRACE DA on the total flow volume and low flows was found to be variable. In general, the use of GRACE observations in the assimilation framework not only improved the simulation of SWE, but also effectively influenced the simulation of streamflow estimates.Les variations dans l'espace et le temps du stock d'eau à travers jouent un rôle important dans le système climatique de la Terre à travers l'échange des flux d'eau et d'énergie entre les compartiments du stock d’eau de la Terre, et entre l'atmosphère, les continents et les océans. Dans les régions nordiques, la fonte de la neige contrôle la disponibilité des ressources en eau. Concernant le processus hydrologique terrestre, l'équivalent en eau de la neige (SWE) est la caractéristique de neige la plus importante pour les hydrologues et les gestionnaires des ressources en eau. Le premier objectif de cette étude a examiné les variations spatio-temporelles des réservoirs terrestres d'eau et leurs liens avec les variabilités de SWE au Canada. Des anomalies de stockage d'eau terrestre (TWSA) provenant de GRACE (Gravity Recovery and Climate Experiment), du modèle hydrologique mondial WaterGAP (WGHM) et du modèle GLDAS (Global Land Data Assimilation System) ont été utilisées. Les produits du SWEA (Snow Water Equiavalent Anomaly) sont fournis par le GlobSnow2 (Global Snow Monitoring for Climate Research version 2), le AMSR-E (Advanced Microwave Scanning Radiometer‐Earth Observing System) et le Centre météorologique canadien (CMC). L'analyse par cellule de grille (1°×1°) a été appliquée pour trouver toute relation possible entre TWSA et SWEA sur le territoire canadien, de décembre 2002 à mars 2011. Les résultats montrent que GRACE par rapport à CMC a fourni le pourcentage le plus élevé de corrélation positive significative (62,4% des 1128 cellules de la grille), avec un coefficient de corrélation positif significatif moyen de 0,5 et un maximum de 0,9. Dans la partie ouest du pays, GRACE a montré un meilleur accord avec plusieurs produits SWE que GLDAS. Pourtant, dans l'est du Canada, principalement dans le nord du Québec (~ 55° N), GRACE a fourni des corrélations faibles ou insignifiantes avec tous les produits SWE, contrairement à GLDAS qui semblait être significativement corrélé. Dans le cas de l’analyse à l'échelle du bassin versant, les relations significatives ont été observées entre TWSA et SWEA pour la plupart des quinze bassins considérés (53% à 80% des bassins, selon les produits SWE considérés). Les meilleurs résultats ont été obtenus avec les produits CMC SWE, par rapport aux données SWE satellitaires. Des relations plus fortes ont été trouvées dans les bassins dominés par la neige (Rs> = 0,7), tels que le bassin versant de Liard [erreur quadratique moyenne (RMSE) = 21,4 mm] et le bassin versant de Peace (RMSE = 26,76 mm). Cependant, malgré une forte accumulation de neige dans le nord du Québec, GRACE a montré des corrélations faibles ou insignifiantes avec SWEA, peu importent les sources de données. Le même comportement a été observé dans le bassin versant ouest de la Baie d’Hudson. Dans les deux régions, il a été constaté que la contribution des compartiments non-SWE, y compris les zones humides, les eaux de surface, ainsi que les stocks d'eau du sol a un effet significatif sur les variations du stock total. Ces composantes ont été estimées à l'aide des simulations du modèle WGHM, puis soustraites des observations GRACE. Ces résultats de corrélation SWEA dérivés de GRACE ont montré une amélioration des relations avec les trois produits SWE (CMC, GlobSnow2, AMSR-E). L'amélioration est particulièrement importante dans les sous-bassins de la Baie d’Hudson, où des résultats très faibles et insignifiants avaient été précédemment trouvés avec les données GRACE TWSA. La SWEA dérivée de GRACE a montré une relation significative avec les données CMC dans 93% des bassins (13% de plus que GRACE TWSA). En somme, les résultats obtenus dans ce premier objectif ont montré le rôle important du SWE dans les variations du stock terrestre de l'eau dans la région d’étude. Le deuxième objectif de cette thèse examine si l'intégration des informations de TWS (terrestrial water storage) dérivées de GRACE (Gravity Recovery and Climate Experiment), peut améliorer les simulations du SWE et du débit d’eau dans un modèle hydrologique semi-distribué de schéma de surface. Un cadre d'assimilation de données (DA) a été développé pour combiner les observations TWS avec le modèle MESH (Modélisation Environnementale Communautaire - Hydrologie de Surface) en utilisant un ensemble Kalman Smoother (EnKS). Cette étude était la première du genre à tenter une assimilation des données GRACE dans le modèle MESH pour améliorer l’estimation du SWE. Le bassin versant de la Liard dominé par la neige a été choisi pour le site d’étude. À l’échelle du bassin versant, la méthodologie d'assimilation proposée a réduit le biais des simulations mensuelles de SWE à 17,5% et amélioré le ubRMSD (unbiased root-mean-square difference) de 23%. À l'échelle de la grille, la méthode DA a amélioré l’estimation du SWE pour les valeurs ubRMSD et les coefficients de corrélation pour 85% et 97% des cellules de la grille, respectivement. Les effets de GRACE DA sur les simulations de débit ont été évalués par rapport aux observations de trois stations des débits, où il pourrait effectivement améliorer la simulation des débits élevés pendant la saison de fonte de la neige d'avril à juin. L'influence de GRACE DA sur le volume total et les faibles débits d’eau a été trouvée variable. En général, l'utilisation des observations GRACE dans le cadre d'assimilation non seulement a amélioré la simulation de SWE, mais a également influencé efficacement la simulation des estimations de débit

Snow Depth Variability in the Northern Hemisphere Mountains Observed from Space

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change

Crystal fabric orientation of the NEGIS ice stream

IASC Workshop on the dynamics and mass budget of Arctic glacier

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling