scholarly journals Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products

2021 ◽  
Vol 13 (22) ◽  
pp. 4513
Author(s):  
Jesús Revuelto ◽  
Esteban Alonso-González ◽  
Simon Gascoin ◽  
Guillermo Rodríguez-López ◽  
Juan Ignacio López-Moreno
Keyword(s):  
Snow Cover ◽  
Grid Cell ◽  
Probabilistic Methods ◽  
Superior Performance ◽  
Mountain Areas ◽  
Main Hypothesis ◽  
Clear Sky ◽  
Lower Accuracy ◽  
Spatial Downscaling ◽  
Sentinel 2

Understanding those processes in which snow dynamics has a significant influence requires long-term and high spatio-temporal resolution observations. While new optical space-borne sensors overcome many previous snow cover monitoring limitations, their short temporal length limits their application in climatological studies. This work describes and evaluates a probabilistic spatial downscaling of MODIS snow cover observations in mountain areas. The approach takes advantage of the already available high spatial resolution Sentinel-2 snow observations to obtain a snow probability occurrence, which is then used to determine the snow-covered areas inside partially snow-covered MODIS pixels. The methodology is supported by one main hypothesis: the snow distribution is strongly controlled by the topographic characteristics and this control has a high interannual persistence. Two approaches are proposed to increase the 500 m resolution MODIS snow cover observations to the 20 m grid resolution of Sentinel-2. The first of these computes the probability inside partially snow-covered MODIS pixels by determining the snow occurrence frequency for the 20 m Sentinel-2 pixels when clear-sky conditions occurred for both platforms. The second approach determines the snow probability occurrence for each Sentinel-2 pixel by computing the number of days in which snow was observed on each grid cell and then dividing it by the total number of clear-sky days per grid cell. The methodology was evaluated in three mountain areas in the Iberian Peninsula from 2015 to 2021. The 20 m resolution snow cover maps derived from the two probabilistic methods provide better results than those obtained with MODIS images downscaled to 20 m with a nearest-neighbor method in the three test sites, but the first provides superior performance. The evaluation showed that mean kappa values were at least 10% better for the two probabilistic methods, improving the scores in one of these sites by 25%. In addition, as the Sentinel-2 dataset becomes longer in time, the probabilistic approaches will become more robust, especially in areas where frequent cloud cover resulted in lower accuracy estimates.

Towards a Pan-European snow cover and melt extent product from Sentinel-1 SAR and Sentinel-3 SLSTR Data

2020 ◽  
Author(s):  
Thomas Nagler ◽  
Lars Keuris ◽  
Helmut Rott ◽  
Gabriele Schwaizer ◽  
David Small ◽  
...  
Keyword(s):  
Change Detection ◽  
Snow Cover ◽  
Cloud Cover ◽  
Forest Cover ◽  
Meteorological Data ◽  
Mountain Areas ◽  
Seasonal Snow ◽  
Dense Forest ◽  
Seasonal Snow Cover ◽  
Sentinel 2

<p>The synergistic use of data from different satellites of the Sentinel series offers excellent capabilities for generating high quality products on key parameters of the global climate system and environment. A main parameter for climate monitoring, hydrology and water management is the seasonal snow cover. In the frame of the ESA project SEOM S1-4-SCI Snow, led by ENVEO, we developed, implemented and tested a novel approach for mapping the total extent and melting areas of the seasonal snow cover by synergistically exploiting Sentinel-1 SAR and Sentinel-3 SLSTR data and apply these tools for snow monitoring over the Pan-European domain.</p><p>Whereas data of medium resolution optical sensors are used for mapping the total snow extent, data of the Copernicus Sentinel-1 mission in Interferometric Wide Swath (IW) mode at co- and cross-polarizations are used for mapping the extent of snowmelt areas applying change detection algorithms. In order to select an optimum procedure for retrieval of snowmelt area, we conducted round-robin experiments for various algorithms over different snow environments, including high mountain areas in the Alps and in Scandinavia, as well as lowland areas in Central Europe covered by grassland, agricultural plots, and forests. In mountain areas the tests show good agreement between snow extent products during the melting period derived from SAR data and from Sentinel-2 and Landsat-8 data. In lowlands ambiguities may arise from temporal changes in backscatter related to soil moisture and agricultural activities. Dense forest cover is a major obstacle for snow detection by SAR because the surface is masked by the canopy layer which is a major scattering source at C-band. Therefore, areas with dense forest cover are masked out. Based on this results we selected for the retrieval of snowmelt area a change-detection algorithm using dual-polarized backscatter data of S1 IW acquisitions. The algorithm applies multi-channel speckle filtering and data fusion procedures for exploiting VV- and VH-polarized multi-temporal ratio images. The binary SAR snowmelt extent product at 100 m grid size is combined with the Sentinel-3 SLSTR and MODIS snow products in order to obtain combined maps of total snow area and melting snow. The optical satellite images provide information on snow extent irrespective of melting state but are impaired by cloud cover. For generating a fractional snow extent product from MODIS and Sentinel-3 SLSTR data we apply multi-spectral algorithms for cloud screening, the discrimination of snow free and snow covered regions and the retrieval of fractional snow extent. In order to fill gaps in the optical snow extent time sequence due to cloud cover we apply a data assimilation procedure using a snow pack model driven by numerical meteorological data of ECMWF, simulating daily changes in the snow extent. We present the results of the Pan-European snow cover and melt extent product derived from optical and SAR data. The performance of this product is evaluated in different environments using independent validation data sets including in-situ snow and meteorological measurements, snow products from Sentinel-2 and Landsat images, as well as high resolution numerical meteorological data.</p>

scholarly journals A Sentinel-2 Dataset for Uganda

Data ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 35
Author(s):  
Jonas Ardö
Keyword(s):  
Environmental Monitoring ◽  
Input Data ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Observation Data ◽  
Output Data ◽  
Radiometric Correction ◽  
Clear Sky ◽  
Earth Observation Data ◽  
Sentinel 2

Earth observation data provide useful information for the monitoring and management of vegetation- and land-related resources. The Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) was used to download, process and composite Sentinel-2 data from 2018–2020 for Uganda. Over 16,500 Sentinel-2 data granules were downloaded and processed from top of the atmosphere reflectance to bottom of the atmosphere reflectance and higher-level products, totalling > 9 TB of input data. The output data include the number of clear sky observations per year, the best available pixel composite per year and vegetation indices (mean of EVI and NDVI) per quarter. The study intention was to provide analysis-ready data for all of Uganda from Sentinel-2 at 10 m spatial resolution, allowing users to bypass some basic processing and, hence, facilitate environmental monitoring.

scholarly journals Snow Cover and Snow Persistence Changes in the Mocho-Choshuenco Volcano (Southern Chile) Derived From 35 Years of Landsat Satellite Images

2021 ◽  
Vol 9 ◽  
Author(s):  
Roberto O. Chávez ◽  
Verónica F. Briceño ◽  
José A. Lastra ◽  
Daniel Harris-Pascal ◽  
Sergio A. Estay
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Landsat Images ◽  
Microbial Composition ◽  
Mountain Areas ◽  
Pixel Resolution ◽  
Temperature Changes ◽  
Soil Microbial ◽  
Landsat Satellite

Mountain regions have experienced above-average warming in the 20th century and this trend is likely to continue. These accelerated temperature changes in alpine areas are causing reduced snowfall and changes in the timing of snowfall and melt. Snow is a critical component of alpine areas - it drives hibernation of animals, determines the length of the growing season for plants and the soil microbial composition. Thus, changes in snow patterns in mountain areas can have serious ecological consequences. Here we use 35 years of Landsat satellite images to study snow changes in the Mocho-Choshuenco Volcano in the Southern Andes of Chile. Landsat images have 30 m pixel resolution and a revisit period of 16 days. We calculated the total snow area in cloud-free Landsat scenes and the snow frequency per pixel, here called “snow persistence” for different periods and seasons. Permanent snow cover in summer was stable over a period of 30 years and decreased below 20 km2 from 2014 onward at middle elevations (1,530–2,000 m a.s.l.). This is confirmed by negative changes in snow persistence detected at the pixel level, concentrated in this altitudinal belt in summer and also in autumn. In winter and spring, negative changes in snow persistence are concentrated at lower elevations (1,200–1,530 m a.s.l.). Considering the snow persistence of the 1984–1990 period as a reference, the last period (2015–2019) is experiencing a −5.75 km2 reduction of permanent snow area (snow persistence > 95%) in summer, −8.75 km2 in autumn, −42.40 km2 in winter, and −18.23 km2 in spring. While permanent snow at the high elevational belt (>2,000 m a.s.l.) has not changed through the years, snow that used to be permanent in the middle elevational belt has become seasonal. In this study, we use a probabilistic snow persistence approach for identifying areas of snow reduction and potential changes in alpine vegetation. This approach permits a more efficient use of remote sensing data, increasing by three times the amount of usable scenes by including images with spatial gaps. Furthermore, we explore some ecological questions regarding alpine ecosystems that this method may help address in a global warming scenario.

An Alternative Machine Learning-Based Methodology for H-SAF H35 Fractional Snow Cover Product

2021 ◽  
Author(s):  
Semih Kuter ◽  
Cansu Aksu ◽  
Kenan Bolat ◽  
Zuhal Akyurek
Keyword(s):  
Machine Learning ◽  
Snow Cover ◽  
Computational Cost ◽  
Multivariate Adaptive Regression Splines ◽  
Pixel Resolution ◽  
European Organization ◽  
Finnish Meteorological Institute ◽  
Fractional Snow Cover ◽  
Turkish State ◽  
Sentinel 2

<p>The fractional snow cover (FSC) product H35 is a daily operational product based on multi-channel analysis of AVHRR onboard to NOAA and MetOp satellites. H35 is supplied by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF). The “traditional” H35 FSC product is generated at pixel resolution by exploiting the brightness intensity, which is the convolution of the snow signal and the fraction of snow within the pixel and the sampling is carried out at 1-km intervals. The product for flat/forested regions is generated by Finnish Meteorological Institute (FMI) and the product for mountainous areas is generated by Turkish State Meteorological Service (TSMS). Both products, thereafter, are merged at FMI. This presentation aims to represent the latest findings of our efforts in developing an “alternative” H35 FSC product for the mountainous part by using two data-driven machine learning methodologies, namely, multivariate adaptive regression splines (MARS) and random forests (RFs). In total, 332 Sentinel 2 images over Alps, Tatra Mountains and Turkey acquired between November 2018 and April 2019 are used in order to generate the necessary reference FSC maps for the training of the MARS and RF models. AVHRR bands 1-5, NDSI and NDVI are used as predictor variables. Binary classified Sentinel 2 snow maps, ERA5 snow depth and MODIS MOD10A1 NDSI data are employed in the validation of the models. The results show that both MARS- and RF-based H35 product are i) in good agreement with reference FSC maps (as indicated by low RMSE and relatively high R values) and ii) able to capture the spatial variability of the snow extend. However, MARS-based H35 is preferred for an operational FSC product generation due to the high computational cost required in RF model.</p>

scholarly journals Observing Snow Cover and Water Resource Changes in the High Mountain Asia Region in Comparison with Global Mountain Trends over 2000–2018

2020 ◽  
Vol 12 (23) ◽  
pp. 3913
Author(s):  
Claudia Notarnicola
Keyword(s):  
Water Resources ◽  
Confidence Interval ◽  
Snow Cover ◽  
Water Storage ◽  
High Mountain ◽  
Global Level ◽  
Total Water ◽  
Mountain Areas ◽  
Snow Cover Extent ◽  
The Impact

The quantification of snow cover changes and of the related water resources in mountain areas has a key role for understanding the impact on several sectors such as ecosystem services, tourism and energy production. By using NASA-Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2018, this study analyzes changes in snow cover in the High Mountain Asia region and compares them with global mountain areas. Globally, snow cover extent and duration are declining with significant trends in around 78% of mountain areas, and the High Mountain Asia region follows similar trends in around 86% of the areas. As an example, Shaluli Shan area in China shows significant negative trends for both snow cover extent and duration, with −11.4% (confidence interval: −17.7%, −5.5%) and −47.3 days (confidence interval: −70.4 days, −24.4 days) at elevations >5500 m a.s.l. respectively. In spring, an earlier snowmelt of −13.5 days (confidence interval: −24.3 days, −2.0 days) in 4000–5500 m a.s.l. is detected. On the other side, Tien Shan area shows an earlier snow onset of −28.8 days (confidence interval: −44.3 days, −8.2 days) between 2500 and 4000 m a.s.l., governed by decreasing temperature and increasing snowfall. In the current analysis, the Tibetan Plateau shows no significant changes. Regarding water resources, by using Gravity Recovery and Climate Experiment (GRACE) data it was found that around 50% of areas in the High Mountain Asia region and 30% at global level are suffering from significant negative temporal trends of total water storage (including groundwater, soil moisture, surface water, snow, and ice) in the period 2002–2015. In the High Mountain Asia region, this negative trend involves around 54% of the areas during spring period, while at a global level this percentage lies between 25% and 30% for all seasons. Positive trends for water storage are detected in a maximum 10% of the areas in High Mountain Asia region and in around 20% of the areas at global level. Overall snow mass changes determine a significant contribution to the total water storage changes up to 30% of the areas in winter and spring time over 2002–2015.

scholarly journals Assessing Snow Phenology over the Large Part of Eurasia Using Satellite Observations from 2000 to 2016

2020 ◽  
Vol 12 (12) ◽  
pp. 2060
Author(s):  
Yanhua Sun ◽  
Tingjun Zhang ◽  
Yijing Liu ◽  
Wenyu Zhao ◽  
Xiaodong Huang
Keyword(s):  
Snow Cover ◽  
Southern China ◽  
Climate Feedback ◽  
Onset Date ◽  
West Siberian Plain ◽  
Positive Trend ◽  
The Tibetan Plateau ◽  
Ecological Processes ◽  
Mountain Areas ◽  
Snow Cover Extent

Snow plays an important role in meteorological, hydrological and ecological processes, and snow phenology variation is critical for improved understanding of climate feedback on snow cover. The main purpose of the study is to explore spatial-temporal changes and variabilities of the extent, timing and duration, as well as phenology of seasonal snow cover across the large part of Eurasia from 2000 through 2016 using a Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-free snow product produced in this study. The results indicate that there are no significant positive or negative interannual trends of snow cover extent (SCE) from 2000 to 2016, but there are large seasonal differences. SCE shows a significant negative trend in spring (p = 0.01) and a positive trend in winter. The stable snow cover areas accounting for 78.8% of the large part of Eurasia, are mainly located north of latitude 45° N and in the mountainous areas. In this stable area, the number of snow-covered days is significantly increasing (p < 0.05) in 6.4% of the region and decreasing in 9.1% of the region, with the decreasing areas being mainly located in high altitude mountain areas and the increasing area occurring mainly in the ephemeral snow cover areas of northeastern and southern China. In central Siberia, Pamir and the Tibetan Plateau, the snow onset date tends to be delayed while the end date is becoming earlier from 2000 to 2016. While in the relatively low altitude plain areas, such as the West Siberian Plain and the Eastern European Plain region, the snow onset date is tending to advance, the end date tends to be delayed, but the increase is not significant.

scholarly journals Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

2019 ◽  
Vol 11 (8) ◽  
pp. 952 ◽  
Author(s):  
Aizhu Zhang ◽  
Genyun Sun ◽  
Ping Ma ◽  
Xiuping Jia ◽  
Jinchang Ren ◽  
...  
Keyword(s):  
Multilayer Perceptron ◽  
Coastal Wetlands ◽  
Coastal Wetland ◽  
Eastern China ◽  
Jiaozhou Bay ◽  
Superior Performance ◽  
Wetland Mapping ◽  
Mlp Classifier ◽  
Morphological Attribute ◽  
Sentinel 2

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.

scholarly journals Typical distribution of the solar erythemal UV radiation over Slovakia

2008 ◽  
Vol 8 (17) ◽  
pp. 5393-5401 ◽  
Author(s):  
A. Pribullová ◽  
M. Chmelík
Keyword(s):  
Snow Cover ◽  
Total Ozone ◽  
Horizontal Resolution ◽  
Clear Sky ◽  
Global Irradiance ◽  
Daily Dose ◽  
Monthly Total ◽  
Modification Factor ◽  
Typical Distribution ◽  
Sky Conditions

Abstract. Maps of solar erythemal ultraviolet (EUV) irradiance daily doses were created for every month with a horizontal resolution of 500 m at the geographical domain 47.15 N–49.86 N×16.94 E–22.81 E covering the territory of Slovakia. The cloud modification factor for the EUV radiation (cmfUV) was modeled utilizing the relation between the cloud modification factor of global and EUV radiation. The maps of the cmfUV factor were created by utilizing measurements of global irradiance performed at nine observatories during the period 1995–2004 and modeling of the cmfUV dependence on altitude. Maps of the EUV irradiance daily dose corresponded to clear-sky conditions and EUV irradiance daily dose affected by average cloudiness were constructed for mean monthly total ozone, its upper and lower monthly limits, for two probability levels of snow cover occurrence as criteria for the snow effect incorporation in the model and for one day representing typical values for every month. The map-set can be regarded as an atlas of solar EUV radiation over Slovakia.

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