Method for constructing an AOD-related atmospheric correction time series for the CLARA-A2 SAL data record

In the Satellite Application Facility on Climate Monitoring (CM SAF) project, financially supported by EUMETSAT, the 34-year long (1982-2015) broadband albedo time series CLARA-A2 SAL (the Surface ALbedo from the CM SAF cLoud, Albedo and RAdiation data record, second version) was produced from Advanced Very High Resolution Radiometer (AVHRR) measurements. CLARA-A2 SAL data record uses a Simplified Method for Atmospheric Correction algorithm SMAC for correcting for atmospheric effects. Aerosol optical depth (AOD) is the main input of the algorithm. Because there were no global AOD time series for the whole needed time period (1982-2015), the AOD-related time series were constructed, and the method for calculating it is described in this report

ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters

Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA – ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (̂ρw). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. This volume of data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errors in ̂ρw(560) and ̂ρw(664) were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15–30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concentration of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20–30% uncertainties in ̂ρw(490 ≤ λ ≤ 743 nm) yielded 25–70% uncertainties in derived Chla and TSS products for topperforming AC processors. We summarize our results using performance matrices guiding the satellite user community through the OWT-specific relative performance of AC processors. Our analysis stresses the need for better representation of aerosols, particularly absorbing ones, and improvements in corrections for sky- (or sun-) glint and adjacency effects, in order to achieve higher quality downstream products in freshwater and coastal ecosystems

ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters

Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA – ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (̂ρw). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. This volume of data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errors in ̂ρw(560) and ̂ρw(664) were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15–30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concentration of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20–30% uncertainties in ̂ρw(490 ≤ λ ≤ 743 nm) yielded 25–70% uncertainties in derived Chla and TSS products for topperforming AC processors. We summarize our results using performance matrices guiding the satellite user community through the OWT-specific relative performance of AC processors. Our analysis stresses the need for better representation of aerosols, particularly absorbing ones, and improvements in corrections for sky- (or sun-) glint and adjacency effects, in order to achieve higher quality downstream products in freshwater and coastal ecosystems

Klassificering av marktäcke med multi-temporal SAR och optisk satellitdata

Satellite data are widely used within remote sensing to respond to the growing need for a deeper understanding of the Earth’s bio- and geophysical parameters. Applications, such as land cover classification has for long been an important task within the field. Optical satellite data have proven to be efficient tools, however, they are unavailable in some conditions, such as cloudy weather. This deficit can be addressed with synthetic aperture radars (SAR), and recently, improvements have been made in their spatial and temporal coverage. Furthermore, a fusion of these data takes advantage of their different characteristics and can lead to even improved outcomes. The aim of this study was to develop and implement an effective land cover classification approach for the boreal forest zone by using multi-temporal SAR and optical data. Optical and SAR satellite data were collected from the area around Hyytiälä, Finland. One Landsat 8 scene and a time series of Sentinel-1 data spanning over a year were used. Co- and cross-polarized data were available. A very high resolution (VHR) reference image was manually interpreted to form training and test data. Features were extracted from both data sets and those from the SAR data were reduced using feature selection. A land cover classification was then performed separately on each data set and with a fused data set. Different features were tested to find an optimal combination. The classifications were performed with the nearest neighbor rule and the maximum likelihood classifier. This resulted in several classification maps which were validated with the test plots. The results showed that the single-sensor classifications were noisy. Classifications with only optical imagery performed better. Additionally, removing some of the original data from the calculations, which can speed up the process, led to worse results. The multi-sensor classifications with the fused data improved the results significantly. Much of the noise was no longer present. The best classification was reached with a fused data set of four SAR features from VH polarized data and four optical features, which gained a final accuracy of 89.8 %. This classification was done with the maximum likelihood classifier. Accuracies up to 97.3 % were also reached but this result had clear flaws in the visual interpretation. It was concluded that fusing optical and SAR data for land cover classification in the boreal zone is a very promising strategy and should be investigated further to reach even better results.Satellitdata används i stor utsträckning inom fjärranalys för att fylla det ständiga behovet av mer ingående kännedom av jordens bio- och geofysiska parametrar. Applikationer, såsom klassificering av marktäcke, har redan länge varit en viktig uppgift inom studieområdet. Optisk satellitdata har visat sig vara ett effektivt redskap, men den är inte tillgänglig i vissa situationer, såsom molnigt väder. Denna brist kan övervinnas med syntetisk aperturradar (SAR) och nyligen har förbättringar skett inom den spatiala och temporala täckningen. Därutöver utnyttjar en fusion av dessa data de olika karaktärerna vilket kan leda till även förbättrade resultat. Denna studies syfte var att utveckla och tillämpa en effektiv metod för klassificering av marktäcke inom boreala skogar med hjälp av multi-temporal SAR och optisk data. Optisk och SAR data samlades från området omkring Hyytiälä, Finland. En Landsat 8 scen och en tidsserie av Sentinel-1 data över ett år användes. Data med olika polarisationer var tillgängliga. En referensbild med hög resolution tolkades manuellt för att bilda tränings- och testdata. Variabler togs fram från båda datauppsättningarna varefter variablerna från SAR datan reducerades genom att välja de bästa. En klassificering av marktäcket utfördes sedan skiljt för de olika datauppsättningarna samt med sammanslagen data. Olika variabler testades för att hitta den bästa kombinationen. Klassificeringen gjordes med regeln för den närmaste grannen samt med maximum likelihood-metoden. Detta resulterade i flera klassificeringskartor vilka sedan validerades med testdatan. Resultaten av klassificeringarna med data från en sensor hade mycket störningar. Den optiska bilden gav bättre resultat. Då en del av den ursprungliga datan togs bort från uträkningarna, vilket kan effektivera processen, blev resultaten sämre. En fusion av datan förbättrade resultaten betydligt då en stor del av störningarna försvann. Den bästa klassificeringen nåddes med en sammanslagen datauppsättning av fyra SAR-variabler från VH polariserad data och fyra optiska variabler med en slutlig noggrannhet på 89.8 %. Denna klassificering gjordes med maximum likelihood-metoden. Noggrannheter upp till 97.3 % nåddes även, men detta resultat hade stora brister i den visuella tolkningen. En slutsats drogs att en sammanslagning av optisk och SAR data för klassificering av marktäcke i boreala områden är en väldigt lovande strategi och bör undersökas vidare för att nå även bättre resultat

Suivi des flux d'énergie, d'eau et de carbone à la surface : apport de la télédétection et de la modélisation du rayonnement solaire absorbé par la végétation

It is known that a global 4% increase of land surface albedo (also called reflectivity) may result approximately in a decrease of 0.7°C in the Earth’s equilibrium temperature. Nowadays the surface properties (including albedo) are changing under climatic and human pressure. At the same time, there is a debate that divides the scientific community about the potential trends (increase or decrease) affecting the surface incoming solar radiation since mid-1980 (resulting of a decrease or increase of aerosol concentration in the atmosphere, respectively). The Earth is a complex system driven at the surface level by three cycles (energy, water, and carbon). These cycles are not insensitive to changes of surface reflectivity, incoming radiation, or aerosol properties. For example, some argue that the increase of diffuse radiation during the last decades would have led to an exceed of carbon uptake by the Earth’s vegetation of 9.3%. The main issue raised here is to assess the added value of the knowledge in absorbed solar radiation by the surface (combination of incoming solar radiation with surface albedo) and, especially, by the vegetation for the monitoring of energy, water and carbon fluxes.In this work, I have used satellite observations and modeled the radiative transfer theory in order to make dynamic mapping of solar radiation absorbed by the surface and through the vertical dimension of the vegetation. First, I quantified each uncertainty source affecting incoming solar radiation, surface albedo and the way radiation is split between horizontal and vertical heterogeneity. In a second step, I measured the added value of using this absorbed radiation mapping of the surface by satellite to estimate the energy and water fluxes at the surface. The resulting improved scores of weather forecast models in the short-range time scale suggested potential feedbacks at the climatic time scale over sensible areas such as the Sahel region. Another significant outcome is that the developments proposed to better characterize the vertical heterogeneity within the canopy led to an improvement of 15% of annual global terrestrial gross primary production (GPP). Moreover, this study has led to measure the impact of the lack of knowledge of spatial and temporal variability of aerosol properties (concentration and type). I have shown that the tracking of temporal changes of directional properties of reflectance allows me to retrieve to the amount of aerosols in the atmosphere as precisely as other widely used methods but with a higher frequency (5 times more) by using data from geostationary satellite. Finally, this study addresses some possibilities to better track temporal changes of properties of reflectivity of surface and aerosol of atmosphere, and to access to a better monitoring of biogeochemical cycles of the terrestrial biosphere.Au niveau global, il a été estimé qu’une augmentation de 4% de l’albédo (ou réflectivité) de la surface provoquerait une diminution de 0,7° de la température d’équilibre de la Terre. Or les propriétés des surfaces (dont l’albédo) changent sous la pression climatique et l’action de l’homme. Parallèlement à ce changement des propriétés de surface un débat divise la communauté scientifique sur une éventuelle diminution ou augmentation du rayonnement incident à la surface depuis le milieu des années 1980 (conséquence d’une augmentation ou diminution de la concentration d’aérosols dans l’atmosphère). La Terre est un système complexe piloté en sa surface par 3 cycles (énergie, eau et carbone). Ces cycles ne sont pas insensibles à ces changements de propriété de réflectivité de surface, de rayonnement solaire incident ou de concentration en aérosols. Certains avancent ainsi qu’une augmentation du rayonnement diffus durant les dernières décennies aurait déjà entraîné un excédent de captation de carbone par la végétation de 9.3%. La problématique ici soulevée est d’évaluer l’apport de la connaissance du flux solaire absorbé par la surface (combinaison du rayonnement solaire et de l’albédo de surface) et plus particulièrement par sa partie végétative pour le suivi des flux d’énergie, d’eau et de carbone. Dans ce travail, j’ai fait appel à l’observation satellitaire et à la modélisation du transfert radiatif pour cartographier la dynamique du rayonnement solaire absorbé par la surface et sur la verticale de la végétation. Dans un premier temps, chacune des sources d’incertitudes sur le rayonnement incident, sur l’albédo de surface mais aussi sur la répartition du rayonnement entre les hétérogénéités horizontales et verticales à la surface furent quantifiées. Puis tout en discutant l’effet de ces incertitudes, j’ai mesuré l’apport de l’utilisation de cette cartographie par satellite du rayonnement solaire absorbé pour estimer les flux d’énergie et d’eau en surface ; ce qui améliora les scores de prévision du temps à court terme et permis également de suggérer des rétroactions à l’échelle climatique sur des zones sensibles tel le Sahel. Aussi une correction de biais de 15% sur l’estimation de la production primaire brute de carbone à l’échelle planétaire démontra l’importance des développements réalisés afin de caractériser les hétérogénéités verticales dans le couvert. Finalement, ce travail m’a conduit à chiffrer l’impact de la méconnaissance des variabilités spatiales et temporelles des propriétés des aérosols (concentration et type). J’ai montré que le suivi au cours du temps des propriétés de directionalité de la réflectivité de surface (tel abordé dans la première partie de mon étude) pouvait aussi permettre de remonter à la quantité d’aérosol dans l’atmosphère. L’utilisation d’observations issues de satellite géostationnaire permet d’estimer la concentration en aérosol avec la même qualité mais avec une fréquence de détection plus élevée (x5 environ) que les méthodes classiques. Enfin, ce travail dresse des pistes pour améliorer la détection des changements des propriétés de réflectivité de surface et d’aérosols de l’atmosphère, et atteindre un suivi encore meilleur des cycles biogéochimiques de la biosphère terrestre

Vliv atmosférické a topografické korekce na přesnost odhadu množství chlorofylu ve smrkových lesních porostech

Odstraňování efektů zemské atmosféry (tzv. atmosférická korekce) je jednou z klíčových součástí předzpracování obrazových dat dálkového průzkumu Země používaných pro kvantitativní nebo semi-kvantitativní analýzu. Přestože v současné době existuje velké množství robustních výpočetních technik kvantitativního odhadu různých parametrů zemského povrchu, vliv atmosférické korekce na výsledky těchto odhadů zpravidla není brán dostatečně v úvahu. Hlavním cílem této práce je zhodnocení vlivu použití různých technik atmosférické korekce na přesnost kvantitativního odhadu množství chlorofylu v lesních porostech smrku ztepilého (Picea abies). Obsah chlorofylu byl určován na podkladě výpočtu vybraných vegetačních indexů, které jsou na obsah chlorofylu citlivé (ANCB650-720, MSR, N718, TCARI/OSAVI a D718/D704). Hodnoty těchto indexů byly simulovány pomocí kombinace modelů radiativního transferu PROSPECT a DART. Výsledné odhady obsahu chlorofylu byly na závěr validovány pomocí výsledků laboratorního stanovení obsahu chlorofylu v odebraných vzorcích smrkových jehlic. Kromě toho byl v rámci práce odvozen nový index pro hodnocení podobnosti dvou srovnávaných spekter nazvaný normalized Area Under Difference Curve (nAUDC). V rámci této práce byla testována potenciální možnost náhrady standardní atmosférické korekce...Removal of atmospheric effects (atmospheric correction) is an essential step in a pre-processing chain of all remotely sensed image data used for any quantitative or semi-quantitative analysis. Although there are many robust computing techniques allowing quantitative estimation of various parameters of the Earth's surface, the influence of atmospheric correction on the accuracy of such estimation is usually not taken into account at all. The main focus of this thesis is to assess the influence of the use of different atmospheric correction techniques on the Norway spruce (Picea abies) canopy chlorophyll content estimation accuracy. Canopy chlorophyll content was estimated using values of chlorophyll sensitive vegetation indices (ANCB650-720, MSR, N718, TCARI/OSAVI and D718/D704) simulated by a coupling of PROSPECT and DART radiative transfer models and validated by a ground-truth dataset. A new spectral similarity index called normalized Area Under Difference Curve (nAUDC) was developed to allow mutual comparison of two spectra originating from hyperspectral datasets corrected by different atmospheric correction methods. Potential substitutability of the standard physically-based ATCOR-4 atmospheric correction by the empirical correction based on the data acquired by the downwelling irradiance...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Empirical approach to satellite snow detection

Lumipeitteellä on huomattava vaikutus säähän, ilmastoon, luontoon ja yhteiskuntaan. Pelkästään sääasemilla tehtävät lumihavainnot (lumen syvyys ja maanpinnan laatu) eivät anna kattavaa kuvaa lumen peittävyydestä tai muista lumipeitteen ominaisuuksista. Sääasemien tuottamia havaintoja voidaan täydentää satelliiteista tehtävillä havainnoilla. Geostationaariset sääsatelliitit tuottavat havaintoja tihein välein, mutta havaintoresoluutio on heikko monilla alueilla, joilla esiintyy kausittaista lunta. Polaariradoilla sääsatelliittien havaintoresoluutio on napa-alueiden läheisyydessä huomattavasti parempi, mutta silloinkaan satelliitit eivät tuota jatkuvaa havaintopeittoa. Tiheimmän havaintoresoluution tuottavat sääsatelliittiradiometrit, jotka toimivat optisilla aallonpituuksilla (näkyvä valo ja infrapuna). Lumipeitteen kaukokartoitusta satelliiteista vaikeuttavat lumipeitteen oman vaihtelun lisäksi pinnan ominaisuuksien vaihtelu (kasvillisuus, vesistöt, topografia) ja valaistusolojen vaihtelu. Epävarma ja osittain puutteellinen tieto pinnan ja kasvipeitteen ominaisuuksista vaikeuttaa luotettavan automaattisen analyyttisen lumentunnistusmenetelmän kehittämistä ja siksi empiirinen lähestymistapa saattaa olla toimivin vaihtoehto automaattista lumentunnistusmenetelmää kehitettäessä. Tässä työssä esitellään kaksi EUMETSATin osittain rahoittamassa H SAFissa kehitettyä lumituotetta ja niissä käytetyt empiiristä lähestymistapaa soveltaen kehitetyt algoritmit. Geostationaarinen MSG/SEVIRI H31 lumituote on saatavilla vuodesta 2008 alkaen ja polaarituote Metop/AVHRR H32 vuodesta 2015 alkaen. Lisäksi esitellään pintahavaintoihin perustuvat validointitulokset, jotka osoittavat tuotteiden saavuttavan määritellyt tavoitteet.Snow cover plays a significant role in the weather and climate system, ecosystems and many human activities, such as traffic. Weather station snow observations (snow depth and state of the ground) do not provide highresolution continental or global snow coverage data. The satellite observations complement in situ observations from weather stations. Geostationary weather satellites provide observations at high temporal resolution, but the spatial resolution is low, especially in polar regions. Polarorbiting weather satellites provide better spatial resolution in polar regions with limited temporal resolution. The best detection resolution is provided by optical and infra-red radiometers onboard weather satellites. Snow cover in itself is highly variable. Also, the variability of the surface properties (such as vegetation, water bodies, topography) and changing light conditions make satellite snow detection challenging. Much of this variability is in subpixel scales, and this uncertainty creates additional challenges for the development of snow detection methods. Thus, an empirical approach may be the most practical option when developing algorithms for automatic snow detection. In this work, which is a part of the EUMETSAT-funded H SAF project, two new empirically developed snow extent products for the EUMETSAT weather satellites are presented. The geostationary MSG/SEVIRI H32 snow product has been in operational production since 2008. The polar product Metop/AVHRR H32 is available since 2015. In addition, validation results based on weather station snow observations between 2015 and 2019 are presented. The results show that both products achieve the requirements set by the H SAF

Vegetation change detection and soil erosion risk assessment modelling in the Man River basin, Central India

Land use change directly increased soil erosion risk, which is a very sensitive environmental issue in Central India. To evaluate the response of land use changes on soil erosion risk, research was implemented using remote sensing techniques, coupled with ground information, to develop an integrated modelling approach to study the factors driving land use changes in the Man River basin, Central India. Results were used to assess the impact of land use change on soil erosion risk. First, a series of sub methods were applied to monitor and verify land use land cover change in the study area which included pre-processing, classification and assessment of land use transaction from 1971 to 2013 using Landsat time series imagery. Additionally, an independent spatial assessment of deforestation, forest degradation and responsible drivers for the period 2009-2013 was conducted to enable a deeper analysis of forestry activates using the GIS based direct interpretation approach. The research also developed a robust accuracy assessment method to check the quality of the 2009 and 2013 classification maps using good quality Google Earth TM imagery and a field measured GPS dataset. These approaches were largely based on the GOFC- GOLD (2010) and IPCC good recommendations for land use land cover mapping and verification. The information obtained from an accuracy assessment was also used to estimate deforestation area and construct confidence intervals that reflect the uncertainty of the area estimates obtained. Such analysis is rarely applied in current published verification assessments. In the second phase of the study, a Geo-spatial interface for process-based Water Erosion Prediction Project (GeoWEPP) was implemented, to estimate the response of land use and land cover change on soil erosion risk in several scenarios derived from both ground and satellite based precipitation, DEMs and vegetation change. GeoWEPP was used at the hillslope scale in three selected watersheds within the Man River basin using Landsat, LISSIII, Cartosat-1, ASTER, SRTM, TRMM and ground based datasets. The results highlight that the study developed a realistic approach using remote sensing techniques to understand the pattern and process of landscape change in the Man River basin and its response on soil erosion risk. Over the last four decades, forest and agriculture areas were found to be the most dynamic land use /land cover categories. During the last four decades, around 54200 ha (33.7 %) forest area has been decreased due to the expansion of agriculture, forest harvesting and infrastructure development. The direct interpretation approach estimated similar patterns of deforestation and forest degradation associated with iii drivers for the 2009 to 2013 time period, but this approach also provided more accurate and location specific information than automatic analysis. The overall correspondence between the map and reference data are a good measure for 2009 and 2013; 94.03 % and 92.8 % respectively. User‘s and producer‘s accuracies of individual classes range from 75 % to 99 %. Using the accuracy assessment data and a simple set of equations, an error-adjusted estimate of the area of deforestation was obtained (± 95% confidence interval) of 23382 ± 550 ha. The estimated average annual soil loss for all three watersheds is 21 T/ha which was found to be comparable to similar studies carried out in the study region. The highest soil loss rates occurred in areas of agriculture (301 T. /ha /yr) and fallow land (158 T/ha/yr), while the lowest rates were recorded in forest land (33.45 T/ha/yr). Agriculture extension (316.5 ha) due to forest harvesting (234 ha) in the last four decades is one of the significant drivers to speed up soil erosion (7.37 T/ha/yr.) in all three watersheds. The spatial pattern of erosion risk indicates that areas with forest cover have minimum rates of soil erosion, while areas with extensive human intervention such as agriculture and fallow land, have high estimated rates of soil erosion. The different DEMs generated varied topographic and hydrologic attributes, which in turn led to significantly different erosion simulations. GeoWEPP using Cartosat-1 (30 m) and SRTM (90 m) produced the most accurate estimation of soil loss which was close to similar already published studies in the area. TRMM rainfall data has good to use as a rainfall parameter for soil erosion risk mapping in study area. Overall, the integrated approach using remote sensing and GIS allowed a clear understanding of the factors that drive land use/land cover change to be developed and enabled the impact of this change on soil erosion risk in the Man River basin, Central India to be assessed

A fractional snow cover mapping method for optical remote sensing data, applicable to continental scale

This thesis focuses on the determination of fractional snow cover (FSC) from optical data provided by satellite instruments. It describes the method development, starting from a simple regionally applicable linear interpolation method and ending at a globally applicable, semi-empirical modeling approach. The development work was motivated by the need for an easily implementable and feasible snow mapping method that could provide reliable information particularly for forested areas. The contribution of the work to the optical remote sensing of snow is mainly associated with accounting for boreal forest canopy effect to the observed reflectance, thus facilitating accurate fractional snow retrievals also for ground beneath the tree canopies. The first proposed approach was based on a linear interpolation technique, which relies on a priori known reference reflectances at a) full snow cover and b) snow-free conditions for each calculation unit-area. An important novelty in the methodology was the utilization of a forest sparseness index determined from AVHRR reflectance data acquired at full dry snow cover conditions. This index was employed to describe the similarity between different unit-areas. In practice, the index was used to determine the reference reflectances for such unit-areas for which the reflectance level could not be determined otherwise, e.g. due to frequent cloud cover. This approach was found to be feasible for Finnish drainage basins characterized by fragmented landscape with moderate canopies. Using a more physical approach instead of linear interpolation would allow the model parameterization using physical quantities (reflectances), and would therefore leave space for further model developments based on measuring and/or modeling of these quantities. The semi-empirical reflectance model-based method SCAmod originates from radiative transfer theory and describes the scene-level reflectance as a mixture of three major constituents: opaque forest canopy, snow and snow-free ground, which are interconnected through transmissivity and snow fraction. Transmissivity, in turn, can be derived from reflectance observations under conditions that highlight the presence of forest canopy, namely the presence of full snow cover on the ground. Thus, SCAmod requires a priori information on transmissivity, but given that it can be determined with the appropriate accuracy, it enables consideration of the obstructing effects of forests in fractional snow estimation. In continental-scale snow mapping, determination of the transmissivity map becomes a key issue. The preliminary demonstration of transmissivity generation using global land cover data was a part of this study. The first implementations and validations for SCAmod were presented for AVHRR data at Finnish drainage basin scale. In subsequent work, determination of the feasible reflectance constituents was addressed, followed by a sensitivity analysis targeting at selection of optimal spectral bands to be applied with SCAmod. Feasibility of the NDSI-based approach in FSC-retrievals over boreal forests is also discussed. Finally, the implementations and validations for MODIS and AATSR data are presented. The results from relative (using high-resolution Earth Observation data to represent the truth) and absolute validation (using in situ observations) indicate a good performance for both forested and non-forested regions in northern Eurasia. Accounting for the effect of forest canopy in the FSC-retrievals is the key issue in snow remote sensing over boreal regions; this study provides a new contribution to this research field and provides one solution for continental scale snow mapping