Normalized Difference Snow Index Research Articles

An efficient algorithm for detection of seasonal snow cover variations over undulating North Indian Himalayas, India

The detection of rapid snow cover variations over undulating Himalayan mountain ranges may get challenging due to the existence of steep slopes and a diverse range of spatial variability. In this paper, a change detection algorithm is proposed for the detection of seasonal snow cover variations over North Indian Himalayas (NIH). The novelty of the proposed model lies within the applicability of fuzzy classification in change vector analysis (CVA) to identify the mixed pixels, termed as mixels which represent the membership of multiple or partial class-categories. Additionally, this model offers various advantages over existing CVA based algorithms such as (a) ease in selection of threshold value; (b) ease in change determination procedure; and (c) effective utilization of coarse-resolution satellite dataset. The efficacy of the algorithm has been established through a case study in Lahaul and Spiti (Himachal Pradesh), India. This analysis has been done using Moderate-Resolution Imaging Spectroradiometer (MODIS) data for the two-winter periods (i.e. 2009–10 and 2017–18). The accuracy of the proposed algorithm is estimated by the performance-matrix (change-category image) and it has been observed that fuzzy based CVA has achieved better accuracy (85.6%) as compared to conventional improved CVA (ICVA) (71.2%) and post-classification comparison (PCC) (69.6%). The relationships of seasonal snow cover variations with respect to altitudes between two-winter seasons have also been summarized to represent a shift in the duration of ablation and accumulation from 2009 to 2018. Moreover, the snow cover area (SCA) computed from fuzzy based CVA has also been compared with the normalized difference snow index (NDSI) maps for the study period. The applications of the present study may include accurate estimation of the snowmelt runoff, water resource management via continuous snow cover monitoring and mapping.

Cross-Comparison between MODIS and VIIRS Snow Cover Products for the 2016 Hydrological Year

The VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on board the Suomi-NPP (National Polar-Orbiting Partnership) satellite aims to provide long-term continuity of several environmental data series including snow cover initiated with MODIS (Moderate Resolution Imaging Spectroradiometer). Although it is speculated that MODIS and VIIRS snow cover products may differ because of their differing spatial resolutions and spectral coverage, quantitative comparisons between their snow products are currently limited. Therefore, this study intercompares MODIS and VIIRS snow products for the 2016 Hydrological Year over the Midwestern United States and southern Canada. Two hundred and forty-four swath snow products from MODIS/Aqua (MYD10L2) and the VIIRS EDR (Environmental Data Records) (VSCMO/binary) were intercompared using confusion matrices, comparison maps and false color imagery. Thresholding the MODIS NDSI (Normalized Difference Snow Index) Snow Cover product at a snow cover fraction of 30% generated binary snow maps are most comparable to the NOAA VIIRS binary snow product. Overall agreement between MODIS and VIIRS was found to be approximately 98%. This exceeds the VIIRS accuracy requirements of 90% probability of correct typing. The agreement was highest during the winter but lower during late fall and spring. MODIS and VIIRS often mapped snow/no-snow transition zones as a cloud. The assessment of total snow and cloud pixels and comparison snow maps of MODIS and VIIRS indicate that VIIRS is mapping more snow cover and less cloud cover compared to MODIS. This is evidenced by the average area of snow in MYD10L2 and VSCMO being 5.72% and 11.43%, no-snow 26.65% and 28.67% and cloud 65.02% and 59.91%, respectively. While VIIRS and MODIS have a similar capacity to map snow cover, VIIRS has the potential to map snow cover area more accurately, for the successful development of climate data records.

MODIS-based Daily Lake Ice Extent and Coverage dataset for Tibetan Plateau

ABSTRACTThe Tibetan Plateau houses numerous lakes, the phenology and duration of lake ice in this region are sensitive to regional and global climate change, and as such are used as key indicators in climate change research, particularly in environment change comparison studies for the Earth three poles. However, due to its harsh natural environment and sparse population, there is a lack of conventional in situ measurement on lake ice phenology. The Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) data, which can be traced back 20 years with a 500 m spatial resolution, were used to monitor lake ice for filling the observation gaps. Daily lake ice extent and coverage under clear-sky conditions was examined by employing the conventional SNOWMAP algorithm, and those under cloud cover conditions were re-determined using the temporal and spatial continuity of lake surface conditions through a series of steps. Through time series analysis of every single lake with size greater than 3 km2 in size, 308 lakes within the Tibetan Plateau were identified as the effective records of lake ice extent and coverage to form the Daily Lake Ice Extent and Coverage dataset, including 216 lakes that can be further retrieved with four determinable lake ice parameters: Freeze-up Start (FUS), Freeze-up End (FUE), Break-up Start (BUS), and Break-up End (BUE), and 92 lakes with two parameters, FUS and BUE. Six lakes of different sizes and locations were selected for verification against the published datasets by passive microwave remote sensing. The lake ice phenology information obtained in this paper was highly consistent with that from passive microwave data at an average correlation coefficient of 0.91 and an RMSE value varying from 0.07 to 0.13. The present dataset is more effective at detecting lake ice parameters for smaller lakes than the coarse resolution passive microwave remote sensing observations. The published data are available in https://data.4tu.nl/repository/uuid:fdfd8c76-6b7c-4bbf-aec8-98ab199d9093 and http://www.sciencedb.cn/dataSet/handle/744.

Historical and recent aufeis in the Indigirka River basin (Russia)

Abstract. A detailed spatial geodatabase of aufeis (or naled in Russian) within the Indigirka River watershed (305 000 km2), Russia, was compiled from historical Russian publications (year 1958), topographic maps (years 1970–1980s) and Landsat images (year 2013–2017). Identification of aufeis by late spring Landsat images was performed with a semi-automated approach according to Normalized Difference Snow Index (NDSI) and additional data. After this, a cross-reference index was set for each aufeis field to link and compare historical and satellite-based aufeis datasets. The aufeis coverage varies from 0.26 % to 1.15 % in different sub-basins within the Indigirka River watershed. The digitized historical archive (Cadastre, 1958) contains the coordinates and characteristics of 896 aufeis fields with a total area of 2064 km2. The Landsat-based dataset included 1213 aufeis fields with a total area of 1287 km2. Accordingly, the satellite-derived total aufeis area is 1.6 times less than the Cadastre (1958) dataset. However, more than 600 aufeis fields identified from Landsat images are missing in the Cadastre (1958) archive. It is therefore possible that the conditions for aufeis formation may have changed from the mid-20th century to the present. Most present and historical aufeis fields are located in the elevation band of 1000–1200 m. About 60 % of the total aufeis area is represented by just 10 % of the largest aufeis fields. Interannual variability of aufeis area for the period of 2001–2016 was assessed for the Bolshaya Momskaya aufeis and for a group of large aufeis fields (11 aufeis fields with areas from 5 to 70 km2) in the basin of the Syuryuktyakh River. The results of this analysis indicate a tendency towards an area decrease in the Bolshaya Momskaya aufeis in recent years, while no reduction in Syuryuktyakh River aufeis area was observed. The combined digital database of the aufeis is available at https://doi.org/10.1594/PANGAEA.891036.

Discrimination of Seasonal Snow Cover in Astore Basin, Western Himalaya using Fuzzy Membership Function of Object-Based Classification

The temporal mapping of seasonal snow cover is generally being delineated through low resolution MODIS data (250-500m resolution) due to daily frequency of image acquisition; however, it sometimes compromises the mapping accuracies. In this study, the time-series of high resolution satellite imagery was used to evaluate the spatio-temporal changes in the snow covered area of Astore basin during summer and winter seasons from 1990 to 2017. The Object Based Image Analysis (OBIA) technique was applied on multi-spectral images of Landsat (TM and OLI sensors) of respective years (1990, 2000, 2010 and 2017) in order to discriminate the snow covered area in both seasons. Although OBIA is a strong technique that has been successfully applied in numerous research problems of remote sensing regarding cryosphere, but due to hindrances (i.e. Clouds and haze), it is sometimes not highly efficient to detect the snow accurately, therefore, Normalized Difference Snow Index (NDSI) has been calculated to distinguish snow covered area from snow free areas. The range of 0.4-1.0 was used as a threshold value for fuzzy membership function in OBIA to delineate the snow cover more precisely. The study suggested that the snow covered area is gradually increasing in winters during past few decades in the basin; however, in summer season as compared to winters, no specific trend has been observed.

The Use of a Convolutional Neural Network for Detecting Snow According to the Data of the Multichannel Satellite Device of Meteor-M No.2 Spacecraft

The paper presents a method for detecting snow using a machine learning algorithm based on a convolutional neural network. As the input data, the satellite images obtained from a multispectral low-resolution scanning device (MSU-MR) installed on the Meteor-M No. 2 spacecraft are used. This unit provides images in six visible and infrared channels at 1 km spatial resolution. The developed algorithm can be an alternative to the traditional methods of snow detection, based on calculation the normalized difference snow index (NDSI), the normalized difference vegetation index (NDVI) and threshold values. Evaluation of the algorithm using the f-measure showed a high level of quality of its work. Calculated using the algorithm, the snow mask allows for the construction of maps that can be successfully applied for snow cover monitoring.

Reflectance variation in boreal landscape during the snow melting period using airborne imaging spectroscopy

We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms.A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI -based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth < 30 cm) the mean NDSI -0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values.

Mapping Snow for Area Where was Detected Ground Deplacement: A Case Studi of Iaşi County

Abstract The main goal of this paper is to detect snow in areas where was detecting and mapping, using Differential Radar Interferometry (DInSAR) technique, ground displacement. DInSAR is a powerful tool to detect and monitor ground deformation. Iaşi county is considered as study area in this research. Study area is geographically situated on latitude 46°48’N to 47°35’N and longitude 26°29’E to 28°07’E. For this paper, to detect and mapping grond displacement, was used Sentinel – 1 images, provided free by The European Space Agency (ESA), for January 2018, with vertical polarization (VV), ascending orbit and Interferometric Wide swath (IW) mode operated. SNAP was used to process the Sentinel – 1 images. Landsat-8 OLI was taken to detect areas cover with snow using Normalized Difference Snow Index (NDSI) - a numerical indicator that shows snow cover over land areas. ArcMap was used to create NDSI map after Landsat-8 data was preprocessed. The presence of snow has been observed both in the areas where it exists vertical displacement positive and negative.

Snow Cover Change as a Climate Indicator in Brunswick Peninsula, Patagonia

Snow cover changes are assessed for the Brunswick Peninsula in southern Patagonia (52.9oS to 53.5oS), located on the transition between the wet Pacific Ocean area and the drier leeward side of the Andes. We use the Normalized Difference Snow Index (NDSI) and a new index which we call SnowPower, combining the NDSI and the Melt Area Detection Index (MADI), to reconstruct the snow cover extent and its temporal distribution for the period 2000-2016, based on Moderate Resolution Imaging Spectroradiometer Sensor (MODIS) MOD09GA products. Comparison of these satellite products with available snow duration and snow height data for 2010-2016 at the local Club Andino ski centre of Punta Arenas shows that the NDSI exhibits the best results. A reasonable and significant linear correlation is found between the MODIS NDSI snow cover extent and the mean monthly temperature at Punta Arenas Airport combined with the monthly snow accumulation at Jorge Schythe station at Punta Arenas city for the extended winter period (April to September) from 2000 to 2016. Snow cover changes within this time series are extended to 1972 and 1958 based on historical climate data of Jorge Schythe and Punta Arenas airport, repectively. The results show a significant decreasing trend of snow extent of 19% for Brunswick Peninsula for the 45-year period (1972-2016), which can be attributed to a statistically significant long-term warming of 0.71oC at Punta Arenas during the extended winter (April to September) in the same period. Multiple correlation with different climate variables indicates that solid precipitation has a relevant role on short-term snow cover variability, but is not related to the observed long-term snow cover decrease.

Snow cover mapped daily at 30 meters resolution using a fusion of multi-temporal MODIS NDSI data and Landsat surface reflectance

Snow fall and melt events are complex meteorological phenomena that help chart the effects of climate change and impact many critical environmental processes including hydrologic and biogeographic systems. Daily snow maps, derived from MODIS imagery, provide managers and researchers with vital snow cover information, but only at spatial scales of 500 m or more. Finer resolution time series maps, however, retain large temporal gaps, particularly during recurrent cloud cover. This paper’s authors have developed the novel algorithm MODSAT-NDSI to harness the strengths of both coarse and finer spatial resolution imagery by fusing MODIS and Landsat normalized difference snow index (NDSI) data. Daily 30 m snow cover maps were thus generated for 2000 – 2017 with an overall accuracy of 90%, using 33 validation sites distributed throughout south-central British Columbia. Snow cover trends were analyzed across stratified elevation bands and land cover types, revealing that snow cover persists under lower elevation forests for an average of 23.5 d longer than in adjacent open areas during spring. We conclude that the MODSAT-NDSI approach captures temporal and spatial advantages of freely available snow cover datasets and can be modified to suit a variety of novel investigations relating to snow cover or other spectral indices.

A Prediction Smooth Method for Blending Landsat and Moderate Resolution Imagine Spectroradiometer Images

Landsat images have been widely used in support of responsible development of natural resources, disaster risk management (e.g., forest fire, flooding etc.), agricultural production monitoring, as well as environmental change studies due to its medium spatial resolution and rich spectral information. However, its availability and usability are largely constrained by its low revisit frequency. On the other hand, MODIS (Moderate Resolution Imaging Spectroradiometer) images for land studies have much more frequent coverage but with a lower spatial resolution of 250–500 m. To take advantages of the two sensors and expand their availability and usability, during the last decade, a number of image fusion methods have been developed for generating Landsat-like images from MODIS observations to supplement clear-sky Landsat imagery. However, available methods are typically effective or applicable for certain applications. For a better result, a new Prediction Smooth Reflectance Fusion Model (PSRFM) for blending Landsat and MODIS images is proposed. PSRFM consists of a dynamic prediction model and a smoothing filter. The dynamic prediction model generates synthetic Landsat images from a pair of Landsat and MODIS images and another MODIS image, either forward or backward in time. The smoothing filter combines the forward and backward predictions by weighted average based on elapsed time or on the estimated prediction uncertainty. Optionally, the smooth filtering can be applied with constraints based on Normalized Difference Snow Index (NDSI) or Normalized Difference Vegetation Index (NDVI). In comparison to some published reflectance fusion methods, PSRFM shows the following desirable characteristics: (1) it can deal with one pair or two pairs of Landsat and MODIS images; (2) it can incorporate input image uncertainty during prediction and estimate prediction uncertainty; (3) it can track gradual vegetation phenological changes and deal with abrupt land-cover type changes; and (4) for predictions using two pairs of input images, the results can be further improved through the constrained smoothing filter based on NDSI or NDVI for certain applications. We tested PSRFM to generate a Landsat-like image time series by using Landsat 8 OLI and MODIS (MOD09GA) images and compared it to two reflectance fusion algorithms: STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) and ESTARFM (Enhanced version of STARFM). The results show that the proposed PSRFM is effective and outperforms STARFM and ESTARFM both visually and quantitatively.

A Spectral Analysis of Snow in Mt. Rainier

Snow cover helps regulate the temperature of the Earth's surface. Snowmelt recharges groundwater, provides run-off for rivers and creeks, and acts as a major source of local water for many communities around the world. Since 2000, there has been a significant decrease in the snow-covered area in the Northern Hemisphere. Climate change is the major factor influencing the change in snow cover amount and distribution. We analyze spectral properties of the remote sensing sensors with respect to the study of snow and examine how data from some of the major remote sensing satellite sensors, such as (Advanced Spaceborne Thermal Emission and Reflection Radiometer) ASTER, Landsat-8, and Sentinel-2, can be used in studying snow. The study was conducted in Mt. Rainier. Although reflectance values recorded were lower due to the timing of the data collection and the aspect of the study site, data can still be used calculate normalized difference snow index (NDSI) to clearly demarcate the snow from other land cover classes. NDSI values in all three satellites ranged from 0.94 to 0.97 in the snow-covered area of the study site. Any pollutants in snow can have a major influence on spectral reflectance in the VIS spectrum because pollutants absorb more than snow.

A Spectral Analysis of Snow in Mt. Rainier

Snow cover helps regulate the temperature of the Earth's surface. Snowmelt recharges groundwater, provides run-off for rivers and creeks, and acts as a major source of local water for many communities around the world. Since 2000, there has been a significant decrease in the snow-covered area in the Northern Hemisphere. Climate change is the major factor influencing the change in snow cover amount and distribution. We analyze spectral properties of the remote sensing sensors with respect to the study of snow and examine how data from some of the major remote sensing satellite sensors, such as (Advanced Spaceborne Thermal Emission and Reflection Radiometer) ASTER, Landsat-8, and Sentinel-2, can be used in studying snow. The study was conducted in Mt. Rainier. Although reflectance values recorded were lower due to the timing of the data collection and the aspect of the study site, data can still be used calculate normalized difference snow index (NDSI) to clearly demarcate the snow from other land cover classes. NDSI values in all three satellites ranged from 0.94 to 0.97 in the snow-covered area of the study site. Any pollutants in snow can have a major influence on spectral reflectance in the VIS spectrum because pollutants absorb more than snow.

Navigating snowscapes: scale-dependent responses of mountain sheep to snowpack properties.

Winters are limiting for many terrestrial animals due to energy deficits brought on by resource scarcity and the increased metabolic costs of thermoregulation and traveling through snow. A better understanding of how animals respond to snow conditions is needed to predict the impacts of climate change on wildlife. We compared the performance of remotely sensed and modeled snow products as predictors of winter movements at multiple spatial and temporal scales using a data set of 20,544 locations from 30 GPS-collared Dall sheep (Ovis dalli dalli) in Lake Clark National Park and Preserve, Alaska, USA from 2005 to 2008. We used daily 500-m MODIS normalized difference snow index (NDSI), and multi-resolution snow depth and density outputs from a snowpack evolution model (SnowModel), as covariates in step selection functions. We predicted that modeled snow depth would perform best across all scales of selection due to more informative spatiotemporal variation and relevance to animal movement. Our results indicated that adding any of the evaluated snow metrics substantially improved model performance and helped characterize winter Dall sheep movements. As expected, SnowModel-simulated snow depth outperformed NDSI at fine-to-moderate scales of selection (step scales < 112 h). At the finest scale, Dall sheep selected for snow depths below mean chest height (<54 cm) when in low-density snows (100kg/m3 ), which may have facilitated access to ground forage and reduced energy expenditure while traveling. However, sheep selected for higher snow densities (>300kg/m3 ) at snow depths above chest height, which likely further reduced energy expenditure by limiting hoof penetration in deeper snows. At moderate-to-coarse scales (112-896 h step scales), however, NDSI was the best-performing snow covariate. Thus, the use of publicly available, remotely sensed, snow cover products can substantially improve models of animal movement, particularly in cases where movement distances exceed the MODIS 500-m grid threshold. However, remote sensing products may require substantial data thinning due to cloud cover, potentially limiting its power in cases where complex models are necessary. Snowpack evolution models such as SnowModel offer users increased flexibility at the expense of added complexity, but can provide critical insights into fine-scale responses to rapidly changing snow properties.

Daily estimates of Landsat fractional snow cover driven by MODIS and dynamic time-warping

Understanding seasonal snow cover dynamics is critical for management of hydrological regimes, habitat availability for wildlife species, forest fire risk assessment and recreational demands. Although data products provided at 500 m spatial resolution by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor provide important and readily available information on snow cover, capturing snow dynamics at finer spatial resolutions remains problematic due to the lack of high temporal and spatial resolution data, which limits the number of available observations each year. In this paper we present a new approach to create a daily time-series of 30-m snow observations (called SNOWARP), derived from daily MODIS Normalised Difference Snow Index (NDSI) snow cover data to capture the temporal dynamics of snow cover and Dynamic Time Warping (DTW) to re-order historical Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) observations to account for inter-annual variability. The SNOWARP product was produced for 2000–2018 for an area of Western Alberta (approximately 30,000 km2) and was calibrated against a network of time-lapse cameras and snow pillows. Results indicate the RMSE of the SNOWARP fractional product ranges from 31.3%–68.3%, while F score of the SNOWARP binary product ranges from 87.7% - 98.6% when compared to ground truth data. Capturing fractional snow cover at a fine spatial and temporal scale is important due to the spatial heterogeneity of snow cover, particularly in mountainous regions with implications for biodiversity assessment and monitoring. SNOWARP demonstrates a novel method to increasing the temporal resolution of Landsat-derived snow cover data, providing valuable insights on regional snow cover dynamics for use in a range of applications.

Impact of land cover change on land surface temperature: A case study of Spiti Valley

Land surface temperature (LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change (LCC) on LST has been assessed using Landsat TM 5, Landsat 8 TIRS/OLI and Digital Elevation Model (ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha (30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha (605.87%) and 45 ha (0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha (23.92%) and 88,589 (14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11°C in 1990 but it rose by 2°C and reached to 13°C in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6°C, 9°C, 1°C, and 7°C, respectively. Further, relationships between LST, Normalized Difference Snow Index (NDSI), and Normalised Difference Vegetation Index (NDVI) were established using Linear Regression.

Uncertainty in satellite remote sensing of snow fraction for water resources management

Snow fraction is an important component of land surface models and hydrologic models. Information on snow fraction also improves downstream products retrieved from remote sensing: vertical atmosphere profiles, soil moisture, heat fluxes, etc. The uncertainty of the fractional snow cover estimates must be determined, quantified, and reported to consider the suitability of the product for modeling, data assimilation and other applications. The reflectances of snow and non-snow are characterized by a very significant local variability and also by changes from one scene to another. The local snow and non-snow endmembers are approximated by the Normalized Difference Snow Index with a high accuracy. The magnitudes of snow and non-snow Normalized Difference Snow Indexes are scene-specific and calculated on the fly to retrieve snow fraction. The development of the Normalized Difference Snow Index based algorithms to estimate snow fraction including a scene-specific approach taking local snow and non-snow properties into account is considered an optimal way to fractional snow retrieval from moderate resolution optical remote sensing observations. The Landsat reference data are used to estimate the performance of the fractional snow cover algorithms at moderate resolution and to compare the quality of alternative algorithms. The validation results demonstrate that the performance of the algorithms using Normalized Difference Snow Index has advantages. The advantages achieved in snow fraction retrieval lead to improved estimate of snow water equivalent and changes in snow cover state contributing to better modeling of land surface and hydrologic regime. The success of managing water resources on the whole depends on coordinating described investigations with the works of other researchers developing further enhanced models.

Monitoring of Snow Cover Variability in Chenab Basin Using IRS AWiFS Sensor

Monitoring of seasonal snow cover is important for many applications such as melt runoff estimation, climate change studies and strategic requirements. Contribution of seasonal snow melt runoff of Chenab River is significant and important to meet hydrological requirement at foothills. Seasonal snow cover of Chandra, Bhaga, Miyar, Bhut, Warwan and Ravi, six major tributaries of Chenab River, becomes crucial to assess the water availability. In addition, altitudinal distribution of snow cover significantly influences the melt runoff which is highly sensitive to minor variations in atmospheric temperature. In this investigation, remote sensing based Normalized Difference Snow Index technique has been used to generate 10 daily snow cover product. Snow cover monitoring of all the sub-basins were carried out for 10 years from 2004–2005 to 2013–2014 during hydrological year (October to June) using Advanced Wide Field Sensor (AWiFS) of Indian remote sensing satellite (IRS). Accumulation and ablation patterns of snow cover have also been analyzed for the six sub-basins. Accumulation and ablation pattern of snow cover, from 2004 to 2014 which shows slightly increasing trend for all the sub-basins. Meteorological data of Kelong at Bhaga sub-basin was also analysed. Average monthly snow line altitude was estimated for all the sub-basins using hypsographic curve. Chandra and Bhaga sub-basins are at higher altitude and Ravi sub-basin is at lower altitude. It was also observed that areal extent of snow reaches to lower altitude during last 5 years, particularly in Ravi sub-basin.

Estimación del Área de la Superficie y el Cambio de Volumen del Glaciar del Nevado Champará (Cordillera Blanca, Perú) a partir de las Imágenes y los Modelos de Elevación Digital del Sensor ASTER / Terra (2000-2010)

El objetivo de este trabajo es la estimación de la variación del área y volumen del glaciar del nevado Champará a partir de las imágenes y los Modelos de Elevación Digital (MED) del sensor ASTER a bordo del satélite TERRA. Se han utilizado siete imágenes ASTER (2000-05, 2003-06, 2003-06, 2006-07, 2007-08, 2009-07, 2010-05) que corresponden al periodo 2000-2010. Para estimar el área del glaciar, se ha utilizado el Índice de Nieve de Diferencia Normalizada (NDSI) y el Índice del Agua de Diferencia Normalizada (NDWI). La variación del volumen del glaciar para el periodo 2003-2010 se determinó a partir de los MED (con resolución espacial 30 m) optenidos que se generaron de las bandas 3B y 3N del sensor ASTER, teniendo en consideración las sombras y las nubes sobre el glaciar que generan incertidumbre y eliminando 1533 pixeles (que representan resoluciones espaciales de altitud mayor de 30 m), quedando con 73,035 de un total de 74,568 pixeles. En cuanto a la desglaciación episódica en el periodo 2000-2010, se ha observado una disminución del área glaciar de aproximadamente 50% con relación al año 2000, y la tendencia del ritmo de cambio episódico es de una disminución exponencial. El cambio de volumen glaciar estimado se ajusta aun comportamiento lineal con una tasa aproximada de -0.085 m3 /año para todos los pixeles del MED, y con el enmascaramiento que permite eliminar pixeles anómalos es de -0.0159 m3 /año con un coeficiente de correlación de aproximadamente R2=0.9303 y R2=0.9954, respectivamente. La disminución de altitud acumulada para el periodo 2003-2010 tiende a un comportamiento lineal de aumento negativo de hielo y nieve glaciar del nevado Champará.

Preliminary Study on a Cloud and Snow Classification of a Solar Radiation Estimation using the Meteorological Satellite Himawari-8 Data

This paper presents a solar radiation estimation method in winter using the meteorological satellite data and cloud and snow classification method. The method is planned to be introduced into the photovoltaic (PV) estimation system developed by the authors in order to reduce the solar radiation estimation error. This paper proposes the cloud and snow detecting method, which uses NDSI (Normalized Difference Snow Index) and BTD (Brightness Temperature Difference) derived from near infrared and infrared wavelength data observed by himawari-8. The proposed method can reduce the solar radiation estimation error by about 36 to 37%.