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Snow Monitoring Using Radar and Optical Satellite Data
Abstract
In 1997 HUT, FEI, and FGI started a joint project that aims to develop an operational snow monitoring system in Finland. This project should apply both traditional methods (in situ measurements and hydrological models) and satellite-borne data (ERS-2 SAR and NOAA AVHRR). The emphasis of the project is to develop methods to monitor the snow melt period during spring time. The test site is located in northern Finland, and it consists of the drainage area of River Kemi, which is the largest river in Finland. A total of 18 ERS-2 SAR and six cloudless AVHRR images have been acquired from the test site during the spring of 1997. These images cover totally the snow melt period starting from dry snow and ending to snow-free ground. The optical and microwave radar data sets were compared to each other and to in situ measurements made on the weather stations which are used for current operative snow mapping. The results show that SAR-derived snow cover maps agree reasonably well with ground based observations, and they have a good correlation with AVHRR reflectance for open areas (r=0.82), and even in the presence of vegetation the correlation is relatively high (r=0.77).
... Guneriussen et al. (1996) have studied SAR volume scattering effects for dry snow cover finding similar results. Koskinen et al. (1999) compared multitemporal ERS-1 SAR images with in situ surveys and a digital land-use map. ...
... Raggam, Almer and Strobel (1994) demonstrated how snow cover retrieved from multiparameter airborne SAR and SPOT HRV can be combined. Koskinen et al (1999) analysed a time series of NOAA AVHRR and ERS-2 SAR images. However, they did no actual combination of the two other studying how the snow cover developed as observed with the two sensors. ...
... Several papers have demonstrated the potential of SAR for wet snow detection using ERS and Radarsat standard mode (see, e.g., Nagler and Rott, 2000;and Koskinen et al., 1999). Wet snow was detected by utilising the high absorption and therefore low backscatter of wet snow pixels and then comparing the backscatter with the corresponding pixel of a reference scene taken during drysnow or snow-free conditions. ...
... Use of weather and light-independent Synthetic Aperture Radar (SAR) data is very attractive in this respect. Snowcovered area mapping has been demonstrated in several papers before (2,3,4), but only for high resolution (30 m) data with limited coverage (100 by 100 km ). In several European projects (Envisnow, EuroClim and SnowMan) we have demonstrated the use of Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS) data for SCA retrieval. ...
... Detection of wet snow by means of multitemporal Synthetic Aperture Radar data from the ERS and Radarsat standard modes (100 km coverage, 30 m resolution) has been demonstrated by several groups (2,3,4).These algorithms use the absorption dependency of the radar signal on the liquid water content of the snow to set a threshold on the differential backscatter between the actual scene and corresponding repeat pass scenes taken under dry snow or snow-free conditions. These algorithms have proved to perform well in identifying wet snow pixels over these limited areas. ...
... Strong sensitivity to vegetation and bare spots causes partly covered pixels to be classified as snow free by the radar SCA algorithm. With improved vegetation maps one could tune the algorithm to deal with some vegetation like in (3). From Figures 6 and 7 we observe that most of the differences are in areas bordering the forest mask and in hillsides down towards the valley floors. ...
It has previously been shown that wet snow can be detected using ERS SAR repeat pass imagery where a reference image is captured during cold dry snow conditions and subtracted from the image to be classified. We have extended and validated this technique for retrieving snow-covered areas (SCA) using repeat pass Envisat ASAR wide swath data (500 by 500 km2 swath coverage, 100 m resolution), covering the mountainous regions of Southern Norway. The algorithm has also been extended to pos- tulate dry snow above areas with wet snow, thus giving a total snow-covered area that is comparable to SCA from optical sensors. A sliding window technique has been applied to facilitate the dry snow classification. The method has been implemented in a near-real time environment and has been run pre-operationally in Norway in 2004. The relatively large coverage allows SAR to become an opera- tional tool for snow monitoring, as opposed to standard modes used in previous works. In order to im- prove snow classification we have used air temperature data from the Norwegian meteorological sta- tion network to create high-resolution surface air temperature maps. These maps are used to filter wet snow from reference images and prevent incorrect classification of dry snow. Snow-covered area maps for South Norway have been derived for the spring melt season with a one-week temporal reso- lution. The results are validated against optical sensor retrievals (MODIS, Landsat) and high accuracy field measurements.
... En effet, les normes en vigueur imposent des conditions particulières concernant les stations météorologiques : emplacement à l'écart de toute végétation, température sous abri placé entre 1.5 et 2 mètres de haut, hauteur de neige relevée autour de la station. Pour souligner les limites de ces mesures de terrain, citons quelques exemples : la forte présence dans ces régions boréales, de forêts associées à un enneigement différent de celui observé à un site clairsemé (Koskinen et al. (1999)) ; l'effet local du vent qui conduit à une redistribution de la neige et ainsi à des inégalités spatiales dans la structure du manteau neigeux Sturm and Liston, 2003) ; l'influence de ce même vent sur les températures alors qu'aux stations ces dernières sont relevées sous abri... Ces conditions locales nécessitent alors un réseau dense pour rendre compte de situations plus globales, ce qui est loin d'être le cas aux hautes latitudes. En plus de leur isolement spatial, les archives de ces stations esseulées présentent souvent des trous, aboutissant dans de nombreux cas à des séries temporelles discontinues. ...
... Notre schéma binaire (neige/sans neige) semble limité pour retranscrire les conditions locales. Par exemple, il n'est pas rare de rencontrer une prairie où la neige a totalement disparu alors que quelques mètres plus loin, la canopée abrite encore un manteau neigeux (Koskinen et al., 1999). Nous sommes conscients de cette limite. ...
Observing sub-polar ecosystems is important as they are suspected to change significantly in response to the expected increase in temperature for the next decades. To bypass the lack of meteorological stations in the Northern High Latitudes, remote sensing is an interesting alternative tool,
covering almost the entire area. This project deals with the development of a method to derive surface parameters (>50°N) from satellite data.
For this study, brightness temperature data acquired by the SSM/I (Special Sensor Microwave Imager) in the microwave spectrum are used because they are independent of solar radiation and weakly influenced by the atmosphere. Methods used are based on brightness temperatures measured at 19 and 37 GHz, which allow to derive three geophysical parameters related to climate variability : daily maps of snowcover between 1988 and 2002 ; a water surface extent (open water, small lakes, reservoirs, wetlands associated with low vegetation) ; a temperature characterizing the surface and the air above the ground. A method to normalize the temperature is presented to overcome the variation of the time of measurement. It leads to hourly series of temperature, This allows to study climate indicators such as the annual sum of positive degree days.Trends confirm observed climate evolution : increase of surface temperature (+0.8 +/- 0.4 °C for Canada/Alaska between 1992 and 2002), decrease in snow extent cover. These original databases could also be useful for validation of regional climate model.
... Use of weather and light independent Synthetic Aperture Radar (SAR) data is very attractive in this respect. Snow covered area mapping has been demonstrated in several papers before [1,2,3], but only for high resolution (30 m) data with poor coverage. In several European projects (Envisnow, EuroClim and SnowMan) the use of Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS) data has been demonstrated. ...
... Wet snow can easily be separated from dry snow or bare soil due to the high absorption of microwaves by the wet snow. Several similar detection schemes have been demonstrated previously (see [1,2,3]). This is achieved by thresholding the ratio between the current SAR scene and a dry snow or snow free reference scene. ...
A near-real time GMES-relevant monitoring system for semi-operational retrieval of snow covered area for hydrological and climatological applications has been developed in the Envisnow EC EESD FP 5 project. The system, using ENVISAT ASAR wide swath data from ESA AOE 785 and from the Kongsberg satellite station, geocodes and classifies Envisat ASAR data automatically, and produces SCA maps with confidence flags. The snow covered area maps are used in hydrological models in Norway to improve run-off forecasting and flood warnings. We show that Envisat ASAR wide swath data can be used to produce snow cover maps with 100 m resolution and 500 km by 500 km coverage. This allows semi- operational use of SAR data for regional and maybe global snow mapping. The applied wet snow detection algorithm (Nagler and Rott, 2000) has been complimented with a dry snow algorithm, predicting dry snow above medium wet snow line. Results and accuracy assessments from campaigns in southern Norway in 2003 and 2004 will be shown. Results from a near-real time multi-sensor demonstration of the system in 2004 will also be shown.
... Raggam, Almer and Strobel demonstrated in [7] how snow cover retrieved from multi-parameter airborne SAR and SPOT HRV can be combined. Koskinen et al. analyzed a time series of NOAA AVHRR and ERS-2 SAR images in [2]. However, they did no actual combination of the two other than studying how the snow cover developed as observed by the two sensors. ...
... Several papers have demonstrated the capability of SAR for wet snow mapping using ERS and Radarsat standard modes (see, e.g., [5] and [2]). Wet snow was detected by utilizing the high absorption, and therefore low backscatter, of wet snow and then comparing the backscatter with a corresponding pixel in a reference image acquired during dry-snow or snow-free conditions. ...
In this study, we have developed an approach for fusion of optical and SAR data for snow cover fraction (SCF) retrieval that avoids the typical blending effects when combining independently retrieved geophysical data from different sensors. Instead of undertaking the sensor fusion at the geophysical parameter level, the fusion is done at the electromagnetic signal level. A state model, based on hidden Markov model theory, has been developed for the simultaneous signal from the optical and the SAR sensors. The model goes through a given set of states through the snowmelt season where transition probability distribution functions of time have been determined for each state transition. A coupling between corresponding models for optical and SAR observations has been developed in order to make a more reliable model of the sensor co-variation.
... In this paper, we highlight the combined use of freely accessible spaceborne C-band RADAR data and in situ optical digital pictures for the observation and the quantification of snowpack melting processes on a glacier surface, in line with past work, emphasizing the complementarity of optical and active microwave measurements [28][29][30]. We start by introducing the automated digital camera network we deployed a decade ago for the systematic imaging of the glacier basin. ...
The snowpack evolution during the melt season on an Arctic glacier is assessed using ground-based oblique-view cameras, spaceborne imaging and spaceborne RADAR. The repeated and systematic Synthetic Aperture RADAR (SAR) imaging by the European Space Agency’s Sentinel-1 spaceborne RADARs allows for all-weather, all-illumination condition monitoring of the snow- covered fraction of the glacier and hence assessing its water production potential. A comparison of the RADAR reflectivity with optical and multispectral imaging highlights the difference between the observed quantities—water content in the former, albedo in the latter—and the complementarity for understanding the snow melt processes. This work highlights the temporal inertia between the visible spring melting of the snowpack and the snow metamorphism. It was found that the snowpack exhibits that approximately 30 days before it starts to fade.
... The SAR backscatter on a surface of snowpack is related to three factors [6]: (1) The parameters of the sensors, which include the frequency, polarization and incidence geometry; (2) snowpack parameters including snow density, particle size and variation in size, net free water content, the characteristics of spatial distribution of particles, viscosity and stratification; and (3) the subsurface parameters, which include the characteristics of the dielectric material, surface roughness, soil on the snow and snow-ice interface. Besides these three general factors determining the SAR backscattering coefficient (or sigma nought) observed is affected by several physical parameters of snow cover [6] [7][8] [9]. These parameters are: (a) depth of the snowpack; (b) surface roughness (air interface snow and snow-ground); (c) the size of snow crystals (grain size) and shape of the snow crystal; ...
This paper proposes an analytical model for the interaction between a microwave beam from Synthetic Aperture Radar in X band (SAR-X) and a snow pack in dry snow conditions with Radiative Transfer Model (RTM). For this purpose, a statistical analysis was performed with SAR-X backscattering data and reference data on snow stratigraphy obtained from snowpits implemented along the pilot study area-Union Glacier, Antarctica. This model was developed by limiting the interaction between the microwave beam and snow in areas of dry snow for simplification purposes, since the dielectric constant in areas of wet snow has a complex character. As a result, a mathematical model was generated based on the radiation transfer model, in order to represent the process of interaction and backscattering of the microwave beam with the snow. This model divided the process of interaction between the SAR-X beam and the snow in three linear effects overlap: Backscatter produced along the surface of the air-snow, Volumetric backscatter produced over the volume of snow and Backscatter produced in snow-ice interface. For the purposes of model testing, it was used to estimate the penetration of the SAR-X beam along the snow package, with a penetration of 0.9663 m ± 0.11247 m being estimated. This fact was proven from the identification of cracks in the glacier that were covered by snow and were not visible in the standard optical images. Even with limited reference data, this result indicates the robustness of the proposed approach, allowing the estimation of the spatial distribution of variations in stratigraphic parameters of snow variables in dry snow areas from SAR data in the X band.
... When the surface is rough, surface scattering becomes the dominant diffusion process. Previous studies have demonstrated the capability of C-band European Remote Sensing Satellites (ERS-1 and 2) SARs [8,9], C-band Spaceborne Imaging Radar (SIR-C) and X-band X-SAR [10] and C-band RADARSAT SAR data [11,12] to discriminate wet snow from dry snow or from ground surfaces [13]. ...
This study compares numerical simulations and observations of C-band radar backscatter in a wide region (2300 km 2 ) in the Northern French Alps. Numerical simulations were performed using a model chain composed of the SAFRAN meteorological reanalysis, the Crocus snowpack model and the radiative transfer model Microwave Emission Model for Layered Snowpacks (MEMLS3&a), operating at a spatial resolution of 250-m. The simulations, without any bias correction, were evaluated against 141 Sentinel-1 synthetic aperture radar observation scenes with a resolution of 20 m over three snow seasons from October 2014 to June 2017. Results show that there is good agreement between observations and simulations under snow-free or dry snow conditions, consistent with the fact that dry snow is almost transparent at C-band. Under wet snow conditions, although the changes in time and space are well correlated, there is a significant deviation, up to 5 dB, between observations and simulations. The reasons for these discrepancies were explored, including a sensitivity analysis on the impact of the liquid water percolation scheme in Crocus. This study demonstrates the feasibility of performing end-to-end simulations of radar backscatter over extended geographical region. This makes it possible to envision data assimilation of radar data into snowpack models in the future, pending that deviations are mitigated, either through bias corrections or improved physical modeling of both snow properties and corresponding radar backscatter.
... This can be obtained through a supervised classification. According to Koskinen et al. (1999), who studied the density of snow distribution, the classification of snow coverage zones was as follows: (a) zones totally covered by snow; (b) more than half of the area covered by snow; (c) less than half of the area covered by snow; (d) snow only exists in forests; and (e) dissipated snow (Fig. 3(a)). ...
... Overall approach for estimation of SCA using RISAT-1 SAR data is given in Fig. 2. This approach is similar to knowledge based image classifiers (Pierce et al. 1994;Baghdadi et al. 1997;Koskinen et al. 1997Koskinen et al. , 1999Al Momani et al. 2007;Tso and Mather 2009;Kulkarni et al. 2010;Thakur et al. 2013a), whereas overall aerial extent of snow was taken from SAR image thresholding and refinement in SCA was done using elevation mask based on optical (MODIS) SCA maps. The RISAT-1 data used for SCA estimation is MRS HV data sets for Beas basin up to Thalot and Bhagirathi River basin up to Uttarkashi. ...
Snow physical properties, snow cover and glacier facies are important parameters which are used to quantify snowpack characteristics, glacier mass balance and seasonal snow and glacier melt. This study has been done using C-band synthetic aperture radar (SAR) data of Indian radar imaging satellite, radar imaging satellite-1 (RISAT)-1, to estimate the seasonal snow cover and retrieve snow physical properties (snow wetness and snow density), and glacier radar zones or facies classification in parts of North West Himalaya (NWH), India. Additional SAR data used are of Radarsat-2 (RS-2) satellite, which was used for glacier facies classification of Smudra Tapu glacier in Himachal Pradesh. RISAT-1 based snow cover area (SCA) mapping, snow wetness and snow density retrieval and glacier facies classification have been done for the first time in NWH region. SAR-based inversion models were used for finding out wet and dry snow dielectric constant, dry and wet SCA, snow wetness and snow density. RISAT-1 medium resolution scan-SAR mode (MRS) in HV polarization was used for first time in NWH for deriving time series of SCA maps in Beas and Bhagirathi river basins for years 2013–2014. The SAR-based inversion models were implemented separately for RISAT-1 quad pol. FRS2, for wet snow and dry snow permittivity retrieval. Masks for layover and shadow were considered in estimating final snow parameters. The overall accuracy in terms of R2 value comes out to be 0.74 for snow wetness and 0.72 for snow density based on the limited ground truth data for subset area of Manali sub-basin of Beas River up to Manali for winter of 2014. Accuracy for SCA was estimated to be 95 % when compared with optical remote sensing based SCA maps with error of ±10 %. The time series data of RISAT-1 MRS and hybrid data in RH/RV mode based decompositions were also used for glacier radar zones classification for Gangotri and Samudra Tapu glaciers. The various glaciers radar zones or facies such as debris covered glacier ice, clean or bare glacier ice radar zone, percolation/refreeze radar zone and wet snow, ice wall etc., were identified. The accuracy of classified maps was estimated using ground truth data collected during 2013 and 2014 glacier field work to Samudra Tapu and Gangotri glaciers and overall accuracy was found to be in range of 82–90 %. This information of various glacier radar zones can be utilized in marking firn line of glaciers, which can be helpful for glacier mass balance studies.
... On the other hand, microwave remote sensing is often a more suitable approach to measure glacier velocity as it is unaffected by the associated high costs logistical restraints that are apparent in ground based gauging networks (Koskinen et al., 1999). ...
Measurements of surface velocity on temperate Skeiðarárjökull Glacier, Vatnajökull Ice sheet, Iceland, over the time period of 1985 to 2012 provide new insight regarding the spatial-temporal patterns of glacier velocity within a glacier with a highly responsive subglacial hydrological system. The data also provides new insight into the relationship between observed glacier velocity and synoptic weather driving parameters.
Velocity measurements were made using cross-correlation feature tracking applied to Landsat and Advanced Spacborne Thermal Emission and Reflection Radiometer (ASTER) imagery, through the use of ENvironment for Visualizing Images (ENVI) software. The time period is composed of individual timeframes that cover a minimum timeframe of 11 days, and a maximum time frame of 418 days, with the data being divided into two groups; yearly timeframes and seasonal timeframes.
The data obtained from the yearly timeframes indicate that the glacier has a mean centreline velocity of 0.31 – 0.89 m d-1, whereas the mean velocity along the glacier centreline within the seasonal timeframes varies from 0.55 – 1.27 m d-1.
When comparing the mean velocity of the yearly timeframes to the climatic parameters, a clear trend can be seen between the mean velocity and the occurrence of rainfall events, with a rainfall event being described as a duration of sustained rainfall over a certain period of time. The data shows that the mean glacier velocity increases with the occurrence of rainfall events that last two or more days with a value of more than 20 mm d-1. Seasonal velocity variations are also highly responsive to rainfall, with the glacier showing a significant increase in velocity with an increasing occurrence of >20 mm d-1 of precipitation. Within both timeframes, a clear relationship between increasing velocity and increasing mean daily temperature can also be seen. Geothermal activity within the Grímsvötn volcanic system is shown to have a significant effect upon Skeiðarárjökull velocity, with jökulhlaups causing a 3-10 threshold increase in velocity throughout the glacier, whereas Grímsvötn volcanic eruptions with widespread tephra fallout are shown to significantly reduce the glacier velocity throughout the entire area covered by tephra.
... Dazu wird oft der Normalized Difference Snow Index NDSI verwendet (Dozier, 1989). In verschiedenen Arbeiten wurden auch bewölkungsunabhängige SAR-Sensoren eingesetzt (Koskinen et al., 1999;Schanda et al., 1983). Die Schneebedeckungskartierung im Schweizer Alpenraum wird heute operationell mit NOAA-AVHRR Daten in Kombination mit in situ Messungen durchgeführt (de Ruyter de Wildt et al., Foppa et al., 2007). ...
Snow-avalanches kill more people in Switzerland than any other natural hazard and threaten buildings and traffic infrastructure. Rapidly available and accurate information about the location and extent of avalanche events is important for avalanche forecasting, safety assessments for roads and ski resorts, verification of warning products as well as for hazard mapping and avalanche model calibration/ validation. Today, isolated observations from individual experts in the field provide information with limited coverage. Only a fraction of all avalanche events can be recorded due to restricted accessibility of many alpine terrain sections during winter season. Information on small to medium size avalanche events within remote regions is collected only sporadically. However, these avalanches notably claim most casualties within the raising number of people pursing off-slope activities. Remote sensing instruments are able to acquire wide-area datasets even over poorly accessible regions. Therefore they are promising tools to close the abovementioned information gap. This research systematically investigates the potential of spatially high resolved remote sensing instruments for the detection and mapping of snow-avalanche deposits. Fieldspectroradiometer data of nine avalanche deposits are analysed to identify universally valid and significant spectral offsets between avalanche deposits and the adjacent undisturbed snow cover. Promising absorption features are found in the near infrared region of the electromagnetic spectrum. Nevertheless, the differences are unlikely to be distinct enough for a detection using air- or spaceborne remote sensing instruments. The directional reflection of rough avalanche deposit surfaces is contrary to the directional reflection of smooth undisturbed snow covers. The potential of multiangular remote sensing data for the detection and mapping of avalanche deposits is demonstrated using multiangular data acquired by the airborne scanner ADS40. However, the difference between observation angles (16°) proves to be insufficient for accurate avalanche detection solely on the base of directional properties. Therefore, auxiliary data has to be utilised. The texture of avalanche deposits and undisturbed snow cover can already be distinguished by the naked eye. Using second order statistics, comprising the spatial distribution of the variation in pixel brightness, textural characteristics in digital image data can be quantified. This is a prerequisite for an automated detection of particular textures. Different established texture measures are tested for their discriminating potential of avalanche deposits and undisturbed snow cover using RC30 aerial images of avalanche deposits acquired within the avalanche winter 1999 in Switzerland. The control parameters such as the size of the filter box are systematically varied to find the ideal settings. The texture measure Entropy is identified as the most distinct and stable indicator to distinguish between rough and smooth snow surfaces. But avalanche deposits are not the only rough snow surfaces within the Alpine winter landscape. For example wind modelled snow surfaces or artificially piled snow at the edge of roads and ski slopes show texture characteristics similar to avalanche deposits. Consequently, a classification approach using texture information only is not sufficient for an accurate identification of avalanche deposits. Based on the findings described above, we develop an avalanche detection and mapping processing chain, combining spectral, directional and textural parameters with auxiliary datasets. The processing chain is tested and improved using data acquired by the airborne scanner ADS40 over the region of Davos, Switzerland. The accuracy assessment, based on ground reference data within three test sites, shows that 94% of all existing avalanche deposits are identified. Even small scale deposits (area < 2000 m2) and deposits within shadowed areas are detected correctly. These results demonstrate the big potential of the proposed approach for an automated detection and mapping of avalanche deposits. Yet, cloud cover constrains the availability of appropriate optical remote sensing data after heavy snowfall while wind modelled snow surfaces, artificially piled snow and sparsely vegetated snow surfaces cause sporadic misclassifications. Despite these constraints, the approach developed within this research shows a big potential to fill existing gaps in avalanche information. Especially within alpine areas of developing countries with almost no reliable information on past avalanche events, such an approach may be used to acquire valuable data for hazard mapping and settlement planning.
... Conversely, the spatial resolution of active microwave sensors, particularly synthetic aperture radar (SAR) sensors, is able to provide useful information at both the regional and drainage basin scales. Accordingly, it can be used to complement optical remote sensing for snow cover mapping in rugged mountain terrain [6]. ...
Snow cover in cold and arid regions is a key factor controlling regional energy balances, hydrological cycle, and water utilization. Interferometric synthetic aperture radar (InSAR) technology offers the ability to monitor snow cover in all weather. In this letter, a support vector machine (SVM) method for extracting snow cover based on SAR and optical data in rugged mountain terrain is introduced. In this method, RadarSat-2 InSAR interferometric coherence images are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth.” The analysis results indicate that the coherence in copolarizations is clearly correlated with the underlying surface type and local incidence angle. These two factors, combined with training samples from GF-1 wide field viewer data, were used to build an SVM to classify coherence images in HH polarization. The classification results demonstrate that snow cover extraction using this method can achieve mean accuracies of 83.8% and 77.5% in areas with low and high vegetation coverage, respectively. These accuracies are significantly higher than those achieved by the typical thresholding algorithm (72.7% and 69.2%, respectively).
... Current algorithms for mapping snow-covered areas by SAR are mainly based on the works by (2) for mountainous areas and by (6) for boreal forests. Means for improving the snow cover maps with inferred dry snow have been suggested by (7) for mountainous areas. ...
In 2003 an extensive campaign was carried out in the mountainous parts of southern Norway to measure snow signatures with multi-polarisation SAR using ESAR. Field campaigns were carried out to support the measurements. Airborne orthophotos and Radarsat imagery was also acquired in the period. Unfortunately, Envisat ASAR was not available at the time due to malfunction. In this paper we will present some of the results from the campaign, specifically multi-polarisation signa-tures for snow and bare soil will be derived. The results from using the multi-polarisation data to derive snow covered area maps will also be presented, using standard classification schemes. The classified results are compared with the orthophoto to assess the overall agreement. Snow cover will also be derived from Radarsat imagery, and compared with orthophoto. Near simultaneous imagery from single polarisation Radarsat imagery (HH-polarisation) will be compared to ESAR data to evaluate the potential of satelliteborne polarimetry to study snow parameters. Issues re-lated to multi-scale observations of fractional snow covers will be discussed.
... Other alternatives have been proposed (e.g. Farmer et al., 2010;Gamon et al., 2013;Kimball et al., 2004;Koskinen et al., 1999;McDonald et al., 2004;Narasimhan and Stow, 2010;Zhao and Fernandes, 2009), though many of these have been dependent on the type of remotely sensed data (i.e. optical, synthetic aperture radar), the temporal resolution of the data (i.e. ...
In the context of a changing climate it is important to be able to monitor and map descriptors of snow seasonality. Because of its relatively low elevation range, Australia’s alpine bioregion is a marginal area for seasonal snow-cover with high interannual variability. It has been predicted that snow-cover will become increasingly ephemeral within the alpine bioregion as warming continues. To assist the monitoring of snow seasonality and ephemeral snow-cover, a remote sensing method is proposed. The method adapted principles of object-based image analysis that have traditionally be used in the spatial domain and applied them in the temporal domain. The method allows for a more comprehensive characterization of snow seasonality relative to other methods. Using high-temporal resolution (daily) MODIS image time-series, remotely sensed descriptors were derived and validated using in situ observations. Overall, moderate to strong relationships were observed between the remotely sensed descriptors of the persistent snow-covered period (start r = 0.70, p < 0.001; end r = 0.88, p < 0.001 and duration r = 0.88, p < 0.001) and their in situ counterparts. Although only weak correspondence (r = 0.39, p < 0.05) was observed for the number of ephemeral events detected using remote sensing, this was thought to be related to differences in the sampling frequency of the in situ observations relative to the remotely sense observations. For 2009, the mapped results for the number of snow-cover events suggested that snow-cover between 1400 – 1799 m was characterized by a high numbers of ephemeral events.
... Despite their fair performance in snow detection, these two algorithms suffer from limitations related to the two data types: the presence of clouds for AVHRR data and insufficient spatial resolution for SSM/I imagery. Several authors have shown the value of combining passive microwave and visible/infrared satellite data to map snow cover and monitor its evolution in time and space [1,[4][5][6][17][18][19][20][21]. Romanov et al. [5] have shown that the multisensor technique is as precise as, or better than, IMS products, especially with respect to data consistency throughout the time series. ...
We present an algorithm for regional snow mapping that combines snow maps derived from Advanced Very High Resolution Radiometer (AVHRR) and Special Sensor Microwave/Imager (SSM/I) data. This merging algorithm combines AVHRR's moderate spatial resolution with SSM/I's ability to penetrate clouds and, thus, benefits from the advantages of the two sensors while minimizing their limitations. First, each of the two detection algorithms were upgraded before developing the methodology to merge the snow mapping results obtained using both algorithms. The merging methodology is based on a membership function calculated over a temporal running window of +/- 4 days from the actual date. The studied algorithms were developed and tested over Eastern Canada for the period from 1988 to 1999. The snow mapping algorithm focused on the spring melt season (1 April to 31 May). The snow maps were validated using snow depth observations from meteorological stations. The overall accuracy of the merging algorithm is about 86%, which is between that of the new versions of the two individual algorithms: AVHRR (90%) and SSM/I (83%). Furthermore, the algorithm was able to locate the end date of the snowmelt season with reasonable accuracy (bias = 0 days; SD = 11 days). Comparison of mapping results with high spatial resolution snow cover from Landsat imagery demonstrates the feasibility of clear-sky snow mapping with relatively good accuracy despite some underestimation of snow extent inherited from the AVHRR algorithm. It was found that the detection limit of the algorithm is 80% snow cover within a 1 x 1 km pixel.
... Year of launch Frequency band and polarisation Significant works SIR-C/X-SAR NASA a / JPL a 1994 L, C (Quad), X Dozier (2000a, 2000b) (Part I and II), Dozier (1995, 1997), Floricioiu and Rott (2001), Matzler et al. (1997), Shi (1998, August), Adam et al. (1997), Baghdadi et al. (1997), Solberg and Andersen (1994), Piesbergen et al. (1995), Koskinen et al. (1997), Guneriussen (1997), Shi Haefner et al. (1997), Guneriussen et al. (1996), Nagler and Rott (2000) ERS-2 ESA 1995 C (VV) Nagler and Rott (2000), Laxon et al. (2003), Nagler et al. (1998), Li et al. (2001), Koskinen et al. (1999), Pivot ...
This work provides an overview of various methods for estimating snow cover and properties in high mountains using remote sensing techniques involving microwaves. Satellite based remote sensing with its characteristics such as synoptic view, repetitive coverage and uniformity over large areas, has great potential for identifying the temporal snow cover. Many sensors have been used in the past with various algorithms and accuracies for this purpose. These methods have been improving with the use of Synthetic Aperture Radar (SAR) sensors, working in different microwave frequencies, polarization and acquisition modes. The limitations, advantages and drawbacks are illustrated while error sources and strategies on how to ease their impacts are also reviewed. An extensive list of references, with an emphasis on studies since 1990s, allows the reader to delve into specific topics.
... Also, the backscatter signature from snowcovered forest areas is the aggregate of backscatter from a number of pathways. Over forested terrain one may be able to detect large total changes in backscatter for dvaried forest types as a result of ground moisture conditions for C-band VV polarisation SAR (Way et al. 1990(Way et al. , 1994; however, the difference in backscattering between wet snow and dry snow surfaces decreases with increasing biomass at all frequencies (Koskinen et al. 1999). Birch forest at the forest-tundra ecotone holds little canopy snow, but tree dielectric conditions also change (e.g. as sap begins to flow), which may affect interpretation of change in the underlying snowpack; doublebounce effects may also occur between trunks and the snow-air interface. ...
Detailed snowpack observations, meteorology, topography and landcover classification were integrated with multi‐temporal SAR data to assess its capability for landscape scale snowmelt mapping at the forest–tundra ecotone. At three sites along an approximately 8° latitudinal gradient in the Fennoscandian mountain range, 16 multi‐temporal spaceborne ERS‐2 synthetic aperture radar (SAR) were used for mapping snowmelt.Comparison of field measurements and backscatter values demonstrates the difficulty of interpreting observed backscatter response because of complex changes in snow properties on diurnal and seasonal temporal scales. Diurnal and seasonal melt–freeze effects in the snowpack, relative to the timing of ERS‐2 SAR image acquisition, effectively reduce the temporal resolution of such data for snow mapping, even at high latitudes.The integration of diverse data sources did reveal significant associations between vegetation, topography and snowmelt. Several problems with the application of thresholding for the automatic identification of snowmelt were encountered. These largely related to changes in backscattering from vegetation in the late stages of snowmelt. Due to the impact of environmental heterogeneity in vegetation at the forest–tundra ecotone, we suggest that the potential to map snow cover using single polarization C‐band SAR at the forest–tundra ecotone may be limited to tundra areas.
... The algorithm currently in use does not work in forested areas. Algorithms for estimation of snow cover in forested areas has been demonstrated in Finland (Koskinen et al., 1999) but these cannot easily be used in Norway due to the different climate regime (Norway has a costal climate with multiple freeze thaw cycles throughout the winter). ...
The paper present results from a series of European and national projects on remote sensing of snow parameters. Currently, satellite borne SAR data are only available at C-band frequencies. Other frequencies such as L-band or Ku-band may be favorable in several snow applications, but current C-band SAR may still be used and further developed to a more mature level. In particular, the advent of wide swath SAR data have provided frequent data sets at medium spatial resolution, that can be used to monitor snow parameters operationally.We will present results from a snow covered area monitoring service developed for Norway and Sweden. The service, which is based on Envisat ASAR wide swath data, produces snow cover maps on average 3–4 times per week. The resulting time series gives a unique data set for studying the snow cover as it rapidly retreats during the melting season, and is of high value to hydro power companies.Snow water equivalent is the key parameter for hydrological applications. Norut IT has developed a technique using repeat pass interferometry to measure SWE, based on the linear relationship between the change in SWE and the change in interferometric phase. The technique has been demonstrated, but scarceness of usable interferometric baseline pairs have so far not allowed wide spread applicability of the technique.A future SAR using Ku-band frequency as carrier will maybe solve the problem of retrieving SWE. Since backscatter at Ku-band frequency is more sensitive to SWE, it is good hope that robust SAR methods can be invented for this purpose. It will, however, be extremely important for the scientific community to validate the retrieval algorithms against in-situ data. The authors have developed an innovative validation concept using ground-penetrating radars at the same carrier frequencies as the space borne SARs to validate EO data in an efficient manner. The concept has been studied at C-band frequencies on glaciers at Svalbard, and we hope to build a similar platform for Ku-band frequencies, which will be used to validate model based retrieval algorithms.
... Since the 1970s, scientists have been studying the dynamic monitoring and mapping of snow changes in spatial and temporal scales, as well as the classification of snow cover (Baumgartner & Rango 1995;Krishna 1996;Jarkko et al. 1999;Fily et al. 1999;Xiao et al. 2002). however, the research has principally focused on snow classification (Krishna 1996;Fily et al. 1999;Xiao et al. 2002). in China, animal husbandry in areas receiving snow mainly relies on natural grazing management and is strongly susceptible to snow disasters because of the lack of basic infrastructure. ...
Monitoring and estimating potential snow disasters in pastoral areas of northern Xinjiang Province are important for decision‐making in hazard reduction and prevention. In this paper, four scenes of NOAA/AVHHR (Advanced Very High Resolution Radiometer) images were combined with ground observation data in the north of Xinjiang Province to establish a model for monitoring snow depth. Using a linear spectral decomposition method, the pixel‐based snow coverage and snow classification were studied. The spatial characteristics of snow, grassland, animal and climate factors were used to develop two new quantified indices for estimating the potential snow hazard grade and for integrated evaluation of snow disasters to grassland animal husbandry. The criteria for snow hazard grade and snow disaster evaluation were established. Results indicated that: (i) a pixel‐based index K1, based on grassland yield, animal capacity, utilisable grassland area coefficient and seasonal grazing utilisation scenarios, can be quantitatively integrated to reflect the grassland capability of resisting snow disasters; (ii) the snow hazard index (K) systematically expresses the spatial and temporal changes of grassland and snow cover, and analyses, and predicts and evaluates the snow hazard grade under conditions where climatic and animal husbandry information may be unavailable during snow disasters. This index plays an important role in studies on early warning of snow hazard in pastoral areas; (iii) the integrated snow disaster evaluation index (E) and related classification criteria reflect the details of snow disaster magnitude in temporal and spatial scales, which provide the basic information for dynamic monitoring and integrated evaluation on snow disasters in pastoral areas.
... This can be obtained through a supervised classification. According to Koskinen et al. (1999), who studied the density of snow distribution, the classification of snow coverage zones was as follows: (a) zones totally covered by snow; (b) more than half of the area covered by snow; (c) less than half of the area covered by snow; (d) snow only exists in forests; and (e) dissipated snow ( Fig. 3(a)). ...
At least one-quarter of the Lebanese terrain is covered by snow annually, thus contributing integrally to feeding surface and subsurface water resources. However, only limited estimates of snow cover have been carried out and applied locally. The use of remote sensing has enhanced significantly the delineation of snow cover over the mountains. Several satellite images and sensors are used in this respect. In this study, SPOT-4 (1-km resolution) satellite images are used. They have the capability to acquire consecutive images every 10 days, thus monitoring the dynamic change of snow and its maximum coverage could be achieved. This was applied to Mount Lebanon for the years 2001–2002. The areas covered by snow were delineated, and then manipulated with the slope angle and altitudes in order to classify five major zones of snowmelt potential. The field investigation was carried out in each zone by measuring depths and snow/water ratio. A volume of around 1100 × 10 m of water was derived from snowmelt over the given period. This is equivalent to a precipitation rate of about 425 mm in the region, revealing the considerable portion of water that is derived from snowmelt.
... This provides favourable conditions for the growth of shrubs. Most of the climatological studies carried out in Finnish Lapland have dealt with snow monitoring (Vehviläinen 1992, Koskinen et al. 1999) and the snow energy balance (Koivusalo et al. 2001), but only few studies have focused on the interaction between snow and the subarctic vegetation. The spatial and temporal variation in snow accumulation , snowmelt and snow water equivalent in Finland was studied by Seppänen (1961) , Kuusisto (1984), Solantie et al. (1996) and Solantie (2000). ...
The presence of permanent snow cover for 200-220 days of the year has a determining role in the energy, hydrological and ecological processes at the climate-driven spruce (Picea abies) timberline in Lapland. Disturbances, such as forest fires or forest harvesting change the vegetation pattern and influence the spatial variation of snow cover. This variability in altered snow conditions (in subarctic Fennoscandia) is still poorly understood. We studied the influence of vegetation on the small-scale spatial variation of snow cover and wind climate in the Tuntsa area that was disturbed by a widespread forest fire in 1960. Radar was applied to measure snow thickness over two vegetation types, the spruce-dominant fire refuge and post-fire treeless tundra. Wind modelling was used to estimate the spatial variation of wind speed and direction. Due to the altered surface roughness and the increased wind velocity, snow drifting was more vigorous on the open tundra, resulting in a 30-cm thinner snow cover and almost half the water equivalent compared to the forest values. The changes in local climate after the fire, particularly in snow cover, may have played an important role in the poor recovery of vegetation: a substantial area is still unforested 40 years after the fire.
... In addition, SAR images are hampered with the effects of speckle and geometric distortion due to layover, foreshortening and shadowing. Several authors (Baghdadi et al. 1997, Koskinen et al. 1999, Nagler and Rott 2000 have shown that wet snow can be mapped accurately with SAR, due to the reduction in backscattering. SAR data have capabilities of detecting the extent of wet snow cover in mountainous areas (Rott et al. 1992, Rott and Davies 1993, Shi et al. 1997. ...
This study has been done using Polarimetric Synthetic Aperture Radar (SAR) data to estimate the snow physical properties (snow wetness and snow density), inManali sub-basin of Himachal Pradesh, India. The SAR data used are of Radarsat-2 (RS2) and Environmental Satellite, Advanced Synthetic Aperture Radar (ASAR). SAR-based inversion models were implemented in Mathematica and MATLAB, and have been used for finding out wet and dry snow dielectric constant, snow wetness and snow density. The SAR-based inversion models were implemented separately for fully polarimetric RS2 and dual polarimetric ASAR Alternate Polarization System datasets. Masks for forest, built area, layover and shadow were considered in estimating snow parameters. The overall accuracy in terms of R 2 value comes out to be 0.86 for snow wetness and 0.84?0.72 for snow density based on the ground truth data for subset area of Manali sub-basin of Beas river up to Manali.
... Remote sensing, specifically from radar, scatterometer and other satellite data, has proven an invaluable tool in snow mapping due to its scope, resolution and temporal characteristics (Koskinen and Hallikainen 1997;Koskinen et al. 1999;Hillard et al. 2003;Wang et al. 2005). Ten 1:50 000 topographic maps covering the study area were used in combination with remotely sensed data. ...
Although snow is known to influence landform genesis and distribution, the spatial associations between snow and landforms within particular cold regions has received limited research attention. We present a case study from the high Drakensberg of southern Africa, comparing the contemporary spatial pattern of longest-lasting cold-season snow patches with the distribution patterns of active and relic cold region landforms. Two 30 m resolution sets of TM images dated 3 and 19 August 1990 and a DEM were used to demonstrate the geographic trends of snow patch depletion during late winter. Geomorphological phenomena with known coordinates were then incorporated into the GIS. The spatial distribution of several periglacial land-forms (earth hummocks, stone-/turf-banked lobes, block deposits, large sorted patterned ground) coincides with topographic positions that limit snow accumulation. However, the strong spatial association between longest-lasting snow patches and palaeo-moraines implies substantial snow accumulation at some high altitude south-facing sites during the last glacial cycle.
... Raggam, Almer and Strobel (1994) demonstrated how snow cover retrieved from multi-parameter airborne SAR and SPOT HRV can be combined. Koskinen et al. (1999) analysed a time series of NOAA AVHRR and ERS-2 SAR images. However, they did no actual combination of the two other than studying how the snow cover developed as observed by the two sensors. ...
We have developed a new approach based on modelling and assimilation to combine SAR and optical data for snow cover area mapping. SAR data would typically be acquired a few times a week, while optical data is acquired daily but is limited by cloud cover. The algorithm we use ana-lyses the current time series to estimate the current Fractional Snow Cover (FSC) per pixel. A set of snow states is defined. Each snow state has a corresponding reflectance model for optical data and a backscatter model for SAR data. The snow states defined are 'dry snow, full snow cover', 'wet snow, full snow cover', 'fractional snow cover' and 'snow-free ground'. A Hidden Markov Model (HMM) has been established to compute the likelihood of a transition from one state to an-other, given the current observations. The backscatter and reflectance observations are processed by an algorithm comparing them to their respective models given by the current state. Based on this, the most likely current FSC is calculated for each pixel being analysed. Each pixel is proc-essed independently and might therefore be in different stages (which is typical for mountainous terrain). The approach has been tested for a mountain plateau in South Norway combining Terra MODIS and ENVISAT ASAR from four snowmelt seasons (2003-2006). The results indicate that it is possible to obtain consistent results of high accuracy from the combination of the two sensors. Further work includes testing and tailoring of the approach to areas with steeper terrain.
... Images, where the calibrations areas are masked out by clouds, will be discharged. Several papers have demonstrated the potential of SAR for wet snow detection using ERS and Radarsat standard mode (see, e.g., [3] and [4]). Wet snow was detected by utilising the high absorption and therefore low backscatter of wet snow pixels and then comparing the backscatter with the corresponding pixel of a reference scene taken during dry-snow or snow-free conditions. ...
The ENVISAT satellite with its many sensors opens for new, interesting approaches of combined multi-sensor, multi-temporal monitoring. In this study, we have focused on monitoring of snow parameters in the snowmelt seasons of 2003 and 2004 (April-June) in South Norway. The sensors used in this study are ENVISAT MERIS and ASAR and Terra MODIS. The study is motivated by operational prospects for snow hydrology, meteorology and climate monitoring. We have developed a generic multi-sensor/multi-temporal approach for monitoring of snow cover area (SCA), snow surface wetness (SSW) and snowmelt onset time (SOT). The objective is to analyse, on a daily basis, a time series of optical and SAR data together producing sensor-independent products. We have defined raster products for each variable and developed a prototype production line. The production line automatically performs data retrieval, pre-processing, parameter retrieval, data aggregation and product generation.
... The coarse spatial resolution restricts their use in snow cover mapping (Rango, 1996). Active microwave data with higher spatial resolution, such as Envisat Advanced Synthetic Aperture Radar (ASAR) and Radarsat and European Remote Sensing Satellite (ERS) can detect snow cover under cloud easily (Nagler and Rott, 1998;Koskinen et al., 1999;Guneriussen et al., 2001;Guneriussen and Johnsen, 2003;Storvold and Malnes, 2004). However, these SAR generally cannot detect dry snow and their temporal resolutions, ranging from 24 to 35 days, are too low, and thus cannot be used for the purpose of mapping detailed snow cover depletion curves. ...
Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud-obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision-tree-like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km2) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.
... Snow cover retrieval by SAR is based on work that demonstrated the potential of SAR for wet snow detection using ERS and Radarsat standard modes (see, e.g. Koskinen et al., 1999; Nagler & Rott, 2000). Wet snow is detected by utilising the high absorption, and, therefore, low backscatter, of wet snow and then comparing the backscatter with the corresponding pixel of a reference image acquired during dry-snow or snow-free conditions. ...
The catchment of Øvre Heimdalsvatn and the surrounding area was established as a site for snow remote sensing algorithm development,
calibration and validation in 1997. Information on snow cover and snowmelt are important for understanding the timing and
scale of many lake ecosystem processes. Field campaigns combined with data from airborne sensors and spaceborne high-resolution
sensors have been used as reference data in experiments over many years. Several satellite sensors have been utilised in the
development of new algorithms, including Terra MODIS and Envisat ASAR. The experiments have been motivated by operational
prospects for snow hydrology, meteorology and climate monitoring by satellite-based remote sensing techniques. This has resulted
in new time-series multi-sensor approaches for monitoring of snow cover area (SCA) and snow surface wetness (SSW). The idea
was to analyse, on a daily basis, a time series of optical and radar satellite data in multi-sensor models. The SCA algorithm
analyses each optical and synthetic aperture radar (SAR) image individually and combines them into a day product based on
a set of confidence functions. The SSW algorithm combines information about the development of the snow surface temperature
and the snow grain size (SGS) in a time-series analysis. The snow cover algorithm is being evaluated for application in a
global climate monitoring system for snow variables. The successful development of these algorithms has led to operational
applications of snow monitoring in Norway and Sweden, as well as enabling the prediction of the spring snowmelt flood and
thus the initiation of many lake production processes.
KeywordsRemote sensing-Retrieval algorithms-Fractional snow cover-Snow surface wetness-Snow surface temperature
... The capabilities of Space-borne SAR in snow monitoring has been widely studied (Rott 1984, Koskinen et al. 1994, Piesbergen at al. 1995, Guneriussen et al. 1996, Koskinen et al. 1997, Nagler and Rott 1998, Koskinen et al. 1999. The suitability of SAR for snow monitoring has been confirmed, and further studies are been made for snow monitoring algorithm development. ...
An accuracy analysis of the HUT Snow Covered Area estimation method is presented in this work. The main emphasis is to resolve the statistical accuracy of the estimation method. Other studied subjects include the effects of reference image selection, forest compensation, topography and satellite flight path on the SCA estimation method. The method is studied by conducting the SCA estimation for 24 ERS-2 SAR intensity images for the boreal forest dominated test area in Northern Finland. The evaluation of the method is conducted by comparing the SCA estimation results with the available reference data.
O propósito deste artigo é discutir a evolução dos métodos de simulação do retroespalhamento radar/SAR em massas de neve e gelo, compreendendo as abordagens físicas e empíricas neste processo. O presente trabalho busca compilar os diversos modelos de retroespalhamento radar/SAR de neve e gelo na faixa de microondas compreendida pela banda X propostos na literatura, comparando seus resultados, contribuições e limitações a fim de promover um material de orientação para pesquisa e emprego dos diversos modelos de retroespalhamento atualmente disponíveis. Assim, os principais modelos de retroespalhamento atuais serão descritos, implementados e finalmente comparados. Como fonte de validação dos modelos, serão considerados dados comuns de entrada, constituídos de dados estratigráficos e de temperatura da neve em um perfil de 2m de profundidade e dados radar/SAR Cosmo-SkyMed na banda X coletados na região da geleira Union no verão antártico de 2011-2012.
The purpose of this paper is to discuss the evolution of simulation methods of SAR backscatter on snow, including the physical and empirical approaches in this process. Therefore, the studyis based on different models of SAR backscatter for snow and ice in the microwave range encompassed by the X-band proposed in the literature, comparing these results, contributions and limitations in order to promote a guidance material for research and employment for many backscatter models currently available. Thus, the main current backscatter models are described, implemented, and finally compared. As a source of validation for the models it was considered common input data, consisting of stratigraphic data and temperature of snow on a 2m depth profile data and SAR Cosmo-SkyMed X-band data acquired on the Union Glacier during the Antarctic summer 2011 -2012.
Cryosphere studies are very critical for understanding the behaviour of the global climate system. The cryosphere components such snow cover, glacier ice, ice sheets, icebergs, etc. need to be studied, from Earth’s energy and water balance point of view. However, their quantification and mapping is tedious on field using tradition survey methods. This review paper summarizes the major research work done in field of remote sensing based cryosphere studies in Indian Himalaya and Polar Regions. The traditional survey methods using terrestrial photogrammetry and aerial photography for snow and glacier studies are presented, initially. Then, remote sensing methods of snow and glacier mapping using visible, infrared and thermal spectral bands are presented. Later, the application of these techniques and method in Indian conditions is discussed. The advanced methods of microwave sensors based inversion models for snow physical properties retrieval are also explained. The separate sub-sections are given to highlight the use of remote sensing in glacier and polar studies. In each section, the current status and advances of the technology are discussed. The future prospects of remote sensing technology for each cryosphere theme are highlighted with emphasis to Indian scenario.
Snow in mountain areas is a key factor controlling regional energy balances, hydrological cycle, and water utilization. Optical remote sensing data offer an effective means of mapping snow cover, although their application is limited by solar illumination conditions, conversely, synthetic aperture radar (SAR) offers the ability to measure snow wetness changes in all weather. In this study, a novel method, which can be approached in two steps by using SAR and optical data, has been developed for dry and wet snow cover recognition in mountain areas. First, two ground-based synchronous observations were implemented, respectively, for snow-accumulation period and snow-melt period. Then, the RADARSAT-2 interferometric coherence images and the backscattering coefficient images of the two periods are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth.” A dynamic thresholding algorithm was proposed to identify snow cover by taking the polarization mode, local incidence angle, and underlying surface type into consideration. Finally, 36 polarimetric parameters obtained from Pauli, H/A/α, Freeman, and Yamaguchi decomposition were analyzed; the results indicate that P $_{\mathrm{vol}}$ from Pauli, λ $_{3}$ from H/A/α, and Y $_{\mathrm{vol}}$ from Yamaguchi are more applicable to discriminate dry and wet snow. These three factors, combined with training samples from Nagler algorithm and in situ data, were used to build a support vector machine to classify the extracted snow cover to dry and wet snow. The classification results demonstrate that the dry and wet snow cover extraction can achieve an accuracy of 90.3% compared with in situ measurements.
This paper presents simulation results of the backscattering coefficient, in order to discriminate between wet snow and dry snow covers sensed at 5.3 GHz by the RADARSAT Synthetic Aperture Radar (SAR) sensor. Snow-field measurements coinciding with the RADARSAT SAR overpasses are used to explore and set out optimal conditions for wet snow detection, as a function of the sensor incidence angles. The conditions concern wet snow surface characteristics, mainly the roughness represented by the surface slope m and the volumetric liquid water content, snwc (vol.%). Based on the 3-dB threshold value used in several wet snow detection algorithms, the results show that in order to be discriminated from dry snow covers, wet snow surfaces must be characterized as: (a) m≤0.058 and snwc≤1.1, if the sensor operates in the S1 mode (20–27° incidence angle range), and (b) m≤0.082 and snwc≤3.0, if the observations are made in the S7 mode (45–49° incidence angle range). For the identification of a very wet snow, it is also shown that the S7 mode of RADARSAT SAR sensor is more suitable than the S1 mode. The latter, however, provides better discrimination for low values of the snow liquid water content. Furthermore, for wet snow detection based on modeling, the present paper demonstrates the importance of using the appropriate methodology to assess the dielectric constant of the background medium.
Snow cover and its associated melt have a variety of important biological, hydrological, economic and hazards implications, which under current global change scenarios are anticipated to continue operating as major environmental agents. To this end, considerable advances have been made in recording and mapping snow cover over the past few decades. This review examines the development of snow mapping over time, and explores the application of modern technology to the measurement of snow distribution and characterization. Some advantages and limitations of current snow mapping methods and techniques are discussed, as are their potential to facilitate future snow mapping. It is demonstrated that advances in remote sensing technology and ground-based measurement devices have upgraded snow mapping to a fully digital process during recent decades, and it is anticipated that three-dimensional snow mapping will facilitate a fundamental step toward a new generation of snow observation techniques in the coming years.
Envisnow is an EU FP5 project focusing its attention on retrieving snow parameters and soil moisture using multisensor earth observation satellites. The project will demonstrate a near real-time retrieval system for snow covered area and snow wetness in the spring 2004. Assimilation og snow parameters with hydrological models and use in run-off forecast will also be demonstrated pre-operationally. In this paper snow detection algorithms for synthetic aperture radar (SAR) imagery will be discussed. In addition examples of SAR and optical SCA data combined in a scalable multi-sensor processing system will also be demonstrated. The project has shown that wide swath SAR data can be used to produce snow cover maps with 100 m resolution and 500 km by 500 km coverage. This allows operational use of SAR data for snow mapping. The applied wet snow detection algorithm (Nagler and Rott, 2000) has been complimented with a dry snow algorithm, predicting dry snow above medium wet snow line. Results from a campaign in southern Norway 2003 using Envisat ASAR and MODIS data combined with aerial SAR imagery and in-situ measurements will be shown. The results demonstrates that a close integration of snow parameter data from several sensors (both radar and optical) may be used to improve temporal resolution and coverage, avoid problems with clouds and improve the overall classification accuracy.
During the melting season in spring, up-to-date information on the areal extent of snow cover is needed; for example for hydrological modelling and hydropower production. A method for estimating drainage area-based snow-cover percentages from NOAA AVHRR images was developed and tested by using data from the melting season of 1999 in Finland. When estimating the snow-cover percentage of a drainage area, the reflectance of the area is compared with the reflectance of the same area in the conditions of full snow cover and with the reflectance of snow-free ground. It is assumed that the mean reflectance of each land-cover class in the drainage area decreases linearly as snow-free ground appears. By estimating the percentages for drainage areas rather than pixels and using local comparisons rather than overall threshold values, some problems related to varying land cover and imaging geometry, as well as small inaccuracies in image registration, can be reduced. The drainage area is also a feasible unit for further use of the results; for example in hydrological models. The results were compared with ground measurements and appeared to be satisfactory. Further research, however, is needed to account for various effects that can change the reflectance of the snow cover and forest during the melting season. Along with the research work, operational use of the developed method began in 2000.
This chapter is dedicated to remote sensing of snow as one of the most
important cryospheric components to be considered in Northern Hydrology.
After a short introduction, general approaches in optical, thermal
infrared, active and passive microwave, and airborne gamma remote
sensing, including the most important systems, are presented and
discussed. Then, specific applications of remote sensing of snow cover
are shown, grouped by the respective snow properties, i.e., snow-covered
area, liquid water content, snow water equivalent, snow reflectance,
snow grain size, snow depth, and snow temperature. Finally, conclusions
are drawn with respect to unresolved issues and future research needs.
The overall idea behind the work presented is to combine the use of optical and SAR sensors and utilise the best features of each sensor when possible in order to map snow cover area (SCA) more frequently and with better spatial coverage than would otherwise be possible. Optical remote sensing sensors are able to map snow cover quite accurately, but are limited by clouds. SAR sen-sors penetrate the clouds, but current satellite-borne sensors are only able to map wet snow accu-rately. In this paper we describe the methodology developed and the results of applying this for SCA mapping through the snowmelt season 2004 in South Norway. The results include the use of ENVISAT ASAR and Terra MODIS. Common for all the experiments is that the sensor fusion has taken place at the level of geophysical parameters. A few algorithms for multi-sensor time-series processing have been developed. One approach is to analyse each image individually and com-bine them into a day product. How each image contributes to the day products is controlled by a pixel-by-pixel confidence value that is computed for each image analysed. The confidence algo-rithm is able to take into account, e.g., information about observation geometry, probability of clouds, prior information about snow state and reliability of the classification. The time series of day products are then combined into a multi-sensor multi-temporal product. The combination of prod-ucts is done on a pixel-by-pixel basis and controlled by each individual pixel's confidence and a decay function of time for the product. The "multi-product" should then represent the most likely status of the monitored variable.
Two regional snow mapping algorithms were developed and tested over the province of Quebec (Canada).: one using AVHRR imagery and the second using SMM/I data. In order to mitigate the disadvantages related to each type of data, we decided then to develop a merging procedure of the snow mapping results obtained by both sensors. This procedure allows combining the advantages of using both high spatial resolution VIR sensor and a high temporal resolution microwaves sensor. The results of the snow mapping using the merging procedure are then validated using high resolution satellite imagery as well as ground observations of snow depth. The quantitative analysis is conducted on a twenty one major watershed over Quebec. These basins have been selected according to their specific climatic and physiographical characteristics.
The standard method for retrieving snow-covered area with C-band SAR is based on thresholding the ratio between a wet snow SAR image and a dry snow reference scene. A new approach is suggested here, where the snow cover fraction is retrieved by using a gradual transition between snow free and snow covered conditions. The paper discusses the method and applies it on a set of Radarsat data acquired in Norway in May 2003. A near simultaneous aerial optical image and data from field campaigns are used to verify the results.
As spaceborne C-band synthetic aperture radar (SAR) observations are used for monitoring the snow cover during the spring melt period, temporal changes in backscatter from forest cover disturb the mapping of snow cover. This paper presents an analysis of snow backscattering properties in eight test areas situated around weather stations. Test areas represent open and forested landscapes in Northern Finland. Analyses are carried out using an extensive multitemporal ERS-2 C-band SAR data set from the snow melt period. We validate the following topics: 1) forest backscattering model for forest compensation; 2) Helsinki University of Technology (TKK) fractional snow-covered area (SCA) method with in situ observations; and 3) inversion of a combined forest/snow/ground backscattering model in an application to yield estimates of the relative changes of snow wetness during full snow cover conditions. The results show that the semiempirical TKK backscattering model describes the average C-band backscattering properties of all test regions well as a function of forest stem volume. Comparison of SCA estimation results with available ground-truth data also shows a good performance. The retrieved relative snow wetness values agree well with temperature observations.
Spatially well-distributed information on the regional fraction of snow-covered area (SCA) is important to snow hydrology during the melting season. One approach for regional SCA estimation using visible and near-infrared reflectances is based on linear interpolation between reference reflectances for full snow cover and snow-free conditions. We present an improved method for National Oceanographic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) imagery with (1) an automated determination of reference reflectances by distinguishing wet and dry snow conditions and, on the other hand, near melt-off and totally melt-off conditions and (2) an employment of Normalized Difference Vegetation Index (NDVI) to avoid overestimations due to vegetation cover at the end of the melting season. The study site covers the area of Finland, which serves as an example of the Eurasian boreal coniferous forest zone. Finnish drainage basins are used as areal calculation units in order to produce feasible information for hydrological models. Since the frequent cloudiness in the northern latitudes reduces the availability of optical data, we developed a technique to generate reference reflectances for basins that were obscured at the actual moment of data retrieval. For a basin without a reference value, the proper values were derived from a basin of the same characteristics; the similarity was described with a special Forest Sparseness Index generated from AVHRR data. The linear interpolation method with the additional features was tested for AVHRR imagery during melting period 2000. Validation against a comprehensive network of ground observations at snow courses and weather stations indicated good performance.
Snow and meteorological measurements collected at six sites in Northeast China were used to compare snow cover area (SCA) retrieved from the Moderate Resolution Imaging SpectroRadiometer (MODIS) and QuikSCAT and snow water equivalent (SWE) retrieved from the Advanced Microwave Scanning Radiometer (AMSR). The SCA retrieved from QuikSCAT and MODIS are in qualitative agreement with each other and with in situ measurements. Based on in situ data from stations Qingyu and Dehui, SWE retrieved from AMSR show a bias of 43% and a R value of 0.91 with in situ data for the early snow season. These results are consistent with previous estimates and point to the need to properly account for other snow properties such as snow density profile and grain size in the retrieval of regional snow parameters in Northeast China.
The structure and function of northern ecosystems are strongly influenced by climate change and variability and by human-induced disturbances. The projected global change is likely to have a pronounced effect on the distribution and productivity of different species, generating large changes in the equilibrium at the tree-line. In turn, movement of the tree-line and the redistribution of species produce feedback to both the local and the regional climate. This research was initiated with the objective of examining the influence of natural conditions on the small-scale spatial variation of climate in Finnish Lapland, and to study the interaction and feedback mechanisms in the climate-disturbances-vegetation system near the climatological border of boreal forest. The high (1 km) resolution spatial variation of climate parameters over northern Finland was determined by applying the Kriging interpolation method that takes into account the effect of external forcing variables, i.e., geographical coordinates, elevation, sea and lake coverage. Of all the natural factors shaping the climate, the geographical position, local topography and altitude proved to be the determining ones. Spatial analyses of temperature- and precipitation-derived parameters based on a 30-year dataset (1971-2000) provide a detailed description of the local climate. Maps of the mean, maximum and minimum temperatures, the frost-free period and the growing season indicate that the most favourable thermal conditions exist in the south-western part of Lapland, around large water bodies and in the Kemijoki basin, while the coldest regions are in highland and fell Lapland. The distribution of precipitation is predominantly longitudinally dependent but with the definite influence of local features. The impact of human-induced disturbances, i.e., forest fires, on local climate and its implication for forest recovery near the northern timberline was evaluated in the Tuntsa area of eastern Lapland, damaged by a widespread forest fire in 1960 and suffering repeatedly-failed vegetation recovery since that. Direct measurements of the local climate and simulated heat and water fluxes indicated the development of a more severe climate and physical conditions on the fire-disturbed site. Removal of the original, predominantly Norway spruce and downy birch vegetation and its substitution by tundra vegetation has generated increased wind velocity and reduced snow accumulation, associated with a large variation in soil temperature and moisture and deep soil frost. The changed structural parameters of the canopy have determined changes in energy fluxes by reducing the latter over the tundra vegetation. The altered surface and soil conditions, as well as the evolved severe local climate, have negatively affected seedling growth and survival, leading to more unfavourable conditions for the reproduction of boreal vegetation and thereby causing deviations in the regional position of the timberline. However it should be noted that other factors, such as an inadequate seed source or seedbed, the poor quality of the soil and the intensive logging of damaged trees could also exacerbate the poor tree regeneration. In spite of the failed forest recovery at Tunsta, the position and composition of the timberline and tree-line in Finnish Lapland may also benefit from present and future changes in climate. The already-observed and the projected increase in temperature, the prolonged growing season, as well as changes in the precipitation regime foster tree growth and new regeneration, resulting in an advance of the timberline and tree-line northward and upward. This shift in the distribution of vegetation might be decelerated or even halted by local topoclimatic conditions and by the expected increase in the frequency of disturbances. Ilmaston vaihtelut ja ilmastonmuutos sekä ulkoiset häiriötekijät vaikuttavat merkittävästi pohjoiseen ekosysteemiin. Ennustetulla maailmanlaajuisella ilmaston-muutoksella on suuri vaikutus eri kasvi- ja eläinlajien elinoloihin myös pohjoisella puurajalla. Puurajalla kasvustossa tapahtuvat muutokset vaikuttavat puolestaan sekä paikalliseen että alueelliseen ilmastoon. Tässä tutkimuksessa on selvitetty mitkä luonnolliset tekijät selittävät ilmaston alueellistan vaihtelua Suomen Lapissa. Tutkimuksessa selvitetään myös kuinka ulkoiset häiriötekijät vaikuttavat ilmaston ja kasvillisuuden muodostaman järjestelmän sisäisiin vuorovaikutuksiin ja palautemekanismeihin lähellä boreaalisen metsän ilmastollista rajaa. Pohjois-Suomen ilmaston pienen mittakaavan (1 km) alueellista vaihtelua tutkittiin soveltamalla Krigingin analyysimenetelmää. Käytetty menetelmä huomioi ulkoisten tekijöiden, kuten maantieteellisten sijainnin, korkeuden ja meren ja järvien vaikutuksen. Ilmastoon vaikuttavien luonnollisten tekijöiden joukosta maantieteellinen sijainti ja maaston korkeus osoittautuivat tärkeimmiksi. Tutkimuksen osana tehty vuosien 1971 2000 havaintoihin pohjautuva lämpötila ja sadeolojen alueellinen analyysi antaa yksityiskohtaisen kuvan Lapin paikallisilmastosta. Keskilämpötilojen sekä ylimpien- ja alimpien lämpötilojen, kasvukauden pituuden ja lämpösumman alueelliset jakaumat sekä pakkasjaksojen ajoittuminen osoittavat, että suotuisimmat kasvuolosuhteet löytyvät Lounais-Lapista suurien vesialueiden ympäriltä sekä Kemijoen alueelta. Kylmimmät alueet taas löytyvät Tunturi-Lapista. Keskimäärin sadetta saadaan useammin ja enemmän Itä-Lapissa kuin alueen länsiosassa. Ulkoisten häiriötekijöiden, kuten esimerkiksi metsäpalojen vaikutusta paikallisilmastoon ja metsien uudistumiseen pohjoisen metsänrajan lähellä arvioitiin Itä-Lapin Tuntsan-alueella. Tällä alueella tapahtui laaja metsäpalo vuonna 1960 ja sen jälkeen tehdyt useat metsien uudistamisyritykset epäonnistuivat. Paikallisilmaston mittaukset ja ilmakehämallilla tehdyt simuloinnit osoittivat, että ilmasto oli muuttunut palon vaurioittamalla alueella ankarammiksi. Alkuperäisen, pääosin kuusen ja hieskoivun muodostuman kasvillisuuden korvautuminen matalalla tundra-kasvillisuudella voimisti tuulen nopeuksia lähellä maanpintaa. Tuulen voimistuessa ajautui kasvustoa suojaava lumipeite pois avoimilta paikoilta, mikä puolestaan lisäsi maaperän lämpötilan, kosteuden ja roudan ajallista vaihtelua. Muuttuneet pinta- ja maaperäolosuhteet sekä äärevöitynyt paikallisilmasto vaikeuttivat taimien kasvuedellytyksiä. Olosuhteet muuttuivat siis epäsuotuisiksi kasvillisuuden elpymisen kannalta. Tämä aiheutti muutoksia metsänrajan paikalliseen sijaintiin. Myös muut tekijät, kuten väärät siemenvalinnat, köyhtynyt maaperä ja vahingoittuneiden puiden intensiivinen hakkuu saattoivat vaikeuttaa metsän uudistumista. Maailmanlaajuisen ilmastonmuutoksen seurauksena tapahtuva lämpötilan nousu, pidentynyt kasvukausi kuten myös sademäärän muutokset suosivat puiden kasvua ja uudistumista mikä voi johtaa metsänrajan ja puurajan siirtymiseen pohjoiseen ja ylöspäin. Myös puulajeissa voi tapahtua muutoksia. Paikallisesti kasvillisuuden levinneisyyden muutokset voivat kuitenkin hidastua tai jopa pysähtyä jos paikallisten häiriötekijöiden, kuten metsäpalojen, esiintymistiheys kasvaa ilmastonmuutoksen seurauksena. Näin tapahtui esimerkiksi Tuntsassa.
This is the author's final draft of the paper published as International Journal of Remote Sensing, 2002, 23 (16), pp. 3185-3208. The final version is available from http://www.informaworld.com/smpp/content~db=all?content=10.1080/01431160110076199. Doi: 10.1080/01431160110076199 Airborne polarimetric Synthetic Aperture Radar (SAR) is used for estimating the stem volume of a Finnish boreal forest by comparing different empirical models. Its capability for retrieval of snow water equivalent is then explored. Fully polarimetric L-and C-band data were acquired over a Finnish test site in March and May 1995. The information content was explored qualitatively by inspecting polarimetric colour composites, and by applying decomposition algorithms to the polarimetric covariance matrices at individual frequencies. Three families of quantitative models were fitted to estimate stem volume: (1) F1P1 models, using a single frequency and a single polarization; (2) F2P1 models, using the difference between HV polarization at C- and L-band related to stem volume; (3) F1P4 models, based on a single frequency and the full polarimetric information, selected by stepwise multiple regression with stem volume. Stem volume estimates from SAR are compared with digital stem volume data by the Finnish Forest Research Institute. Prior information about the stem volume distribution addresses the saturation problem of the microwave response. The L-band F1P4 models in March and May 1995 have the smallest rms errors, around 22 m 3 /ha - 1 . Three multiple regression models to retrieve snow water equivalent from backscatter are presented: (1) EU model, an explorative, uncorrected multiple regression model; (2) EC model, an explorative, stem volume corrected multiple regression model; (3) CC model, a statistically conservative, stem volume corrected multiple regression model. The accuracy of snow water equivalent estimates was improved significantly by a simple linear correction for stem volume. The statistically conservative CC model showed that only L-band in HH polarization explained a significant ( p <0.05) proportion of snow water equivalent ( r 2 =0.51). The explorative EC model resulted in r 2 =0.68 ( p >0.05). Conclusions are that (1) decomposition algorithms of the polarimetric covariance matrix result in information on scattering mechanisms in the vegetation canopy and on the ground, so being potentially of great value for land cover mapping; (2) satellite polarimetric SARs, for example those to be flown on Envisat and ALOS, will be able to estimate stem volume on continental and global scales; and (3) L-band SAR has a potential for snow cover mapping and runoff prediction.
This paper presents a method to estimate the snow covered area (SCA) for small urban catchments. The method uses images taken with a digital camera positioned on top of a tall building. The camera is stationary and takes overview images of the same area every fifteen minutes throughout the winter season. The images were read into an image-processing program and a three-layered feed-forward perceptron artificial neural network (ANN) was used to calculate fractional snow cover within three different land cover types (road, park and roofs). The SCA was estimated from the number of pixels with snow cover relative to the total number of pixels. The method was tested for a small urban catchment, Risvollan in Trondheim, Norway. A time series of images taken during spring of 2001 and the 2001-2002 winter season was used to generate a time series of SCA. Snow covered area was also estimated from aerial photos. The results showed a strong correlation between SCA estimated from the digital camera and the aerial photos. The time series of SCA can be used for verification of urban snowmelt models.
It is of significance to establish an integrated evaluation system of snow disaster in northern pastoral areas. Based on the NOAA satellite digital images, field observation data, and maps of grassland type and seasonal pastureland, this paper selected the winter and spring pasturelands in Aletai region of Xinjiang as the main area of snow disaster-remote sensing monitoring. With affecting factors of economy and the characteristics of natural resource distribution comprehensively analyzed, and using 3S techniques and field survey information, a fundamental information processing model for integrated evaluation of snow disaster was built up, and snow disaster-spatial evaluation indices and damage level systems were constructed. Natural and social systems and 20 indices were selected in snow disaster evaluation indicator system. Four principal factors, i.e., snow cover area, snow depth on grassland, persistence days of low temperature, and livestock death rate, were used as the grading indices of snow disaster damage level, and the models of snow disaster identification and loss estimatation were set up to quantitatively analyze snow disaster. The results indicated that the system could accurately reflect the details of snow hazard grade and the situation of a disaster in temporal and spatial scales, which would help to carry out the dynamic monitoring and scientific estimatation of big area's snow disaster in pastoral region.
A procedure for mapping melting snow areas is described which is
based on change detection methods. The algorithm was applied for snow
mapping in alpine drainage basins and on glaciers, using ERS SAR images.
The comparison with snow maps from Landsat Thematic Mapper images shows
in general good agreement. Differences are observed mainly in areas with
patchy snow cover where SAR tends to underestimate, and Landsat to
overestimate, the snow extent. The developed software aims at
operational generation of SAR snow maps for snowmelt runoff forecasting
The authors have organized several airborne campaigns with their
dual-frequency scatterometer and multifrequency radiometer under various
snow conditions. These data are complemented with ERS-1 SAR images of
the same test site. The effect of land use categories (especially
forests) to the snow mapping capability of radar and radiometer has been
investigated
ERS-1 SAR data, airborne data and in situ snow data were acquired
for the Sodankyla test site in northern Finland for the winters of
1991-1992 and 1992-1993. The test area consists of sparsely forested
areas (pine, mixed forests, and mires) and open areas (bogs, lakes,
clear-cut areas, and urban area). A set of multitemporal ERS-1 SAR
images covering the two winters have been analyzed and the results have
been compared with in situ surveys and a digital land-use map. The
results indicate that even in the presence of forest canopies (1) wet
snow can be distinguished from other soil/snow conditions (dry snow and
bare ground), and (2) snow melt maps can be derived from SAR images.
Snow-melt maps indicate areas fully covered with wet snow, partly melted
areas and snow-free areas
This paper describes a computationally fast and accurate technique for the atmospheric correction of satellite measurements in the solar spectrum. The main advantage of the method is that it is several hundred times faster than more detailed radiative transfer models like 5S and that it does not require precalculated look-up tables. The method is especially useful for correcting the huge amounts of data acquired by large-field-of-view high-repetitivity sensors, like the ones on board polar orbiting and geostationary meteorological satellites.The technique is based on a set of equations with coefficients which depend on the spectral band of the sensor. Semi-empirical formulations are used to describe the different interactions (absorption, scattering, etc.) of solar radiation with atmospheric constituents during its traverse through the atmosphere. Sensor specific coefficients of each equation are determined using a best fit technique against the computations of the 5S code (Simulation of Satellite Signal in the Solar Spectrum, Tanré et al. 1990). Other radiative transfer models could be used. Once coefficients for a specific spectral band are determined, the inputs of the model are vertically integrated gaseous contents, aerosol optical depth at 550 nm, geometric conditions and reflectance at the top of the atmosphere (TOA). TOA reflectances were calculated using our method and then compared to the TOA reflectances calculated by 5S for a wide range of gaseous and aerosol contents, illumination and observation conditions for various sensor spectral bands. In the case of NOAA-9 AVHRR visible data the maximum relative error is 2·35 per cent (i.e. 0·01 for a reflectance value of 0·4) and the corresponding rmse is 0·0018. For NOAA-9 AVHRR near-infrared, Meteosat-1 visible, Landsat-5 TM band 1 and Landsat-5 TM band 4 the maximum relative errors are 3·11, 4·0, 1·65 and 2·37per cent respectively. The corresponding values of the rmse are 0·0022, 0·0015, 0·0017 and 0·0012.The method can be used both in the direct and in the inverse mode, i.e., to compute TOA reflectance knowing the surface reflectance (e.g., for fast sensitivity studies), or conversely to retrieve surface reflectance from the TOA reflectance. It can easily be implemented in operational data preprocessing computer code, since only band specific coefficients need to be updated when new sensors are flown, while the routines remain the same.
ERS-1 SAR data, airborne data and in situ snow data were acquired for the Sodankylä test site in northern Finland for the winters of 1991–1992 and 1992–1993. The test area consists of sparsely forested areas (pine, mixed forests and mires) and open areas (bogs, lakes, clear-cut areas and an urban area).A set of multitemporal ERS-1 SAR images covering two winters have been analyzed and the results have been compared with in situ surveys and a digital land-use map. The results indicate that even in the presence of forest canopies (1) wet snow can be distinguished from other classes (dry snow and bare ground), (2) snow-free areas can be identified and (3) snow melt maps can be derived from SAR images. Snow-melt maps indicate areas fully covered with wet snow, partly melted and snow free areas.
The objectives of the study reported here were to prepare for the future SAR satellites and to develop image processing and analysis methods to be used with the data from these satellites. Satellite SARs with small incidence angle suffer from large distortions due to terrain relief. To combine SAR images to other high-resolution satellite data, the relief displacement must be removed using a digital elevation model. There are various approaches to rectification of SAR images (see e.g. Triebnig 1987). From the implementation point of view, the simplest approach is the polynomial rectification. In addition to distorting the geometry of a SAR scene, terrain topography also affects its radiometry. The effects of topography must be strongly reduced before the small variation in backscatter due to land cover can be analyzed. -Author
A method is presented for the application of ERS-1 SAR images to
snow cover monitoring in high mountain areas. It is based on an image
coregistration as well as a geometric and radiometric correction to
remove the relief induced distortions. Using the optimal resolution
approach ORA synthetic SAR images are calculated to significantly
improve the thematic information content. The multitemporal optimal
resolution approach MORA uses a sequence of ascending and descending
scenes. It builds on the functionality of ORA by coregistering image
pairs and extracting geoecophysical parameters through successive
ratioing of the backscatter information. Wet snow cover monitoring was
done by calculating ratios between the backscattering coefficients of
the synthetic SAR images and a snow-free reference scene. The method was
applied to a dataset covering the 1993 melting season. The climbing of
the snow-line could clearly be detected. The potentials and limitations
of the approach are discussed
Describes an operational snow-cover monitoring system using
satellite imagery. The system has for several years been developed for
the largest Norwegian hydroelectric company, the Norwegian Energy
Corporation (Statkraft). The snow-cover data is an important information
source for the hydrological simulation models run in the melting season
from April to August. Accurate predictions of water amounts from the
snow-covered areas are crucial for power station production planning and
planning of long-term contracts. The system can be operated in
semi-automatic and automatic modes and is based on NOAA AVHRR imagery.
Geocoded imagery is calibrated against pre-selected stable calibration
areas before the classification. The classification algorithm is based
on an empirical reflectance-to-snow-cover model. The classified result
is then combined with various GIS data sources depending on the desired
result. Ongoing work for further development is also presented: Image
data aggregation from a time series of partially clouded imagery,
terrain normalization, automatic geocoding, and utilization of microwave
data
Snow algorithms method for the atmospheric correction of satellite measure-ments in the solar spectrum
- H Rahman
- G R Dedieu
- D Hiltbrunner
- J Koskinen
- T Gunneriussen
- K Rautiainen
- M Hallikainen
Rahman, H., and Dedieu, G. (1994), SMAC: a simplified Solberg, R., Hiltbrunner, D., Koskinen, J., Gunneriussen, T., Rautiainen, K., and Hallikainen, M. (1997), Snow algorithms method for the atmospheric correction of satellite measure-ments in the solar spectrum. Int. J. Remote Sens. 15:123–143.
Digital land-use map, product specification
- J Paavilainen
- T Siltala
- A Vertanen