Snow Identification Research Articles

Elevation-dependent effects of snowfall and snow cover changes on runoff variations at the source regions of the Yellow River basin

ABSTRACT Accurate simulation of the snowmelt runoff process is of great significance in understanding the evolution of water resources in high-altitude cold regions and achieving efficient utilization of water resources. This study focuses on the source regions of the Yellow River basin (SYRB) and aims to improve the snow identification and snowmelt simulation methods in the WEP-L hydrological model. The results show a significant decrease in the snowfall ratio from 2002 to 2018. The fraction of snow cover decreased at lower altitudes but increased at higher altitudes, displaying an exponential relationship with negative accumulated temperature. Snowmelt was found to be negatively correlated with snowfall and snow cover, with a stronger negative correlation at higher altitudes. The decrease in the snowfall ratio intensified with increasing elevation, while snow cover increased with elevation. However, the overall trend of snowmelt runoff was not significant. These findings highlight the dynamic relationship between snowfall, snow cover, and temperature in the SYRB. By incorporating the established response function, the accuracy of snow identification and snowmelt simulation in the WEP-L model has been enhanced. This study contributes to a better understanding of water resource evolution and the efficient utilization of water resources in high-altitude cold regions.

Snow‐ or ice‐covered road detection in winter road surface conditions using deep neural networks

AbstractTraffic accidents occur frequently in cold and snow‐ or ice‐covered regions due to weather changes that occur during the winter season. To detect the snow‐ or ice‐covered roads in road surface conditions, road surface images captured using fixed‐point cameras installed along the route are sufficient. This paper proposes a snow‐ or ice‐covered road detection method that uses the deep convolutional autoencoding Gaussian mixture model (DCAGMM) with structural similarity (SSIM). The DCAGMM method, which is an unsupervised anomaly detection method, is unaffected by imbalance in the training data. In addition, the end‐to‐end convolutional neural network implemented in the DCAGMM enables the capture of the unique characteristics of the road surface images. Finally, by reconstructing the input images as normal images, the comparison of the input and reconstructed images enables identification of snow‐ or ice‐covered road areas without requiring pixel‐level annotations. Furthermore, the road surface images include complex characteristics for reconstruction, and the SSIM‐based reconstruction error allows us to preserve the image quality of the reconstructed image. Experimental results obtained on real‐world road surface images demonstrate the effectiveness of the proposed method.

Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning

Fine spatiotemporal resolution snow monitoring at the watershed scale is crucial for the management of snow water resources. This research proposes a cloud removal algorithm via snow grain size (SGS) gap-filling based on a space–time extra tree, which aims to address the issue of cloud occlusion that limits the coverage and time resolution of long-time series snow products. To fully characterize the geomorphic characteristics and snow duration time of the Kaidu River Basin (KRB), we designed dimensional data that incorporate spatiotemporal information. Combining other geographic and snow phenological information as input for estimating SGS. A spatiotemporal extreme tree model was constructed and trained to simulate the nonlinear mapping relationship between multidimensional inputs and SGS. The estimation results of SGS can characterize the snow cover under clouds. This study found that when the cloud cover is less than 70%, the model’s estimation of SGS meets expectations, and snow cover reconstruction achieves good results. In specific cloud removal cases, compared to traditional spatiotemporal filtering and multi-sensor fusion, the proposed method has better detail characterization ability and exhibits better performance in snow cover reconstruction and cloud removal in complex mountainous environments. Overall, from 2000 to 2020, 66.75% of snow products successfully removed cloud coverage. This resulted in a decrease in the annual average cloud coverage rate from 52.46% to 34.41% when compared with the MOD10A1 snow product. Additionally, there was an increase in snow coverage rate from 21.52% to 33.84%. This improvement in cloud removal greatly enhanced the time resolution of snow cover data without compromising the accuracy of snow identification.

Snow identification from unattended automatic weather stations images using DANet

Snow identification from unattended automatic weather stations images using DANet

Comparison of varied complexity parameterizations in estimating blowing snow occurrences

Blowing snow has a major impact on the spatial–temporal evolution of snow cover. An accurate prediction of the initiation of snow particle movement is one of the prerequisites for blowing snow modeling. Previous studies have proposed varied complexity parameterizations to estimate the occurrence of blowing snow. However, a quantitative evaluation of the performance of different schemes in blowing snow identification remains lacking, particularly outside the regions where empirical schemes were proposed. This study details a comparative study of the performance of five distinct parameterizations with varying degrees of complexity in estimating the occurrence of blowing snow over the central French Alps. Our results show that less than half of blowing snow events can be accurately detected at most stations by traditional schemes, for example, the constant and temperature-based empirical threshold wind speed schemes, probability estimation, and physically based methods. In addition, the spatial variability of wind speed can result in considerable spatial variability in the occurrence of blowing snow, which cannot be captured by traditional methods. Decision tree-based models can detect blowing snow occurrences with higher accuracy, and the spatial variation in snow transport can be captured by including information on blowing snow occurrences at low wind speed stations. However, regardless of the training strategies employed, the decision tree-based model employed here estimated far too many false alarms. This study contributes to blowing snow modeling by presenting the current state-of-the-art and limitations of various schemes for detecting blowing snow occurrences, and highlights the importance of parameterization modification, key parameter calibration, and optimization.

Identifying Wet and Dry Snow With Dual-Polarized C-Band SAR Data Based on Markov Random Field Model

Quad-pol SAR is one of the most effective approaches for dry and wet snow identification data, but its applicability is limited by the high cost of quad-pol SAR data. Dual-pol SAR such as Sentinel-1 has larger spatial coverage, longer time sequences and freely accessible data, but there is still a highly uncertainty in dual-pol SAR to distinguish dry and wet snow due to limited polarimetric information. In this study, a pixel neighborhood-based snow identification algorithm was developed and verified using dual-pol C-band SAR data in Northern Xinjiang, China. A total of six decomposed parameters were obtained to characterize the polarimetric information of dual-pol SAR data by modifying the H-α decomposition applicable to dual-pol SAR data. In the case of limited training samples, polarimetric features that were most sensitive to snow identification were selected as the optimal features for support vector machine (SVM), and the result derived from SVM was employed as the initial labels of markov random field (MRF) model to separate dry and wet snow using iterative conditional mode (ICM). Then, the proposed algorithm, dual-pol SVM-MRF (DSVM-MRF), was validated and compared with previously published methods. The results show that the DSVM-MRF acquires the superior snow recognition with the overall accuracy and kappa coefficient of 84.5% and 0.58, respectively.

An Unsupervised Snow Segmentation Approach Based on Dual-Polarized Scattering Mechanism and Deep Neural Network

Distribution of snow and its melting is a critical factor affecting local weather, avalanche and flood forecasting, livelihood of people residing, and hydropower production. Most of the existing dry and wet snow identification methods were based on expensive quad-pol SAR with finite generalizability, while dual-pol SAR with larger coverage, longer time series and open availability has more advantages. In this study, an unsupervised algorithm for dry and wet snow discrimination, NSAE-WFCM, is proposed based on a variety of polarimetric features derived from H-α decomposition in dual-pol mode using C-band Sentinel-1 SAR data. NSAE-WFCM constructs a deep training network using the pixel neighborhood-based sparse autoencoder (NSAE) to optimize polarimetric parameters, and inputs reconstructed features with different weights into feature-weighted fuzzy C-means clustering (WFCM) to distinguish dry and wet snow for each underlying surface. Ground observation was carried out during the snow melting period of March 2021 in Altay, China, to validate dual-pol NSAE-WFCM method with an overall accuracy and kappa coefficient of 88.8% and 0.68, respectively. The results show that NSAE-WFCM’s accuracy is similar to that of the quad-pol SAR-based dry and wet snow result (90.0%), and significantly better than that of previously published approaches extended to dual-pol SAR, such as SVM (76.7%), H-α-Wishart (65.5%), SPAN-based threshold method (51.7%), and wet snow-based method (43.1%). Therefore, the NSAE-WFCM algorithm improves the ability to classify wet and dry snow based on dual-pol polarimetric features, overcomes the high dependence of existing methods on quad-pol SAR data, and reduces manual interpretation by using unsupervised clustering.

Cloud and Snow Identification Based on DeepLab V3+ and CRF Combined Model for GF-1 WFV Images

Cloud and snow identification in remote sensing images is critical for snow mapping and snow hydrology research. Aimed at the problem that the semantic segmentation model is prone to producing blurred boundaries, slicing traces and isolated small patches for cloud and snow identification in high-resolution remote sensing images, the feasibility of combining DeepLab v3+ and conditional random field (CRF) models for cloud and snow identification based on GF-1 WFV images is studied. For GF-1 WFV images, the model training and testing experiments under the conditions of different sample numbers, sample sizes and loss functions are compared. The results show that, firstly, when the number of samples is 10,000, the sample size is 256 × 256, and the loss function is the Focal function, the model accuracy is the optimal and the Mean Intersection over Union (MIoU) and the Mean Pixel Accuracy (MPA) reach 0.816 and 0.918, respectively. Secondly, after post-processing with the CRF model, the MIoU and the MPA are improved to 0.836 and 0.941, respectively, compared with those without post-processing. Moreover, the misclassifications such as blurred boundaries, slicing traces and isolated small patches are significantly reduced, which indicates that the combination of the DeepLab v3+ and CRF models has high accuracy and strong feasibility for cloud and snow identification in high-resolution remote sensing images. The conclusions can provide a reference for high-resolution snow mapping and hydrology applications using deep learning models.

IMPROVED ON SNOW COVER EXTRACTION IN MOUNTAINOUS AREAS BASED ON MULTI-FACTOR NDSI DYNAMIC THRESHOLD

Abstract. Snow cover is one of the most active elements of the cryosphere and plays an important role in the surface radiation budget and water balance. Optical satellite remote sensing has become an important tool for snow identification and monitoring. The Sentinel-2 A/B satellite has become an important data source for snow cover extraction because of its high radiation resolution, which reduces the problem of snow/ice saturation in remote sensing observations. The normalized differential snow cover index (NDSI) and the snow cover index considering the forest cover area (NDFSI) are important methods for snow cover extraction, but due to the strong spatial heterogeneity and fast change speed of snow cover in mountainous areas, using the classical fixed threshold method to extract snow cover will result in a large omission error. In this paper, a dynamic threshold method is constructed by synthesizing the effects of the type of snow cover period, aspect and snow underlying land coverage type on the snow cover NDSI/NDFSI. Compared with the high-resolution GF-2 snow map results, the dynamic threshold method has higher accuracy in extracting snow cover, and the overall classification accuracy, omission error, commission error and Kappa coefficient are 97.70%, 0.20%, 11.17% and 0.93 respectively. The dynamic threshold method is used to extract snow cover in a snow cover period in the Babao River Basin. The snow cover rate in the basin fluctuates greatly with time, and the spatial distribution of snow cover is uneven, with more snow cover in mountainous areas and rapid changes in snow cover in river valleys.

Automatic cloud and snow detection for GF-1 and PRSS-1 remote sensing images

In the remote sensing image processing field, cloud and snow detection for high-resolution sensors and cloud and snow morphology in different latitudes of the world is challenging. A deep learning training model (Softmax) was developed to improve the accuracy of cloud and snow identification from Gaofen-1 and Pakistan Remote Sensing Satellite-1 images. First, more than 1800 scenes remote sensing images in various regions over the world are collected. Next, the texture details and spectral information of the objects are extracted. Finally, the Softmax model is applied to process the features to obtain the final cloud and snow masks. The cloud and snow detection results are evaluated by performing statistical analysis. The overall accuracy for cloud detection reaches 92.64% (kappa coefficient = 0.83) and for snow detection reaches 93.94% (kappa coefficient = 0.8). The algorithm is not only accurate but also computationally efficient. It is of great importance for image processing in ground segment and corresponding applications.

Canopy Adjustment and Improved Cloud Detection for Remotely Sensed Snow Cover Mapping

AbstractMaps of snow cover serve as early indicators for hydrologic forecasts and as inputs to hydrologic models that inform water management strategies. Advances in snow cover mapping have led to increasing accuracy, but unsatisfactory treatment of vegetation's interference when mapping snow has led to maps that have limited utility for water forecasting. Vegetation affects snow mapping because ground surfaces not visible to the satellite produce uncertainty as to whether the ground is snow covered. At nadir, the forest canopy obscures the satellite view below the canopy. At oblique viewing angles, the forest floor is obscured by both the canopy and the projection of tree profiles onto the forest floor. We present a canopy correction method based on Moderate Resolution Imaging Spectroradiometer satellite imagery validated with field observations that mitigates geometric and georegistration issues associated with changing satellite acquisition angles in forested areas. The largest effect from a variable viewing zenith angle on the viewable gap fraction in forested areas occurs in moderately forested areas with 30–40% tree canopy coverage. Cloud cover frequently causes errors in snow identification, with some clouds identified as snow and some snow identified as cloud. A snow‐cloud identification method utilizes a time series of fractional vegetation and rock land‐surface data to flag snow‐cloud identification errors and improve snow‐map accuracy reducing bias by 20% over previous methods. Together, these contributions to snow‐mapping techniques could advance hydrologic forecasting in forested, snow‐dominated basins that comprise an estimated one fifth of Northern Hemisphere snow‐covered areas.

Improvement of snow/haze confusion data gaps in MODIS Dark Target aerosol retrievals in East China

The MODerate resolution Imaging Spectroradiometer (MODIS) is one of the most widely used meteorological remote sensing instruments. Its Dark Target aerosol optical depth (AOD) product has been widely used in environment and meteorology researches, such as model evaluation and data assimilation. However, this product has a low coverage under conditions of heavy haze in China. This is because the haze can be misidentified as snow under some circumstances by the algorithm and therefore rejected, leading to large-scale data omission. In the most polluted regions, misidentified snow cover exceeded 8%. Regarding this issue, a new method combining the snow mask derived from the MODIS cloud mask product and Fisher discrimination analysis was developed to give a more accurate identification of snow and ice cover. Applying this new method increases the AOD data coverage significantly. Comparisons with AOD values from ground-based observations showed that the newly produced data under haze conditions had a similar accuracy with the original data in the MODIS AOD product. Because the newly supplemented data are more distributed at the seriously polluted regions, the average AOD increased significantly after data filling in many regions. In winter, AOD in the most severely polluted regions (average air quality index >130) increased by 0.2–0.3 after improvement, about 30–50% of the original value. In 49 haze cases with large-scale pollution, the increase reached 0.3–0.6, about 50%–70% of the original value. During the haze episodes, the data omission led to an underestimation of the regional average AOD by 19–40%. The improvement in AOD coverage helps to provide a better reflection of the air pollution condition in East China through the perspective of AOD.

Identification of Snow Using Fully Polarimetric SAR Data Based On Entropy and Anisotropy

AbstractRecently, with the extensive availability of fully polarimetric synthetic aperture radar (SAR) data, methods that are simple and efficient, and involve lesser computation and data processing, are needed to be explored for snow cover mapping. This paper analyzes different polarimetric parameters such as entropy, anisotropy, and the mean scattering angle for the identification of snow cover area. We present a novel index for mapping snow cover based on the assessment of entropy (H) and anisotropy (A) using fully polarimetric SAR data and refer to it as the radar snow fraction (RSF). The RSF is proposed as an extension of the H(1‐A) metric by applying a sigmoidal function to this metric. The experiments to evaluate the applicability of the proposed RSF are carried out using fully polarimetric SAR data of L‐band ALOS‐2/PALSAR‐2 and C‐band RADARSAT‐2 data sets corresponding to different geographical locations in the Indian Himalayas. The developed snow cover maps from the proposed method were validated with respect to reference snow cover maps derived by thresholding the Normalized Differenced Snow Index developed from multispectral data (e.g., Landsat‐8 imagery). These maps were also statistically compared with those obtained from the conventional radar snow index, which is based on the polarization fraction. We determined a mean overall accuracy of 0.8 between the developed snow cover maps and the reference maps for the different data sets used for experiments. The results showed that, in general, the RSF outperformed the other polarimetric parameters for snow cover detection.

An Adaptive Snow Identification Algorithm in the Forests of Northeast China

Northeast China is one of the primary snow-covered regions, and its forest coverage is over 40%. Forest snow identification is usually a challenging problem, and the SNOMAP algorithm tends to underestimate the amount of snow cover in forest regions for the lower normalized difference snow index. In this article, an improved method of the snow-cover identification based on the Landsat operational land imager is proposed. One improvement includes using the normalized difference forest snow index (NDFSI) to discriminate between snow-covered and snow-free forests. The threshold value of the NDFSI in different forest types is set according to the normalized difference vegetation index. On the other hand, the sun elevation is very low in winter in Northeast China with high latitude; as a result, the snow in shadow areas is usually classified as liquid water for its low near-infrared reflectance in the current SNOMAP algorithm. Then, another improvement is introducing the land surface temperature, which is retrieved from the thermal infrared band to distinguish liquid water from snow in shadow areas. We applied this improved method to evaluate forest areas in the Daxinganling, Xiaoxinganling, and Changbai Mountain areas in different seasons. The total classification accuracy reached 97.5%, and the pixels that introduce omission error and commission error were mainly distributed in areas of dense forest shadows. This improved method retains the computational simplicity and effectiveness of the SNOMAP algorithm in nonforest areas and improves the underestimation of snow cover in forest regions and shadow areas.

Assessment of Methods for Passive Microwave Snow Cover Mapping Using FY-3C/MWRI Data in China

Ongoing information on snow and its extent is critical for understanding global water and energy cycles. Passive microwave data have been widely used in snow cover mapping given their long-time observation capabilities under all-weather conditions. However, assessments of different passive microwave (PMW) snow cover area (SCA) mapping algorithms have rarely been reported, especially in China. In this study, the performances of seven PMW SCA mapping algorithms were tested using in situ snow depth measurements and a one-kilometer Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product over China. The selected algorithms are the FY3 algorithm, Grody’s algorithm, the South China algorithm, Kelly’s algorithm, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm. During the test period, most algorithms performed reasonably well. The overall accuracy of all algorithms is higher than 0.895 against in situ observations and higher than 0.713 against the IMS product. In general, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm had poor performance during the test. Their misclassification errors were larger than those of the remaining algorithms. Grody’s algorithm, the South China algorithm and Kelly’s algorithm had higher positive predictive values and lower omission errors than those of the others. The errors of these three algorithms were mainly caused by variations in commission errors. Comparing to Grody’s algorithm, the South China algorithm and Kelly’s algorithm, the FY3 algorithm presented a conservative snow cover estimation to balance the problem between snow identification and overestimation. As a result, the overall accuracy of the FY3 algorithm was the highest of all the tested algorithms. The accuracy of all algorithms tended to decline with a decreased snow cover fraction as well as SD < 5 cm. All tested algorithms have severe omission errors over barren land and grasslands. The results shown in this study contribute to ongoing efforts to improve the performance and applicability of PMW SCA algorithms.

Breaking Ranks: C. P. Snow and the Crisis of Mid-Century Liberalism, 1930–1980

C. P. Snow's identification of ‘two cultures’, as the literary critic F. R. Leavis pointed out in 1962, represents not an insight but a cliché, one that invites the repetition of further clichés about the origins of a divided culture, the need to bridge cultures, the emergence of a third culture, or the reality of one culture. Yet this recurrent feature of ‘two cultures’ talk does not nullify the concept's value as an object of study, if these discussions are treated as revealing points of entry into foreign historical contexts. This article adopts this approach, unearthing the liberal position that Snow developed as a novelist and critic from the 1930s, that he advanced in the form of a disciplinary lament in The Two Cultures (Snow, C.P. 1959. The two cultures and the scientific revolution. Cambridge: Cambridge University Press.), and that — to his distress — increasingly came under radical critique from the mid-1960s. Ultimately, the technocratic liberalism that Snow associated with science at mid-century came to be closer to American neo-conservatism by 1980. By tracking the fortunes of the ideological position that structured The Two Cultures, rather than lifting that text out of its moment in an attempt to engage its arguments today, this article testifies to the abiding value of contextual analysis at a moment when intellectual historians are increasingly inclined to question and even displace it.

Deriving long term snow cover extent dataset from AVHRR and MODIS data: Central Asia case study

We computed the daily AVHRR snow cover from all available AVHRR level1b raw data over central Asia (CA) with an aggregated rating based snow identification scheme. The daily AVHRR snow cover was further processed into an 8-day maximum-snow-extent data, and then went through a set of spatial and temporal filters to fill cloud or gap pixels. A correction method based on known long term snow probability in small sub-regions has been developed for computing corrected 8-day AVHRR snow cover, which is comparable to 8-day MODIS snow data. Validation of the daily AVHRR snow product against ground snow survey suggested a high accuracy of the snow identification scheme. Comparison of the daily AVHRR snow cover with the daily MODIS snow cover in Amu Dar'ya River basin within CA showed high accuracy of daily AVHRR snow, with a general accuracy of 99.60% and Kappa Coefficient of 0.92 in the basin. Comparison of the corrected 8-day AVHRR snow cover with 8-day MODIS cloud/gap free snow cover in the same basin also showed high comparability between both data, with a general accuracy of 95.61% and Kappa Coefficient of 0.84. Seasonal snow cover analysis in Amu Dar'ya River basin revealed the spatial and temporal patterns of snow distribution and negative trends in snow cover duration in 1986 to 2008 due to earlier snow melting dates. The newly developed long-term snow dataset from AVHRR and MODIS data over CA, with its high accuracy and internal comparability, is suitable for seasonal snow cover studies in mountainous regions over CA.

A Supplementary Clear-Sky Snow and Ice Recognition Technique for CERES Level 2 Products

Abstract Identification of clear-sky snow and ice is an important step in the production of cryosphere radiation budget products, which are used in the derivation of long-term data series for climate research. In this paper, a new method of clear-sky snow/ice identification for Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. The algorithm’s goal is to enhance the identification of snow and ice within the Clouds and the Earth’s Radiant Energy System (CERES) data after application of the standard CERES scene identification scheme. The input of the algorithm uses spectral radiances from five MODIS bands and surface skin temperature available in the CERES Single Scanner Footprint (SSF) product. The algorithm produces a cryosphere rating from an aggregated test: a higher rating corresponds to a more certain identification of the clear-sky snow/ice-covered scene. Empirical analysis of regions of interest representing distinctive targets such as snow, ice, ice and water clouds, open waters, and snow-free land selected from a number of MODIS images shows that the cryosphere rating of snow/ice targets falls into 95% confidence intervals lying above the same confidence intervals of all other targets. This enables recognition of clear-sky cryosphere by using a single threshold applied to the rating, which makes this technique different from traditional branching techniques based on multiple thresholds. Limited tests show that the established threshold clearly separates the cryosphere rating values computed for the cryosphere from those computed for noncryosphere scenes, whereas individual tests applied consequently cannot reliably identify the cryosphere for complex scenes.

Satellite-based snow identification and its impact on monitoring photovoltaic systems

Earth observation allows the separation of snow cover and cloudiness using multispectral measurements. Several satellite-based snow monitoring services are available, ranging from regional to world-wide scales. Using these data enables photovoltaic (PV) plant management to differentiate between failures due to snow coverage on a PV system and other error sources. Additionally, yield estimates for solar siting are improved. This paper presents a validation study from January to April 2006 comparing satellite-based datasets with ground measurements from German and Swiss meteorological stations. A false alarm rate, an error due to irradiance underestimation, the availability of daily data, and the classification accuracy are introduced as quality metrics. Compared to Switzerland, generally a higher accuracy is found in all datasets for Southern Germany. The most significant difference among the datasets is found in the error pattern shifting from too much snow (which results in an error due to underestimation of irradiance) to too little snow detection, causing a false alarm in PV monitoring. Overall, the data records of the Land Surface Analysis Satellite Application Facility (LSA SAF), the German Aerospace Center (DLR) and the Interactive Multisensor Snow and Ice Mapping System (IMS) are found to be most suitable for solar energy purposes. The IMS dataset has a low false alarm rate (4%) and a good data availability (100%) making it a good choice for power plant monitoring, but the error due to underestimation relevant in site auditing is large with 59%. If a cumulative snow cover algorithm is applied to achieve information every day as needed both for power plant monitoring and site auditing, both the DLR and the LSA SAF datasets are comparable with classification accuracies of 70%, false alarm rates of 37% and 34%, respectively, and errors due to irradiance underestimation in 26% and 27% of all coincidences.

Field based spectral reflectance studies to develop NDSI method for snow cover monitoring

Snow is highly reflective in the visible region of the electromagnetic spectrum making it possible to easily distinguish on a satellite image. However, cloud cover and mountain shadows pose a serious problem in the identification of snow in a mountainous region. Therefore, to identify snow in such an environment, a Normalized Difference Snow Index (NDSI) has been applied. The NDSI is based on the high reflectance of snow in the visible region and its low reflectance in the SWIR region, whereas, reflectance of cloud remains high compared to snow in the SWIR region. Efforts have been made to carry out field observations on reflectance of various land features near Manali in Himachal Pradesh (HP) to develop NDSI values for identifying snow. Field data have been collected using three field radiometers, viz., Multi-band Ground Truth Radiometer (GTR) operating in the 12 spectral bands ranging from visible to near-infrared wavelengths, Near-Infrared Ground Truth Radiometer (NIGTR) operating in the SWIR range, and Ratio-Radiometer (RR) operating in two spectral bands, one in the visible range, and another band in the SWIR range. All these three field radiometers have been designed and developed indigenously at the Space Applications Centre (ISRO), Ahmedabad. NDSI values for all types of snow, such as, fresh, clear, patchy and wet, have been found to be in the range 0.9 to 0.96. In addition, the NDSI value for snow under mountain shadow is found to be more than 0.9. This suggests the use of NDSI method for snow cover monitoring under mountain shadow. NDSI values for other land features such as soil, vegetation, and rock were substantially different than snow. However, water bodies have NDSI values close to snow and they need to be masked during snow cover delineation using NIR band.