Accuracy assessment of MODIS/Terra snow cover product for parts of Indian Himalayas

Snow cover mapping is important for snow and glacier-related research. The spatial and temporal distribution of snow cover area is a fundamental input to the atmospheric models, snowmelt runoff models and climate models, as well as other applications. Daily snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite were retrieved for the period between 2004 and 2007, and pixels in these images were classified as cloud, snow or snow-free. These images have then been compared with ground snow depth (SD) measurements from the four observatories located at different parts of Himalayas. Comparison of snow maps with in situ data showed good agreement with overall accuracies in between 78.15 and 95.60%. When snow cover was less, MODIS data were found to be less accurate in mapping snow cover region. As the SD increases, the accuracy of MODIS snow cover maps also increases.     

Snow and glacier melt runoff model to estimate hydropower potential

Himalayan region has high concentrations of mountain glaciers. Large extent of this region is covered by seasonal snow during winter. Runoff generates from melting of these snow and glaciers is one of the important sources of water for the Himalayan Rivers. Glaciers and snowfields are distributed throughout the Himalayas and form a source of numerous streams. Due to steep slopes, all such streams have potential sites for hydropower generation. If this potential is fully utilized, it will help in generating power from environmentally friendly Run-of-River (RoR) hydropower stations. Considering these aspects, a stream flow simulation model was developed for small streams. This will help in estimation of average seasonal unrestricted hydropower potential of snow and glaciated streams for winter, summer, monsoon and autumn seasons. Information generated through remote sensing technique as glacier, permanent snow cover, seasonal snow cover, altitude of snow and glaciers were used in conjunction with daily maximum and minimum temperature, rainfall and discharge. The model was developed for Malana nala located in Parbati River basin near Kullu in Himachal Pradesh. It was validated at adjacent Tosh nala in the same basin. Seasonal runoff computed from the model is comparable with observed data for all seasons except Monsoon. Good results in autumn, winter and summer seasons demonstrates usefulness of runoff model to assess hydropower potential of snow and glaciated streams and therefore, the model was applied to ungauged Sorang Gad and Kirang Khad. In winter runoff was estimated as 1.8 and 1.69 cumecs for Kirang Khad and Sorang Gad, respectively. This is important, as viability of hydropower station depends upon winter stream runoff. These results suggest that the model is useful tool to assess initial estimate of hydropower potential for large number of snow and glaciated streams, for which no hydrological data is available.     

Analysis of snow cover and climatic variability in Bhaga basin located in western Himalaya

Bhaga Basin has complex mountainous terrain; little study has been done on the spatial and temporal characteristics of snow cover in the region. The Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day snow cover products between 2001 and 2012 for winter period (November–April) have been used to study the variation in snow cover area (SCA). The statistical analysis based on non-parametric Mann Kendall and Sen’s slope methods have been used for detecting and estimating trends for climatic variables (temperature and snowfall) and SCA for winter period. Results of statistical analysis indicate rise in minimum temperature (0.02 °C year^?1) and fall in maximum temperature (0.17 °C year^?1). It also shows decrease in mean seasonal snowfall (0.07 cm year^?1). The seasonal SCA was found to decrease at the rate of 0.002% year^?1. This study indicates that the climate change is probably one of the major causes for depleting SCA.     

Evaluating MODIS snow products for modelling snowmelt runoff: Case study of the Rio Grande headwaters

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM + ) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS’ coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered-Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCA_MG), with reference SCA maps (SCA_REF) generated from binary classification of TM imagery. SCA_MG showed strong agreement with SCA_REF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCA_REF and SCA_MG ranged between −2.4% and 4.7%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCA_REF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM simulation results from the SCA_MG runs yielded the best results of all the MODIS products and only slightly underestimated discharge volume (between 7 and 11% of measured annual discharge). SRM simulations that used SCA derived from Collection 6 NDSI Snow Cover also yielded promising results, with efficiency coefficients ranging between 0.73 and 0.91.In conclusion, we recommend that when simulating snowmelt runoff from small basins (<4000 km²) with SRM, we recommend that users select either canopy-corrected MODSCAG or create their own site-specific products from the Collection 6 MOD10A1 NDSI.     

Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes. The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover. We used 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) datasets during 2001–2010 to examine the snow–vegetation relationships, specifically, (1) the influence of snow melting date on vegetation green-up date and (2) the effects of snow cover duration on vegetation greenness. The results showed that the alpine vegetation responded strongly to snow phenology (i.e., snow melting date and snow cover duration) over large areas of the Qinghai-Tibetan Plateau. Snow melting date and vegetation green-up date were significantly correlated (p < 0.1) in 39.9% of meadow areas (accounting for 26.2% of vegetated areas) and 36.7% of steppe areas (28.1% of vegetated areas). Vegetation growth was influenced by different seasonal snow cover durations (SCDs) in different regions. Generally, the December–February and March–May SCDs played a significantly role in vegetation growth, both positively and negatively, depending on different water source regions. Snow's positive impact on vegetation was larger than the negative impact.     

Using cosmo-skymed images to detect wet snow cover on the Kraków Ice Field,King George Island,Antarctica

This work analysed the spatio-temporal variation of snow cover on the Kraków Ice Field, located in the King George Island, Antarctica. High spatial resolution images of COSMO-SkyMed were used in this study. These X-band images are vertically and horizontally co-polarized and their intensity data were converted to amplitude (dB). The COSMO-SkyMed images were classified by a minimum distance algorithm and post-classified based on knowledge of adjacency relationships of snow zones. Hypsometric, slope, aspect and solar radiation maps to support the interpretation of backscatter patterns in the COSMO-SkyMed images. Three radar zones were classified in these images: percolation, slush and wet snow radar zone. Positive surface air temperatures and rainfall events, registered from a meteorological station, lead to increase in wet snow and slush zones. The COSMO-SkyMed images and minimum distance algorithm were adequate to discriminate the snow cover and to assess the supraglacial melting pattern during the ablation season in the study area.     

基于遥感数据的积雪信息提取:以新疆北疆为例(英文)

This paper proposes an applicable approach for snow information abstraction in northern Xinjiang Basin using MODIS data. Linear spectral mixture analysis (LSMA) was used to calculate snow cover fractions (SF) within a pixel, which was used to establish a regression function with NDSI. In addition, 80 snow depths samples were collected in the study region. The correlation between image spectra reflectance and snow depth as well as the comparison between measured snow spectra and image spectra was analyzed. An algorithm was developed for snow depth inversion on the basis of the correlation between snow depth and snow spectra in the region. The results indicated that the model of SF had a high accuracy with the mean absolute error 0.06 tested by 26 true measured values and the validation for snow depth model using another dataset with 50 sampling sites showed an RMSE of 1.63. Our study showed that MODIS data provide an alternative method for snow information abstraction through development of algorithms suitable for local application. Supported by the National Natural Science Foundation of China (No.70361001).     

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km² river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.     

Impact of climatic parameters on statistical stream flow sensitivity analysis for hydro power

Large area of Himalayas covered with seasonal snow during winter are rapidly changing during summer, significantly affects the stream flow of many rivers originating from Himalayas. This necessitates the efficient time series monitoring of seasonal snow cover in the rugged mountainous region throughout the winter and summer periods for weekly/monthly as well as seasonal forecast of stream run-off for water management and other developmental activities.     

MTSAT-2静止气象卫星中国区域雪盖监测

利用MTSAT-2静止气象卫星数据开展了中国区域的雪盖监测研究,结合MODIS雪盖产品及站点雪深观测数据对判识结果进行对比分析和验证。首先,根据MTSAT-2静止气象卫星数据特点,进行角度效应校正及多时相数据合成,以减少云对图像的影响;其次,根据多个雪盖判识因子建立中国区域雪盖判识算法;最后,对比分析2011年1月份MTSAT-2和MODIS雪盖判识结果,并使用站点观测数据进行精度验证。研究表明:(1)MTSAT-2雪盖判识受云影响比例约30%,MODIS雪盖产品受云影响比例约60%,MTSAT-2去云效果明显。(2)无云情况下,MTSAT-2雪盖判识和MODIS雪盖产品判识精度均高于92%;有云覆盖时,MTSAT-2判识精度约65%,优于MODIS雪盖产品35%的判识精度。(3)MTSAT-2静止气象卫星在保持高积雪判识精度的前提下,可以更有效减少云对雪盖判识影响,实时获取更多地表真实信息。该研究对中国区域雪盖信息准确监测、气候变化研究以及防灾减灾等具有重要意义。     

1979年—2019年兴凯湖湖冰物候变化的被动微波遥感监测

季节性冻结与消融的湖冰是气候变化的重要指示器。本文以兴凯湖为例,基于1979年—2019年的被动微波遥感数据获取了兴凯湖的冻融日期,用2000年—2019年的中等分辨率成像光谱仪MODIS(Moderate-resolution Imaging Spectroradiometer)数据进行了验证,并用气候数据分析了湖冰物候变化的原因。结果表明被动微波与MODIS遥感数据在湖冰物候提取方面具有较好的一致性,也即MODIS的验证结果表明用低频被动微波亮度温度数据获取湖冰物候的方法是可行的,结果也是可靠的。平均而言,兴凯湖湖冰每年11-13左右开始冻结,11-23左右完全冻结,湖冰冻结持续时间9.80 d;次年04-23左右湖冰开始消融,04-30左右湖冰完全消融,消融持续时间8.03 d;湖冰完全封冻时间150.50 d,湖冰覆盖时间168.03 d。过去41 a,兴凯湖开始冻结日期没有明显变化,完全冻结日期平均推后了0.19 d/a,开始消融日期和完全消融日期分别提前了0.16 d/a和0.13 d/a,湖泊完全封冻时间和湖冰覆盖时间分别缩短了12.71 d和2.87 d。湖冰冻结日期推后与风速增大密切相关,消融日期提前和湖冰持续时间缩短与气温升高显著相关。     

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

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

基于MODIS数据的天山北坡经济带雪深反演研究

根据实测雪深不同的积雪反射光谱值分析雪深和积雪反射率的关系,探讨利用MOD IS数据反演雪深的可行性和反演雪深的MOD IS最佳通道。利用2004年12月至2005年3月的MOD IS数据,以天山北坡经济带为实验区,结合该区域实测雪深数据、气象台地面雪深观测记录建立雪深反演数学模型,分析雪深空间分布特征对农业开发具有重要意义。     

基于MODIS数据的雪面温度遥感反演(英文)

On the basis of simplification of the Planck function in a low temperature range, this paper revises the practical split-window algorithm and presents a method for retrieving snow surface temperature （Ts） based on MODIS data in the middle-latitude region. The application of this method in Qinghai Lake region reveals that it is feasible for the retrieval of Ts. Results of correlation analysis indicate that there was strong negative relationship between Ts and altitude. By analyzing three typical areas in which land cover was relatively homogenous, this paper discusses the relationship between Ts and normalized difference snow index （NDSI） and then presents a new concept named ＂NDSI-Ts space＂.     

Accuracy assessment of four cloud-free snow cover products over the Qinghai-Tibetan Plateau

Four up-to-date daily cloud-free snow products – IMS (Interactive Multisensor Snow products), MOD-SSM/I (combination of the MODIS and SSM/I snow products), MOD-B (Blending method basing on the MODIS snow cover products) and TAI (Terra–Aqua–IMS) – with high-resolutions over the Qinghai-Tibetan Plateau (QTP) were comprehensively assessed. Comparisons of the IMS, MOD-SSM/I, MOD-B and TAI cloud-free snow products against meteorological stations observations over 10 snow seasons (2004–2013) over the QTP indicated overall accuracies of 76.0%, 89.3%, 92.0% and 92.0%, respectively. The K_hat values of the IMS, MOD-SSM/I, MOD-B and TAI products were 0.084, 0.463, 0.428 and 0.526, respectively. The TAI products appear to have the best cloud-removal ability among the four snow products over the QTP. Based on the assessment, an I-TAI (Improvement of Terra–Aqua–IMS) snow product was proposed, which can improve the accuracy to some extent. However, the algorithms of the MODIS series products show instability when identifying wet snow and snow under forest cover over the QTP. The snow misclassification is an important limitation of MODIS snow cover products and requires additional improvements.     

多样地表和大气状况下的MODIS数据云检测

云覆盖作为天气和气候变化的一个重要因子,对地表-大气能量平衡和水循环有着重要的影响,因此,快速、准确地利用卫星遥感技术检测云覆盖具有重要的实用价值和科学意义。利用卫星遥感数据,尤其是常用的Moderate Resolution Imaging Spectroradiometer(MODIS)影像数据,因其具有较高的光谱和时间分辨率,以及2330 km扫描幅宽,为大范围实时、准确地进行云检测提供了可能。目前,基于MODIS数据发展了大量的云检测方法,但因地表类型的多样性和大气状况(如空气污染和沙尘事件等)的复杂性,目前已有的云检测方法,检测精度通常具有较大的不确定性,且针对不同地表和大气状况缺乏普适性,同时也缺乏对检测精度的定量化评估。因此,本文首先比较了常用的3种云检测算法,并基于前人经验提出了两种改进方法(方法4和方法5),首先区分出云和冰雹,摒弃了不稳定的亮温波段,两种算法均适用于复杂地表和大气状况的云检测算法。结果显示,方法5可以较好地应用于基于MODIS数据的云检测,总体精度达92.6±7%,改进了现有基于MODIS数据的云检测算法;方法4平均总体精度82.9±13%,虽然精度相对较低,但云残留少,适合作为对云敏感度高的研究工作的云检测方法。     

中国西部积雪SMMR微波遥感的评价与初步应用

本文根据1978—1987年OSL的可见光影像及地面台站积雪记录,评价了NASA的用微波亮温反演积雪深度的算式。还根据DEM资料,用GIS技术把中国西部分成高山、高原、低山、丘陵及盆地五个单元,分别求得各区域订正算式,并以此计算了中国西部1978—1987年各季的平均雪盖率与雪量及它们的年际变化,为本区积雪影响东亚气候研究,提供了可靠基础资料。     

Estimation of Snow Cover Area,Snow Physical Properties and Glacier Classification in Parts of Western Himalayas Using C-Band SAR Data

Snow physical properties, snow cover and glacier facies are important parameters which are used to quantify snowpack characteristics, glacier mass balance and seasonal snow and glacier melt. This study has been done using C-band synthetic aperture radar (SAR) data of Indian radar imaging satellite, radar imaging satellite-1 (RISAT)-1, to estimate the seasonal snow cover and retrieve snow physical properties (snow wetness and snow density), and glacier radar zones or facies classification in parts of North West Himalaya (NWH), India. Additional SAR data used are of Radarsat-2 (RS-2) satellite, which was used for glacier facies classification of Smudra Tapu glacier in Himachal Pradesh. RISAT-1 based snow cover area (SCA) mapping, snow wetness and snow density retrieval and glacier facies classification have been done for the first time in NWH region. SAR-based inversion models were used for finding out wet and dry snow dielectric constant, dry and wet SCA, snow wetness and snow density. RISAT-1 medium resolution scan-SAR mode (MRS) in HV polarization was used for first time in NWH for deriving time series of SCA maps in Beas and Bhagirathi river basins for years 2013–2014. The SAR-based inversion models were implemented separately for RISAT-1 quad pol. FRS2, for wet snow and dry snow permittivity retrieval. Masks for layover and shadow were considered in estimating final snow parameters. The overall accuracy in terms of R² value comes out to be 0.74 for snow wetness and 0.72 for snow density based on the limited ground truth data for subset area of Manali sub-basin of Beas River up to Manali for winter of 2014. Accuracy for SCA was estimated to be 95 % when compared with optical remote sensing based SCA maps with error of ±10 %. The time series data of RISAT-1 MRS and hybrid data in RH/RV mode based decompositions were also used for glacier radar zones classification for Gangotri and Samudra Tapu glaciers. The various glaciers radar zones or facies such as debris covered glacier ice, clean or bare glacier ice radar zone, percolation/refreeze radar zone and wet snow, ice wall etc., were identified. The accuracy of classified maps was estimated using ground truth data collected during 2013 and 2014 glacier field work to Samudra Tapu and Gangotri glaciers and overall accuracy was found to be in range of 82–90 %. This information of various glacier radar zones can be utilized in marking firn line of glaciers, which can be helpful for glacier mass balance studies.     

How much variation in land surface phenology can climate oscillation modes explain at the scale of mountain pastures in Kyrgyzstan?

Climate oscillation modes can shape weather across the globe due to atmospheric teleconnections. We built on the findings of a recent study to assess whether the impacts of teleconnections are detectable and significant in the early season dynamics of highland pastures across five rayons in Kyrgyzstan. Specifically, since land surface phenology (LSP) has already shown to be influenced by snow cover seasonality and terrain, we investigated here how much more explanatory and predictive power information about climatic oscillation modes might add to explain variation in LSP. We focused on seasonal values of five climate oscillation indices that influence vegetation dynamics in Central Asia. We characterized the phenology in highland pastures with metrics derived from LSP modeling using Landsat NDVI time series together with MODIS land surface temperature (LST) data: Peak Height (PH), the maximum modeled NDVI and Thermal Time to Peak (TTP), the quantity of accumulated growing degree-days based on LST required to reach PH. Next, we calculated two metrics of snow cover seasonality from MODIS snow cover composites: last date of snow (LDoS), and the number of snow covered dates (SCD). For terrain features, we derived elevation, slope, and TRASP index as linearization of aspect. First, we used Spearman’s rank correlation to assess the geographical differentiation of land surface phenology metrics responses to environmental variables. PH showed weak correlations with TTP (positive in western but negative in eastern rayons), and moderate relationships with LDoS and SCD only in one northeastern rayon. Slope was weakly related to PH, while TRASP showed a consistent moderate negative correlation with PH. A significant but weak negative correlation was found between PH and SCAND JJA, and a significant weak positive correlation with MEI MAM. TTP showed consistently strong negative relationships with LDoS, SCD, and elevation. Very weak positive correlations with TTP were found for EAWR DJF, AMO DJF, and MEI DJF in western rayons only. Second, we used Partial Least Squares regression to investigate the role of oscillation modes altogether. PLS modelling of TTP showed that thermal time accumulation could be explained mostly by elevation and snow cover metrics, leading to reduced models explaining 55 to 70% of observed variation in TTP. Variable selection indicated that NAO JJA, AMO JJA and SCAND MAM had significant relationships with TTP, but their input of predictive power was neglible. PLS models were able to explain up to 29% of variability in PH. SCAND JJA and MEI MAM were shown to be significant predictors, but adding them into models did not influence modeling performance. We concluded the impacts of climate oscillation anomalies were not detectable or significant in mountain pastures using LSP metrics at fine spatial resolution. Rather, at a 30 m resolution, the indirect effects of seasonal climatic oscillations are overridden by terrain influences (mostly elevation) and snow cover timing. Whether climate oscillation mode indices can provide some new and useful information about growing season conditions remains a provocative question, particularly in light of the multiple environmental challenges facing the agropastoralism livelihood in montane Central Asia.     

基于MODIS影像的内蒙古草原积雪监测

光学遥感源MODIS具有高光谱分辨率、高时间分辨率、高空间分辨率、全球范围内免费接收等优势,被广泛应用于洪涝、干旱、森林草原火灾、雪灾等自然灾害的动态监测领域。MODIS数据用于内蒙古草原积雪监测,提取积雪信息在国内尚属空白。本文利用MODIS L1B 500m分辨率数据,经过几何校正、去"双眼皮"预处理,根据归一化差分积雪指数(NDSI)算法和综合阈值判别法对内蒙古自治区2008年1月下旬大范围降雪进行积雪信息提取,制作积雪覆盖图。利用内蒙古生态与农业气象中心发布的雪情遥感监测信息验证积雪覆盖图的准确度。验证结果表明,MODIS数据用于大范围积雪监测非常有效。