Estimating fractional snow cover from MODIS using the normalized difference snow index

Snow-cover information is important for a wide variety of scientific studies, water supply and management applications. The NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) provides improved capabilities to observe snow cover from space and has been successfully using a normalized difference snow index (NDSI), along with threshold tests, to provide global, automated binary maps of snow cover. The NDSI is a spectral band ratio that takes advantage of the spectral differences of snow in short-wave infrared and visible MODIS spectral bands to identify snow versus other features in a scene. This study has evaluated whether there is a “signal” in the NDSI that could be used to estimate the fraction of snow within a 500 m MODIS pixel and thereby enhance the use of the NDSI approach in monitoring snow cover. Using Landsat 30-m observations as “ground truth,” the percentage of snow cover was calculated for 500-m cells. Then a regression relationship between 500-m NDSI observations and fractional snow cover was developed over three different snow-covered regions and tested over other areas. The overall results indicate that the relationship between fractional snow cover and NDSI is reasonably robust when applied locally and over large areas like North America. The relationship offers advantages relative to other published fractional snow cover algorithms developed for global-scale use with MODIS. This study indicates that the fraction of snow cover within a MODIS pixel using this approach can be provided with a mean absolute error less than 0.1 over the range from 0.0 to 1.0 in fractional snow cover.     

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.     

Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery

Knowledge of snow cover is essential to understanding the global water and energy cycle. Thresholding the normalized difference snow index (NDSI) image is a method frequently used to map snow cover from remotely sensed data. However, the threshold is dependent on the scenario and needs to be determined accordingly. In this study, nine automatic thresholding methods were tested on the NDSI. Comparisons of the automatic thresholding methods, optimal threshold, and support vector machine (SVM) classification show that Otsu's and Nie's methods appear to be the most robust among the nine automatic thresholding methods, achieving comparable accuracies with the latter two approaches. In addition, NDSI from the digital number (DN) can be an efficient substitution for NDSI obtained from atmospherically or topographically corrected data, with similar accuracy.     

Satellite-derived snow coverage related to hydropower production in Norway: Present and future

Hydropower derived from snow-melt runoff is a major source of electricity in Norway. Therefore, amount of snow-melt runoff is key to the prediction of available water. The prediction of water quantity may be accomplished through the use of hydrological models. These models, which may be run for individual basins, use satellite-derived snow-covered area in combination with snow-cover depletion curves. While it is known that snow albedo information would increase the accuracy of the models, large-scale albedo measurements have not yet been obtained from satellites on a regular basis. This paper presents Landsat-5 Thematic Mapper (TM) reflectances recorded in May 1989 from a mountainous catchment at Kvikne, Norway. Satellite-derived albedo values are analysed, and compared with simultaneously measured in situ albedo. The satellite-derived shortwave snow albedo is comparable with bare ground albedo and values as low as 0.19 were found in areas where the snow was highly metamorphosed and heavily blackened by organic material. To map snow-covered areas, the contrast between snow and snow-free areas can be improved by using a normalized TM Band 2-5 difference image. While TM Band 2 alone shows varying degrees of snow surface contamination within the study area, the normalized difference snow index (NDSI) is not affected by impurities. This paper also discusses the use of NASA's EOS (Earth Observing System) Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which is planned to be launched in the summer of 1999 for mapping of large-scale geophysical parameters including snow-cover. MODIS will enable snow cover and albedo to be mapped in Norway on a daily basis, and should enhance our ability to estimate snow coverage and thus manage hydropower production.     

新疆玛纳斯河流域卫星雪盖信息提取的影响因素分析

卫星雪盖信息的准确提取受到很多因素的影响,本文选用11幅玛纳斯河流域Landsat ETM＋影像,应用归一化差值积雪指数NDSI区分积雪与其他地物,分析传感器增益、大气因素、地形效应对雪盖信息提取造成影响的原因,并定量计算各因素影响程度的大小。研究结果表明,成像过程传感器高低增益对卫星雪盖信息提取的影响非常大,大气因素的影响相对较小,NDSI对地形不具有适应性,尤其在阴影区进行积雪判读不适用。     

Comparison of methods of snow cover mapping by analysing the solar spectrum of satellite remote sensing data in China

Three methods, supervised classification (SC), digital number (DN) statistics and Normalized Difference Snow Index (NDSI), are used to map snow cover and then calculate snow cover area. Data sets from Landsat TM, Moderate Resolution Imaging Spectroradiometer (MODIS) and NOAA/AVHRR are selected because these sensors of different spatial resolution provide the most up to date remote sensing data for China. The results show that the best method for obtaining the snow index is different for each of these sensor products because of their different spatial and temporal resolutions and objectives of application. Reflectivity threshold statistics (RTs) should be used if the data series is incomplete; whereas SC needs a relatively accurate signature file for classification. A valid and rational method has been certified which selects NDSI for extracting snow pixels. Meanwhile, we introduce the brightness compensation method for decreasing the impact of topographic shading on distinguishing of snow pixels.     

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.     

Field-based spectral reflectance measurements of seasonal snow cover in the Indian Himalaya

In the present study, spectroradiometer (350–2500 nm) experiments are carried out in the field to understand the influence of snow grain size, contamination, moisture, ageing, snow depth, slope / aspect on spectral reflectance and to determine the sensitive wavelengths for mapping of snow and estimation of snow characteristics using satellite data. The observations suggest that, due to ageing and grain-size variation, the maximum variations in reflectance are observed in the near-infrared region, i.e. around 1040–1050 nm. For varying contamination and snow depth, the maximum variations are observed in the visible region, i.e. around 470 and 590 nm, respectively. For the moisture changes, the maximum variations are observed around 980 and 1160 nm. Based on the spectral signatures of seasonal snow, the normalized difference snow index (NDSI) is studied, and snow indexes, such as grain and contamination indexes, are proposed. The study also suggests that the NDSI increases with ageing, grain size and moisture content. The NDSI values remain constant with variations in slope and aspect. Attempts are made to estimate seasonal snow characteristics using multispectral Advanced Wide Field Sensor (AWiFS) Indian Remote Sensing (IRS-P6) and Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite data and validated with snow-meteorological observatory data of the study area.     

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.     

A blended global snow product using visible,passive microwave and scatterometer satellite data

A joint US Air Force/National Aeronautics and Space Administration (NASA) blended global snow product that uses Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and Quick Scatterometer (QuikSCAT or QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by using a newly developed Air Force Weather Agency (AFWA)/NASA Snow Algorithm (ANSA). This initial blended snow product uses minimal modelling to expeditiously yield improved snow products, which include, or will include, snow-cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the USA, from Colorado obtained during the Cold Land Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow-cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or diurnal-amplitude variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.     

Land cover mapping of North and Central America—Global Land Cover 2000

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere-Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.     

卫星遥感雪盖制图方法对比与分析

利用ＬａｎｄｓａｔＴＭ、ＮＯＡＡ／ＡＶＨＲＲ和中分辨率成像光谱仪（ＭＯＤＩＳ）三个平台传感器的遥感数据,分别使用训练样本监督分类、阈值数字信号统计、雪盖指数方法制作雪盖图和提取积雪面积。结果表明：不同传感器遥感图像因时相和时空分辨率的差异,提取积雪信息的有效方法有所不同。但基于反射特性的雪盖指数计算法具有普遍的实际操作性意义,即雪盖制图精度高,分类合理,是提取积雪信息的最佳技术手段;当使用监督积雪分类时,只有取得精确的信号文件,分类结果才是可信的;而阈值数字信号统计的雪的阈值确定具有很大的经验性和随机性,但对数据不完整或只有单波段时也不失为有效和简便的途径;山影补偿处理法基本可以消除地形阴影的影响;而去云后其覆盖下的积雪恢复技术值得进一步讨论。     

Using crowdsourced and weather station data to fill cloud gaps in MODIS snow cover datasets

We present the design, development, and testing of a new software package for generating snow cover maps. Using a custom inverse distance weighting method, we combine volunteer snow reports, cross-country ski track reports and station measurements to fill cloud gaps in the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product. The method is demonstrated by producing a continuous daily time step snow probability map dataset for the Czech Republic region. For validation, we checked the ability of our method to reconstruct MODIS snow cover under cloud by simulating cloud cover datasets and comparing estimated snow cover to actual MODIS snow cover. The percent correctly classified indicator showed accuracy between 80 and 90% using this method. The software is available as an R package. The output data sets are published on the HydroShare website for download and through a web map service for re-use in third-party applications.     

Determination of phenological dates in boreal regions using normalized difference water index

Monitoring and understanding plant phenology are important in the context of studies of terrestrial productivity and global change. Vegetation phenology, such as dates of onsets of greening up and leaf senescence, has been determined by remote sensing using mainly the normalized difference vegetation index (NDVI). In boreal regions, the results suffer from significant uncertainties because of the effect of snow on NDVI. In this paper, SPOT VEGETATION S10 data over Siberia have been analysed to define a more appropriate method. The analysis of time series of NDVI, normalized difference snow index (NDSI), and normalized difference water index (NDWI), together with an analysis of in situ phenological records in Siberia, shows that the vegetation phenology can be detected using NDWI, with small effect of snow. In spring, the date of onset of greening up is taken as the date at which NDWI starts increasing, since NDWI decreases with snowmelt and increases with greening up. In the fall, the date of onset of leaf coloring is taken as the date at which NDWI starts decreasing, since NDWI decreases with senescence and increases with snow accumulation. The results are compared to the results obtained using NDVI-based methods, taking in situ phenological records as the reference. NDWI gives better estimations of the start of greening up than NDVI (reduced RMSE, bias and dispersions, and higher correlation), whereas it does not improve the determination of the start of leaf coloring. A map of greening up dates in central Siberia obtained from NDWI is shown for year 2002 and the reliability of the method is discussed.     

提高森林覆盖区积雪提取精度的方法研究——以玛纳斯河流域为例

森林覆盖区积雪的提取精度很低,由于植被冠层的遮挡,冠层下的积雪很难被提取出来。基于Landsat 8OLI数据,针对玛纳斯河流域下游有大面积森林覆盖的特点,通过传统的积雪指数法,结合NDVI数据的积雪指数法和面向对象图像特征法分别提取积雪面积。结果表明:1传统的NDSI和S3积雪指数法无法较好地提取出森林覆盖下的积雪,提取精度分别为85.23%和87.54%。这两种方法适用于空间尺度较大、植被覆盖面积较大的区域,并不适合所选研究区;2结合NDVI数据后的NDSI、S3积雪指数模型能大大提高森林覆盖下的积雪面积,提取精度分别达到91.47%和90.60%。在影像空间分辨率较高,流域尺度较小,林区覆盖较多的情况下可采用此方法提取积雪;3随着海拔的升高,地形阴影影响逐渐增大,NDVI辅助积雪指数方法提取林区覆盖下积雪面积逐渐减小。因此采用光谱、纹理和空间信息结合的面向对象图像特征方法提取积雪,能够较好地识别出受地形影响下的雪像元,精度达到89.75%,可以满足实际应用的需求。     

Comparison and assessment of coarse resolution land cover maps for Northern Eurasia

Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse region covering a wide range of climate zones and ecosystems: from arctic deserts, tundra, boreal forest, and wetlands, to semi-arid steppes and the deserts of Central Asia. In this study, we assessed four of the most recent global land cover datasets: GLC-2000, GLOBCOVER, and the MODIS Collection 4 and Collection 5 Land Cover Product using cross-comparison analyses and Landsat-based reference maps distributed throughout the region. A consistent comparison of these maps was challenging because of disparities in class definitions, thematic detail, and spatial resolution. We found that the choice of sampling unit significantly influenced accuracy estimates, which indicates that comparisons of reported global map accuracies might be misleading. To minimize classification ambiguities, we devised a generalized legend based on dominant life form types (LFT) (tree, shrub, and herbaceous vegetation, barren land and water). LFT served as a necessary common denominator in the analyzed map legends, but significantly decreased the thematic detail. We found significant differences in the spatial representation of LFT's between global maps with high spatial agreement (above 0.8) concentrated in the forest belt of Northern Eurasia and low agreement (below 0.5) concentrated in the northern taiga-tundra zone, and the southern dry lands. Total pixel-level agreement between global maps and six test sites was moderate to fair (overall agreement: 0.67-0.74, Kappa: 0.41-0.52) and increased by 0.09-0.45 when only homogenous land cover types were analyzed. Low map accuracies at our tundra test site confirmed regional disagreements and difficulties of current global maps in accurately mapping shrub and herbaceous vegetation types at the biome borders of Northern Eurasia. In comparison, tree dominated vegetation classes in the forest belt of the region were accurately mapped, but were slightly overestimated (10%-20%), in all maps. Low agreement of global maps in the northern and southern vegetation transition zones of Northern Eurasia is likely to have important implications for global change research, as those areas are vulnerable to both climate and socio-economic changes.     

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.     

Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision

This work presents a new algorithm designed to detect clouds in satellite visible and infrared (IR) imagery of ice sheets. The approach identifies possible cloud pixels through the use of the normalized difference snow index (NDSI). Possible cloud pixels are grown into regions and edges are determined. Possible cloud edges are then matched with possible cloud shadow regions using knowledge of the solar illumination azimuth. A scoring index quantifies the quality of each match resulting in a classified image. The best value of the NDSI threshold is shown to vary significantly, forcing the algorithm to be iterated through many threshold values. Computational efficiency is achieved by using sub-sampled images with only minor degradation in cloud-detection performance. The algorithm detects all clouds in each of eight test Landsat-7 images and makes no incorrect cloud classifications.     

An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China

Snow is an important land cover on the earth's surface. It is characterized by its changing nature. Monitoring snow cover extent plays a significant role in dynamic studies and prevention of snow-caused disasters in pastoral areas. Using NASA EOS Terra/MODIS snow cover products and in situ observation data during the four snow seasons from November 1 to March 31 of year 2001 to 2005 in northern Xinjiang area, the accuracy of MODIS snow cover mapping algorithm under varied snow depth and land cover types was analyzed. The overall accuracy of MODIS daily snow cover mapping algorithm in clear sky condition is high at 98.5%; snow agreement reaches 98.2%, and ranges from 77.8% to 100% over the 4-year period for individual sites. Snow depth (SD) is one of the major factors affecting the accuracy of MODIS snow cover maps. MODIS does not identify any snow for SD less than 0.5 cm. The overall accuracy increases with snow depth if SD is equal to or greater than 3 cm, and decreases for SD below 3 cm. Land cover has an important influence in the accuracy of MODIS snow cover maps. The use of MOD10A1 snow cover products is severely affected by cloud cover. The 8-day composite products of MOD10A2 can effectively minimize the effect of cloud cover in most cases. Cloud cover in excess of 10% occurs on 99% of the MOD10A1 products and 14.7% of the MOD10A2 products analyzed during the four snow seasons. User-defined multiple day composite images based on MOD10A1, with flexibilities of selecting composite period, starting and ending date and composite sequence of MOD10A1 products, have an advantage in effectively monitoring snow cover extent for regional snow-caused disasters in pastoral areas.