The optical behaviour of snow during a melting season at Ny Ålesund (Svalbard, Norway)

2021 ◽  
Author(s):  
Roberto Salzano ◽  
Christian Lanconelli ◽  
Giulio Esposito ◽  
Marco Giusto ◽  
Mauro Montagnoli ◽  
...  
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Full Range ◽  
Time Lapse ◽  
Test Site ◽  
Remotely Sensed ◽  
Optical Behaviour ◽  
Remotely Sensed Data ◽  
Snow Cover Area ◽  
Significant Information

<p><span>Polar areas are the most sensitive targets of </span><span>the </span><span>climate change and the continuous monitoring of the cryosphere represents a critical issue. The satellite remote sensing can fill this gap but further integration between remotely-sensed multi-spectral images and field data is crucial to validate retrieval algorithms and climatological models. The optical behaviour of snow, at different wavelengths, provides significant information about the micro-physical characteristics of the surface and this allow to discriminate different snow/ice covers. The aim of this work is to present an approach based on combining unmanned observations on spectral albedo and on the analysis of time-lapse images of sky and ground conditions in a</span><span>n </span><span>Ar</span><span>c</span><span>tic </span><span>test-site </span><span>(Svalbard, Norway). Terrestrial photography can provide, in fact, important information about the cloud cover and support the discrimination between white-sky or clear-sky illuminating conditions. Similarly, time-lapse cameras can provide a detailed description of the snow cover, estimating the fractional snow cover area. The spectral albedo was obtained by a narrow band device that was compared to a full-range commercial system and to remotely sensed data acquired during the 2015 spring/summer period at the </span><span>Amundsen - Nobile</span><span> Climate Change Tower (Ny </span><span>Å</span><span>lesund). The results confirmed the possibility to have continuous observations of the snow surface (microphisical) characteristics and highlighted the opportunity to monitor the spectral variations of snowed surfaces during the melting period. It was possible, </span><span>therefore,</span><span> to estimate spectral indexes, such as NDSI and SWIR albedo, and to found interesting links between both features and air/ground temperatures, wind-speed and precipitations. Different melting phases were detected and different processes were associated with the observed spectral variations.</span></p>

scholarly journals On the Seasonality of the Snow Optical Behaviour at Ny Ålesund (Svalbard Islands, Norway)

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 112
Author(s):  
Roberto Salzano ◽  
Christian Lanconelli ◽  
Giulio Esposito ◽  
Marco Giusto ◽  
Mauro Montagnoli ◽  
...  
Keyword(s):  
Climate Change ◽  
Continuous Monitoring ◽  
Surface Albedo ◽  
Narrow Band ◽  
Full Range ◽  
Remotely Sensed Data ◽  
Summer Period ◽  
Significant Information ◽  
Melting Period ◽  
Optical Behavior

Polar areas are the most sensitive targets of climate change. From this perspective, the continuous monitoring of the cryosphere represents a critical need, which, now, we can only partially supply with specific satellite missions. The integration between remote-sensed multi-spectral images and field data is crucial to validate retrieval algorithms and climatological models. The optical behavior of snow, at different wavelengths, provides significant information about the microphysical characteristics of the surface in addition to the spatial distribution of snow/ice covers. This work presents the unmanned apparatus installed at Ny Ålesund (Svalbard) that provides continuous spectral surface albedo. A narrow band device was compared to a full-range system, to remotely sensed data during the 2015 spring/summer period at the Amundsen-Nobile Climate Change Tower. The system was integrated with a camera aimed to acquire sky and ground images. The results confirmed the possibility of making continuous observations of the snow surface and highlighted the opportunity to monitor the spectral variations of snowed surfaces during the melting period.

scholarly journals Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska

2019 ◽  
Vol 23 (5) ◽  
pp. 2439-2459
Author(s):  
Katrina E. Bennett ◽  
Jessica E. Cherry ◽  
Ben Balk ◽  
Scott Lindsey
Keyword(s):  
Snow Cover ◽  
The United States ◽  
Remotely Sensed ◽  
Streamflow Forecasting ◽  
Snow Cover Area ◽  
Interior Alaska ◽  
Fractional Snow Cover ◽  
The Us ◽  
Physically Based ◽  
Streamflow Forecasts

Abstract. Remotely sensed snow cover observations provide an opportunity to improve operational snowmelt and streamflow forecasting in remote regions. This is particularly true in Alaska, where remote basins and a spatially and temporally sparse gaging network plague efforts to understand and forecast the hydrology of subarctic boreal basins and where climate change is leading to rapid shifts in basin function. In this study, the operational framework employed by the United States (US) National Weather Service, including the Alaska Pacific River Forecast Center, is adapted to integrate Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed observations of fractional snow cover area (fSCA) to determine if these data improve streamflow forecasts in interior Alaska river basins. Two versions of MODIS fSCA are tested against a base case extent of snow cover derived by aerial depletion curves: the MODIS 10A1 (MOD10A1) and the MODIS Snow Cover Area and Grain size (MODSCAG) product over the period 2000–2010. Observed runoff is compared to simulated runoff to calibrate both iterations of the model. MODIS-forced simulations have improved snow depletion timing compared with snow telemetry sites in the basins, with discernable increases in skill for the streamflow simulations. The MODSCAG fSCA version provides moderate increases in skill but is similar to the MOD10A1 results. The basins with the largest improvement in streamflow simulations have the sparsest streamflow observations. Considering the numerous low-quality gages (discontinuous, short, or unreliable) and ungauged systems throughout the high-latitude regions of the globe, this result is valuable and indicates the utility of the MODIS fSCA data in these regions. Additionally, while improvements in predicted discharge values are subtle, the snow model better represents the physical conditions of the snowpack and therefore provides more robust simulations, which are consistent with the US National Weather Service's move toward a physically based National Water Model. Physically based models may also be more capable of adapting to changing climates than statistical models corrected to past regimes. This work provides direction for both the Alaska Pacific River Forecast Center and other forecast centers across the US to implement remote-sensing observations within their operational framework, to refine the representation of snow, and to improve streamflow forecasting skill in basins with few or poor-quality observations.

scholarly journals Snow-Cover Area and Runoff Variation under Climate Change in the West Kunlun Mountains

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2246 ◽  
Author(s):  
Ma ◽  
Yan ◽  
Zhao ◽  
Kundzewicz
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
River Basin ◽  
River Flow ◽  
Initial Increase ◽  
Runoff Generation ◽  
The West ◽  
Snow Cover Area ◽  
Kunlun Mountains ◽  
West Kunlun

In recent years, the climate in the arid region of Northwest China has become warmer and wetter; however, glaciers in the north slope of the West Kunlun Mountains (NSWKM) show no obvious recession, and river flow is decreasing or stable. This contrasts with the prevalent response of glaciers to climate change, which is recession and initial increase in glacier discharge followed by decline as retreat continues. We comparatively analyzed multi-timescale variation in temperature–precipitation–snow cover-runoff in the Yarkant River Basin (YRK), Karakax River Basin (KRK), Yurungkax River Basin (YUK), and Keriya River Basin (KRY) in the NSWKM. The Mann–Kendall trend and the mutation–detection method were applied to data obtained from an observation station over the last 60 years (1957–2017) and MODIS snow data (2001–2016). NSWKM temperature and precipitation have continued to increase for nearly 60 years at a mean rate of 0.26 °C/decade and 5.50 mm/decade, respectively, with the most obvious trend (R2 > 0.82) attributed to the KRK and YUK. Regarding changes in the average snow-cover fraction (SCF): YUK (SCF = 44.14%) > YRK (SCF = 38.73%) > KRY (SCF = 33.42%) > KRK (SCF = 33.40%). Between them, the YRK and YUK had decreasing SCA values (slope < −15.39), while the KRK and KRY had increasing SCA values (slope > 1.87). In seasonal variation, the SCF of the three of the basins reaches the maximum value in spring, with the most significant performance in YUK (SCF = 26.4%), except for YRK where SCF in spring was lower than that in winter (−2.6%). The runoff depth of all river basins presented an increasing trend, with the greatest value appearing in the YRK (5.78 mm/decade), and the least value in the YUK (1.58 mm/decade). With the runoff response to climate change, temperature was the main influencing factor of annual and monthly (summer) runoff variations in the YRK, which is consistent with the runoff-generation rule of rivers in arid areas, which mainly rely on ice and snow melt for water supply. However, this rule was not consistent for the YUK and KRK, as it was disturbed by other factors (e.g., slope and slope direction) during runoff generation, resulting in disruptions of their relationship with runoff. This research promotes the study of the response of cold and arid alpine regions to global change and thus better serve regional water resources management.

Prediction of Crop Yield for New Mexico Based on Climate and Remote Sensing Data for the 1920–2019 Period

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1389
Author(s):  
Kamini Yadav ◽  
Hatim M. E. Geli
Keyword(s):  
Climate Change ◽  
New Mexico ◽  
Crop Yield ◽  
Crop Yields ◽  
Production Systems ◽  
Remotely Sensed ◽  
Adaptation Strategies ◽  
Yield Prediction ◽  
Climate Variables ◽  
Remotely Sensed Data

Agricultural production systems in New Mexico (NM) are under increased pressure due to climate change, drought, increased temperature, and variable precipitation, which can affect crop yields, feeds, and livestock grazing. Developing more sustainable production systems requires long-term measurements and assessment of climate change impacts on yields, especially over such a vulnerable region. Providing accurate yield predictions plays a key role in addressing a critical sustainability gap. The goal of this study is the development of effective crop yield predictions to allow for a better-informed cropland management and future production potential, and to develop climate-smart adaptation strategies for increased food security. The objectives were to (1) identify the most important climate variables that significantly influence and can be used to effectively predict yield, (2) evaluate the advantage of using remotely sensed data alone and in combination with climate variables for yield prediction, and (3) determine the significance of using short compared to long historical data records for yield prediction. This study focused on yield prediction for corn, sorghum, alfalfa, and wheat using climate and remotely sensed data for the 1920–2019 period. The results indicated that the use of normalized difference vegetation index (NDVI) alone is less accurate in predicting crop yields. The combination of climate and NDVI variables provided better predictions compared to the use of NDVI only to predict wheat, sorghum, and corn yields. However, the use of a climate only model performed better in predicting alfalfa yield. Yield predictions can be more accurate with the use of shorter data periods that are based on region-specific trends. The identification of the most important climate variables and accurate yield prediction pertaining to New Mexico’s agricultural systems can aid the state in developing climate change mitigation and adaptation strategies to enhance the sustainability of these systems.

scholarly journals Response of snow cover to climate change in the periphery mountains of Tarim river basin, China, over the past four decades

2008 ◽  
Vol 49 ◽  
pp. 166-172 ◽  
Author(s):  
Chen Yaning ◽  
Xu Changchun ◽  
Chen Yapeng ◽  
Li Zhongqin ◽  
Pang Zhonghe
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Winter Season ◽  
Precipitation Change ◽  
Western China ◽  
Tarim River ◽  
Tarim River Basin ◽  
Snow Cover Area ◽  
Temperature And Precipitation ◽  
Annual Total

AbstractData of annual mean temperature, annual total precipitation and snow-cover area (SCA) in the winter season from 1982 to 2001 have been analyzed to examine the response of SCA to climate change in the Tarim basin, western China. The results show that over the entire basin SCA exhibits a slowly decreasing trend. The responses of SCA to temperature and precipitation in the northern, western and southern parts of the basin show a stronger effect of precipitation change on SCA than that of temperature. SCA has slowly increased below 2500ma.s.l., but has decreased at higher altitudes. The lowest-altitude zone was apt to be affected by precipitation, while the highest-altitude zone seems to have been influenced mainly by temperature. The middle zone from 2500 to 5000 m was the most sensitive to climate change. Snowfall and melt rates were higher in the 1990s than in the 1980s. In the winter season, SCA change was positively correlated with precipitation change but not with temperature change.

scholarly journals A Preliminary Assessment of Forest Canopy Structure in Grand Teton National Park

1994 ◽  
Vol 18 ◽  
pp. 37-41
Author(s):  
Clayton Blodgett ◽  
Mark Jakubauskas
Keyword(s):  
Climate Change ◽  
Spatial Autocorrelation ◽  
Global Climate Change ◽  
Spatial Dependence ◽  
Forest Canopy ◽  
Global Climate ◽  
Canopy Structure ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Geostatistical Techniques

The potential impact of environmental change on human welfare has renewed interest in understanding the patterns and processes associated with global climate change. Goals of the Committee on Earth Sciences (1989) regarding the U.S. Global Climate Change Program concentrated on the development of sound scientific strategies for monitoring and predicting environmental change. The scaling of ecological characteristics from local to regional and global scales were identified by the Committee as key priorities. The scaling of ecological information is not simply done by integrating or aggregating information from local scale investigations to regional and global scales (Caldwell et al., 1993). The complexity of the effects of scale variations rules out the use of simple generalizations (Foody and Curran, 1994). Information that is significant at local scales may be trivial when evaluated at regional or global scales. Biological interactions with the environment occur over many scales, suggesting a role for multiscale analysis in the description of these interactions (Sclmeider, 1994). Methods must be developed to better understand and evaluate ecological processes operating at multiple scales. Forest structure attributes have been measured using remotely sensed data. Leaf area index (LAI), for example, has been related to the infrared/red ratio (Running et al., 1986 Peterson et al., 1987), the normalized difference vegetation index (NDVI) (Leblon et al., 1993), and gap fractions (Nel and Wessman, 1993). These methods generate values for each pixel in a satellite scene based on the relationship between one or more spectral and/or ancillary data channels and the attribute of interest. The spatial autocorrelation or spatial dependence present in surface phenomena and satellite data are usually not ex-ploited during attribute assignment because of difficulty in quantifying the spatial patterns present (Woodcock et al., 1988). Geostatistics provides a statistically based technique to quantify spatial pattern. Geostatistical techniques, in particular cokriging, can serve as an efficient means of modeling forest canopy structure at a variety of spatial scales to serve as inputs to global change models. The key issue will be to determine the factors that influence remotely sensed spectral reflectance and relating them to the ecological model across scales (Ustin et al., 1993). The geostatistical techniques considered in this research include the following: the semivariograrn, which allows the user to compare values of a random variable at two points separated by a given lag distance (Milne, 1991); kriging which uses the information on spatial dependence present in the semivariogram to estimate values at unsampled locations based on scattered sample data (lsaaks and Srivastava, 1989); and cokriging, the multivariate extension of kriging, which is appropriate when two or more variables are spatially interdependent and the variable of interest is undersampled (McBratney and Webster, 1983; Leenaers et al., 1989). Geostatistical techniques have been successfully applied to remotely sensed data. Variograms have been used to determine components of coniferous canopy structure (Cohen et al., 1990), and to determine the spatial autocorrelation structure of Landsat Thematic Mapper (IM) imagery and intercepted photosynthetically active radiation (IPAR) (Lathrop and Pierce, 1991). Atkinson et al. (1992) used cokriging of ground-based radiometer data to estimate LAI, dry biomass and percent cover. Satellite imagery is an excellent candidate for inclusion as an explanatory variable in the cokriging process because it is an exhaustive sample of a given area.

scholarly journals Using MODIS estimates of fractional snow cover extent to improve streamflow forecasts in Interior Alaska

2018 ◽  
Author(s):  
Katrina E. Bennett ◽  
Jessica E. Cherry ◽  
Ben Balk ◽  
Scott Lindsey
Keyword(s):  
Snow Cover ◽  
Remotely Sensed ◽  
Poor Quality ◽  
Water Model ◽  
Streamflow Forecasting ◽  
Snow Cover Area ◽  
Snow Cover Extent ◽  
The Us ◽  
Physically Based ◽  
Streamflow Forecasts

Abstract. Remotely sensed snow cover observations provide an opportunity to improve operational snowmelt and streamflow forecasting in remote regions. This is particularly true in Alaska, where remote basins and a spatially and temporally sparse gaging network plague efforts to understand and forecast the hydrology of subarctic boreal watersheds and where climate change is leading to rapid shifts in watershed function. In this study, the operational framework employed by the US National Weather Service, including the Alaska Pacific River Forecast Center, is adapted to integrate Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed observations of snow cover extent (SCE) to determine if these data improve streamflow forecasts in Interior Alaskan river basins. Two versions of MODIS fractional SCE are tested in this study: the MODIS 10A1 (MOD10A1), and the MODIS Snow Cover Area and Grain size (MODSCAG) product. Observed runoff is compared to simulated runoff to calibrate both iterations of the model. MODIS-forced runs have improved snow depletion timing compared with snow telemetry sites in the basins, with discernable increases in skill for the streamflow simulations. The MODSCAG SCE version provides moderate increases in skill, but is similar to the MOD10A1 results in these watersheds. The basins with the greatest improvement in streamflow simulations have the sparsest streamflow observations. Considering the numerous low-quality gages (discontinuous, short, or unreliable) and ungaged systems throughout the high latitude regions of the globe, this result is of great value and indicates the utility of the MODIS SCE data in these regions. Additionally, while improvements in predicted discharge values are subtle, the snow model better represents the physical conditions of the snow pack and therefore provides more robust simulations, which are consistent with the US National Weather Service's move toward a physically-based National Water Model. Physically-based models may be more capable of adapting to changing climates than statistical models tuned to past regimes. This work provides direction for both the Alaska Pacific River Forecast Center and other forecast centers across the US to implement remote sensing observations within their operational framework, to refine the representation of snow, and to improve streamflow forecasting skill in basins with few or poor-quality observations.

Snow Cover Dynamics and its Spatio-Temporal Distribution in the Central Higher Himalaya of India

2021 ◽  
Vol 14 (9) ◽  
pp. 15-22
Author(s):  
Masoom Reza ◽  
Ramesh Chandra Joshi
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Geographical Area ◽  
Temporal Distribution ◽  
Melting Rate ◽  
Optical Data ◽  
Snow Cover Area ◽  
Central Himalayas ◽  
Spatio Temporal ◽  
The Impact

Retreating glaciers, changing timber line and decreasing accumulation of snow in the Himalaya are considered the indicators of climate change. In this study, an attempt is made to observe the snow cover change in the higher reaches of the Central Himalayas. Investigation of climate change through snow cover is very important to understand the impact and adaptation in an area. Landsat thematic and multi spectral optical data with a spatial resolution of 60m and 30m are considered for the estimation and extraction of snow cover. Total 3,369 Km2 snow cover area is lost since 1972 out of total geographical area i.e. 17,227 Km2. The accumulation of snow during winter is lower than the melting rate during summer. The current study identified the decrease of 19.6 % snow cover in 47 years since 1972 to 2019. Composite satellite imageries of September to December show that the major part of the study area covered with snow lies above 3600m. Overall observation indicates that in 47 years, permanent snow cover is decreasing in Central Himalayas.

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