Resolution Imaging Spectroradiometer Snow Cover Research Articles

Mapping reveals contrasting change patterns of rain-on-snow events in China during 2001–2018

Rain-on-snow (ROS) events can accelerate snow melting, which in turn leads to disasters such as floods, avalanches, and landslides. However, little is known about the characteristics of and changes in ROS events in China. In this study, the temporal and spatial variations of ROS days in China from 2001 to 2018 were investigated using the improved Moderate Resolution Imaging Spectroradiometer snow cover data, and China Meteorological Forcing Dataset. The results suggested that ROS days in China mainly occurred from November to June and were mainly distributed in northern Xinjiang (6.1 days/year), Qinghai-Tibet Plateau (2.8 days/year), northeast China (3.5 days/year), and east China (1.1 days/year). ROS days in each region have different seasonal distribution patterns. In the past 18 years, ROS days have shown a trend of increasing at higher latitudes and decreasing at lower latitudes in China, especially in northern Xinjiang and the Qinghai-Tibet Plateau. ROS days are decreasing below 3000 m in northern Xinjiang (-0.16 days/year) and below 4000 m in the Qinghai-Tibet Plateau (-0.21 days/year), which is explained by the decrease of snow days and rain days. ROS days are increasing at 3000–4000 m altitudes (0.15 days/year) in the Tianshan Mountains and 4000–5500 m altitudes (0.06 days/year) in the eastern Tibetan Mountains, mainly explained by more snowfall converted to rainfall. The ROS days along the Greater Khingan Mountains in Northeast China showed a decreasing trend (-0.12 days/year), and along the Changbai Mountains showed an increasing trend (0.07 days/year). The variation of snow cover days is the dominant factor. East China as a whole shows a decreasing trend (-0.1 days/year), explained by a decrease in snow cover time. In China, ROS days at low altitudes are negatively correlated with annual average temperature, and positively at high altitudes. The changes of ROS were also correlated with Arctic Oscillation and North Atlantic Oscillation. This study reveals the response of ROS to climate change in China, and provides an important reference for coping with the related disasters and changes caused by ROS events.

Application of moderate resolution imaging spectroradiometer snow cover maps in modeling snowmelt runoff process in the central Zab basin, Iran (Erratum)

The <i>Journal of Applied Remote Sensing</i> (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications.

Snow climatology in Lithuania based on the cloud‐free moderate resolution imaging spectroradiometer snow cover product

AbstractSnow cover plays an important role in environmental, hydrological and climate systems. To monitor the inter‐annual and seasonal variation of snow in Lithuania, daily moderate resolution imaging spectroradiometer (MODIS) snow cover products MOD10A1 and MYD10A1 were employed during the period from 2002 to 2018. The main disadvantage of the MODIS sensor is that it is unable to determine the surface conditions under cloud cover. In this study, a four‐step procedure was implemented to remove clouds from the MODIS snow products and estimated the annual and monthly number of snow cover days (SCDs). The steps included a combination of MODIS data from Aqua and Terra satellites, spatial and temporal filtering of cloud covered pixels. Additional daily minimum temperature control filter helped to reduce the misclassification snow errors. The final daily cloud‐free MODIS snow maps showed an overall accuracy of 89% when compared with manual ground observations. The lowest validation scores were determined on the Baltic Sea coast, where snow cover is ephemeral, and the highest scores were in the eastern part of the country where the climate is continental. The MODIS‐based SCD had larger relative errors in autumn, smaller errors in spring and very good agreement with the in situ observations in the middle of winter. Although the generated cloud‐free MODIS snow cover product tended to overestimate the annual SCD on average by 8.5 days, we consider it adequate to describe the inter‐annual and monthly variation of snow cover in Lithuania.

Recurrent Snowmelt Pattern Synthesis Using Principal Component Analysis of Multiyear Remotely Sensed Snow Cover

AbstractSnow acts as a vital source of water especially in areas where streamflow relies on snowmelt. The spatiotemporal pattern of snow cover has tremendous value for snowmelt modeling. Instantaneous snow extent can be observed by remote sensing. Cloud cover often interferes. Many complex methods exist to resolve this but often have requirements which delay the availability of the data and prohibit its use for real‐time modeling. In this research, we propose a new method for spatially modeling snow cover throughout the melting season. The method ingests multiple years of MODerate Resolution Imaging Spectroradiometer snow cover data and combines it using principal component analysis to produce a spatial melt pattern model. Development and application of this model relies on the interannual recurrence of the seasonal melting pattern. This recurrence has long been accepted as fact but to our knowledge has not been utilized in remote sensing of snow. We develop and test the model in a large watershed in Wyoming using 17 years of remotely sensed snow cover images. When applied to images from 2 years that were not used in its development, the model represents snow‐covered area with accuracy of 84.9–97.5% at varied snow‐covered areas. The model also effectively removes cloud cover if any portion of the interface between land and snow is visible in a cloudy image. This new principal component analysis method for modeling the interannually recurring spatial melt pattern exclusively from remotely sensed images possesses its own intrinsic merit, in addition to those associated with its applications.

Constraining Conceptual Hydrological Models With Multiple Information Sources

AbstractThe calibration of hydrological models without streamflow observations is problematic, and the simultaneous, combined use of remotely sensed products for this purpose has not been exhaustively tested thus far. Our hypothesis is that the combined use of products can (1) reduce the parameter search space and (2) improve the representation of internal model dynamics and hydrological signatures. Five different conceptual hydrological models were applied to 27 catchments across Europe. A parameter selection process, similar to a likelihood weighting procedure, was applied for 1,023 possible combinations of 10 different data sources, ranging from using 1 to all 10 of these products. Distances between the two empirical distributions of model performance metrics with and without using a specific product were determined to assess the added value of a specific product. In a similar way, the performance of the models to reproduce 27 hydrological signatures was evaluated relative to the unconstrained model. Significant reductions in the parameter space were obtained when combinations included Advanced Microwave Scanning Radiometer ‐ Earth Observing System and Advanced Scatterometer soil moisture, Gravity Recovery and Climate Experiment total water storage anomalies, and, in snow‐dominated catchments, the Moderate Resolution Imaging Spectroradiometer snow cover products. The evaporation products of Land Surface Analysis ‐ Satellite Application Facility and MOD16 were less effective for deriving meaningful, well‐constrained posterior parameter distributions. The hydrological signature analysis indicated that most models profited from constraining with an increasing number of data sources. Concluding, constraining models with multiple data sources simultaneously was shown to be valuable for at least four of the five hydrological models to determine model parameters in absence of streamflow.

Variations in the Ice Phenology and Water Level of Ayakekumu Lake, Tibetan Plateau, Derived from MODIS and Satellite Altimetry Data

The alpine lakes on the Tibetan Plateau (TP) are highly sensitive to variations in climate changes, and the lake ice phenology and water level are considered to be direct indicators of regional climate variability. In this study, we first used 14 years of moderate resolution imaging spectroradiometer snow cover products to analyse the freeze dates, ablation dates, and ice coverage durations. The lake level changes during 2002–2015 were estimated, derived from satellite altimetry and Hydroweb data. Unexpectedly, the freeze dates of lake ice greatly advanced, and the ablation dates were markedly delayed. The complete freezing duration lengthened by approximately 77 days. As a result of the warm-wet climate in the northern TP, the lake area expanded from 770 to 995 km2 during 2002–2015, and the water levels rose by 4.2 m in total, at a rate of 0.3 m/year. The progressive expansion of Ayakekumu Lake profoundly affected the ice phenology. Larger water volume with larger thermal capacity likely led to the delaying of ablation dates, with the freezing point depression caused by decreasing salinity. Some new narrow and shallow bays located in southern and eastern Ayakekumu Lake were conducive to early freezing of ice. Additionally, the changes in air temperature, precipitation, potential evaporation, and sunshine duration may be related to the prolonged ice cover duration since 2002. In sum, accurate measurements of lake ice and water levels are critical for understanding the water resource balance and hydrologic cycle in arid or semi-arid regions of China.

Coupling the WRF model with a temperature index model based on remote sensing for snowmelt simulations in a river basin in the Altay Mountains, north‐west China

AbstractSnowmelt water is an important freshwater resource in the Altay Mountains in north‐west China; however, warming climate and rapid spring snowmelt can cause floods that endanger both public and personal property and safety. This study simulates snowmelt in the Kayiertesi River catchment using a temperature index model based on remote sensing coupled with high‐resolution meteorological data obtained from National Centers for Environmental Prediction (NCEP) reanalysis fields that were downscaled using the Weather Research Forecasting model and then bias corrected using a statistical downscaled model. Validation of the forcing data revealed that the high‐resolution meteorological fields derived from the downscaled NCEP reanalysis were reliable for driving the snowmelt model. Parameters of the temperature index model based on remote sensing were calibrated for spring 2014, and model performance was validated using Moderate Resolution Imaging Spectroradiometer snow cover and snow observations from spring 2012. The results show that the temperature index model based on remote sensing performed well, with a simulation mean relative error of 6.7% and a Nash–Sutcliffe efficiency of 0.98 in spring 2012 in the river of Altay Mountains. Based on the reliable distributed snow water equivalent simulation, daily snowmelt run‐off was calculated for spring 2012 in the basin. In the study catchment, spring snowmelt run‐off accounts for 72% of spring run‐off and 21% of annual run‐off. Snowmelt is the main source of run‐off for the catchment and should be managed and utilized effectively. The results provide a basis for snowmelt run‐off predictions, so as to prevent snowmelt‐induced floods, and also provide a generalizable approach that can be applied to other remote locations where high‐density, long‐term observational data are lacking. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Extracting Snow Cover Time Series Data from Open Access Web Mapping Tile Services

The probability of the presence of snow cover at a given location over time is a critical input to hydrologic simulation models in snowpack-driven watersheds. While a number of open access web mapping tile services exist for viewing images of current and historical snow cover over large regions, no equally accessible tools exist for extracting numerical time series data of snow cover probability defined at particular point locations. This article presents the design, development, and testing of a new open source script and web application for snow cover probability time series extraction from map images. The script is deployed as a web app using the Tethys framework making it accessible to novice users through a user interface. A WaterML web-API gives access to third-party applications for automation and embedding in modeling tools. The full design of the script is presented such that it can serve as a model for similar or extended tools that may be developed by others. A set of use case experiments is presented demonstrating the full functionality of the script and its limitations, and an example application for ground validation of the Moderate Resolution Imaging Spectroradiometer snow cover dataset is discussed.

Snow cover variability and snowmelt in a high‐altitude ungauged catchment

AbstractSnow variability is an integrated indicator of climate change, and it has important impacts on runoff regimes and water availability in high‐altitude catchments. Remote sensing techniques can make it possible to quantitatively detect the snow cover changes and associated hydrological effects in those poorly gauged regions. In this study, the spatial–temporal variations of snow cover and snow melting time in the Tuotuo River basin, which is the headwater of the Yangtze River, were evaluated based on satellite information from the Moderate Resolution Imaging Spectroradiometer snow cover product, and the snow melting equivalent and its contribution to the total runoff and baseflow were estimated by using degree–day model. The results showed that the snow cover percentage and the tendency of snow cover variability increased with rising altitude. From 2000 to 2012, warmer and wetter climate change resulted in an increase of the snow cover area. Since the 1960s, the start time for snow melt has become earlier by 0.9–3 days/10a and the end time of snow melt has become later by 0.6–2.3 days/10a. Under the control of snow cover and snow melting time, the equivalent of snow melting runoff in the Tuotuo River basin has been fluctuating. The average contributions of snowmelt to baseflow and total runoff were 19.6% and 6.8%, respectively. Findings from this study will serve as a reference for future research in areas where observational data are deficient and for planning of future water management strategies for the source region of the Yangtze River. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Spatiotemporal variations of snow cover in northeast China based on flexible multiday combinations of moderate resolution imaging spectroradiometer snow cover products

Daily moderate resolution imaging spectroradiometer (MODIS) (Terra and Aqua) snow cover products are used to produce flexible multiday combinations for each hydrological year between October 2003 and September 2013 in northeast China. Compared with in situ observations, the daily and flexible multiday combinations achieve overall accuracy of 47.51 and 76.52%, respectively, which is >34.45% of MODIS Terra and 30.57% of MODIS Aqua under all-sky conditions. Snow cover fraction, snow cover day map (SCD), snow cover onset date map (SCOD), snow cover end date map (SCED), and snow cover index (SCI) are then generated. The highest SCD, earliest SCOD, and latest SCED are always located in the southern edge of Da Hinggan Mountain and Changbai Mountain, followed by Xiao Hinggan Mountain. SCI negatively correlated with air temperature, with a correlation coefficient of −0.73 (p<0.05) in snowfall season; SCI positively correlated with precipitation, but is insignificant at the 95% level. This suggests that snow cover is more sensitive to air temperature than precipitation. In other words, continuous increase in air temperature due to global warming in the long term will result in continuous reducing of snow cover, a gracious water resource for northeast China, although a relative decrease in air temperature in the recent 10 years has resulted in snow cover increase.

A GIS-based method for defining snow zones: application to the western United States

This study maps the geographic extent of intermittent and seasonal snow cover in the western United States using thresholds of 2000–2010 average snow persistence derived from moderate resolution imaging spectroradiometer snow cover area data from 1 January to 3 July. Results show seasonal snow covers 13% of the region, and intermittent snow covers 25%. The lower elevation boundaries of intermittent and seasonal snow zones increase from north-west to south-east. Intermittent snow is primarily found where average winter land surface temperatures are above freezing, whereas seasonal snow is primarily where winter temperatures are below freezing. However, temperatures at the boundary between intermittent and seasonal snow exhibit high regional variability, with average winter seasonal snow zone temperatures above freezing in west coast mountain ranges. Snow cover extent at peak accumulation is most variable at the upper elevations of the intermittent snow zone, highlighting the sensitivity of this snow zone boundary to climate conditions.

Application of moderate resolution imaging spectroradiometer snow cover maps in modeling snowmelt runoff process in the central Zab basin, Iran

Snow, as one form of precipitation, plays a very significant role in the water cycle and in water resource management. However, the spatial and temporal variations in snow cover complicate the monitoring of this role. Field measurements, especially in mountainous areas, are often impossible without the use of new technologies. In this study, moderate resolution imaging spectroradiometer (MODIS) at 500-m resolution has been used to provide a map of snow cover area (SCA) using the normalized difference snow index in the central Zab basin in West Azerbaijan, Iran. Eight-day composite data are used to minimize the effect of cloud cover and maximize the amount of useable SCA images. The importance of snow in this basin was simulated using a snowmelt runoff model (SRM) as one of the major applications of daily MODIS-8 images based on various algorithms. The location of snow gauge stations on digital elevation model (DEM) of central Zab basin extracted from advanced space borne thermal emission and reflection radiometer images by using bilinear interpolation method. The SCA index, along with spectral threshold on bands 2 and 4, provided a stable relationship for extraction of the snow cover map in the study area. The simulated flow in the water year 2010 to 2011 had a coefficient of determination (R2) of 0.8953 and a volume difference (Dv) of 0.1498%, which shows a good correlation between the measured and computed runoff by using the SRM in the central Zab basin. The first results of the modeling process show that MODIS snow covered area product can be used for simulation and measuring value of snowmelt runoff in central Zab basin. The studies found that the SCA results were more reliable in the study area.

Mapping exposure to snow in a developing African context: implications for human and livestock vulnerability in Lesotho

Alpine cryospheric hazards are becoming increasingly prominent under current global/regional climate change scenarios and receiving wide scientific coverage from, in particular, northern hemisphere mountain regions associated with glaciers, permafrost, and extensive seasonal snow cover. However, there is a general paucity of knowledge and attention on cryospheric hazards associated with mountain environments only occasionally/rarely impacted by heavy seasonal snowfalls or severe frost events, particularly those in developing and southern hemisphere regions. Prolonged snow cover in the Lesotho Highlands sometimes carries the consequence of human and livestock deaths owing to isolation and exposure in this developing region. We use daily Moderate Resolution Imaging Spectroradiometer snow cover images for the period 2003–2010, to establish the frequency, extent, and timing of snowfalls across Lesotho. In addition, a digital shape file containing the location, name, and district attributes of 2,016 villages across Lesotho was used to assist in the construction of a village exposure to snow index. A ranking system was applied to each village according to the seasonal duration of snow cover, and the accessibility and proximity to the nearest road. Snowfalls occur on average between 1 and 8 times per annum, with village exposure to snow (potential vulnerability) being generally low, particularly for the lowlands and Senqu River Valley. However, the study identifies that some high-altitude (>2,500 m) villages such as Thoteng, Letseng-la-Terae, and Mabalane are, on occasion, highly exposed to prolonged snow cover, and particularly so during the mid-snow season of July/August. We demonstrate the importance of applying spatiotemporal assessments on infrequent snow occurrences (which carry associated hazards) in developing mountain regions such as Lesotho, with implications to reduce livelihood risks through improved disaster preparedness and a well-informed, focused emergency response.

Assessment of ice mapping system and moderate resolution imaging spectroradiometer snow cover maps over Colorado Plateau

Satellite snow cover area (SCA) mapping using optical sensors has been known to suffer severe obstruction due to vegetation canopy and cloud cover. Several algorithms have been developed to reduce cloud cover contamination and enhance the SCA mapping. In this study we introduce the use of a daily SCA product from the Multisensor Snow and Ice Mapping System (IMS) at a nominal resolution of 4 km, assess its accuracy and error levels against in situ observations, and compare the IMS SCA product with the SCA products from moderate resolution imaging spectroradiometer (MODIS), a combination of daily Terra and Aqua satellites. The results show that the snow accuracies are higher during winter for both IMS and MODIS, and that there is not much difference between MODIS at 500 m and upscaled at 4 km. The IMS SCA mapping accuracies are significantly higher than MODIS accuracies for all sky conditions, while they are similar to or slightly lower than MODIS accuracies in clear sky conditions. The overestimate error of snow cover using IMS is higher (lower) than that of MODIS during ablation (accumulation) periods. Both MODIS and IMS show a similar pattern of underestimation errors of snow cover with the IMS being slightly higher than the MODIS. It is concluded that the IMS SCA product has potential as a good alternative for the MODIS daily SCA products or replacing those cloud pixels in the MODIS daily or multiday products.

Multiscale assimilation of Advanced Microwave Scanning Radiometer–EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado

Eight years (2002–2010) of Advanced Microwave Scanning Radiometer–EOS (AMSR‐E) snow water equivalent (SWE) retrievals and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule‐based update. The satellite data are either left unscaled or are scaled for anomaly assimilation. The results are validated against in situ observations at 14 high‐elevation Snowpack Telemetry (SNOTEL) sites with typically deep snow and at 4 lower‐elevation Cooperative Observer Program (COOP) sites. Assimilation of coarse‐scale AMSR‐E SWE and fine‐scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with shallow snowpacks, AMSR‐E SWE and MODIS SCF data assimilation are beneficial separately, and joint SWE and SCF assimilation yields significantly improved root‐mean‐square error and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however, AMSR‐E retrievals are typically biased low and assimilation without prior scaling leads to degraded SWE estimates. Anomaly SWE assimilation could not improve the interannual SWE variations in the assimilation results because the AMSR‐E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near‐complete snow cover. Across all sites, SCF assimilation improves the timing of the onset of the snow season but without a net improvement of SWE amounts.