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Reliable cleaning processes are an absolute prerequisite to ensure the production of safe foods. In particular, the cleaning of heat exchangers is a crucial process step since temperature induces the formation of strongly bound food-borne deposits. Aiming on relationships between soil-specific physicochemical properties and cleaning behavior, four native starches of different botanical origin (maize, waxy maize, potato, wheat) were selected for plane channel flow (similar to the flow regime in plate heat exchangers) and falling film cleaning experiments. In addition, their chemical composition and swelling behavior was analyzed. Different cleaning mechanisms could be observed, whereby diffusive dissolution was identified as dominant cleaning mechanism for most starch deposits in both cleaning experiment setups. The cleaning of such soils is characterized by an initial reptation time, where the cleaning fluid penetrates the soil and no cleaning occurs, followed by a period with constant cleaning rate. Since these cleaning parameters where overlapped by swelling-induced processes, a correction model was used to identify the unaffected cleaning progress. The cleaning and swelling-induced parameters were influenced by the origin of the starch as well as by temperature and sodium hydroxide concentration of the cleaning fluid. Multivariate statistics was helpful to unravel interrelationships and interdependencies between results of cleaning experiments, and compositional and physicochemical properties. [ABSTRACT FROM AUTHOR]

This paper describes the methodology for deriving yearly pixel-wise snow melt-off day maps from optical data-based FSC (Fractional Snow Cover) without conducting any interpolation for cloud-obscured pixels or otherwise missing data. The Copernicus CryoLand Pan-European FSC time series for 2001–2016 re-gridded to 0.1° serves as input for the production of 16 years of melt-off day maps for Europe. These maps are compared with passive microwave radiometer (MWR) melt retrievals, to compare the performance of these two independent datasets, particularly concerning the effect of physiographic and snow conditions on the accuracy of the melt-off day estimates. Both these datasets are evaluated against melt-off day derived from in situ snow depth (SD) time series observed at European weather stations. We also present the relationship of these snow melt-off day products to a passive microwave radiometer-derived landscape freeze/thaw product. Our results show that the melt-off day derived from optical springtime FSC time series provides the strongest correlation with the snow melt-off day with respect to the in situ data. Overall the deviation of CryoLand FSC data derived melt-off day to that indicated by in situ observations is quite small, with positive bias of 0.9 days, and RMSE of 13.1 days. For 85% of the analyzed cases the differences are between ±10 days. Across Europe the MWR-based detection of melt-off day is less accurate; the investigated method performs the best for areas with sustained seasonal snow cover. Based on the time series for MWR-based melt-off day (1980–2016) and FT-ESDR (1980–2014), separately for boreal forests and tundra, we also found a clear trend towards earlier snow clearance: a decrease of melt-off day by as much as ~5 days per decade in boreal forest region was observed. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess the performance of the simulated start of the photosynthetically active season by a large-scale biosphere model in boreal forests in Finland with remote sensing observations. The start of season for two forest types, evergreen needle- and deciduous broad-leaf, was obtained for the period 2003-2011 from regional JSBACH (Jena Scheme for Biosphere-Atmosphere Hamburg) runs, driven with climate variables from a regional climate model. The satellite-derived start of season was determined from daily Moderate Resolution Imaging Spectrometer (MODIS) time series of Fractional Snow Cover and the Normalized Difference Water Index by applying methods that were targeted to the two forest types. The accuracy of the satellite-derived start of season in deciduous forest was assessed with bud break observations of birch and a root mean square error of seven days was obtained. The evaluation of JSBACH modelled start of season dates with satellite observations revealed high spatial correspondence. The bias was less than five days for both forest types but showed regional differences that need further consideration. The agreement with satellite observations was slightly better for the evergreen than for the deciduous forest. Nonetheless, comparison with gross primary production (GPP) determined from CO2 flux measurements at two eddy covariance sites in evergreen forest revealed that the JSBACH-simulated GPP was higher in early spring and led to too-early simulated start of season dates. Photosynthetic activity recovers differently in evergreen and deciduous forests. While for the deciduous forest calibration of phenology alone could improve the performance of JSBACH, for the evergreen forest, changes such as seasonality of temperature response, would need to be introduced to the photosynthetic capacity to improve the temporal development of gross primary production. [ABSTRACT FROM AUTHOR]

Abstract: Spring phenological events are important for the assessment of the carbon budget in forest ecosystems. In large parts of the boreal zone the evergreen conifers are dominant, but most available satellite-based methods for the detection of the start of the growing season have focussed on the greening-up of deciduous species. We investigated the possibility of determining spring phenological events of boreal evergreen coniferous forest from Moderate Resolution Imaging Spectroradiometer (MODIS) time-series based on test sites in Finland. The beginning of the photosynthetically active period, determined from the CO2 fluxes measured with the eddy covariance (EC) method, was used as a primary reference for the onset of the growing season (growing season start date, GSSD). Furthermore, phenological observations of pine trees, such as the beginning of shoot elongation (growth of pine start date, GPSD) were included in the comparison. The corresponding indicators were derived from MODIS data and compared with in situ phenological observations. This was carried out using cloud-filtered daily MODIS time-series of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Fractional Snow Cover (FSC) from homogenous areas around in situ sites (3 EC sites and 4 phenological sites in Finland). GSSD in coniferous forest showed correspondence with the time during snowmelt when the ground starts to be exposed, which was indicated by a decrease in FSC and the spring-rise in NDVI time-series. GPSD occurred in general after complete snow melt. Satellite-derived GSSD from NDVI (GSSDNDVI) and FSC (GSSDFSC) showed good correspondence with in situ observations for both NDVI and FSC (R2 >0.8, RMSE<7days) including observations from two CO2 flux measurement sites in Northern Finland and one in Southern Finland. The highest site-wise correlations were obtained for the satellite indicator GSSDFSC. Finally, the national-scale mapping of GSSD was demonstrated based on the satellite-derived indicator GSSDFSC. [Copyright &y& Elsevier]

With new environmental perspectives and policies, such as the Water Framework Directive, the need to understand the processes governing the transfer of water from soils to freshwater systems at a catchment and regional scale has never been more important. It is widely understood that soil texture and structure strongly influence water flow in soils. However, less is known how specific size, shape and aggregation of particles and the associated distribution of pores affect the path of light transmission. In this study, we hypothesize that reflectance spectra exhibit a primary response to soil structure. Using a field study site near Nottingham, United Kingdom that comprised of randomized plots with different crop histories, soil reflectance factor measurements were taken between 350 and 2500 nm on undisturbed soil samples from the top horizon using an Analytical Spectral Devices (ASDs) Field Spec® Pro spectrometer (ASD Inc., Boulder, CO, USA). The undisturbed soils were subsequently impregnated to preserve their structure, and the soil structure properties (in terms of macropore characteristics) were measured in horizontally orientated two-dimensional sections through the soil at different depths. The soil reflectance and transformed spectra (first- and second-order derivatives) were correlated with the soil structural properties and empirical relationships were found between the surface macroporosity, pore size and shape attributes and reflectance values and derivatives of reflectance. Surface macroporosity was highly negatively correlated with reflectance in the visible domain at water absorption bands at ∼1400 and ∼1900 nm and throughout the shortwave-infrared region from 1915 nm onwards. [ABSTRACT FROM AUTHOR]

Gas flaring in the oil and gas industry has been identified as an important source of anthropogenic black carbon (BC) affecting the climate, particularly in the Arctic. Our study provides spatially-explicit estimates of BC emissions from flaring in Russia utilising state-of-the-art methodology for determining the emission factors. We utilised satellite time series of the flared gas volume from Visible Infrared Imaging Radiometer Suite (VIIRS) for the period 2012 to 2017, supplemented with information on the gas and oil field type. BC emissions at flaring locations were calculated based on field type-specific emission factors, taking into account different gas compositions in each field type. We estimate that the average annual BC emissions from flaring in Russia were 68.3 Gg/year, with the largest proportion stemming from oil fields (82%). We observed a decrease in the yearly emissions during the period 2012 to 2017 with regional differences in the trend. Our results highlight the importance of detailed information on gas composition and the stage of oil and gas separation of the flared gas to reduce uncertainties in the BC emission estimates. [Display omitted] • New estimate for black carbon emissions from flaring in Russia. • Enhanced temporal profiles of flared gas volume from VIIRS. • Oil and gas field-type specific emission factors developed. • Average BC emissions from 2012 to 2017 are estimated at 68.3 Gg/year. [ABSTRACT FROM AUTHOR]

We publish and describe a surface spectral reflectance data record of seasonal snow (dry, wet, shadowed), forest ground (lichen, moss) and forest canopy (spruce and pine, branches) constituting the main elements of the boreal landscape. The reflectances are measured with spectro(radio)meters covering the wavelengths from visible (VIS) to short-wave infrared (SWIR) (350 to 2500 nm). In this paper, we describe the instruments used and how the spectral observations at different scales along with the concurrent in situ reference data have been collected, processed and archived. Information on the quality of the data and factors causing uncertainty are discussed. The main experimental site is located in the Sodankylä Arctic Space Centre in northern Finland (67.37 ∘ N, 26.63 ∘ E; 179 m a.s.l) and the surrounding region. The collection includes highly controlled snow and conifer branch laboratory spectral measurements, portable field spectroradiometer observations of snow and snow-free ground at different locations, and continuous mast-borne reflectance time series data of a pine forest and forest opening. In addition to the surface level spectral reflectance, data from airborne imaging spectrometer campaigns over the Sodankylä boreal forest and Saariselkä fell region at selected spectral bands are included in the collection. All measurements of the data record correspond to a typical polar-orbiting satellite observation event in the high-latitude spring season regarding their Sun or illumination source (calibrated lamp) zenith angle and close-to-nadir instrument viewing angle. For all measurement geometries, observations are given in surface reflectance quantity corresponding to the typical representation of a satellite observation quantity to facilitate their comparison with other data sources. The openly accessible spectral reflectance data at multiple scales are suitable to climate and hydrological research and remote sensing model validation and development. To facilitate easy access to the data record the four datasets described here are deposited in a permanent data repository (http://www.zenodo.org/communities/boreal%5freflectances/) (Hannula et al., 2019). Each dataset of a distinct scale has its own unique DOI – laboratory: 10.5281/zenodo.3580078 (Hannula and Heinilä, 2018a); field: 10.5281/zenodo.3580825 (Heinilä et al., 2019a); mast-borne: 10.5281/zenodo.3580096 (Hannula and Heinilä, 2018b); and airborne: 10.5281/zenodo.3580451 (Heinilä, 2019a) and 10.5281/zenodo.3580419 (Heinilä, 2019b). [ABSTRACT FROM AUTHOR]

We publish and describe a surface spectral reflectance data record of seasonal snow (dry, wet, shadowed), forest ground (lichen, moss) and forest canopy (spruce and pine, branches) constituting the main elements of the boreal landscape. The reflectances are measured with spectro(radio)meters covering the wavelengths from visible (VIS) to short-wave infrared (SWIR) (350 to 2500 nm). In this paper, we describe the instruments used and how the spectral observations at different scales along with the concurrent in situ reference data have been collected, processed and archived. Information on the quality of the data and factors causing uncertainty are discussed. The main experimental site is located in Sodankylä Arctic Space Centre in northern Finland (67.37° N, 26.63° E) and the surrounding region. The collection includes highly controlled snow and conifer branch laboratory spectral measurements, portable field spectroradiometer observations of snow and snow-free ground at different locations and continuous mast-borne reflectance time series data of a pine forest and forest opening. In addition to the surface level spectral reflectance, data from airborne imaging spectrometer campaigns over Sodankylä boreal forest and Saariselkä fell region at selected spectral bands are included in the collection. All measurements of the data record correspond to a typical polar orbiting satellite observation event in high latitude spring season regarding their sun or illumination source (calibrated lamp) zenith angle and close to nadir instrument viewing angle. For all measurement geometries, observations are given in surface reflectance quantity corresponding to the typical representation of a satellite observation quantity to facilitate their comparison with other data sources. The openly accessible spectral reflectance data at multiple scales are suitable e.g. to climate and hydrological research and remote sensing model validation and development. [ABSTRACT FROM AUTHOR]

Ecosystems’ potential to provide services, e.g. to sequester carbon, is largely driven by the phenological cycle of vegetation. Timing of phenological events is required for understanding and predicting the influence of climate change on ecosystems and to support analyses of ecosystem functioning. Analyses of conventional camera time series mounted near vegetation has been suggested as a means of monitoring phenological events and supporting wider monitoring of phenological cycle of biomes that is frequently done with satellite earth observation (EO). Especially in the boreal biome, sparsely scattered deciduous trees amongst conifer-dominant forests pose a problem for EO techniques as species phenological signal mix, and render EO data difficult to interpret. Therefore, deriving phenological information from on the ground measurements would provide valuable reference data for earth observed phenology products in a larger scale. Keeping this in mind, we established a network of digital cameras for automated monitoring of phenological activity of vegetation in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1–3 cameras. In this study, we used data from 12 sites to investigate how well networked cameras can detect the phenological development of birches ( Betula spp.) along a latitudinal gradient. Birches typically appear in small quantities within the dominant species. We tested whether the small, scattered birch image elements allow a reliable extraction of colour indices and the temporal changes therein. We compared automatically derived phenological dates from these birch image elements both to visually determined dates from the same image time series and to independent observations recorded in the phenological monitoring network covering the same region. Automatically extracted season start dates, which were based on the change of green colour fraction in spring, corresponded well with the visually interpreted start of the season, and also to the budburst dates observed in the field. Red colour fraction turned out to be superior to the green colour-based indices in predicting leaf yellowing and fall. The latitudinal gradients derived using automated phenological date extraction corresponded well with the gradients estimated from the phenological field observations. We conclude that small and scattered birch image elements allow reliable extraction of key phenological dates for the season start and end of deciduous species studied here, thus providing important species-specific data for model validation and for explaining the temporal variation in EO phenology products. [ABSTRACT FROM AUTHOR]

In recent years, monitoring of the status of ecosystems using low-cost web (IP) or time lapse cameras has received wide interest. Networked cameras can provide information about snow cover and vegetation status with a broad spatial coverage and high temporal resolution, and serve as ground truths to earth observations, and be useful for gap-filling of cloudy areas in earth observation time series. Networked cameras can also play an important role in supplementing laborious phenological field surveys and citizen-science projects, which also suffer from observer-dependent observation bias. We established a network of digital surveillance cameras for automated monitoring of phenological activity of vegetation and snow cover in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1-3 cameras. Here, we document the network, basic camera information and access to images (see, https://doi.org/10.5281/zenodo.777952) in the permanent data repository (https://www.zenodo.org/communities/phenology_camera/). Individual DOI-referenced image time series from cameras are consisted of half-hourly images collected between 2014 and 2016. Additionally, we present example colour index time series derived from image time series from two contrasting sites. [ABSTRACT FROM AUTHOR]

Abstract: This study adopts the “syndrome approach”, originally defined by the Potsdam Institute for Climate Impact Research (PIK), () to assess and map rural land abandonment (RLA), that occurred during the period 1990–2005 within the wider Mediterranean area. The basic idea behind the syndrome approach is to describe change processes by archetypical, dynamic, and co-evolutionary patterns of civilization–nature interactions. In the frame of the Rural Exodus Syndrome the RLA can be interpreted as the occurrence of environmental degradation through the abandonment of traditional agricultural practices. Multi-source spatial data, including biophysical-related variables mainly derived from Earth Observation as well as socio-economical GIS-based data, were used to define proxies for expected underlying processes and drivers of the mentioned syndrome. The analysis of data is rooted in the fuzzy set theory and approximate reasoning techniques which allows for the handling of uncertain and imprecise knowledge of environmental systems. Generalized Conjunction/Disjunction operators (GCD) were applied to compute intermediate indicator score maps representing the conditions that may affect the RLA, and a bipolar operator was used to combine mandatory and favouring conditions with the aim of generating a RLA indicator. The indicator expresses the detailed location and severity, or degree, of the syndrome. The Northern Mediterranean was generally found to suffer from RLA to a distinctly higher degree than the Southern Mediterranean. Reported abandonment studies from the existing literature, the European CORINE land cover map, and the Less Favoured Areas (LFA) map all supported the findings by confirming plausibility through convergence of evidence from comparisons with different types of independent information. This spatially highly-detailed results obtained may be of particular interest to policy and decision makers involved in rural development planning in the conceived areas. [Copyright &y& Elsevier]

Climate change effects on seasonal activity in terrestrial ecosystems are significant and well documented, especially in the middle and higher latitudes. Temperature is a main driver of many plant developmental processes, and in many cases higher temperatures have been shown to speed up plant development and lead to earlier switching to the next ontogenetic stage. Qualitatively consistent advancement of vegetation activity in spring has been documented using three independent methods, based on ground observations, remote sensing, and analysis of the atmospheric CO2 signal. However, estimates of the trends for advancement obtained using the same method differ substantially. We propose that a high fraction of this uncertainty is related to the time frame analysed and changes in trends at decadal time scales. Furthermore, the correlation between estimates of the initiation of spring activity derived from ground observations and remote sensing at interannual time scales is often weak. We propose that this is caused by qualitative differences in the traits observed using the two methods, as well as the mixture of different ecosystems and species within the satellite scenes. © New Phytologist (2004) doi: 10.1111/j.1469-8137.2004.01059.x [ABSTRACT FROM AUTHOR]

Fractional snow cover (FSC) is an important parameter to estimate snow water equivalent (SWE) and surface albedo important to climatic and hydrological applications. The presence of forest creates challenges to retrieve FSC accurately from satellite data, as forest canopy can block the sensor's view of snow cover. In addition to the challenge related to presence of forest, in situ data of FSC--necessary for algorithm development and validation--are very limited. This paper investigates the estimation of FSC using digital imagery to overcome the obstacle caused by forest canopy, and the possibility to use this imagery in the validation of FSC derived from satellite data. FSC is calculated here using an algorithm based on defining a threshold value according to the histogram of an image, to classify a pixel as snow-covered or snow-free. Images from the MONIMET camera network, producing a continuous image series in Finland, are used in the analysis of FSC. The results obtained from automated image analysis of snow cover are compared with reference data estimated by visual inspection of same images. The results show the applicability and usefulness of digital imagery in the estimation of fractional snow cover in forested areas, with a Root Mean Squared Error (RMSE) in the range of 0.1-0.3 (with the full range of 0-1). [ABSTRACT FROM AUTHOR]

Digital repeat photography has become a widely used tool for assessing the annual course of vegetation phenology of different ecosystems. By using the green chromatic coordinate (GCC) as a greenness measure, we examined the feasibility of digital repeat photography for assessing the vegetation phenology in two contrasting high-latitude ecosystems. Ecosystem-atmosphere CO2 fluxes and various meteorological variables were continuously measured at both sites. While the seasonal changes in GCC were more obvious for the ecosystem that is dominated by annual plants (open wetland), clear seasonal patterns were also observed for the evergreen ecosystem (coniferous forest). Daily and seasonal time periods with sufficient solar radiation were determined based on images of a grey reference plate. The variability in cloudiness had only a minor effect on GCC, and GCC did not depend on the sun angle and direction either. The daily GCC of wetland correlated well with the daily photosynthetic capacity estimated from the CO2 flux measurements. At the forest site, the correlation was high in 2015 but there were discernible deviations during the course of the summer of 2014. The year-to-year differences were most likely generated by meteorological conditions, with higher temperatures coinciding with higher GCCs. In addition to depicting the seasonal course of ecosystem functioning, GCC was shown to respond to environmental changes on a timescale of days. Overall, monitoring of phenological variations with digital images provides a powerful tool for linking gross primary production and phenology. [ABSTRACT FROM AUTHOR]

The European Space Agency's Data User Element (DUE) project GlobSnow was established to create a global database of Snow Extent and Snow Water Equivalent. The Snow Extent (SE) product portfolio provided within ESA DUE GlobSnow (2008–2014) is introduced and described, with a special focus on the Daily Fractional Snow Cover (DFSC) of the SE version 2.0 and its successor 2.1 released in 2013–2014. The fractional snow retrieval uses the SCAmod method designed ecpecially to enable accurate snow mapping including forests. The basics of the methodology are presented, as well as the cloud screening method applied in SE production. Considerations for future validations together with discussion on some current issues and potential inaccuracies are presented. One focus of the investigation is on the representativeness of reference FSC generated from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM +) data, with a particular interest in forested areas. Two methods for reference data generation are investigated. When comparing the GlobSnow Daily Fractional Snow Cover to these reference data, we try to identify how the comparison reflects the possible inaccuracies of the DFSC and to define the conditions where the reference data are not representative. It is obvious that the evaluation result strongly depends on the quality of the reference data, and that the two methods investigated cannot provide representative reference data for dense forests. For fully snow-covered dense conifer forest area in Finland, a Root Mean Squared Error of 20–30% was obtained from comparisons although DFSC indicated full snow cover correctly. These first evaluations would indicate a good performance of GlobSnow SE products in forests; however, this does not necessarily show up in validations due to the non-representativeness of the reference data. It is also concluded that GlobSnow SE products are sensitive to the representativeness of the applied SCAmod parameters and that FSC overestimations may occur in dense forests. GlobSnow SE products are available at www.globsnow.info/se/ . [ABSTRACT FROM AUTHOR]

This work aims at the improvement of the reflectance model-based methodology for mapping of fractional snow cover (FSC) in seasonally snow covered areas in the Northern Hemisphere. The investigations are focused to the determination and analyses of snow-free ground reflectance (ρground ). The objective is to derive information on typical average snow-free ground reflectance for predominant non-forested land cover classes in Eurasia and to investigate the implications of their variations to the FSC estimation accuracy. The snow-free ground reflectances are derived from MODIS time series, using single band reflectances and their related indices. Our approach for the determination of ρground is based on the assumption that the behavior of ground reflectance in conditions occurring directly after the snow melt adequately represents the snow-free ground reflectance during snow ablation i.e. melting snow cover including snow-free patches. Additionally, the effects of the ρground variability to snow mapping accuracy for different land cover types are analyzed focusing to the SCAmod algorithm. The current operational implementations of SCAmod use a fixed value for snow-free ground reflectance at the visible wavelengths around 555nm (corresponding to Terra/MODIS band 4). The deviation between the true snow-free ground value and the fixed value causes error in FSC estimation and, thus, it is necessary to investigate the magnitude and variations of the true ρground for different land covers. Even so, our investigation for the target area of Europe, shows that the currently used fixed value of ρground (10.0%-units) in SCAmod seems to work adequately for land cover classes investigated here. For example, the obtained ρground mean and standard deviation 10.0±1.3%-units for the agricultural areas and steppe at 555nm seem to widely coincide with the fixed value. According to SCAmod, the error in FSC estimation caused by the deviation between the fixed and the land cover class-specific value of ρground for agricultural areas and steppe yields a systematic error close to zero and a random error ranging from 2.5 to 1.5%-units with the corresponding FSC range of 0–50%. However, in the case of wetlands (7.4±0.8%-units), the systematic error caused to FSC estimation using the fixed value compared to using the estimated class-specific value is as large as from 5 to 2.5%-units with the FSC range of 0–50%. For FSC retrievals larger than 50% the error caused by the variability in ρground is considerably smaller for all of the studied land cover classes. Our results show that the performance of SCAmod improves if land cover-specific ρground values are applied at least for wetlands. We also suggest that the global application of SCAmod would benefit from the generation of a ρground map for all land cover types by using the methods presented in this paper. This post-winter reflectance climatology map can be constructed by calculating and using the class-specific ρground statistics together with suitable land cover information. [ABSTRACT FROM AUTHOR]

Abstract: Forest canopy impairs the detection of snow underneath and optical interpretation methods tend to underestimate the snow coverage. The effect of measurement conditions on snow reflectance is considerably well understood, but the sensitivity of scene reflectance (when forest canopy present) to these properties is less examined. To investigate scene reflectance of a boreal forest, an Analytical Spectral Devices (ASD) Field Spec Pro JR was installed at the top of a 30-metre-high mast in Sodankylä, Northern Finland. Two sites – sparse forest and a forest opening – were monitored. The data consists of average reflectance spectra in the range 350–2500nm, as well as of simultaneously acquired digital images. The Moderate Resolution Imaging Spectroradiometer (MODIS) is widely used for the monitoring of snow‐covered area (SCA). Therefore, ASD spectra were resampled to the spectral characteristics of MODIS. Time series of normalised difference snow index (NDSI), normalised difference vegetation index (NDVI) and the reflectance at selected wavelengths were investigated in order to understand their behaviour in a snow-covered forest in spring. In order to discriminate between the effects of illumination conditions and the actual snow characteristics, reflectance spectra were measured both under direct and diffuse illumination. A specific focus was on the behaviour of NDSI, which is typically used in SCA retrieval – e.g. in MODIS snow mapping algorithms by NASA''s Goddard Space Flight Center (GSFC). We further modelled the scene reflectance as a function of surface reflectances considering areal proportions of Scots Pines, shadowed snow and directly illuminated snow. In the forest opening, NDSI was found to be relatively insensitive to illumination conditions, while in the forest stand it showed stronger dependence. This indicates that NDSI-based snow algorithms may provide a lower accuracy over forested areas. We demonstrated this by comparing the mast-observed indices and reflectances with the corresponding thresholds applied in MODIS snow mapping. The current thresholds led to underestimated snow cover in our test forest. It was also noticed that over the forest stand, variations in snow reflectance induced particularly great variation in the scene reflectance, even though visible snow-covered ground accounted for only half of the observed area as a result of the blocking effect of the trees. Comparison between the mast-borne and the modelled scene reflectance suggest that the surface reflectance observations could offer a feasible means to predict scene reflectance characteristics of snow-covered boreal forests observed by space-borne instruments. [Copyright &y& Elsevier]

Abstract: An advanced approach for estimating the sub-pixel fraction of snow covered area for boreal forest and tundra belt from optical data is presented. The method named SCAmod by the Finnish Environment Institute (SYKE) is based on a forward semi-empirical model where the at-satellite observed reflectance is expressed as a function of the fractional snow cover (FSC). The effective forest transmissivity determined for each target unit-area and the generally applicable reflectances of three major contributors (wet snow, forest canopy and snow-free ground) serve as model parameters. The forest transmissivity describes the visibility of the ground through the forest canopy from above, and can be determined from visible reflectance data acquired at full snow cover conditions. SCAmod can be applied to data from various sensors operating at optical and near-infrared region. We apply the method to Envisat/AATSR and Terra/MODIS data and validate the resulting FSC against ground truth data over Finland, with considerations of feasibility of these ground truth data in a scale of the MODIS/AATSR pixel. In comparison, also NASA MOD10_L2 fractional snow product is validated. The results indicate that SCAmod performs better than MOD10_L2 particularly in forested areas; an RMSE of 0.11 for the fraction of snow covered area (range is 0–1) is achieved. For large-scale snow mapping, using reflectance data for transmissivity determination would evidently be very time-consuming. Here we present also a new method for transmissivity generation using global land cover data and demonstrate its use for snow cover mapping in continental scale. The resulting FSC data are compared against FSC from high-resolution Landsat TM/ETM+ data for selected areas in Northern and Eastern Europe. The comparison indicates that SCAmod is feasible also in continental-scale snow mapping and is superior in identifying snow in dense forests. [Copyright &y& Elsevier]