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Snow observation by satellite: A review
Abstract
Surface snow cover is able to be detected within the spectral and thermal wavelengths by a range of satellite sensors and the area and frequency of observation is a function of both spatial and temporal resolution. The Landsat, NOAA and GOES satellite are primarily employed for routine snow mapping, although each sensor has specific limitations.
Snow observation is inhibited by sensor saturation problems, and also cloud cover which both obscures the snow surface and exhibits some spectral overlap with snow. A number of developed techniques allow snow/cloud discrimination, with varying degrees of success, although the most promising of these include the middle infrared (1.6 μm) channel in the analysis. The close correspondence of the distribution of snow cover with terrain has also enabled the interpolation of snow cover into cloud obscured regions.
Shadows from terrain generally confuse the location of snow covered pixels and procedures correcting for the variation in illumination have been generated. Vegetation cover, particularly conifer forest, also reduces the reflectance of snow covered surfaces and prevents reliable calibration of pixel intensity to snow depth or percentage snow cover. Many studies have therefore developed techniques to identify snow in vegetation covers.
The detection of the snow/no snow boundary and subsequent estimation of snow area has been achieved by using a variety of approaches ranging from interactive delineation and planimetry or thresholding to multi‐temporal analysis and more sophisticated grid‐ding or digital techniques.
... Further, these images cover large areas at short time intervals. Until quite recently, the problem facing interpretation of snow cover from satellite imagery involved the differentiation of snow from other features, especially those of cloud cover, bare rocks and shadows on sloping terrain, as well as vegetation cover which reduces the reflectance (Lucas & Harrison, 1990). ...
... In addition to estimating the snow volume, it is necessary to estimate the water-snow equivalent (or snow/water ratio, SWR = ratio of water volume to snow volume) to establish the volume of water in the form of snow. In mountainous regions, the snow line/altitude method (Fig. 1) Lucas & Harrison, 1990). Gurney, 1985;Lucas & Harrison, 1990;Maxfield, 1994). ...
... In mountainous regions, the snow line/altitude method (Fig. 1) Lucas & Harrison, 1990). Gurney, 1985;Lucas & Harrison, 1990;Maxfield, 1994). This method depends on classifying the snow cover distribution with respect to different altitudes. ...
... Further, these images cover large areas at short time intervals. Until quite recently, the problem facing interpretation of snow cover from satellite imagery involved the differentiation of snow from other features, especially those of cloud cover, bare rocks and shadows on sloping terrain, as well as vegetation cover which reduces the reflectance (Lucas & Harrison, 1990). ...
... In addition to estimating the snow volume, it is necessary to estimate the water-snow equivalent (or snow/water ratio, SWR = ratio of water volume to snow volume) to establish the volume of water in the form of snow. In mountainous regions, the snow line/altitude method ( Fig. 1) is generally applied (Engman & Gurney, 1985;Lucas & Harrison, 1990;Maxfield, 1994). This method depends on classifying the snow cover distribution with respect to different altitudes. ...
... Snow line/altitude curve to estimate the average area of snow cover (according toLucas & Harrison, 1990). ...
At least one-quarter of the Lebanese terrain is covered by snow annually, thus contributing integrally to feeding surface and subsurface water resources. However, only limited estimates of snow cover have been carried out and applied locally. The use of remote sensing has enhanced significantly the delineation of snow cover over the mountains. Several satellite images and sensors are used in this respect. In this study, SPOT-4 (1-km resolution) satellite images are used. They have the capability to acquire consecutive images every 10 days, thus monitoring the dynamic change of snow and its maximum coverage could be achieved. This was applied to Mount Lebanon for the years 2001–2002. The areas covered by snow were delineated, and then manipulated with the slope angle and altitudes in order to classify five major zones of snowmelt potential. The field investigation was carried out in each zone by measuring depths and snow/water ratio. A volume of around 1100 × 10 m of water was derived from snowmelt over the given period. This is equivalent to a precipitation rate of about 425 mm in the region, revealing the considerable portion of water that is derived from snowmelt.
... Satellite remote sensing also offers consistent snow cover observation over large and remote areas, which can be used in long term environmental studies [14]. Satellite snow cover mapping was initiated when TIROS-1 (Television and InfraRed Observation Satellite) captured the first snow cover image of Eastern Canada on April 1, 1960 [16,17]. In the mid-1960s, weekly snow maps at 3.7 km spatial resolution were produced following the launch of the Environmental Science Service Administration (ESSA) satellite in 1965. ...
... In 1982, short wave infrared (SWIR) band of Landsat-4 TM allowed for the snow/cloud discrimination and snow cover mapping at 30 m spatial resolution [9]. Since then, regional and global snow cover maps have been developed using satellite data from Geostationary Operational Environmental Satellite (GEOS), AVHRR, Landsat, Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) [13,16] and most significantly from the Moderate Resolution Imaging Spectroradiometer (MODIS). ...
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.
... The distinct optical properties of snow, marked by its high reflectance in the visible spectrum and lower reflectance in the mid-infrared, allow its identification from space, using varied optical sensors, each with its own spatial and temporal resolutions (Boudhar et al., 2009;Marchane et al., 2017;Bousbaa et al., 2022;Hanich et al., 2022). This notable spectral difference between snow and other natural surfaces has been historically employed to map snow cover using diverse approaches (Lucas and Harrison, 1990). In the last two decades, a variety of products for snow monitoring have been developed (Frei et al., 2012). ...
In semi-arid Mediterranean areas, a significant proportion of the population living downstream depends on water resources from snowmelt and precipitation as their main source of water. Consequently, snow-covered mountain regions can be considered as a vital water tower, providing a steady supply of water, and contributing significantly to streamflow and groundwater recharge. Given the scarcity of ground-based hydroclimatic measurements, remote sensing could be an effective technique for mapping and monitoring snow cover. This study evaluates the last version of MODIS (version 6, called V6) snow cover product, optimizing the NDSI threshold for accurate snow cover mapping and developing models for local fractional snow cover estimation in the southern Mediterranean region, particularly in the Moroccan Atlas Mountains. For this purpose, 448 Sentinel-2 (S2) scenes from six different regions across the Atlas Range were used to adjust the NDSI threshold and to develop FSC estimation models. In addition, a total of 8419 MOD10A1 images from March 2000 to June 2023, and 7561 MYD10A1 images from September 2002 to June 2023, were processed to improve cloud filtering and to develop a highly accurate daily snow cover product suitable for the Moroccan Atlas Mountains. The cloud correction approach significantly reduced the number of cloud-covered pixels, from 25.7% to 0.4% after filtering. Two schemes for selecting the MODIS NDSI threshold were tested: (1) the global reference of 0.4 and (2) the locally optimal threshold of 0.2. The average snow cover estimation errors using the optimal and global NDSI thresholds for Terra are an average overestimation of 0.34% and a significant underestimation of 6.13%, respectively. For Aqua, the corresponding errors are an overestimation of 1.4% and an underestimation of 6.8%. Thus, the optimal NDSI threshold of 0.2 could be more appropriate than the threshold of 0.4 for use in the southern Mediterranean region. The new FSC estimation models developed showed satisfactory performance with significant correlation coefficients (mean of 0.85 for Terra and 0.83 for Aqua), and with low RMSE and MAE values (mean of 0.17 and 0.12 for Terra and mean of 0.19 and 0.14 for Aqua) when comparing FSC derived from high-resolution S2 data with predicted FSC from MODIS NDSI. The daily snow cover product developed was compared with the high-resolution snow maps obtained from S2 satellite imagery in different regions of the Moroccan Atlas. On average, the product showed a mean correlation coefficient of 0.96, a mean absolute error of 0.22%, and a mean reasonable negative bias of −0.17%. This research concludes that the enhanced daily snow cover product could improve the understanding of spatiotemporal dynamics of snow extent and, therefore, contribute to quantifying the snowmelt contribution to the water budget through modeling approaches in the southern Mediterranean region.
... 测. 光学遥感发展迅速且较为成熟, 但众多研究表明, 在可见光波段积雪反射率信息和雪深之间没有直接的 物理函数关系, 近红外光子在积雪中也只有微弱的穿 透效果 [5,6] . Romanov和Tarpley [7] 基于积雪反射率和积 效捕获积雪特征变化及体积信息 [8,9] , 被广泛地应用于 雪深估算研究. ...
Snow depth is an important parameter that reflects the law of spatiotemporal variation in snow cover and is an indispensable observation variable for studying global and regional climate change and the hydrological cycle. Traditional manual field measurements and ground station observations have limited scopes and cannot fully reflect regional-scale snow conditions. Satellite remote sensing can simultaneously observe surface information over a large area and has been widely used in snow monitoring since the 1970s. Optical remote sensing has difficulty obtaining snow depth information directly through the visible light band and is limited by atmospheric conditions. Microwave remote sensing can penetrate clouds and fog and can effectively capture changes in snow depth. Differential interferometric synthetic aperture radar (D-InSAR) technology uses the differential interferometric phase formed by microwave penetration through the snow layer before and after snowfall to establish a geometric function relationship with the snow depth; it is widely used in regional small-scale snow depth estimation research. However, its estimation accuracy is affected by many factors, such as interferometric image pair coherence, local topography, and snow dielect capability at microwave wavelengths.
Based on high-resolution Sentinel-1 SAR data, this study optimizes the snow depth differential interferometric phase unwrapping accuracy by introducing Sentinel-2 optical images and high coherence coefficient regions to select ground control points, which are phase correction benchmarks. Furthermore, introducing field-measured snow parameters such as snow density and satellite local terrain incidence angles based on digital elevation model (DEM) data reduces the error of the differential interferometric phase-slope distance relationship model, thereby enabling estimations of the spatiotemporal distribution of snow depth is estimated in the Babao River Basin in the northeastern Qinghai-Tibet Plateau during the 2021 ablation period. Simultaneously, the accuracy of the snow depth estimation ability is evaluated based on the synchronous field measurement data of snow cover satellites. The main factors affecting the accuracy of snow depth estimation are discussed and analysed. Data from 122 ground snow depth measurements (meteorological stations + field measurements) are used to verify the results. The results show that the optimized D-InSAR differential interferometry can improve the snow depth estimation accuracy. The RMSE is 3.9 cm, the MAPE is 20.03%, and the R2 is 0.92. However, the estimated snow depth is generally underestimated (MBE%= -16.8%), and the maximum underestimation error of the snow depth is 9.1 cm. In addition to the influencing factors of D-InSAR system interferometric decorrelation, the estimations are affected by the penetration ability of microwaves in the snow and snow parameters such as stratigraphy structure, temperature and humidity. This limits the ability of differential interferometry to estimate snow depth and is more suitable for dry and homogeneous snow cover.
This method allows for more precise and faster monitoring of centimetre-level snow depth changes; at the same time, the underlying surface of the study is relatively simple, and the influence of forest and other vegetation coverage is not considered. In follow-up research, snow cover should be optimized with multi-property and multi-environment interferometric models. An InSAR scattering model with a layered factor is introduced to improve the propagation information inside the snow layer then combined with the multiangle radar measurement of the ascending and descending orbits; experiments are carried out using the penetration ability of more bands in microwaves to reduce interferometric error sources and improve the accuracy of snow depth estimation. This study can provide a scientific reference for D-InSAR snow depth estimation research.
... With the fast development of satellite technology, it is becoming much easier to obtain remote sensing images, and they have been widely used in various fields including agriculture, geology, hydrology, transportation, and disaster monitoring (ZHENG et al. 2021). The first satellite employed to map snow cover is the Television and Infra-Red Observation Satellite (TIROS-1) active from 1 April 1960 (LUCAS and HARRISON 1990). After that, various mature satellite technologies with different spatial and temporal resolution have been gradually developed in this field ( Table 1). ...
Accurately monitoring the variation of snow cover from remote sensing is vital since it assists in various fields including prediction of floods, control of runoff values, and the ice regime of rivers. Spectral indices methods are traditional ways to realize snow segmentation, including the most common one – the Normalized Difference Snow Index (NDSI), which utilizes the combination of green and short-wave infrared (SWIR) bands. In addition, spectral indices methods heavily depend on the optimal threshold to determine the accuracy, making it time-consuming to find optimal values for different places. Convolutional neural networks ensemble model with DeepLabV3+ was employed as sub-models for snow segmentation using (Sentinel-2), which aims to distinguish clouds and water body from snow. The imagery dataset generated in this article contains sites in global alpine regions such as Tibetan Plateau in China, the Alps in Switzerland, Alaska in the United States, Southern Patagonian Icefield in Chile, Tsylos Provincial Park, Tatsamenie Peak, and Dalton Peak in Canada. To overcome the limitation of DeepLabV3+, which only accepts three channels as input features, and the need to use six features: green, red, blue, near-infraRed, SWIR, and NDSI, 20 three-channel DeepLabV3+ sub-models, were constructed with different combinations of three features and then ensembled together. The proposed ensemble model showed superior performance than benchmark spectral indices method, with mIoU values ranging from 0.8075 to 0.9538 in different test sites. The results of this project contribute to the development of automated snow segmentation tools to assist earth observation applications.
... Remote sensing technology have been actively applied to estimate the accumulated snow depth and spatial extent. Microwave sensors [15][16][17][18] and optical sensors [19][20][21][22][23][24] on-board satellites or the combination of both [25][26][27][28] have been used. In addition, on-board aircraft sensors have been used [29,30]. ...
Factors influencing the accuracy of UAV-photogrammetry-based snow depth distribution maps were investigated. First, UAV-based surveys were performed on the 0.04 km2 snow-covered study site in South Korea for 37 times over the period of 13 days under 16 prescribed conditions composed of various photographing times, flight altitudes, and photograph overlap ratios. Then, multi-temporal Digital Surface Models (DSMs) of the study area covered with shallow snow were obtained using digital photogrammetric techniques. Next, the multi-temporal snow depth distribution maps were created by subtracting the snow-free DSM from the multi-temporal DSMs of the study area. Then, snow depth in these UAV-Photogrammetry-based snow maps were compared to the in situ measurements at 21 locations. The accuracy of each of the multi-temporal snow maps were quantified in terms of bias (median of residuals, QΔD) and precision (the Normalized Median Absolute Deviation, NMAD). Lastly, various factors influencing these performance metrics were investigated. The results are as follows: (1) the QΔD and NMAD of the eight surveys performed at the optimal condition (50 m flight altitude and 80% overlap ratio) ranged from −2.30 cm to 5.90 cm and from 1.78 cm to 4.89 cm, respectively. The best survey case had −2.30 cm of QΔD and 1.78 cm of NMAD; (2) Lower UAV flight altitude and greater photograph overlap lower the NMAD and QΔD; (3) Greater number of Ground Control Points (GCPs) lowers the NMAD and QΔD; (4) Spatial configuration and accuracy of GCP coordinates influenced the accuracy of the snow depth distribution map; (5) Greater number of tie-points leads to higher accuracy; (6) Smooth fresh snow cover did not provide many tie-points, either resulting in a significant error or making the entire photogrammetry process impossible.
... Hence, five altitude classes were considered (Table 5.4). It is; therefore, well evidenced by the snow line/altitude curve adopted by (Lucas and Harrison 1990). ...
Usually the regime of the atmosphere, including mainly the precipitation and temperature, controls the volume of water. While the characteristics of terrain, including surface and sub-surface properties govern water flow and storage. These two physical pillars must be primarily identified in order to reach optimal water resources assessment. Hence, the relatively humid climate is interlinked with the rugged topography and both govern water discharge, including water flow energy, accumulation, and infiltration, storage in the substratum and even in water loss to the sea. Therefore, the atmospheric regime and the characteristics of a terrain are principal generators for the entire water cycle like the case in Lebanon. The climate of Lebanon is known by wet periods that are pronounced by the rainfall and the snow cover for a considerable number of months over the year. Besides, the terrain has different responding features. This implies accelerating surface water flow and regular water infiltration among the rock masses, as well as the chaotic groundwater flow into the karstic conduits. This chapter illustrates the major atmospheric variables and the terrain characteristics-induced water resources for the entire Lebanon.
... Hence, five altitude classes were considered (Table 5.4). It is; therefore, well evidenced by the snow line/altitude curve adopted by (Lucas and Harrison 1990). ...
Among many features of water resources in Lebanon, snow is still the principal resource which has a significant role in the replenishment of rivers, springs and groundwater reservoirs. Therefore, without snow, Lebanon will lose the largest part of its water, which can be estimated to more than 60%. Recently, the exacerbated challenges on water supply makes it necessary to give concerns to snow cover and its accumulation/melting regime. This must be normal since several studies in Lebanon pointed out that snow shares to 50–60% of the water volume in rivers and springs, and then in feeding groundwater aquifers. For this reason, recent researches and studies have been applied; in particular for monitoring snow cover, field investigations, modeling and the interact with climate change. However, data analysis is still a crucial matter for research. Therefore, remote sensing techniques in combination with the advanced ground measuring stations have been utilized. This chapter will present a detailed discussion on snow cover in Lebanon depending on several research studies obtained by the author. Most of these studies utilized many types of satellite images with diverse spatial and temporal resolution. In addition, field investigations to determine snow density, depth and its relationship to different topographic and geologic features were studied. These are mostly the first of their type applied in Lebanon.
... Hence, five altitude classes were considered (Table 5.4). It is; therefore, well evidenced by the snow line/altitude curve adopted by (Lucas and Harrison 1990). ...
There are seventeen rock formations exposed in Lebanon. Thus, matching the precipitated water with the geological characteristics of Lebanon created typical hydrogeological sequence where permeable and porous rocks are interbedded with rock of diverse properties and then resulting a number of groundwater reservoirs and the aquiferous rock formations. There are contradictory estimations for the volume of the renewable groundwater in Lebanon. It ranges between 0.5 and 4.84 billion m³/year, besides 3.65 billion m³ in rivers and springs, but it must be clear that this groundwater volume is interrelated with those in rivers and springs and cannot be hydro-logically separated. Apart from the problem of groundwater flow into the sea as invisible rivers; yet, the groundwater resources in Lebanon are severely exhausted and the uncontrolled pumping became widespread. This added negative impact on groundwater re-sources which often found with high contamination rate. Still, the investment of groundwater does not follow scientifically-based approaches whether to explore potential reservoirs or to pump water from zones with no impact on other water resources. This chapter will present a detailed discussion on groundwater resources of Lebanon including mainly aquifer characteristics and their hydraulic properties, plus estimations for water volume and recharge rate in addition, the relationship of groundwater to faults, and then groundwater level and discharge.
... Hence, five altitude classes were considered (Table 5.4). It is; therefore, well evidenced by the snow line/altitude curve adopted by (Lucas and Harrison 1990). ...
“Water in Lebanon is in Jeopardy”. Since the beginning of 1990s the water crisis has been occurred in Lebanon and still continuing. Up to date, no improvement in the water sector can be touched. The discharge in rivers and springs has been significantly decreased, it is also the case for many wetlands and lakes. Groundwater is under depletion with sharp decline in the discharge and the abrupt lowering of water table. Quality deterioration became widespread including a surface and groundwater resources where it reached the bottled water. In the view of this unfavorable situation in water sector in Lebanon, questions are always raised: Why there is such a status? What are the reasons behind it? What are the solutions to alleviate the impact of water crisis in Lebanon? What the outcomes of measures and water policies adopted by the government? The most important question remains: What will happen if this deteriorating situation in continues as it is? Actually, there is no define answers for all these questions, notably it is not determined who should answer on them. There must be clear and practical solutions, based on scientific concepts, to face this situation. This chapter presents proposed solutions which are based on the author’s expertise and observations. Data and information mentioned in this document were used as a background to build the proposed solutions which represent scientific outlines for further actions.
... Hence, five altitude classes were considered (Table 5.4). It is; therefore, well evidenced by the snow line/altitude curve adopted by (Lucas and Harrison 1990). ...
A major aspect of water resources, springs are widespread in Lebanon, and they are characterized by different hydrogeological mechanism, and thus by diverse flow rate and regime. The natural setting of Lebanon with its complicated geology, and more certainly the dominant rock deformations that interrupt the lithostratigarphic sequence, gives a chance for groundwater to seep on terrain surfaces as surface water flow. As per their hydrogeological linkage, it is not precise to distinguish water in springs from those in rivers and groundwater, yet these springs occupy an essential part of water budget in Lebanon and they discharge considerable water volume estimated at about 1410 million m³/year, which is equivalent to about 36% of rivers’ water. There are about 1800–2000 major springs in Lebanon, which are attributed mainly to karstic and fault springs type. The largest part of them is considered as the primary source of water for rivers, and all rivers in Lebanon are substantially fed from springs where, in many instances, one or two springs fulfill the stream flow all year long in these rivers. The majority of water in these springs is derived from snowmelt that accumulated on the mountainous regions of Lebanon (Shaban 2003). This chapter will give a detailed discussion on the springs of Lebanon including their lithostratigraphy and rock structures controls as well as the discharge regime for springs locate on terrestrial environment an even those off-shore.
... Hence, five altitude classes were considered (Table 5.4). It is; therefore, well evidenced by the snow line/altitude curve adopted by (Lucas and Harrison 1990). ...
The occurrence of water bodies on terrain surfaces have different aspects. Thus, some of them permanently occur all year long and then they are described with define names (e.g. lake, river, etc.). However, there are, sometimes, water bodies immerse terrain surfaces intermittently and usually these bodies do not comprise a specific shape or dimension; therefore, lands where these bodies occur are described as wetlands. Lebanon, the region with relatively humid climate, encompasses a number of wetlands that spread on diverse geographic locations, and they are controlled by different hydrogeological conditions. Yet, there is no define number of wetlands in Lebanon, notably that they usually immerse terrain surfaces with irregular shapes and with different water amounts; this is in addition to the relative dimensions which often make it difficult to decide whether these are wetland or not. Moreover, the existing climatic conditions and the rapid population increase accompanied with chaotic water abstraction affected the mechanism of feeding for most of the known wetlands in Lebanon (Shaban et al. Assessment of coastal wetlands in Lebanon. In: Moran G (ed) Coastal zones: management, assessment and current challenges. Nova Science Publishers, Inc, New York, pp 27–97. ISBN:978-1-63485-611-9, 2016). There are four wetlands that were designated in the RAMSAR list. Nevertheless, the hydrogeological settings of these wetlands have not been determined yet. This chapter will present a detailed explanation on the wetlands in Lebanon and their hydrogeological interrelation; in addition to a case-study for a major wetlands in Lebanon.
... Hence, five altitude classes were considered (Table 5.4). It is; therefore, well evidenced by the snow line/altitude curve adopted by (Lucas and Harrison 1990). ...
When Lebanon is described as the country with plenty water resources, this is usually inspired from the remarkable number of rivers spread on its territory. Even though, the Lebanese rivers are with small dimensions; however, it can be said that there is one river in each 750 km². The most creditable estimation of the discharge from these rivers is about 2800 million m³ per year, which constitutes a substantial part of the water balance in Lebanon. Less than 15% of this amount is exploited and the rest is either lost to sea or shared with the neighboring countries. Other than the domestic uses, rivers’ water in Lebanon is used mainly for agriculture. Lebanon rivers are also used for hydro-power generation where is contributes to about 10% of electricity needs for the entire country. The discharge from the Lebanese rivers is sharply decreased and some rivers lost more than 60% of its average annual discharge. This can be attributed either to the direct abstraction from these rivers or pumping form groundwater reservoirs. In addition, the changing hydrologic regimes of the terrain surface plays a major role in controlling the amount of water in rivers. This chapter reveals a detailed discussion on Lebanese rivers including their watersheds, and even those for the major streams as well as the related geometric measures. This is also accompanied with quantitative estimations on the amounts of water in the Lebanese rivers.
... Hence, five altitude classes were considered (Table 5.4). It is; therefore, well evidenced by the snow line/altitude curve adopted by (Lucas and Harrison 1990). ...
Artificial storage of surface water is a common hydrologic feature that is often observed in many arid and even humid regions. This can be also found naturally where water is accumulated by the existing terrain features. It is also widespread as an engineering implementation whereas many types of constructions are established to collect surface water. However, in both cases, this aspect of surface water resource usually contributes substantially in the water budget. Lakes and reservoirs are well known in Lebanon, where they are located in different geographic regions with remarkable existence in the mountainous ones. There are several aspects of man-made water storage which are principally governed by the topography and geology of the terrine. This water harvesting approach is adopted either on the individual or national levels. Thus, the volume of surface water storage in Lebanon has been estimated at 475 million m³/year (Shaban, Hamzé. Shared water resources of Lebanon, Nova Publishing, New York, p 150, 2017). This is theoretically contributes to about 110 m³/capita/year. Even though, man-made surface water storage is important for Lebanon in order to reduce surface water lose, yet there is a debate about the construction of dams. This remains a result of the lack to knowledge for the hydrological concepts. This chapter will introduce a detailed discussion and in-formation about surface water storage, artificial and man-made, in Lebanon in order to clearly provide its feasibility as a supporting water resources.
... Since TIROS-1 (Television Infrared Observation Satellite) was first used for monitoring snow cover in Canada in 1964 (Lucas et al., 1990), snow cover has been mapped from various optical sensors, i.e., Landsat (Dozier, 1980;Rosethal and Dozier, 1996), AVHRR (Advanced Very High Resolution Radiometer) (Brest et al., 1992), MODIS (Moderate Resolution Imaging Spectroradiometer) (Hall et al., 1995;, SPOT (Systeme Probatoire d'Observation de la Terre) (Dankers et al., 2010) and microwave sensors, i.e., SMMR (Scanning Multichannel Microwave Radiometer), SSM/I (Special Sensor Microwave/Imager) (Pulliainen and Hallikainen, 2001;Chang et al., 2016), AMSR-E (Advanced Microwave Scanning Radiometer) (Chang et al., 2000;Derksen, 2008). Due to its fine temporal resolution, MODIS data have become a major source of optical data for the monitoring of snow cover, which supply daily, 8-day and monthly snow cover 'binary' and fractional products (Hall et al., 2001;. ...
The dynamics of snow cover differs greatly from basin to basin in the Songhua River of Northeast China, which is attributable to the differences in the topographic shift as well as changes in the vegetation and climate since the hydrological year (HY) 2003. Daily and flexible multi-day combinations from the HY 2003 to 2014 were produced using Moderate Resolution Imaging Spectroradiometer (MODIS) from Terra and Aqua remote sensing satellites for the snow cover products in the three basins including the Nenjiang River Basin (NJ), Downstream Songhua River Basin (SD) and Upstream Songhua River Basin (SU). Snow cover duration (SCD) was derived from flexible multiday combination each year. The results showed that SCD was significantly associated with elevation, and higher SCD values were found out in the mountainous areas. Further, the average SCDs of NJ, SU and SD basins were 69.43, 98.14 and 88.84 d with an annual growth of 1.36, 2.04 and 2.71 d, respectively. Binary decision tree was used to analyze the nonlinear relationships between SCD and six impact factors, which were successfully applied to simulate the spatial distribution of depth and water equivalent of snow. The impact factors included three topographic factors (elevation, aspect and slope), two climatic factors (precipitation and air temperature) and one vegetation index (Normalized Difference Vegetation Index, NDVI). By treating yearly SCD values as dependent variables and six climatic factors as independent variables, six binary decision trees were built through the combination classification and regression tree (CART) with and without the consideration of climate effect. The results from the model show that elevation, precipitation and air temperature are the three most influential factors, among which air temperature is the most important and ranks first in two of the three studied basins. It is suggested that SCD in the mountainous areas might be more sensitive to climate warming, since precipitation and air temperature are the major factors controlling the persistence of snow cover in the mountainous areas.
... Hence, five altitude classes were considered (Table 5.4). It is; therefore, well evidenced by the snow line/altitude curve adopted by (Lucas and Harrison 1990). ...
Water has become a challenging resource that many countries worldwide are concerned with. Thus, water is often linked with health, society development, national income and even international geo-politics. Sometimes, water resources are unavailable, but successful management involves developing approaches and projects to assure water supply. However, there are some countries with available water resources, but unsatisfactory management, and thus complain about water supply becoming a national problem. This situation is prevalent in Lebanon, a country characterized by abundant water resources whether on the surface or sub-surface.
It is a paradox that there is still imbalance in water supply/demand in Lebanon, and water resources are now under stress due to chaotic use. This has been exacerbated by the oscillating climatic conditions, increased population and improper management. Therefore, people receive less than one-third of their water needs, and most water supplied is of poor quality. The current status shows a descending trend. Undoubtedly, if the water sector in Lebanon continues this way, we should anticipate unfavourable (and may be severe) consequences.
Many studies have been conducted on water and related disciplines in Lebanon; however, all of them focus on specific themes and sometimes defined regions. Nevertheless, the occurred changes on the influencers (natural and man-made) have not been considered.
This book is the first of its type for Lebanon, and it shows all aspects of water resources with updated measurements and findings obtained by adopting new techniques. It diagnoses in-depth the major elements of water flow/storage mechanism that have never been covered in such a comprehensive manner before. Also, this book introduces and analyses the existing challenges and proposes solutions. It represents a comprehensive investigation of the water resources in Lebanon.
... With the development of technology, remote sensing is widely used in real-time monitoring snow cover [10][11][12][13]. By spectrum curve analysis, snow has higher reflectivity in visible band and lower reflectivity in shortwave and infrared band, snow information can be extracted by using spectral differences. ...
Most of Tianshan highway is located in Xinjiang where is the high and cold mountain area. Drifting snow and other disaster continue to occur, seriously affecting road traffic. Conventional monitoring is difficult to meet the emergency needs due to the influence of climate, topography, etc. The article selected MODIS data to inverse snow depth and temporal-spatial distribution in Dushanzi-Kuqa region of Tianshan Highway in recent years. Analysis revealed that the snowfall mainly was concentrated from the end of October to the beginning of April in the next year. The snow area is the least in summer of July, but the largest in winter, accounting for about 35.7% of the entire study area, the snow depth in most areas at 12 to 15 cm, the maximum snow cover depth is about 23 cm in the Dushanzi region of Wusu County. The results of study has a very important theoretical and practical significance to monitoring and control of diseases.
... In Step 4, the dynamic coefficients 1/log 10 (Tb 18V − Tb 18H ) and 1/log 10 (Tb 36V − Tb 36H ) in (6) of the standard algorithm were mainly based on the data from Yakutsk and Siberian, Russia, collected during the period of 2002-2003 [19], [20], [48]. However, these coefficients are largely site-specific. ...
It is still a challenge to accurately map snow depth using passive microwave remote sensing. This study first validates the standard AMSR2 snow depth product in Northern Xinjiang, China, and then proposes an improved snow depth retrieving algorithm using the AMSR2 brightness temperature data in combination with in-situ measurements in the same region. The results show that: 1) in the past 15 years the mean snow depth based on the metrological stations ranges from 1.1 to 20.4 cm (mean 8.9 cm); 2) the standard AMSR2 snow depth product overestimates (underestimates) snow depth when snow depth thinner (thicker) than 30 cm, with an overall increased estimation error (root mean squared error) as snow depth increase; and 3) for the ascending mode (AMSR2_A) and descending mode (AMSR2_D), our improved algorithm shows smaller bias (2.5 and 3.9 cm) and smaller error (6.9 and 8.2 cm) as compared with the standard AMSR2 products (5.7 and 6.7 cm) and (11.2 and 12.1 cm), respectively. This suggests that the improved algorithm based on brightness temperature data of AMSR2_A has better accuracy and smaller error and can be used to retrieve snow depth of nonforest areas in cold period in the Northern Xinjiang.
... Streamflow resulted from snowmelt takes up as much as 10%-15% of yearly streamflow in Northeast China [3]. The snowmelt runoff provides important water source for human activity and agricultural irrigation in mountain regions, especially in mid and high latitude regions [4]. Snowmelt runoff model was first developed by Matinec in 1975, and has been successfully applied in mountain basins of almost any size (so far from 0.76 to 917,444 km 2 ) and any elevation range (form 305 to 7690 meters) [5][6][7]. ...
... Hence, five altitude classes were considered (Table 5.4). It is; therefore, well evidenced by the snow line/altitude curve adopted by (Lucas and Harrison 1990). ...
The magnitude of water discharges from rivers into the sea varies in space and time. The principal atmospheric-related function that causes this relates to precipitation rate and intensity and results in two types of land/ocean water flow regimes. The first is a rapid flow that leads to freshwater discharges in the sea of vast areal extent. The second appears as gradual freshwater discharges resulting from the percolation of rain water into the subsurface that is then stored as groundwater. The groundwater may subsequently migrate slowly along gradients through the host rocks and into the sea. In order to analyze the two processes, a systematic, monitoring approach is applied that considers the water input/output scheme, i.e., rainfall and the nature of freshwater discharges. For this purpose, two space tools are utilized. First, Tropical Rainfall Mapping Mission (TRMM) satellite data that provides daily rainfall records, and second, Moderate Resolution Imaging Spectroradiometer (MODIS-Terra) satellite data which provides daily thermal images that allow identification of freshwater discharges. In this study, the monitoring approach analyzes atmospheric and hydrologic interactions for the coastal rivers of Lebanon. Results obtained for a one-year time period reveal an obvious correlation between precipitation rates and intensity and the magnitude and duration of freshwater discharges from all Lebanese coastal rivers. Thus, the approach can be used to monitor both climatic and hydrologic behavior including monitoring climatic changes that occur over time as it relates to rainfall.
... In the visible and infrared domain, spectral properties of snow are well-known, with high reflectance in the visible part of the electromagnetic spectrum and, by contrast, low reflectance in the medium infrared (Dumont, Arnaud, Six, & Corripio, 2009;Painter & Dozier, 2004). This contrasted spectral behavior with regards to other natural surfaces was exploited in the past to map snow-covered areas (Lucas & Harrison, 1990), either following empirical approaches based on the Normalized Difference Snow Index (NDSI; Hall, Riggs, & Salomonson, 2001;Salomonson & Appel, 2004 further modified by Chaponnière et al., 2005;Dobreva & Klein, 2011), semi-empirical methods based on spectral deconvolution with explicit consideration of topographic effects on the signal (Sirguey, Mathieu, & Arnaud, 2009) or even more complex physicallybased approaches based on radiative-transfer modeling as a function of various characteristics of snow, such as water content and grain size (Painter & Dozier, 2004;Painter et al., 2009). Various operational snow products exist nowadays (Frei et al., 2012) but the only one with spatial resolution (500 m) and short revisit time (1-2 days) compatible with the monitoring of the highly dynamic behavior of snow pack in the South Mediterranean mountainous areas, is provided by the National Snow and Ice Data Center and is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations (Hall et al., 2001). ...
In semi-arid Mediterranean areas, the snow in the mountains represents an important source of water supply for many people living downstream. This study assessed the daily MODIS fractional snow-covered area (FSC) products over seven catchments with a mixed snow–rain hydrological regime, covering the Atlas chain in Morocco. For this purpose, more than 4760 daily MODIS tiles (MOD10A1 version 5) from September 2000 to June 2013 were processed, based on a spatio-temporal filtering algorithm aiming at reducing cloud coverage and the problem of discrimination between snow and cloud. The number of pixels identified as cloudy was reduced by 96% from 22.6% to 0.8%. In situ data from five snow stations were used to investigate the relative accuracy of MODIS snow products. The overall accuracy is equal to 89% (with a 0.1 m. threshold for snow depth). The timing of the seasonal snow was also correctly detected with 11.4 days and 9.4 days of average errors with almost no bias for onset and ablation dates, respectively. The comparison of the FSC products to a series of 15 clear sky FORMOSAT-2 images at 8 m resolution in the Rheraya sub-basin near to Marrakech showed a good correlation of the two datasets (r = 0.97) and a reasonable negative bias of − 27 km2. Finally, the FSC products were analyzed through seasonal indicators including onset and melt-out dates, the Snow Cover Duration (SCD) and the maximum snow cover extent (SCAmax) at the catchment level: (1) the dynamic of the snow cover area is characterized by a very strong inter-annual signal with a variation coefficient of the SCAmax reaching 77%; (2) there is no evidence of a statistically significant long-term trend although results have pointed out that the SCD increased in February–March and, to a lesser extent, decreased in April–May for the 2000–2013 period. The study concludes that the daily MODIS product can be used with reasonable confidence to map snow cover in the South Mediterranean area despite difficult detection conditions.
... * as estimated for 2014, data source [16]; ** as estimated for 2010, data source [13]. Techniques for mapping snow from remotely sensed data have been developed since the 1960s when TIROS-1 (Television and InfraRed Observation Satellite) allowed for snow cover detection [17]. Methods and sensors have been improved continuously since that time. ...
Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan,
Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km2. The
continental climate is characterized by hot and dry summer months and cold winter seasons with
most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending
on the amount of accumulated snow as well as the moment of its release after snowmelt. The
aim of the presented study is to identify possible changes in snow cover characteristics,
consisting of snow cover duration, onset and offset of snow cover season within the last 28 years.
Relying on remotely sensed data originating from medium resolution imagers, these snow cover
characteristics are extracted on a daily basis. The resolution of 500–1000 m allows for a
subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term
changes are identified from this unique dataset, revealing an ongoing shift towards earlier
snowmelt within the Central Asian Mountains. This shift can be observed in most upstream
hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change
of freshwater availability in the downstream regions, exerting additional pressure on the
already tensed situation.
... Visible and infrared imageries have been successfully utilised for the determination and mapping of snow area and the monitoring of several snow surface characteristics (e.g. Armstrong, and Brodzik, 2001;Barrett et al, 1994;Lucas and Harrison, 1990). ...
Although snow is one of the most important parameters in hydrological and climatological studies in Iran, and as an indicator of climate change, the sparse network of ground observing stations and the common problems caused by severe environment make it difficult to monitor. A new passive microwave satellite algorithm based on single channel (V37 GHz) for retrieving snow depth was developed. To help identify appropriate method, a statistical comparison was made between the results of this algorithm and the available in situ data. Data from two sources were obtained: F11-SSM/I data from the Centre for Remote Sensing and ground truth data from the Water Resource Research Organization under the authority of the Iranian Ministry of Energy. The snow depth product compared with in situ data confirmed that passive microwave remote sensing has a great potential (R 2 = 0.92) for the detection of snow depth, especially in regions where few observations are made on the ground. In contrast to earlier work (Chang et al., 1987), which has suggested that passive microwave snow depth algorithm is able to detect snow depths only up to about 50 cm, results from this research evidenced that new algorithm, are able to identify snow depths more than 50 cm in Iran. The results of the research also provide a useful basis for continuous snow depth monitoring and longer term-data in Iran, particularly through real-time or near real-time operations.
... Earth observation from space is being operationally used for monitoring snow cover dynamics from catchment scale to the global scale (Dozier 1989; Konig et al 2001). Satellite monitoring of seasonal snow cover makes it possible to predict future changes of the snow coverage and the runoff regime for a desired climate change scenario (Lucas and Harrison 1990; Rabatel et al. 2005). Spring snowpack conditions, temperature, rate of snowmelt, and amount and aerial extent of precipitation in April, May, and June are the final components of the water supply equation. ...
Mountain areas are sensitive to climate change. Implications of climate change can be seen in less snow, receding glaciers, increasing temperatures, and decreasing precipitation. Climate change is also a severe threat to snow-related winter sports such as skiing, snowboarding, and cross-country skiing. The change in climate will put further pressure on the sensitive environment of high mountains. Therefore, in this study, an attempt has been made to know the impact of climate change on the snow precipitation, water resources, and winter tourism in the two famous tourist resorts of the Kashmir Valley. Our findings show that winters are getting prolonged with little snow falls on account of climate change. The average minimum and maximum temperatures are showing statistically significant increasing trends for winter months. The precipitation is showing decreasing trends in both the regions. A considerable area in these regions remains under the snow and glacier cover throughout the year especially during the winter and spring seasons. However, time series analysis of LandSat MODIS images using Normalized Difference Snow Index shows a decreasing trend in snow cover in both the regions from past few years. Similarly, the stream discharge, comprising predominantly of snow- and glacier-melt, is showing a statistically significant declining trend despite the melting of these glaciers. The predicted futuristic trends of temperature from Predicting Regional Climates for Impact Studies regional climate model are showing an increase which may enhance snow-melting in the near future posing a serious threat to the sustainability of winter tourism in the region. Hence, it becomes essential to monitor the changes in temperature and snow cover depletion in these basins in order to evaluate their effect on the winter tourism and water resources in the region.
... Of course, the cloud obscuration problem is not intrinsic to MODIS SCA products due to the fact that sensors for the visible portion of the electromagnetic spectrum cannot see through clouds and also due to the similarities in the spectral signatures of snow and clouds. The visible and thermal bands can be used to discriminate between snow and clouds (see reviews by Lucas and Harrison, 1990;Klein et al., 1998Klein et al., , 2000Riggs and Hall, 2002;Schmugge et al., 2002) and passive microwave can be used to infer snow extent, depth, water equivalent, and state (Schmugge et al., 2002). ...
Satellite remote sensing can be used to investigate spatially
distributed hydrological states for use in modeling, assessment, and
management. However, in the visual wavelengths, cloud cover can often
obscure significant portions of the images. This study develops a
rule-based, multistep method for removing clouds from MODIS snow cover
area (SCA) images. The methods used include combining images from more
than one satellite, time interpolation, spatial interpolation, and
estimation of the probability of snow occurrence based on topographic
information. Applied over the upper Salt River basin in Arizona, the
method reduced the degree of cloud obscuration by 93.8%, while
maintaining a similar degree of image accuracy to that of the original
images.
... Of course, the cloud obscuration problem is not intrinsic to MODIS SCA products, due to the fact that sensors for the visible portion of the electromagnetic spectrum cannot see through clouds, and due to the similarities in the spectral signatures of 20 snow and clouds. The visible and thermal bands can be used to discriminate between snow and clouds (see reviews by Lucas and Harrison, 1990;Klein et al., 1998Klein et al., , 2000Rigs and Hall 2002;Schmugge et al., 2002;etc.) and passive microwave can be used to infer snow extent, depth, water equivalent and state (Schmugge et al., 2002). ...
Satellite remote sensing can be used to investigate spatially
distributed hydrological states and fluxes for use in modeling,
assessment and management. However, in the visual wavelengths, cloud
cover can often obscure significant portions of the images. This study
develops a rule-based, multi-step method for removing clouds from MODIS
Snow Cover Area (SCA) images. The methods used include a combining
images from more than one satellite, time interpolation, spatial
interpolation, and estimation of the probability of snow occurrence
based on topographic information. Applied over the Upper Salt River
Basin in Arizona, the method reduced the degree of cloud obscuration by
93.8% while maintaining a similar degree of image accuracy to that of
the original images.
... Sd=0.78(T1SH-T37H)/(1.00-J) (2) Where,fis the fractional forest cover. For North America and Eurasia, the value ofthe constant is different, that is: In the Tibetan Plateau, we use the algorithms developed by Chang et al. to retrieve snow depth, because this simple and applicable algorithm has been applied to many regions and got good results (Tait and Armstrong, 1996). ...
The estimation of snow parameters such as snow extent, snow depth and snow water equivalent are very important. They are parameters in land surface schemes and are very useful in snow disaster assessment. Passive microwave remote sensing has advantages in retrieving these parameters, especially snow depth. However, this technique has not been applied to monitor snow in Tibetan Plateau so far. So since last winter we tried to operationally monitor snow in this area by using SSM/I data, providing daily snow depth maps to the concerning sections of local government. In the meantime, the in-situ measurements of snow depth data in the Tibetan Plateau were collected to validate the retrieval algorithm employed in this study. In the paper, SSM/I images before and after a heavy snowfall were analyzed and compared with MODIS images. The results showed that the snow extent from SSM/I data is consistent with that from MODIS data, and snow depths from SSM/I are helpful for the assessment of snow disaster. However, compared with in-situ observations SSM/I derived snow depths are significantly overestimated. Since passive microwave remote sensing is almost transparently to atmosphere and cloud, it will play an important role in monitoring snow in the Tibetan Plateau, wih the retreival algorithm being improved. This will be more dominant when AMSR data are available.
... TIROS-1 (Television and InfraRed Observation Satellite) was the first satellite that enabled mapping of snow cover from space in April 1960 (Lucas and Harrison 1990). Since then, many sensors with various spectral channels and spatial and temporal resolution have been used to improve these first efforts. ...
The use of satellite remote sensing for the mapping of snow-cover characteristics has a long-lasting history reaching back until the 1960s. Because snow cover plays an important role in the Earth’s climate system, it is necessary to map snow-cover extent and snow mass in both high temporal and high spatial resolutions. This task can only be achieved by the use of remotely sensed data. Many different sensors have been used in the past decades with various algorithms and respective
accuracies. This article provides an overview of the most common methods. The
limitations, advantages and drawbacks will be illustrated while error sources and
strategies on how to ease their impact will be reviewed. Beginning with a short summary
of the physical and spectral properties of snow, methods to map snow extent
from the reflective part of the spectrum, algorithms to estimate snow water equivalent
(SWE) frompassive microwave (PM) data and the combination of both spectra
will be delineated. At the end, the reader should have an overarching overview
of what is currently possible and the difficulties that can occur in the context of
snow-cover mapping from the reflective and microwave parts of the spectrum.
... The daily availability, easy access and the low costs make AVHRR data a fundamental source of information for operational monitoring of snow-covered areas and, indirectly, of snow depth. The majority of operational snow mapping projects, where satellite data were employed, have experienced difficulties with separation of snow from cloud (Lucas & Harrison, 1990). Visible and infrared imagery have been utilized for the determination and mapping of snow-covered areas and the monitoring of several snow surface characteristics (Barrett et al., 1994). ...
Monitoring snow-covered areas and estimating the snow water equivalent play an important role in predicting discharges during spring months, especially in regions where snow is an important resource. This study has been conducted in the Upper Euphrates River basin, of 10 200 km area, and elevation range of 1125–3500 m. In estimating snow-covered areas, besides semi-supervised multispectral classification of NOAA-AVHRR data, a theta algorithm, developed by the US National Weather Service, has been used. The two classification techniques were applied to the Advanced Very High Resolution Radiometer (AVHRR) data obtained directly from the satellite receiver located at the university campus in Ankara, Turkey. The corrected images were rectified according to the UTM coordinate system. Snow-covered areas were obtained for cloud-free and partial cloudy images for April 1998 within the project area located in the eastern part of Turkey. Furthermore, the threshold to separate clouds from cloud-free areas is determined. The effects of elevation, aspect, slope and prevailing winds in determining the snow-covered areas for April 1998 are explained and the changes in the snow line are determined considering the effects of these topographic and meteorological factors. Snow depletion curves were obtained by using the proper classification technique for all the other cloud-free and partial cloudy images for 1998. These curves were used with other meteorological parameters as input to a snowmelt runoff model in order to predict the daily discharges, which were compared with the records at the streamgauge of the basin. The effects of aspect and slope on the snow depletion curves for different elevation zones are also shown and, considering this effect, the depletion curves are improved.
... The NOAA/AVHRR satellite sensor was chosen because its spatial resolution of 1 km 2 allows snow classification on a regional scale (Rango et al., 1983; Baumgartner and Seidel, 1988) and the whole study area can be covered in one image. Moreover, many authors have shown that if a multispectral classification algorithm is used, the different satellite sensors (channels) allow snow-covered areas to be accurately separated from their surroundings (Allen et al., 1990; Kidder and Wu, 1987; Lucas and Harrison, 1990). In this study snow discrimination was done by using NOAA/AVHRR channel 1–4 and applying a Maximum Likelihood Classification algorithm (Duda and Hart, 1973). ...
Since 1990 the project ‘Climate Change in the Arid Andes’ has been focusing on past climate and environmental conditions in the high mountain range of the north Chilean Andes (18° S28° S). The extreme aridity of this region is shown by the absence of glaciers, even at the highest altitudes above 6700 m a.s.l. More knowledge of the present climatic situation is needed to interpret the proxy data of different paleoarchives in this transition belt between tropical and extratropical circulation.
Precipitation events in this arid region are mainly registered during southern hemisphere summer, when the ITCZ reaches its southernmost position. Winter precipitation (snowfall) has so far not been considered an important factor in the hydrologic system of the area, because snow is seldom accurately registered by climatic stations. To fill this gap in our information, winter snowfall activity was analysed for a period of 6 years using digital NOAA/AVHRR satellite data. The results show that snowfall during winter (May-September) is a quite regular phenomenon mainly linked to northward displacements or cut-offs of cold air-masses from the Pacific. The areal distribution of snowfall is determined by the synoptic situation that produces precipitation. During cold frontal events, snowfall is most frequent in the southernmost part of the research area and on the western Chilean side of the Andes. Cold air that has been cut off from the westerlies often interacts with warmer and more humid air over the continent and therefore gives rise to a different snowfall distribution, with the greatest snowfall frequency between 23° S-25° S, decreasing polewards as well as towards the equator. These two winter snowfall patterns show that reconstruction of paleoclimate has to take into account the different mechanisms that may cause precipitation in the research area. Intensification of winter precipitation (e.g., the west wind zone) can induce largely different precipitation patterns, depending on which mechanisms (cut-offs, cold-fronts or both) within the west wind zone are strengthened.
Most of Tianshan Highway is located in the high and cold mountain areas over 2,000 m above the sea level in Xinjiang. Highway distress in late stage is exacerbated and frequently occurs especially avalanche, wind blowing snow, geohazard, flood damage, and other disasters along the highway, which seriously affects the traffic. Restricted by the climate and topography and other factors, conventional ground monitoring is hard to be applied into dealing with the emergency situation. In contrast, remote sensing technology can monitor snows in large-scale and real-time. Based on the remote sensing data sources and studied areas, MODIS data is selected to reversely derive snow depth and analyze the temporal-spatial snow distribution in Dushanzi-Kuqa region of Tianshan Highway of northern Xinjiang in recent years. Analysis revealed that the snowfall was mainly concentrated on late October to the early April in the next year. The snow area in July is the least in summer, and reaches largest in winter, accounting for about 35.7% of the entire studied area. Most of the snow depth is at 12 to 15 cm, and the maximum snow cover depth is about 23 cm in the Duzishan region of Wusu County. The study conclusion is of great theoretical and practical significance for distress monitoring and controlling.
Snow-depth retrieval from passive microwave remote-sensing data has always been an active research area, though there are still several problems that need to be solved. Due to its concision and expansibility, the National Aeronautics and Space Administration (NASA) algorithm has become the most widely used among the existing snow-depth retrieval algorithms. However, this algorithm still has its limitations: first, since it is based on linear fitting, the NASA algorithm needs to be re-fitted when we need to accurately measure snow depth greater than 1 m. Second, because the difference between the 19 GHz and 37 GHz brightness temperature measurements is completely saturated at different snow-depth ranges, the NASA algorithm will underestimate snow depth. In order to make improvements to these existing algorithms, the research in this article has attempted to develop a new algorithm of snow-depth retrieval based on the Ant Colony Optimization. Moreover, with respect to the underestimation of snow depth of the NASA algorithm, this article introduces 10.7 GHz brightness temperature measurements taken by AMSR-E. Simulations from the Microwave Emission Model of Layered Snowpacks (MEMLS) and the brightness temperature measurements of AMSR-E are applied to the snow-depth retrieval experiment. The retrieval accuracy of the algorithm is evaluated using the fieldmeasured data and the AMSR-E Snow Water Equivalent (SWE) product. Our results indicate that both of the algorithms produce accurate results, and the inversion results have improved to a certain extent compared to the AMSR-E product.
Based on related literatures from home and abroad, this paper reviews the present status of monitoring snow disaster, snow depth in Tibetan Plateau and snow disaster-causing mechanism by using satellite remote sensing, and accordingly presents deficiencies of the previous research. The feasibility and necessity of establishment a fast estimating system of snow disaster by remote sensing in Tibet is fully analyzed. With a view of the current development of remote sensing and GIS, especially the development of civil society and economy of Tibetan autonomous region, a primary design for the system contents and technical methods is provided.
Snow cover is one of the most dynamic land cover parameters that can be monitored from space and plays an important role for the Earth’s climate system and hydrological circle. While the spatial extent can be limited to narrow mountain ridges during summer, the snow cover percentage on the Northern Hemisphere may exceed 50 % (Lemke et al., Observations: changes in snow, ice and frozen ground. In: Solomon S, Qin D, Manning M, Chen Z, Marquis MC, Averyt K, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contributions of Working Group 1 to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge and New York, pp 337–383, 2007) of the total land surface (~45 million km2) during winter seasons (Barry et al., Global outlook for ice & snow. United Nations Environment Programme, Hertfordshire, 2007). Remote sensing has been used since the early 1970s to map terrestrial snow cover (Brown, J Clim 13:2339–2355, 2000) and both – sensors as well as retrieval algorithms – have undergone a substantial development since that time. This chapter will give a short introduction on how snow cover can be monitored from space. Furthermore, techniques will be outlined that show how time series analyses can be applied to remotely sensed snow cover products to reduce the compromising effect of cloud cover and to investigate the fundamental characteristics of snow. Time series of snow cover data allow for various analyses covering the fields of hydrology, climate research, flood prediction and management, and economy. Short term variations and extreme events can be analysed as well as long term climatological trends, constituting time series of snow cover data a valuable tool for a large bandwidth of applications.
This paper reviews measurement techniques and corresponding devices used to determine the physical properties of the seasonal snowpack from distances close to the ground surface. The review is placed in the context of the need for scientific observations of snowpack variables that provide inputs for predictive hydrological models that help to advance scientific understanding of geophysical processes related to snow in the near-surface cryosphere. Many of these devices used to measure snow are invasive and require the snowpack to be disrupted, thereby precluding the possibility for multiple measurements to be made at the same sampling location. Moreover, many devices rely on the use of empirical calibration equations that may not be valid at all geographic locations. The spatial density of observations with most snow measurement devices is often inadequate. There is a need for improved automation of snowpack measurement instrumentation with an emphasis on field-based feedback of measurement validity in lieu of post-processing of samples or data at a lab or office location. The scientific future of snow measurement instrumentation thereby requires a synthesis between science and engineering principles that takes into consideration geophysics and the physics of device operation.
Some research achievements in satellite remote sensing in Tibetan Plateau are described in this paper. It mainly covers remote sensing of the surface characteristic parameters, remote sensing of vegetation parameters, and remote sensing of snow parameters. Besides those, the problems demanding prompt solution about the applications of satellite data over Tibetan Plateau are given.
The extent, and variability of seasonal snow cover are important parameters in the climate system. Changes in snow cover may provide an indicator of global climatic trends and are of considerable practical significance. The question of the most suitable indices of changes in snow cover conditions, in terms of their use for change detection and for monitoring applications, is discussed. The use of passive microwave-derived estimates of snow cover extent and water equivalent for continental and regional-scale mapping is illustrated. Problems in interpreting the microwave signatures, as well as difficulties in comparing such data to ground observations, are also noted.
Up to now analyses have focused primarily on trends in Northern Hemisphere snow extent based on monthly averages using the NOAA weekly snow charts 1972-present, or on station data spanning 50-100 years. However, the latter are generally less readily available, or accessible. An overview is provided of current information on recent hemispheric trends and, for the former USSR, the relationship of changes in snow depth, to variations in temperature and precipitation since the late nineteenth century are described, based on newly available station records. Interpretation of these changes and comparisons with other records are presented.
Model projections of changes in snow cover conditions and associated snowmelt runoff that may occur as a result of greenhouse gas-induced warming are discussed for several mountain regions. Long-term station records of snow depth variability provided a valuable context within which such modeling results can be examined.
In Alpine regions, snow is a predominant environmental factor. High accurate snow monitoring in the Alpine Region is of great importance as temporal and spatial variations in snow coverage. It is required for various purposes such as meteorological modelling, climate studies, snow mapping estimation of stored water equivalent or snowmelt runoff prediction. In contrast to conventional in situ snow observations, remote sensing data regularly provide spatial snow cover information which can be used for climate induced studies on snow cover variability. The main objectives of this study are to assess the accuracy of chronological sequences derived from fractional snow cover maps as well as to detect and analyze temporal and spatial variability patterns within the Alpine Region based on different statistical applications. Time series of more than 20 years (1985 - 2007) are used to derive spatial and temporal snow cover dynamics.
Large areas on earth are covered permanently or seasonally by snow, ice sheets, or sea ice. The widespread presence of snow and ice increases drastically the surface albedo of these areas and significantly influences climate. Consequently, variations of snow and ice cover are good indications of climatic change. A knowledge of the distribution and abundance of snow and ice are critical to such practical concerns as water supply, avalanche forecasting and routing of ships over ice-infested seas. It is often difficult to get ground data over such inaccessible, large and highly variable areas. Remote sensing from space is a good candidate for their study because satellite data can provide spatial and temporal coverage on a global scale.
This article reports recent progress in monitoring snow area and snow depth over the U.K. using NOAA AVHRR data. A series of transportable programmes have been developed and updated for data calibration and navigation. The establishment of a database registered to the British National Grid enables the generation of subsequent thematic overlays for any selected area. Results of snow area mapping and estimation have been validated by means of high-resolution Landsal TM data and data from U.K. Meteorological Office Snow Survey Reports. Assignment of snow depth values has also been attempted. The integration of remote sensing and GIS has facilitated the work, and will be further deployed.
Two radar experiments were performed in the French Alps in December 1989 and April 1990. The main objective was to study the signature of snow-covered terrain for different snow conditions. The E-SAR (Band X) from DLR was used, and the data were processed by CNES. The study area was situated at Les Arcs, a ski resort, with elevations ranging from 1600 m to 2600 m. Ground data included snow depth, liquid water content, and snow and air temperature. A SPOT image was available for December and aerial photographs for the following July. In December there was almost no snow, while in April the area was completely snow covered. Because of the highly varying topography, a DEM (digital elevation model) was used to correct the image geometrically and radiometrically. A simple scheme was developed for these corrections. The snow was slightly wet in April and many features appearing on the image are due to the snow itself or to the snow-ground interface. In particular, the ski runs are clearly visible due to the different structure of the packed snow. Only a small area could be compared between December and April due to technical problems in December. It appears that the snow free and snow-covered signatures are very similar for this particular terrain, which is a golf course in summer, that is, smooth wet soil with short grass. These experiments show that geometrical corrections are necessary when studying mountainous area. Furthermore calibrated data are required to compare snow free and snow-covered terrain when the snow is slightly wet and therefore slightly absorbant with respect to the electromagnetic waves.
It is of significance to establish an integrated evaluation system of snow disaster in northern pastoral areas. Based on the NOAA satellite digital images, field observation data, and maps of grassland type and seasonal pastureland, this paper selected the winter and spring pasturelands in Aletai region of Xinjiang as the main area of snow disaster-remote sensing monitoring. With affecting factors of economy and the characteristics of natural resource distribution comprehensively analyzed, and using 3S techniques and field survey information, a fundamental information processing model for integrated evaluation of snow disaster was built up, and snow disaster-spatial evaluation indices and damage level systems were constructed. Natural and social systems and 20 indices were selected in snow disaster evaluation indicator system. Four principal factors, i.e., snow cover area, snow depth on grassland, persistence days of low temperature, and livestock death rate, were used as the grading indices of snow disaster damage level, and the models of snow disaster identification and loss estimatation were set up to quantitatively analyze snow disaster. The results indicated that the system could accurately reflect the details of snow hazard grade and the situation of a disaster in temporal and spatial scales, which would help to carry out the dynamic monitoring and scientific estimatation of big area's snow disaster in pastoral region.
Use of satellite multi-spectral remote-sensing data to map snow and estimate snow characteristics over remote and inaccessible areas requires that we distinguish snow from other surface cover and from clouds, and compensate for the effects of the atmosphere and rugged terrain. Because our space-borne radiometers typically measure reflectance in a few wavelength bands, for climate modeling we must use inferences of snow grain-size and contaminant amount to estimate snow albedo throughout the solar spectrum. Although digital elevation data may be used to simulate typical conditions for a satellite image, precise registration of an elevation data set with satellite data is usually impossible. Instead, an atmospheric model simulates combinations of Thematic Mapper (TM) band radiances for snow of various grain-sizes and contaminant amounts. These can be recognized in TM images and snow can automatically be distinguished from other surfaces and classified into clean new snow, older metamorphosed snow, or snow mixed with vegetation.
A series of 12 monthly flights along a fixed path in Wisconsin and a series of 4 long-range flights over extensive areas of the United States and Canada were performed during 1963 to measure systematically the surface albedo over various types of the earth's surface, using an instrumented light airplane operating at a low level. An approximate total of 24,000 mi. was flown and roughly 210,000 sets of the measurements were processed for this study. Techniques of measurement and data treatment are discussed.
It is shown, and discussed in detail, that the regional differences and seasonal variations of the surface albedo due to thc structure and state of the earth's surface cover are significant. The snow cover is the most important modification of the earth's surface, giving a significantly higher albedo. A quantitative relationship between the increase of surface albedo and snow cover is examined. The surface albedo measured during the flights over typical surface covers over North America, including cities, is presented. The surface covers and their textures over the North American Continent were studied mainly in terms of land use, vegetation type and phenology, soil type, and ground snow cover. The surface albedo values were estimated for various regions of the continent from the flight measurement data, considering the similarity and differences in surface structure among the regions. The resulting seasonal albedo maps of North America are presented and discussed, along with the seasonal variation of the meridional profile of thc continental surface albedo.
Deux méthodes permettant de corriger géométriquement les images AVHRR transmises par satellite sur orbite méridienne, en fonction d'une projection cartographique choisie, font l'objet d'une comparaison. Une méthode utilise des éphémérides de grande qualité et tient compte de l'aplatissement de la Terre alors que l'autre méthode présume une orbite circulaire fondée sur des paramètres orbitaux qui ne sont qu'approximatifs. Dans les deux méthodes, le procédé de correction au moyen de points de contrôle au sol choisis est utilisé aux fins du redressement final des images. Pour produire des images exactes à 1,5 km près, on a eu besoin d'un seul point de contrôle au sol dans la méthodé des éphémérides alors qu'on a dû utiliser sept points de contrôle au sol avec la technique de l'orbite circulaire.SUMMARYTwo methods for geometrically correcting polar-orbiting AVHRR imagery to a selected map projection are compared. One method utilizes high quality emphemeris data and includes earth oblateness while the other assumes a circular orbit based on only approximate orbital parameters. Both methods employ selected ground control point correction for final image rectification. In order to produce images accurate to within 1.5 km only one ground control point was used with the ephemeris method while seven ground control points were used with the circular orbit technique.
Recent work on snow pack energy exchange has involved detailed investigations on snow albedo and attempts to integrate energy energy balance calculations over drainage basins. Along with our better under- standing of the electromagnetic properties of snow, research in remote sensing has become more focused toward estimation of snow pack properties. In snow metamorphism, analyses of the physical processes must now be coupled to better descriptions of the geometry of the snow' s microstincture. The dilution method now appears to be the best direct technique for measuring the liquid water content of snow; work on electromagnetic methods continues. Increasing attention to the chemistry of the snow pack has come with the general focus on acid precipitation in hydrology.
A potentially operational snow mapping scheme applicable to areas of temporary snow cover (e.g. UK) using daily NOAA AVHRR imagery for the production of weekly composite snow area estimates is presented. This technique is based upon an unsupervised multi- spectral classification of AVHRR channels allowing snow/cloud and snow type discriminations together with the accurate location of snowpack margins. Supporting sub-routines have been developed also for the correction of forest masking and shadowing of the snow by
We present a method for calculating the spectral albedo of snow which can be used at any wavelength in the solar spectrum and which accounts for diffusely or directly incident radiation at any zenith angle. For deep snow, the model contains only one adjustable parameter, an effective grain size, which is close to observed grain sizes. A second parameter, the liquid-equivalent depth, is required only for relatively thin snow.In order for the model to make realistic predictions, it must account for the extreme anisotropy of scattering by snow particles. This is done by using the `delta-Eddington' approximation for multiple scattering, together with Mie theory for single scattering.The spectral albedo from 0.3 to 5 m wavelength is examined as a function of the effective grain size, the solar zenith angle, the snowpack thickness, and the ratio of diffuse to direct solar incidence. The decrease in albedo due to snow aging can be mimicked by reasonable increases in grain size (50-100 m for new snow, growing to 1 mm for melting old snow).The model agrees well with observations for wavelengths above 0.8 m. In the visible and near-UV, on the other hand, the model may predict albedos up to 15% higher than those which are actually observed. Increased grain size alone cannot lower the model albedo sufficiently to match these observations. It is also argued that the two major effects which are neglected in the model, namely nonsphericity of snow grains and near-field scattering, cannot be responsible for the discrepancy. Insufficient snow depth and error in measured absorption coefficient are also ruled out as the explanation. The remaining hypothesis is that visible snow albedo is reduced by trace amounts of absorptive impurities (Warren and Wiscombe, 1980, Part II).
Measurement of polar cloud cover is important because of its strong radiative influence on the energy balance of the snow and ice surface. Conventional satellite cloud detection schemes often fail in the polar regions because the visible and thermal contrasts between cloud and surface are typically small. Nevertheless, experts looking at satellite imagery can distinguish clouds from the surface by examining the textural characteristics of the scene.This paper describes an automated pattern recognition algorithm winch identities regions of various surface and cloud types at high latitudes from visible, near-infrared, and infrared AVHRR satellite data. Five spectral features give information about the magnitude of albedos and brightness temperatures, while three textural features describe the variability and `bumpiness' in a scene. The maximum likelihood decision rule is used to classify that region into one of seven surface categories or 11 cloud categories.The algorithm was able to classify 870 training samples with a skill of 84%. Eighteen hundred artificer samples created using a Monte Carlo technique were classified with a skill of 92%, which represents the theoretical limit of class separability using the given features. Both the near-infrared information and the textural information proved to be especially useful in detecting high-latitude cloudiness. The algorithm experienced some difficulty identifying thin stratus over snow and ice and thin cirrus over land and water, situations which also prove difficult for most other cloud detection schemes.When tested on AVHRR imagery from a different date, the algorithm showed a skill of 83% as verified against the analyses of three independent experts. Significant variability was encountered among the experts, underlining the need for an objective routine. This algorithm performed more accurately thin others constructed with alternate feature sets corresponding to various existing cloud detection schemes.
The snow cover is an important parameter for water run-off prediction in high mountain areas. At present its seasonal changes can be monitored regularly by satellite. The paper presents a new method for a more accurate classification of snow from multispectral Landsat data with the aid of a digital terrain model (DTM). A reflectance map derived from the DTM was used to register the Landsat data onto the map applying the cross-correlation function. The DTM makes the independent evaluation of snow possible with respect to elevation, exposure and slope angle. For areas with climatologically similar conditions, it becomes possible to extrapolate the snow cover of the total catchment area supposed to be partially obscured by clouds from a small “cloud free” zone.
This paper will consider the following topics: mapping snow-cover using Landsat data; snow/cloud differentiation using Skylab data; mapping snow-cover using Skylab plus topographic data; areal extent of water bodies; defining water condition with Landsat data; water temperature mapping with Skylab data; water temperature mapping using aircraft MSS data. -from Author
Reviews snowcover monitoring by satellite as basic information for water management and run-off forecasting under the specific demands and conditions of Europe. Discusses appropriate satellite systems, and gives examples from the Alps. - Ray Harris
Areal snow-cover data provided by remote sensing enable the areal water equivalent at the start of the snow melt season to be evaluated. To this end, the time scale in the graphical representation of the snow coverage curves is replaced by the totalized computed daily melt depths. These refer to the seasonal snow cover at the starting date and disregard subsequent snowfalls.
Remote-sensing research in glacial and pro-glacial environments raises several methodological problems relating to the handling of ground and satellite radiometric data. An evaluation is undertaken of the use of ground radiometry to elucidate properties of relevant surface types in order to interpret satellite imagery. It identifies the influence that geometric correction and re-sampling have on the radiometric purity of the resulting data set. Methodological problems inherent in deriving catchment terrain characteristics are discussed with reference to currently glacierized and pro-glacial zones of south-western Switzerland.
The utility of NOAA weather satellite imagery to monitor snowpack depletion in the Alberta Rockies was evaluated for the 1985 run- off season. Snow cover area estimates for the Bow River watershed above Banff were generated from A.E.S. digital NOAA imagery using an ARIES II image analysis system. A series of near real time NOAA-derived snow cover maps provided river forecasters with a better perception of the seasonal progression of snowpack depletion than the low-resolution small scale hard-copy images commonly available.- from Authors
Meteorological satellites have made possible the recognition of cloud formations on scales which are not susceptible to ordinary single observer reporting or discovery by weather chart analysis. These formations are of fundamental importance because they represent many of the meso-scale mechanisms. The most obvious formations are described together with a section on the use of a satellite archive for climatological purposes. -Author
Describes the metamorphism of dry snow, the thermal and mechanical properties of dry snow, the behaviour of blowing snow, the physics of snowmelt, and the metamorphism and percolation through wet snow.-K.Clayton
Interpretation of images is an essential process through which information is obtained. To draw accurate conclusions from the images one needs the fundamentals of image interpretation. The authors introduce a conceptual framework on manual as well as automated image analysis. -M.Alam
Earth resources satellites have provided a means for the timely, efficient, and accurate monitoring of watershed snowcovered area. Several techniques are available for analysing the data ranging from simple photointerpretation to automated digital methods. Although photointerpretation methods are completely suitable and recommended for many applications, it is believed that a highly interactive digital processing system, when combined with a skilled remote sensing applications specialist, will provide maximum operational use of satellite snowcover observations. Several studies have shown that satellite-observed snowcovered area is significantly related to various expressions of seasonal streamflow. The results indicate that snowcovered area could be incorporated into conventional snowmelt runoff prediction methods as an additional index parameter for improving streamflow estimates. Refs.
GEOS and TIROS-N/NOAA-6 satellite data were evaluated both visually and digitally to assess their potential for aiding the process of statewide snow extent mapping in Minnesota. Snow depth vs. scene radiance correlations were calculated using GEOS image data for four cloud-free dates covering the open-agricultural area. Various data normalization techniques were employed, with resulting R2 values ranging up to 0.88. The optimum normalization technique yielded R2 values averaging 0.71.-from Authors
The Skylab EREP S192 Multispectral Scanner data have provided for the first time an opportunity to examine the reflectance characteristics of snowcover in several spectral bands extending from the visible into the near-infrared spectral region. The analysis of the S192 im-agery and digital tape data indicates a sharp drop in reflectance of snow in the near-infrared, with snow be-coming essentially non-reflective in Bands 11 (1.55-1.75 urn) and 12 (2.10-2.35 vim). Two potential applica-tions to snow mapping of measurements in the near-infra-red spectral region are possible: (1) the use of a near-infrared band in conjunction with a visible band to distinguish automatically between snow and water droplet clouds; and (2) the use of one or more near-infrared bands to detect areas of melting snow.
The spatial and spectral characteristics of the GOES visible and infrared images are examined. From that analysis, a scheme is developed which identifies and separates the following classes: clear skies/no snow cover, clear skies/snow cover and clouds. Clouds are then classified as high or low broken clouds and overcast. The scheme is tested for various weather situations. Comparison of the classification results with reports from ground synoptic stations and maps reflect an average accuracy of approximately 72%, and a higher accuracy (87%) when high or low broken clouds and overcast are considered as one class (i.e., clouds). The differentiation between clouds and snow, or no snow-covered ground has been found to be very satisfactory, even in cases of temperature inversions.
The NOAA 2 environmental satellite and subsequent environmental satellites provide daily coverage of the earth in the visible (0.6–0.7 μm) and thermal (10.5–12.5 μm) spectral bands. The ground resolution of the very high resolution radiometer is 1 km at nadir. This improved resolution in the visible spectral band permits more detailed observations of snow features than were possible with the previous operational satellites. A visible band image from February 11, 1973, depicts the heavy snow cover in considerable detail in areas from Alabama to North Carolina. Digitized reflectance data from the study area have been compared with prestorm bare ground digitized reflectance data of February 6, 1973, to examine the relation of snow reflectivity to snow depths. A regression analysis of greatest brightness versus greatest snow depth for 201 data pairs produced a power correlation coefficient of 0.86. Reflectance values from snow deeper than 30 cm tended to be uniformly high; values from snow less than 30 cm tended to increase rapidly as snow depth increased.
A long-standing problem in satellite meteorology has been the discrimination of low clouds from snow cover. The basic difficulty is that clouds and snow have very similar radiometric properties in regions of the electromagnetic spectrum most often used for observation. In the visible window (near 0.5 micrometer) both clouds and snow have high albedos; in the 11 micrometer infrared window both clouds and snow have high emittances. Thus, low clouds and snow, which have similar thermometric temperatures, present little contrast in visible or 11 micrometer infrared satellite images. In the 3.7 micrometer window, however, clouds and snow can have different radiometric properties.-Authors
Estimates of snow cover area (SCA) from analyses of operational environmental satellite data have been produced since 1974. Daily, and even half hourly, images monitor changes in the SCA where continuous surveillance of the snowpack is necessary to accurately predict runoff and flood potential. A case of rapid melt of an extensive, but shallow, snow cover in Arizona is examined.
Using photographic terminology for channel 3 pictures in sunshine, one notes that most ice clouds appear black and that cloud shadows are equally dark, but water droplet clouds appear in all shades. These shades also vary greatly with the direction of sunshine relative to the line of sight because scatter is almost entirely by diffraction. Droplets and ice crystals larger than about 10 fan absorb the incident radiation almost completely and it does not penetrate through clouds unless there exist plenty of unobstructed ray paths through the clouds. The reflection from a water surface is almost metallic in intensity so that glint completely saturates the radiometer. There is no evidence of comparable reflection from ice. All snow-covered surfaces, including sea ice, appear black. Stratus cloud shows large variations in reflectance depending on the state of the convection in it which brings very small droplets to the surface. Small particle size causes some contrails and orographic cirrus to appear white although most appear black; old cumulonimbus tops develop pale areas when gravitational settling leaves predominantly very small crystals at the top while still active areas remain black.
Data from the 3.7-micron channel of the AVHRR are contaminated by interference which appears in the images as a herring-bone pattern. A measure of the quality of the imagery, estimated from the NOAA-7 and NOAA-9 instruments' calibration data, is used to gain an idea of their progressive deterioration over each satellite's lifetime. The spectral properties of the Fourier transform of the data are examined and used to attenuate the unwanted components of the signal to an acceptable level. Two simple restoration algorithms are applied to a particularly severely contaminated NOAA-7 image and the resulting images presented.
The AVHRR (Advanced Very High Resolution Radiometer) Processing scheme Over Land, cLbud and Ocean (APOLLO) is used to extract surface and cloud parameters from satellite data. Before these parameters can be computed, it is necessary to distinguish between land and ocean surfaces and to apply algorithms for the detection of partially cloudy and cloud-filled pixels. In regions with seasonal or permanent snow and ice coverage the separation of clouds becomes much more difficult or often impossible. For this reason, and to find cloud-free and partly cloudy snow and ice pixels,- a day-time algorithm has been developed which uses all five AVHRR channels as follows: The threshold testing of the reflected part of channel-3 radiance leads to a definite distinction between snow/ice and water clouds due, to the clouds much higher reflectivity at 3.7 μm. The detection; of sea ice is based on threshold tests of visible reflectances and, in particular, of the temperature difference between channels-4 and -5. Snow is identified if a high visible reflectance is combined with a low reflectance in channel-3 and with a ratio of channel-2 to channel-1 reflectances similar to that of a cloud. The latter criterion is also mostly suitable to distinguish between snow-covered and snow-free ice areas. Some tests of this algorithm applied to AVHRR data from the 1987 Baltic Sea ice season have shown reasonable classification results with the exception of a few areas with ice clouds or with ice topped water clouds.
Utilizing data from three winter seasons, snow cover distributions in the relatively flat terrain of the Missouri and Upper Mississippi River basins are mapped from satellite photography. Techniques are developed for identifying snow cover and for differentiating between snow and cloud. Comparisons between snow data observed by satellites and snow data conventionally acquired indicate that, in almost all cases, snow can be distinguished from cloud on the bases of current cloud reports, pattern continuity, landmark identification, and texture. Snow cover can be mapped from Tiros or Essa resolution photography with an accuracy of approximately ±20 miles, often providing a more detailed mapping than can be obtained from conventional station networks. Qualitative estimates of snow depth can also be obtained from satellite photography. In nonforested areas, reflectivity increases with snow depth, until an accumulation of about 4 to 6 inches is reached ; after that, the reflectivity remains unchanged. In forested areas, no discernible relationship between reflectivity and snow depth is observed.
The penetration of radiation into snow is assumed to follow Fick's second law of dif- fusion with a term for simultaneous absorption. From this, the distribution of radiant energy in a deep homogeneous snow cover, a laminated snow cover, and a snow cover with an absorbing sur- face beneath is deduced. An equation for albedo is also derived. The penetration phenomenon is then analyzed in terms of the physical processes occurring and formulated as a random walk process. After a correction is made for a certain amount of nondiffuse radiation in the snow, more accurate equations for energy distribution and albedo are obtained. These results are applied to the theory of radiation measurement in snow. After experimental procedures were established on the Blue Glacier summer firn, field data were collected from winter snow at Alta, Utah, and used to check the theoretical equations for albedo and transmission to a black surface. The agreement of theory and experiment is very satisfactory.
Albedos and equivalent blackbody temperatures from the 5-channel Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA-7 satellite have been examined for two consecutive passes of the satellite over the St. Louis area when snow was on the ground. The albedo difference for channel 1 (visible) between St. Louis and a typical rural area was − 16%, much larger than urban-rural albedo differences found in previous studies under snow-free conditions. The albedo difference between channel 1 and channel 2 (near-infrared) appears to be a better indicator of the snow boundary than either channel alone. The channel 4 (11 μm) equivalent blackbody temperature difference between the warmest part of St. Louis and a typical rural area was 3 K during the day and 2.5 K at night, about the same as urban-rural temperature differences found under snow-free conditions. Day-night temperature differences were nearly the same in urban as in rural areas. Largest day-night differences were found in forested portions of the snow-covered area. Slightly larger (about 0.5 K) day-night differences were observed in the commercial-industrial area of St. Louis than in the residential area. This may be caused by residential heating. Finally, hot industrial targets were easily observed in 3.7 μm images. These targets may be useful for accurate registration of AVHRR images.
The results presented in this study indicate the possibility of seasonal runoff prediction when satellite-derived basin snow-cover data are related to point source river discharge data for a number of years. NOAA-VHRR satellite images have been used to delineate the areal extent of snow cover for early April over the Indus and Kabul River basins in Pakistan. Simple photo- interpretation techniques, using a zoom transfer scope, were employed in transferring satellite snow-cover boundaries onto base map overlays. A linear regression model with April 1 through July 31 seasonal runoff (1974-1979) as a function of early April snow cover explains 73% and 82% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. The correla- tion between seasonal runoff and snow cover is significant at the 97% level for the lndus River and at the 99% level for the Kabul River. Combining Rango et al.'s (1977) data for 1969-73 with the above period, the April snow cover explains 60% and 90% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. In an attempt to improve the Indus relationship, a multiple regression model, with April 1 through July 31, 1969-79, seasonal runoff in the Indus River as a function of early April snow-covered area of the basin and concurrent runoff in the adjoining Kabul River, explains 79% of the -variability in flow. Moreover, a significant reduction (27%) in the standard error of estimate results from using the multi-variate model. For each year of the study period, 1969-79, a separate multiple regression equation is developed dropping the data for the year in question from the data-base and using those for the rest of the years. The snow cover area and concurrent
In spite of the Impressive volume of ice on the earth, the seasonal snow cover is more important in particular in industrialized countries of the Northern Hemisphere. It constitutes a valuable natur al resource, but has also adverse effects like avalanches and loads on structures. Measurements of the occurrence, depth and water equivalent of snow are regularly car ried out but need further improvement, espe cially for operational river flow forecasts. Systematic gathering of snow data in esta blished centres facilitates their use and the evaluation of return periods for extreme events. Efficient large scale monitoring of the seasonal snow cover becomes possible by an increasing application of remote sensing from satellites.
This paper discusses the measurement of important snow properties using electromagnetic radiation. Snow areal extent can be measured using manual, optical, electronic, or digital techniques from data supplied by visible and near-visible light sensors carried on Earth resources and meteorological satellites, but these techniques cannot routinely detect snow under clouds or a forest canopy. Gamma-ray techniques used at stations or from low-flying aircraft permit measurement of water equivalent of snow (depth times density). Active or passive microwave systems may permit this to be done over larger areas, but the physics of this possible technique is not yet sufficiently understood. Wetness or temperature, of a snow surface is measurable with thermal infrared radiometers; wetness throughout a snow pack may be measurable with microwave radiometers. Much research needs to be done on the electrical (including scattering) properties of snow before efficient, all-weather, remote-sensing systems can be designed.
A method is proposed to determine the areal distribution of the maximum seasonal water equivalent of snow in mountain basins. Because a sufficient density of direct measurements is not available in remote, inaccessible areas, the accumulation of snow at the start of the melting season is reconstituted. The disappearance of snow in grid units is monitored using Landsat data, the number of degree days necessary to melt the snow is totalized, and the water equivalent of the snow melted is calculated. The reconstituted water equivalent values can be used to correct precipitation measurements in winter. Together with limited point measurements these new areal data can improve the evaluation of snow reserves for seasonal discharge forecasts.
The Advanced Very High Resolution Radiometer (AVHRR) on the NOAA 7 satellite acquires 1-km spatial resolution data in "split window" channels at 10.8 and 11.9 #m. Data from these spectral channels may be used to estimate surface temperature and the atmospheric correction to radiation from the earth's surface. Analysis of a data set from July 1981 shows that (1) there is satisfactory agreement between the equation resulting from radiative transfer theory and the atmospheric correction algorithm as obtained by analysis of an area of incipient cloud street formation; (2) agreement is also satisfactory between this algorithm and the statistically derived NOAA algorithm used to obtain sea surface temperatures from the satellite data (However, the comparison assumes the NOAA algorithm is valid outside its range of derivation.); (3) in areas of cloud street formation, variations of atmospheric moisture produce radiance temperature differences of order 2-3øC, which if neglected would cause errors in the derivation of surface thermal characteristics. This meteorological variation over distances of 5-10 km would not be inferred from conventional radiosonde measurements or from lower-resolution satellite soundings. ,
Air temperature appears to be the most important factor in a snowmelt-runoff model. In an alpine representative basin, an automatic meteorological station situated at 2420 m above sea level greatly improves the evaluation of temperatures throughout the whole range of elevation. If temperatures must be extrapolated from a distant station, the uncertain vertical lapse rate affects the results. The range of discharge forecasts depends on a quick transmission of data and on temperature forecasts. It is also possible to assume temperatures of several months ahead, to adjust the depletion curves of the snow coverage and to simulate the resulting runoff.
Problems of accurate discrimination between snow and cloud, together with the detection of the snow pack boundary, have handicapped the use of satellite data in operational snow-cover mapping systems. A technique, involving an unsupervised clustering procedure, is described which allows the removal of cloud areas using NOAA-9 Advanced Very High Resolution Radiometer (AVHRR) channel-1, channel-3 and channel-4 data in conditions of recent snow lie and a difference channel (channel-2 —channel-1 with channel-3 and channel-4) during periods of advanced snow melt. Accurate delineation of snow extent is provided by the techniques if these specified snow conditions are taken into account. A method for the identification of areas of marginal snow melt is also presented, based on comparisons with Landsat Thematic Mapper data. The classifications also enable the determination of snow areas influenced by cloud shadows and conifer forest in addition to separating areas of differing snow depth and percentage cover.
Use of near-infrared data in conjunction with reflected visible radiation appears to allow detection of melting snow and ice. Under normal conditions , snow and ice are highly reflective in both the visible and near-infrared areas of the electromagnetic spectrum. Under melting conditions , however , near-infrared radiation is strong- ly absorbed, whereas visible radiation is strongly reflected. Com- parison of simultaneous visible and near-infrared imagery from the Nimbus III satellite provides a method for monitoring the melting of snow and ice that may be applied to snowpack-runoff prediction, flood forecasting, and lake n,avigation. Several examples (Lake Winnipeg, the Alps, and northwest Canada) are provided to illustrate the use of this spectral difference.
Differentiation between cloud cover and snow surfaces using remotely sensed data is complicated by the similarity of their radiative temperatures, and also by their similar reflectances at visible wavelengths. A method of cloud analysis over snow-covered regions is presented, using 1.51-1.63 micron data from an experimental sensor on board a U.S. Air Force Defense Meteorological Satellite Program platform. At these wavelengths, snow appears relatively 'black' while clouds are highly reflective. The spatial structure of the 1.51-1.63 micron reflectivity fields over a continuous snow surface are examined. Plots of mean reflectance against coefficients of variation for 4 x 4 pixel areas reveals a cluster of points have low reflectivity and low variability, corresponding to snow-covered (cloud free) areas, and a similar cluster with high reflectances corresponding to 100 per cent cloud cover. For the case of a single layered cloud, the radiances associated with partially filled fields of view are also inferred.
Six different automated classifiers, three of which were suggested by previous studies, were tested on a special data base of DMSP SSC (near IR channel, 1.51 to 1.63 micrometers) and OLS (visible channel, 0.4 to 1.0 micrometers, and IR channel, 10.2 to 12.8 micrometers). The classifiers were used to distinguish water clouds, ice clouds, snow cover, and other cloud-free surfaces. A test sample of 433 known cases was found with the aid of the AFGL man-Computer Interactive Data Access System.