Subpixel mapping of snow cover in forests by optical remote sensing

Forest represents a challenging problem for snow-cover mapping by optical satellite remote sensing. To investigate reflectance variability and to improve the mapping of snow in forested areas, a method for subpixel mapping of snow cover in forests (SnowFrac) has been developed. The SnowFrac method is based on linear spectral mixing modelling of snow, trees and snow-free ground. The focus has been on developing a physically based reflectance model which uses a forest-cover map as prior information. The method was tested in flat terrain covered by spruce, pine and birch forests, close to the Jotunheimen region of South Norway. Experiments were carried out using a completely snow-covered Landsat Thematic Mapper (TM) scene, aerial photos and in situ reflectance measurements. A detailed forest model was photogrammetrically derived from the aerial photos. Modelled and observed TM reflectances were compared. In the given situation, the results demonstrate that snow and individual tree species, in addition to cast shadows on the snow surface from single trees, are the most influencing factors on visible and near-infrared reflectance. Modelling of diffuse radiation reduced by surrounding trees slightly improve the results, indicating that this effect is less important. The best results are obtained for pine forest and mixed pine and birch forest. Future work will focus on deriving a simplified reflectance model suitable for operational snow-cover mapping in forests.     

Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: A Normalized Difference Snow and Ice Index

The VEGETATION (VGT) sensor in SPOT 4 has four spectral bands that are equivalent to Landsat Thematic Mapper (TM) bands (blue, red, near-infrared and mid-infrared spectral bands) and provides daily images of the global land surface at a 1-km spatial resolution. We propose a new index for identifying and mapping of snow/ice cover, namely the Normalized Difference Snow/Ice Index (NDSII), which uses reflectance values of red and mid-infrared spectral bands of Landsat TM and VGT. For Landsat TM data, NDSII is calculated as NDSII_TM=(TM3-TM5)/(TM3+TM5); for VGT data, NDSII is calculated as NDSII_VGT=(B2-MIR)/(B2+MIR). As a case study we used a Landsat TM image that covers the eastern part of the Qilian mountain range in the Qinghai-Xizang (Tibetan) plateau of China. NDSIITM gave similar estimates of the area and spatial distribution of snow/ice cover to the Normalized Difference Snow Index (NDSI=(TM2-TM5)/(TM2+TM5)) which has been proposed by Hall et al. The results indicated that the VGT sensor might have the potential for operational monitoring and mapping of snow/ice cover from regional to global scales, when using NDSII_VGT.     

Fractional snow cover mapping through artificial neural network analysis of MODIS surface reflectance

Accurate areal measurements of snow cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is considered either snow-covered or snow-free. Fractional snow cover (FSC) mapping can achieve a more precise estimate of areal snow cover extent by estimating the fraction of a pixel that is snow-covered. The most common snow fraction methods applied to Moderate Resolution Imaging Spectroradiometer (MODIS) images have been spectral unmixing and an empirical Normalized Difference Snow Index (NDSI). Machine learning is an alternative for estimating FSC as artificial neural networks (ANNs) have been successfully used for estimating the subpixel abundances of other surfaces. The advantages of ANNs are that they can easily incorporate auxiliary information such as land cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed a multilayer feed-forward ANN trained through backpropagation to estimate FSC using MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with higher spatial-resolution FSC maps derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow cover maps. Testing of the network was accomplished over training and independent test areas. The developed network performed adequately with RMSE of 12% over training areas and slightly less accurately over the independent test scenes with RMSE of 14%. The developed ANN also compared favorably to the standard MODIS FSC product. The study also presents a comprehensive validation of the standard MODIS snow fraction product whose performance was found to be similar to that of the ANN.     

Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements

Taking three snow seasons from November 1 to March 31 of year 2002 to 2005 in northern Xinjiang, China as an example, this study develops a new daily snow cover product (500 m) through combining MODIS daily snow cover data and AMSR-E daily snow water equivalent (SWE) data. By taking advantage of both high spatial resolution of optical data and cloud transparency of passive microwave data, the new daily snow cover product greatly complements the deficiency of MODIS product when cloud cover is present especially for snow cover product on a daily basis and effectively improves daily snow detection accuracy. In our example, the daily snow agreement of the new product with the in situ measurements at 20 stations is 75.4%, which is much higher than the 33.7% of the MODIS daily product in all weather conditions, even a little higher than the 71% of the MODIS 8-day product (cloud cover of ~ 5%). Our results also indicate that i) AMSR-E daily SWE imagery generally agrees with MOD10A1 data in detecting snow cover, with overall agreement of 93.4% and snow agreement of 96.6% in the study area; ii) AMSR-E daily SWE imagery underestimates the snow covered area (SCA) due to its coarse spatial resolution; iii) The new snow cover product can better and effectively capture daily SCA dynamics during the snow seasons, which plays a significant role in reduction, mitigation, and prevention of snow-caused disasters in pastoral areas.     

A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model

A feasible method for mapping the fraction of Snow Covered Area (SCA) in the boreal zone is presented. The method (SCAmod) is based on a semi-empirical model, where three reflectance contributors (wet snow, snow-free ground and forest canopy), interconnected by an effective canopy transmissivity and SCA, constitute the observed reflectance from the target area. Given the reflectance observation, SCA is solved from the model. The predetermined values for the reflectance contributors can be adjusted to an optional wavelength region, which makes SCAmod adaptable to various optical sensors. The effective forest canopy transmissivity specifies the effect of forests on the local reflectance observation; it is estimated using Earth observation data similar to that employed in the actual SCA estimation. This approach enables operational snow mapping for extensive areas, as auxiliary forest data are not needed.Our study area covers 404 000 km², comprising all drainage basins of Finland (with an average area of 60 km²) and some transboundary drainage basins common with Russia, Norway and Sweden. Applying SCAmod to NOAA/AVHRR cloud-free data acquired during melting periods 2001-2003, we estimated the areal fraction of snow cover for all the 5845 basins. The validation against in situ SCA from the Finnish snow course network indicates that with SCAmod, 15% (absolute SCA-units) accuracy for SCA is gained. Good results were also obtained from the validation against snow cover information provided by the Finnish weather station network, for example, 94% of snow-free and fully snow-covered basins were recognized. A general formula for deriving the statistical accuracy of SCA estimates provided by SCAmod is presented, complemented by the results when the AVHRR data are employed.Snow melting in Finland has been operatively monitored with SCAmod in Finnish Environment Institute (SYKE) since year 2001. The SCA estimates have been assimilated to the Finnish national hydrological modelling and forecasting system since 2003, showing a substantial improvement in forecasts.     

Subjective operational reliability assessment of maritime transportation system

System reliability assessment is one of the major acts in the operation and maintenance of every industrial and service sector, which also holds true for maritime transportation system. The complexity of the maritime transportation system is a prime obstacle in the evaluation of the operational reliability of the system; mainly due to the fact that statistical data on the important parameters and variables is scarce. This makes the application of fuzzy sets and fuzzy logic a viable option to overcome the data problem with regards to imprecision or vagueness in parameters and variables values. In this paper, the different decisive factors, affecting maritime transportation systems, are modeled in the form of linguistic variables. Techniques such as aggregation, mapping of fuzzy sets using distance measure and fuzzy logic rule base are used to arrive at subjective operational reliability value. The complete procedure is demonstrated with an example.     

Mapping snow cover in the pan-Arctic zone,using multi-year (1998-2001) images from optical VEGETATION sensor

Timely information on spatial distribution and temporal dynamics of snow cover in the pan-Arctic zone is needed, as snow cover plays an important role in climate, hydrology and ecological processes. Here we report estimates of snow cover in the pan-Arctic zone (north of 45° N) at 1-km spatial resolution and at a 10-day temporal interval over the period of April 1998 to December 2001, using 10-day composite images of VEGETATION sensor onboard Système Pour l'Observation de la Terre (SPOT)-4 satellite. The results show that snow covered area (SCA) in North America (north of 45° N) increased from 1998 to 2001, while SCA in Eurasia (north of 45° N) decreased from 1998 to 2000 but increased in 2001. There were large spatial and temporal variations of snow cover in the pan-Arctic zone during 1998-2001.     

View angle effects on MODIS snow mapping in forests

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.     

Mobility-assisted minimum connected cover in a wireless sensor network

All properties of mobile wireless sensor networks (MWSNs) are inherited from static wireless sensor networks (WSNs) and meanwhile have their own uniqueness and node mobility. Sensor nodes in these networks monitor different regions of an area of interest and collectively present a global overview of monitored activities. Since failure of a sensor node leads to loss of connectivity, it may cause a partitioning of the network. Adding mobility to WSNs can significantly increase the capability of the WSN by making it resilient to failures, reactive to events, and able to support disparate missions with a common set of sensor nodes. In this paper, we propose a new algorithm based on the divide-and-conquer approach, in which the whole region is divided into sub-regions and in each sub-region the minimum connected sensor cover set is selected through energy-aware selection method. Also, we propose a new technique for mobility assisted minimum connected sensor cover considering the network energy. We provide performance metrics to analyze the performance of our approach and the simulation results clearly indicate the benefits of our new approach in terms of energy consumption, communication complexity, and number of active nodes over existing algorithms.     

Multitemporal ensemble learning for snow cover extraction from high-spatial-resolution images in mountain areas

ABSTRACT

High-spatial and -temporal resolution snow cover products in mountain areas are important to hydrological applications. The GF-1 satellite provides multispectral images with 8-m resolution and a revisit up to 2 days, which makes it possible to produce snow cover products. However, it is challenging to extract snow cover from these images because of limited spectral bands, severe mountain shadows, and dataset-shift problem in multitemporal classification. To overcome the limitations above, this study proposes a multitemporal ensemble learning framework to extract snow cover from high-spatial-resolution images in mountain areas. The principle behind ensemble learning, i.e. learning from disagreement, is extended from single image classification to multitemporal ones. We assume that multitemporal training samples selected within time-invariant classes at the same locations can be different in feature space. Such disagreements are used in multitemporal ensemble learning to improve classification accuracy. To enhance both accuracy and diversity of the multiple classifiers trained on these samples, a joint feature selection method is suggested to select the optimal multitemporal feature space and a joint parameter optimization method is designed to ensemble classifiers trained for multitemporal images. The experiments show that the performances of multitemporal ensemble classifiers are superior to that of single classifiers, confirming the effectiveness of the proposed framework.     

Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the 2000-2001 snow year

Snow cover represents an important water resource for the Upper Rio Grande River Basin of Colorado and New Mexico. Accuracy assessment of MODIS snow products was accomplished using Geographic Information System (GIS) techniques. Daily snow cover maps produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data were compared with operational snow cover maps produced by the National Operational Hydrologic Remote Sensing Center (NOHRSC) and against in situ Snowpack Telemetry (SNOTEL) measurements for the 2000-2001 snow season. Over the snow season, agreement between the MODIS and NOHRSC snow maps was high with an overall agreement of 86%. However, MODIS snow maps typically indicate a higher proportion of the basin as being snow-covered than do the NOHRSC snow maps. In particular, large tracts of evergreen forest on the western slopes of the San de Cristo Range, which comprise a large portion of the eastern margin of the basin, are more consistently mapped as snow-covered in the MODIS snow products than in the NOHRSC snow products. NOHRSC snow maps, however, typically indicate a greater proportion of the central portion of the basin, predominately in cultivated areas, as snow. Comparisons of both snow maps with in situ SNOTEL measurements over the snow season show good overall agreement with overall accuracies of 94% and 76% for MODIS and NOHRSC, respectively. A lengthened comparison of MODIS against SNOTEL sites, which increases the number of comparisons of snow-free conditions, indicates a slightly lower overall classification accuracy of 88%. Errors in mapping extra snow and missing snow by MODIS are comparable, with MODIS missing snow in approximately 12% of the cases and mapping too much snow in 15% of the cases. The majority of the days when MODIS fails to map snow occurs at snow depths of less than 4 cm.     

覆盖层材料对触觉传感器空间分辨率的影响

通过有限元计算 ,分析了弹性覆盖层材料对触觉传感器空间分辨率的影响 ,给出了一些覆盖层材料的选择准则。     

Mapping dry/wet snow cover in the Indian Himalayas using IRS multispectral imagery

Application of remote sensing data has been made to differentiate between dry/wet snows in a glacierized basin. The present study has been carried out in the Gangotri glacier, Himalayas, using IRS-LISS-III multispectral data for the period March-November 2000 and the digital elevation model. The methodology involves conversion of satellite sensor data into reflectance values, computation of NDSI, determination of the boundary between dry/wet snows from spectral response data, and threshold slicing of the image data. The areas of dry snow cover and wet snow cover for different dates of satellite overpasses have been computed. The dry snow area has been compared with non-melting area obtained from the temperature lapse rate method, and the two are found to be in close mutual correspondence (< 15%). It is observed that there occur four water-bearing zones in the glacierized basin: dry snow zone, wet snow zone, exposed glacial ice and moraine-covered glacial ice, each of which possesses unique hydrological characteristics and can be distinguished and mapped from satellite sensor data. It is suggested that input of data on the position and extent of specifically wet snow and exposed glacial ice, which can be directly derived from remote sensing, should improve hydrological simulation of such basins.     

Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing

Various remote sensing products are used to identify spatial-temporal trends in snow cover in river basins originating in the Himalayas and adjacent Tibetan-Qinghai plateau. It is shown that remote sensing allows detection of spatial-temporal patterns of snow cover across large areas in inaccessible terrain, providing useful information on a critical component of the hydrological cycle. Results show large variation in snow cover between years while an increasing trend from west to east is observed. Of all river basins the Indus basin is, for its water resources, most dependent on snow and ice melt and large parts are snow covered for prolonged periods of the year. A significant negative winter snow cover trend was identified for the upper Indus basin. For this basin a hydrological model is used and forced with remotely sensed derived precipitation and snow cover. The model is calibrated using daily discharges from 2000 to 2005 and stream flow in the upper Indus basin can be predicted with a high degree of accuracy. From the analysis it is concluded that there are indications that regional warming is affecting the hydrology of the upper Indus basin due to accelerated glacial melting during the simulation period. This warming may be associated with global changes in air temperature resulting from anthropogenic forcings. This conclusion is primarily based on the observation that the average annual precipitation over a five year period is less than the observed stream flow and supported by positive temperature trends in all seasons.     

NOAA-AVHRR data processing for the mapping of vegetation cover

The NOAA-AVHRR images have been widely used for global studies due to their low cost, suitable wavebands and high temporal resolution. Data from the AVHRR sensor (Bands 1 and 2) transformed to the Normalized Difference Vegetation Index (NDVI) are the most common product used in global land cover studies. The purpose of this Letter is to present the vegetation, soil, and shade fraction images derived from AVHRR, in addition to NDVI, to monitor land cover. Six AVHRR images from the period of 21 to 26 June 1993 were composed and used to obtain the above mentioned products over Sa o Paulo State, in the south-east of Brazil. Vegetation fraction component values were strongly correlated with NDVI values ( r 0.95; n 60). Also, the fraction image presented a good agreement with the available global vegetation map of Sao Paulo State derived from Landsat TM images.     

Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey

Water perhaps is the most valuable natural asset in the Middle East as it was a historical key for settlement and survival in Mesopotamia, “the land between two rivers”. At present, the Euphrates and Tigris are the two largest trans-boundary rivers in Western Asia where Turkey, Syria, Iran, Iraq and Saudi Arabia are the riparian countries. The Euphrates and Tigris basins are largely fed from snow precipitation whereby nearly two-thirds occur in winter and may remain in the form of snow for half of the year. The concentration of discharge mainly from snowmelt during spring and early summer months causes not only extensive flooding, inundating large areas, but also the loss of much needed water required for irrigation and power generation purposes during the summer season. Accordingly, modeling of snow-covered area in the mountainous regions of Eastern Turkey, as being one of the major headwaters of Euphrates-Tigris basin, has significant importance in order to forecast snowmelt discharge especially for energy production, flood control, irrigation and reservoir operation optimization.A pilot basin, located on the upper Euphrates River, is selected where five automated meteorological and snow stations are installed for real time operations. The daily snow cover maps obtained from Moderate Resolution Imaging Spectroradiometer MODIS at 500 m resolution are compared with ground information for the winter of 2002-2003 both during accumulation and ablation and at accumulation stage for the winter of 2003-2004. The snow presence on the ground is determined from the snow courses performed. Such measurements were made at 19 points in and around the upper Euphrates River in Turkey and at 20 points in the upper portion of the pilot basin for the winters of 2002-2003 and 2003-2004, respectively. Comparison of MODIS snow maps with in situ measurements over the snow season show good agreement with overall accuracies ranging between 62% and 82% considering the shift in the days of comparison. The main reasons to have disagreement between MODIS and in situ data are the high cloud cover frequency in the area and the current version of the MODIS cloud-mask that appears to frequently map edges of snow-covered areas and land surfaces. The effect of elevation and land cover types on validation of MODIS snow cover maps is also analyzed. In order to minimize the cloud cover and maximize the snow cover, MODIS-8 daily snow cover products are used in deriving the snow depletion curve, which is one of the input parameters of the snowmelt runoff model (SRM). The initial results of modeling process show that MODIS snow-covered area product can be used for simulation and also for forecasting of snowmelt runoff in basins of Turkey.     

Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets

When mining large spatio-temporal datasets, interesting patterns typically emerge where the dataset is most dynamic. These dynamic regions can be characterized by a location or set of locations that exhibit different behaviors from their neighbors and the time periods where these differences are most pronounced. Examples include locally intense areas of precipitation, anomalous sea surface temperature (SST) readings, and locally high levels of water pollution, to name a few. The focus of this paper is to find and analyze the pattern of moving dynamic spatio-temporal regions in large sensor datasets. The approach presented in this paper uses a measure of local spatial autocorrelation over time to determine how pronounced the difference in measurements taken at a spatial location is with those taken at neighboring locations. Dynamic regions are analyzed both globally, in the form of spatial locations and time periods that have the largest difference in local spatial autocorrelation, and locally, in the form of dynamic spatial locations for a particular time period or dynamic time periods for a particular spatial node. Then, moving dynamic regions are identified by determining the spatio-temporal connectivity, extent, and trajectory for groups of locally dynamic spatial locations whose position has shifted from one time period to the next. The efficacy of the approach is demonstrated on two real-world spatio-temporal datasets (a) NEXRAD precipitation and (b) SST. Promising results were found in discovering highly dynamic regions in these datasets depicting several real environmental phenomenon which are validated as actual events of interest.     

Evaluation of multi-satellite precipitation products in Xinjiang,China

ABSTRACT

Satellite precipitation retrieval is a critical approach to understanding the spatial distribution of precipitation in Xinjiang, an arid area located in Northwest China, where weather stations are sparsely distributed. However, satellite precipitation retrieval in arid areas is a challenging task. The goal of this study is to evaluate the estimates of four satellite precipitation products, namely, the Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis 3B42 (3B42), and Climate Prediction Center Morphing Technique (CMORPH), on half-hourly, hourly, 3-hourly, and daily scales based on rain gauge data. The findings of this study are as follows. (1) The four products generally display a declining trend from northwest to southeast. IMERG exhibits a higher accuracy than CMORPH for all indexes at the half-hourly scale, while GSMaP performs better than other products based on most indexes at hourly and daily scales. (2) In three sub-regions, i.e. Southern Xinjiang (SX), Northern Xinjiang (NX) and the Tianshan Mountains (Tianshan), these products exhibit significant regional characteristics. The precipitation in SX, where rainfall observations are scarce, is overestimated by all products; in contrast, all products underestimate precipitation in Tianshan in NX, except for the underestimation by 3B42, precipitation was overestimated by the studied products. (3) All satellite products performed better in the warm season than in the cold season at each time scale. During the warm season, apart from the apparent overestimation by CMORPH, the relative bias values of the other products are all within ±10%. During the cold season, all products underestimate precipitation mainly composed of snowfall, especially 3B42, which yields the most underestimated values. (4) IMERG performs well in the retrieval of the distribution of the probability density function (PDF) of the occurrence (PDFc) of gauge observations, especially at low precipitation intensities, and the difference between the estimated and observed precipitation volumes at the hourly scale is the smallest. However, GSMaP performed better at the daily scale according to the PDF for the volume of precipitation (PDFv). This study is the first to evaluate IMERG and CMORPH products at the half-hourly scale and is one of the few sub-daily evaluations of various satellite precipitation products in arid areas of China. Thus, our results provide a significant reference for the satellite retrieval of precipitation in arid areas.     

Learning automata-based algorithms for finding cover sets in wireless sensor networks

Wireless sensor networks (WSNs) have been widely used in different applications. One of the most significant issues in WSNs is developing an efficient algorithm to monitor all the targets and, at the same time, extend the network lifetime. As sensors are often densely deployed, employing scheduling algorithms can be considered a promising approach that is able ultimately to result in extending total network lifetime. In this paper, we propose three learning automata-based scheduling algorithms for solving target coverage problem in WSNs. The proposed algorithms employ learning automata (LA) to determine the sensors that should be activated at each stage for monitoring all the targets. Additionally, we design a pruning rule and manage critical targets in order to maximize network lifetime. In order to evaluate the performance of the proposed algorithms, extensive simulation experiments were carried out, which demonstrated the effectiveness of the proposed algorithms in terms of extending the network lifetime. Simulation results also revealed that by a proper choice of the learning rate, a proper trade-off could be achieved between the network lifetime and running time.     

Evaluation of MODIS snow cover and cloud mask and its application in Northern Xinjiang, China

Using five-year (2001-2005) ground-observed snow depth and cloud cover data at 20 climatic stations in Northern Xinjiang, China, this study: 1) evaluates the accuracy of the 8-day snow cover product (MOD10A2) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite, 2) generates a new snow cover time series by separating the MODIS cloud masked pixels as snow and land, and 3) examines the temporal variability of snow area extent (SAE) and correlations of air temperature and elevation with SAE. Results show that, under clear sky conditions, the MOD10A2 has high accuracies when mapping snow (94%) and land (99%) at snow depth ≥ 4 cm, but a very low accuracy (< 39%) for patchy snow or thin snow depth (< 4 cm). Most of the patchy snow is misclassified as land. The mean accuracy of the cloud mask used in MOD10A2 for December, January and February is very low (19%). Based on the ratio of snow to land of ground observations in each month, the new snow cover time series generated in this study provides a better representation of actual snow cover for the study area. The SAE (%) time series exhibits similar patterns during six hydrologic years (2001-2006), even though the accumulation and melt periods do not exactly coincide. The variation of SAE is negatively associated with air temperature over the range of − 10 °C to 5 °C. An increase in elevation generally results in longer periods of snow cover, but the influence of elevation on SAE decreases as elevation exceeds 4 km in the Ili River Watershed (IRW). The number of days with snow cover shows either a decreasing trend or no trend in the IRW and the entire study area in the study period. This result is inconsistent with a reported increasing trend based on limited in situ observations. Long-term continuance of the MODIS snow cover product is critical to resolve this dilemma because the in situ observations appear to undersample the region.