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This study investigated the use of optical remote sensing for estimating leaf and canopy scale light use efficiency (LUE) and carbon exchange. In addition, a new leaf level model capable of predicting dynamic changes in apparent reflectance due to chlorophyll fluorescence was developed. A leaf level study was conducted to assess the applicability of passive remote sensing as a tool to measure the reduction, and the subsequent recovery, of photosynthetic efficiency during the weeks following transplantation. Spectral data were collected on newly planted saplings for a period of 8 weeks, as well as gas exchange measurements of LUE and PAM fluorescence measurements. A set of spectral indices, including the Photochemical Reflectance Index (PRI), were calculated from the reflectance measurements. A marked depression in photosynthetic rate occurred in the weeks after outplanting followed by a gradual increase, with recovery occurring in the later stages of the experimental period. As with photosynthetic rate, there was a marked trend in PRI values over the study period but no trend was observed in chlorophyll based indices. The study demonstrated that hyperspectral remote sensing has the potential to be a useful tool in the detection and monitoring of the dynamic effects of transplant shock. Relationships between hyperspectral reflectance indices, airborne carbon exchange measurements and satellite observations of ground cover were then explored across a heterogeneous Arctic landscape. Measurements were collected during August 2008, using the University of Edinburgh’s research aircraft, from an Arctic forest tundra zone in northern Finland as part of the Arctic Biosphere Atmosphere Coupling at Multiple Scales (ABACUS) study. Surface fluxes of CO2 were calculated using the eddy covariance method from airborne data that were collected from the same platform as hyperspectral reflectance measurements. Airborne CO2 fluxes were compared to MODIS vegetation indices. In addition, LUE was estimated from airborne flux data and compared to airborne measurements of PRI. There were no significant relationships between MODIS vegetation indices and airborne flux observations. There were weak to moderate (R2 = 0.4 in both cases) correlations between PRI and LUE and between PRI and incident radiation. A new coupled physiological radiative transfer model that predicts changes in the apparent reflectance of a leaf, due to chlorophyll fluorescence, was developed. The model relates a physically observable quantity, chlorophyll fluorescence, to the sub leaf level processes that cause the emission. An understanding of the dynamics of the processes that control fluorescence emission on multiple timescales should aid in the interpretation of this complex signal. A Markov Chain Monte Carlo (MCMC) algorithm was used to optimise biochemical model parameters by fitting model simulations of transient chlorophyll fluorescence to measured reflectance spectra. The model was then validated against an independent data set. The model was developed as a precursor to a full canopy scheme. To scale to the canopy and to use the model on trans-seasonal time scales, the effects of temperature and photoinhibition on the model biochemistry needs to be taken into account, and a full canopy radiative transfer scheme, such as FluorMOD, must be developed.

A imprecisão das estimativas de carbono estocado em florestas naturais no ciclo global de carbono vem criando uma demanda de desenvolvimento e padronização de métodos indiretos para modelagem deste ciclo e de emissões de CO2 provenientes de mudanças de uso da terra e florestas. O trabalho teve como objetivo estabelecer as relações empíricas existentes entre a biomassa e o estoque de carbono de uma Floresta Ombrófila Mista (FOM) e os dados ópticos provenientes de sensores remotos de média resolução espacial (ASTER, LiSSIII e TM) por meio de análise de regressão. Além disso, criou-se um cenário hipotético de Redução de Emissões por Desmatamento, Degradação Florestal e Aumento de Estoque de Carbono (REDD+). O estudo foi desenvolvido na Estação Experimental de São João do Triunfo, no estado do Paraná. As equações de regressão envolveram como variáveis dependentes (y): a biomassa e o carbono florestal, obtidos indiretamente do inventário florestal contínuo do Programa de Pesquisas Ecológicas de Longa Duração (PELD), e como variáveis independentes (x) as bandas espectrais e os índices de vegetação (IV). O tratamento estatístico envolveu a análise da matriz de correlação (r) entre as variáveis x e y; a análise de regressão linear simples, não linear e múltipla, com as seguintes estatísticas: R², R²aj., Syx, Syx% e dispersão dos resíduos, Por fim, elaboraram-se mapas temáticos para estas variáveis biofísicas. Como as correlações (r) entre as variáveis biofísicas e espectrais do sensor ASTER (15m) foram baixas, a imagem foi degradada para 30m e 45m. Na resolução de 30m, o uso dos dados ASTER foi superior ao seu uso na resolução original. Não houve diferenças significativas nos valores de r entre o uso das bandas ou dos IVs para predizer as variáveis biofísicas. Regressões lineares simples se mostraram mais adequadas do que as regressões não lineares (exponenciais e logarítmicas) e múltiplas para estimar as variáveis biofísicas, apresentando erros inferiores aos estabelecidos nas campanhas de inventários tradicionais (α < 5%). Os mapas gerados a partir do sensor ASTER 30m foram mais fidedignos ao retratar a distribuição espacial destas variáveis na área de estudo devido à alta correspondência destes com os valores observados no inventário (PELD). Assim, a equação de regressão de carbono florestal a partir do ASTER foi usada na criação do projeto REDD+. A estimativa de biomassa e de carbono florestal da FOM mediante uso de dados de sensores ópticos foi adequada, com possibilidades de ser expandida para extensas áreas. A metodologia, portanto, se mostrou apropriada para ao monitoramento, relatório e verificação de estoques de carbono em florestas. / The imprecision of the estimates of carbon stock in natural forests in the global carbon cycle has created a demand for development and standardization of indirect methods for modeling this cycle and CO2 emissions from land use change and forestry. The work had as objective to establish empirical relationships between biomass and carbon stock of an Araucaria Forest (FOM) and medium spatial resolution remote sensing data (ASTER, and LiSSIII TM) through regression analysis. In addition, we created a hypothetical scenario of Reducing Emissions from Deforestation and Forest Degradation and Enhanced Carbon Stocks (REDD+). The study was developed at the Experimental Station of São João do Triunfo, state of Paraná. The regression analysis involved the forest biomass and forest carbon obtained from continuous forest inventory of the Long Term Ecological Research Program (LTER) as dependent variables (y) and spectral bands and vegetation indices (VIs) as independent variables (x). The statistical analysis comprised correlation analysis (r) between the variables x and y; regression analysis from linear, nonlinear and multiple regressions with the following statistics: R², R²adj, Syx, Syx% and residual dispersion. Furthermore thematic maps were made. Correlations between the biophysical variables and the spectral ASTER data were weak therefore ASTER was scaling up to 30m and 45m. The resolution of 30m, using ASTER data was higher than its use in the original resolution. There were not significant differences in r values between use of bands or VIs to predict the biophysical variables. Linear regressions were more suitable than nonlinear regressions (exponential and logarithmic) and multiple to estimate the biophysical variables, with errors lower than established in traditional inventories campaigns (α <5%). Maps generated from ASTER 30m were more reliable in portraying the spatial distribution of these variables in the study area due to the high correlation of these with the values observed in the inventory (LTER). Thus, the forest carbon equation from ASTER data was used in the creation of REDD+. The estimated biomass and forest carbon by using optical sensors data was adequate, with possibilities to be expanded to large areas. The methodology thus proved suitable for the monitoring, reporting and verification of carbon stocks in forests.

A imprecisão das estimativas de carbono estocado em florestas naturais no ciclo global de carbono vem criando uma demanda de desenvolvimento e padronização de métodos indiretos para modelagem deste ciclo e de emissões de CO2 provenientes de mudanças de uso da terra e florestas. O trabalho teve como objetivo estabelecer as relações empíricas existentes entre a biomassa e o estoque de carbono de uma Floresta Ombrófila Mista (FOM) e os dados ópticos provenientes de sensores remotos de média resolução espacial (ASTER, LiSSIII e TM) por meio de análise de regressão. Além disso, criou-se um cenário hipotético de Redução de Emissões por Desmatamento, Degradação Florestal e Aumento de Estoque de Carbono (REDD+). O estudo foi desenvolvido na Estação Experimental de São João do Triunfo, no estado do Paraná. As equações de regressão envolveram como variáveis dependentes (y): a biomassa e o carbono florestal, obtidos indiretamente do inventário florestal contínuo do Programa de Pesquisas Ecológicas de Longa Duração (PELD), e como variáveis independentes (x) as bandas espectrais e os índices de vegetação (IV). O tratamento estatístico envolveu a análise da matriz de correlação (r) entre as variáveis x e y; a análise de regressão linear simples, não linear e múltipla, com as seguintes estatísticas: R², R²aj., Syx, Syx% e dispersão dos resíduos, Por fim, elaboraram-se mapas temáticos para estas variáveis biofísicas. Como as correlações (r) entre as variáveis biofísicas e espectrais do sensor ASTER (15m) foram baixas, a imagem foi degradada para 30m e 45m. Na resolução de 30m, o uso dos dados ASTER foi superior ao seu uso na resolução original. Não houve diferenças significativas nos valores de r entre o uso das bandas ou dos IVs para predizer as variáveis biofísicas. Regressões lineares simples se mostraram mais adequadas do que as regressões não lineares (exponenciais e logarítmicas) e múltiplas para estimar as variáveis biofísicas, apresentando erros inferiores aos estabelecidos nas campanhas de inventários tradicionais (α < 5%). Os mapas gerados a partir do sensor ASTER 30m foram mais fidedignos ao retratar a distribuição espacial destas variáveis na área de estudo devido à alta correspondência destes com os valores observados no inventário (PELD). Assim, a equação de regressão de carbono florestal a partir do ASTER foi usada na criação do projeto REDD+. A estimativa de biomassa e de carbono florestal da FOM mediante uso de dados de sensores ópticos foi adequada, com possibilidades de ser expandida para extensas áreas. A metodologia, portanto, se mostrou apropriada para ao monitoramento, relatório e verificação de estoques de carbono em florestas. / The imprecision of the estimates of carbon stock in natural forests in the global carbon cycle has created a demand for development and standardization of indirect methods for modeling this cycle and CO2 emissions from land use change and forestry. The work had as objective to establish empirical relationships between biomass and carbon stock of an Araucaria Forest (FOM) and medium spatial resolution remote sensing data (ASTER, and LiSSIII TM) through regression analysis. In addition, we created a hypothetical scenario of Reducing Emissions from Deforestation and Forest Degradation and Enhanced Carbon Stocks (REDD+). The study was developed at the Experimental Station of São João do Triunfo, state of Paraná. The regression analysis involved the forest biomass and forest carbon obtained from continuous forest inventory of the Long Term Ecological Research Program (LTER) as dependent variables (y) and spectral bands and vegetation indices (VIs) as independent variables (x). The statistical analysis comprised correlation analysis (r) between the variables x and y; regression analysis from linear, nonlinear and multiple regressions with the following statistics: R², R²adj, Syx, Syx% and residual dispersion. Furthermore thematic maps were made. Correlations between the biophysical variables and the spectral ASTER data were weak therefore ASTER was scaling up to 30m and 45m. The resolution of 30m, using ASTER data was higher than its use in the original resolution. There were not significant differences in r values between use of bands or VIs to predict the biophysical variables. Linear regressions were more suitable than nonlinear regressions (exponential and logarithmic) and multiple to estimate the biophysical variables, with errors lower than established in traditional inventories campaigns (α <5%). Maps generated from ASTER 30m were more reliable in portraying the spatial distribution of these variables in the study area due to the high correlation of these with the values observed in the inventory (LTER). Thus, the forest carbon equation from ASTER data was used in the creation of REDD+. The estimated biomass and forest carbon by using optical sensors data was adequate, with possibilities to be expanded to large areas. The methodology thus proved suitable for the monitoring, reporting and verification of carbon stocks in forests.

A imprecisão das estimativas de carbono estocado em florestas naturais no ciclo global de carbono vem criando uma demanda de desenvolvimento e padronização de métodos indiretos para modelagem deste ciclo e de emissões de CO2 provenientes de mudanças de uso da terra e florestas. O trabalho teve como objetivo estabelecer as relações empíricas existentes entre a biomassa e o estoque de carbono de uma Floresta Ombrófila Mista (FOM) e os dados ópticos provenientes de sensores remotos de média resolução espacial (ASTER, LiSSIII e TM) por meio de análise de regressão. Além disso, criou-se um cenário hipotético de Redução de Emissões por Desmatamento, Degradação Florestal e Aumento de Estoque de Carbono (REDD+). O estudo foi desenvolvido na Estação Experimental de São João do Triunfo, no estado do Paraná. As equações de regressão envolveram como variáveis dependentes (y): a biomassa e o carbono florestal, obtidos indiretamente do inventário florestal contínuo do Programa de Pesquisas Ecológicas de Longa Duração (PELD), e como variáveis independentes (x) as bandas espectrais e os índices de vegetação (IV). O tratamento estatístico envolveu a análise da matriz de correlação (r) entre as variáveis x e y; a análise de regressão linear simples, não linear e múltipla, com as seguintes estatísticas: R², R²aj., Syx, Syx% e dispersão dos resíduos, Por fim, elaboraram-se mapas temáticos para estas variáveis biofísicas. Como as correlações (r) entre as variáveis biofísicas e espectrais do sensor ASTER (15m) foram baixas, a imagem foi degradada para 30m e 45m. Na resolução de 30m, o uso dos dados ASTER foi superior ao seu uso na resolução original. Não houve diferenças significativas nos valores de r entre o uso das bandas ou dos IVs para predizer as variáveis biofísicas. Regressões lineares simples se mostraram mais adequadas do que as regressões não lineares (exponenciais e logarítmicas) e múltiplas para estimar as variáveis biofísicas, apresentando erros inferiores aos estabelecidos nas campanhas de inventários tradicionais (α < 5%). Os mapas gerados a partir do sensor ASTER 30m foram mais fidedignos ao retratar a distribuição espacial destas variáveis na área de estudo devido à alta correspondência destes com os valores observados no inventário (PELD). Assim, a equação de regressão de carbono florestal a partir do ASTER foi usada na criação do projeto REDD+. A estimativa de biomassa e de carbono florestal da FOM mediante uso de dados de sensores ópticos foi adequada, com possibilidades de ser expandida para extensas áreas. A metodologia, portanto, se mostrou apropriada para ao monitoramento, relatório e verificação de estoques de carbono em florestas. / The imprecision of the estimates of carbon stock in natural forests in the global carbon cycle has created a demand for development and standardization of indirect methods for modeling this cycle and CO2 emissions from land use change and forestry. The work had as objective to establish empirical relationships between biomass and carbon stock of an Araucaria Forest (FOM) and medium spatial resolution remote sensing data (ASTER, and LiSSIII TM) through regression analysis. In addition, we created a hypothetical scenario of Reducing Emissions from Deforestation and Forest Degradation and Enhanced Carbon Stocks (REDD+). The study was developed at the Experimental Station of São João do Triunfo, state of Paraná. The regression analysis involved the forest biomass and forest carbon obtained from continuous forest inventory of the Long Term Ecological Research Program (LTER) as dependent variables (y) and spectral bands and vegetation indices (VIs) as independent variables (x). The statistical analysis comprised correlation analysis (r) between the variables x and y; regression analysis from linear, nonlinear and multiple regressions with the following statistics: R², R²adj, Syx, Syx% and residual dispersion. Furthermore thematic maps were made. Correlations between the biophysical variables and the spectral ASTER data were weak therefore ASTER was scaling up to 30m and 45m. The resolution of 30m, using ASTER data was higher than its use in the original resolution. There were not significant differences in r values between use of bands or VIs to predict the biophysical variables. Linear regressions were more suitable than nonlinear regressions (exponential and logarithmic) and multiple to estimate the biophysical variables, with errors lower than established in traditional inventories campaigns (α <5%). Maps generated from ASTER 30m were more reliable in portraying the spatial distribution of these variables in the study area due to the high correlation of these with the values observed in the inventory (LTER). Thus, the forest carbon equation from ASTER data was used in the creation of REDD+. The estimated biomass and forest carbon by using optical sensors data was adequate, with possibilities to be expanded to large areas. The methodology thus proved suitable for the monitoring, reporting and verification of carbon stocks in forests.

Inland water has for a long time showed vast stress due to eutrophication, mainly caused by increased levels of phosphorus. Applying remote sensing as a tool for monitoring water parameters has long been used. In the past, inland watercourses measurements have proven to be challenging, often due to the limitations of satellite missions' spectral resolution or difficulties in implementing the appropriate methodology. This project investigates the potential to use a high-resolution satellite mission, Sentinel 2, to monitor phosphorus with the proxies total suspended matter (TSM) and turbidity in two smaller watercourses, Fyrisån and Sävjaån, in Uppsala, Sweden. From April to November, a period spanning three years (2018, 2019, and 2021), empirical modeling was employed to conduct investigations. The three years all represent different weather patterns and discharge velocities. The bands 2 to 8 were investigated individually and together to see if there was a potential using a single band correlation or multiple to correlate with turbidity or TSM. The two optically active water parameters are known to have a high correlation with the non-optically active phosphorus. There was no correlation found between the proxies and each band individually for any of the years investigated. Using a multi regression analysis both 2018 and 2019 showed high correlation for TSM, and 2019 for turbidity. While the results for 2021 were not significant for any of the proxies. The conclusion indicates that with right surrounding factors it’s possible to use TSM and turbidity as a proxy for phosphorus when using Sentinel 2 in these smaller watercourses. Nevertheless, further studies are needed to investigate how the proxy and the nutrient acts together with satellite data for peaks etc. before using Sentinel 2 results as a direct interpretation.

With the advent of very high spatial resolution (VHR) satellite, spatial details within the image scene have increased considerably. This led to the development of object-based image analysis (OBIA) for the analysis of VHR satellite images. Image segmentation is the fundamental step for OBIA. However, a large number of techniques exist for RS image segmentation. To identify the best ones for VHR imagery, a comprehensive literature review on image segmentation is performed. Based on that review, it is found that the multiresolution segmentation, as implemented in the commercial software eCognition, is the most widely-used technique and has been successfully applied for wide variety of VHR images. However, the multiresolution segmentation suffers from the parameter estimation problem. Therefore, this study proposes a solution to the problem of the parameter estimation for improving its efficiency in VHR image segmentation. The solution aims to identify the optimal parameters, which correspond to optimal segmentation. The solution to the parameter estimation is drawn from the Equations related to the merging of any two adjacent objects in multiresolution segmentation. The solution utilizes spectral, shape, size, and neighbourhood relationships for a supervised solution. In order to justify the results of the solution, a global segmentation accuracy evaluation technique is also proposed. The solution performs excellently with the VHR images of different sensors, scenes, and land cover classes. In order to justify the applicability of solution to a real life problem, a building detection application based on multiresolution segmentation from the estimated parameters, is carried out. The accuracy of the building detection is found nearly to be eighty percent. Finally, it can be concluded that the proposed solution is fast, easy to implement and effective for the intended applications.

Le projet INDESO (Développement de l'océanographie spatiale en Indonésie) en collaboration entre le gouvernement indonésien (Ministère des affaires maritimes et des pêches - MMAF) et la société française CLS (Collecte Localisation Satellite) promeut l'utilisation des technologies spatiales pour la surveillance des côtes et des mers indonésiennes. Cette thèse fait partie du volet sur la surveillance des récifs coralliens, mené par l'IRD (Institut de Recherche pour le Développement). L'objectif principal était de déterminer si les habitats des récifs coralliens dans l'île de Bunaken dans le nord de Sulawesi sont résilients, en utilisant i) des cartes d'habitat nouvellement conçues, ii) des données in situ et une série chronologique de 15 ans unique d'images satellites de différents capteurs très haute résolution (THR), iii) des données auxiliaires qui pourraient expliquer les changements détectés. Les résultats comprennent des cartes très détaillées de l'habitat des récifs de Bunaken (194 polygones cartographiés et un recensement de 175 habitats). L'influence de la chute du niveau de la mer sur la mortalité des coraux pendant l'événement El Niño de 2015-2016 est présentée en détail, et l'importance de ce processus est également discutée à partir de l'interprétation d'une série chronologique unique de 15 ans d'images THR. La série temporelle met en évidence des trajectoires très différentes des habitats coralliens. Nous avons conclu que le récif de Bunaken démontre une capacité de résilience et sans déphasage, mais qu'un diagnostic définitif de sa résilience reste difficile à déterminer par imagerie. Des trajectoires de l'habitat ne peuvent pas être totalement interprétées sans changer certains paradigmes de surveillance, et sans utiliser une combinaison d'observations de télédétection et de données in situ. / The INDESO (Infrastructure Development of Space Oceanography) project, in collaboration with the Indonesian Government (Ministry of Marine Affairs and Fisheries - MMAF) and the French company CLS (Collecte Localisation Satellites), promotes the use of space technologies for monitoring coastlines and Indonesian seas. This thesis is part of coral reef monitoring component, led by the IRD (Institute de Recherche pour le Développement). The main objective was to determine wether coral reef habitats on Bunaken Island in Northern Sulawesi are resilient, using (i) newly desgined habitat maps, (ii) in situ data, and a unique 15-year time series of satellite images of different very high resolution (VHR) sensors, and (iii) ancillary data that could explain the changes detected. The results include highly detailed maps of the Bunaken reefs habitat (194 polygons mapped and a census of 175 habitats). The influence of sea level fall on coral mortality during the El-Nino event of 2015 - 2016 is presented in detail, and the importance of this process is also discussed from the interpretation of a unique time series of 15 years of VHR images. The temporal series reveals very different trajectories of the coral habitats. We conclude that Bunaken reefs demonstrate an ability to resileince and without phase shift, but that a definitive diagnosis of their resilience remains difficult to determine by imagery. Habitat trajectories can not be fully interpreted without changing some monitoring paradigms and without using a combination of remote sensing and in situ data.

En régions méditerranéennes, la gestion de l'eau à partir d'outils d'aide à la décision requiert la connaissance des échanges d'énergie et de masse entre la surface et l'atmosphère, dont l'évapotranspiration, qui représente la composante majeure du cycle hydrologique. Les avancées récentes, en termes de modélisation des Transferts Sol-Végétation-Atmosphère (TSVA) pour des couverts homogènes et d'assimilation des données de télédétection, principalement à l'échelle subrégionale, permettent d'envisager le passage à l'échelle régionale pour des couverts complexes de type cultures en rang. L'objectif de ce travail est de développer une modélisation versatile et de la caler par télédétection à l'échelle régionale sur un bassin versant viticole. Les investigations sont menées sur le bassin versant de la Peyne, dans le cadre de l'ORE OMERE. Une analyse bibliographique a permis de sélectionner un modèle TSVA à vocation régionale avec un nombre réduit de paramètres. L'implémentation de ce modèle est motivée par des objectifs de versatilité mais aussi d'inclusion dans une plateforme de simulation. Parallèlement, l'évapotranspiration a été spatialisée à l'échelle régionale par synergie des données télédétectées infrarouge thermique ASTER et Landsat. Les chroniques d'évapotranspiration obtenues sont ensuite utilisées pour caler le modèle TSVA. / In Mediterranean regions, decision making tools for water management require knowledge of water and mass exchanges between land surface and atmosphere, where evapotranspiration is the main component of the hydrological cycle. Recent advances, in terms of modeling and remote sensing, mainly at the subregional scale for homogeneous canopies, allow foreseeing the regional extent for complex landscapes such as row crops. This work aims to propose and calibrate a versatile modeling at the regional scale over a vineyard watershed, the calibration relying on remote sensing. A literature review allows selecting a SVAT model with a regional scope and a limited number of parameters. Model implementation is motivated by versatility and further inclusion into a simulation platform. Then, evapotranspiration is spatialized synergistically by using thermal infrared data from ASTER and Landsat remote sensors. Next, the time series obtained for evapotranspiration are used for calibrating the selected SVAT model. These investigations are conducted over the Peyne watershed, within the framework of the OMERE Observatory for environmental research.

Water stored in snow is a critical contribution to the world’s available freshwater supply and is fundamental to the sustenance of natural ecosystems, agriculture and human societies. The importance of snow for the natural environment and for many socio-economic sectors in several mid‐ to high‐latitude mountain regions around the world, leads scientists to continuously develop new approaches to monitor and study snow and its properties. The need to develop new monitoring methods arises from the limitations of in situ measurements, which are pointwise, only possible in accessible and safe locations and do not allow for a continuous monitoring of the evolution of the snowpack and its characteristics. These limitations have been overcome by the increasingly used methods of remote monitoring with space-borne sensors that allow monitoring the wide spatial and temporal variability of the snowpack. Snow models, based on modeling the physical processes that occur in the snowpack, are an alternative to remote sensing for studying snow characteristics. However, from literature it is evident that both remote sensing and snow models suffer from limitations as well as have significant strengths that it would be worth jointly exploiting to achieve improved snow products. Accordingly, the main objective of this thesis is the development of novel methods for the estimation of snow parameters by exploiting the different properties of remote sensing and snow model data. In particular, the following specific novel contributions are presented in this thesis: i. A novel data fusion technique for improving the snow cover mapping. The proposed method is based on the exploitation of the snow cover maps derived from the AMUNDSEN snow model and the MODIS product together with their quality layer in a decision level fusion approach by mean of a machine learning technique, namely the Support Vector Machine (SVM). ii. A new approach has been developed for improving the snow water equivalent (SWE) product obtained from AMUNDSEN model simulations. The proposed method exploits some auxiliary information from optical remote sensing and from topographic characteristics of the study area in a new approach that differs from the classical data assimilation approaches and is based on the estimation of AMUNDSEN error with respect to the ground data through a k-NN algorithm. The new product has been validated with ground measurement data and by a comparison with MODIS snow cover maps. In a second step, the contribution of information derived from X-band SAR imagery acquired by COSMO-SkyMed constellation has been evaluated, by exploiting simulations from a theoretical model to enlarge the dataset.