scholarly journals Icelandic Snow Cover Characteristics derived from a gap-filled MODIS Daily Snow Cover Product

2019 ◽  
Author(s):  
Andri Gunnarsson ◽  
Sigurður M. Garðarsson ◽  
Óli G. B. Sveinsson
Keyword(s):  
Snow Cover ◽  
Cloud Cover ◽  
Data Availability ◽  
Statistical Testing ◽  
Optical Data ◽  
Temporal Aggregation ◽  
Data Set ◽  
Snow Cover Duration ◽  
Sentinel 2

Abstract. This study presents a spatio-temporal continuous data set for snow cover in Iceland based on the Moderate Resolution Imaging Spectroradiometer (Modis) from 2000–2018. Cloud cover and polar darkness are the main limiting factors for data availability of remotely sensed optical data at higher latitudes. In Iceland the average cloud cover is 75 % with some spatial variations and polar darkness reduces data availability from the Modis sensor from late November until mid January. In this study Modis snow cover data were validated over Iceland with comparison to manned in-situ observations, Landsat 7/8 and Sentinel 2 data. Overall a good agreement was found between in-situ observed snow cover with an average agreement of 0.925. Agreement of Landsat 7,8 and Sentinel 2 was found to be acceptable with R2 values 0.96, 0.92 and 0.95, respectively, and in agreement with other studies. By applying daily data merging from Terra and Aqua and temporal aggregation of 7 days, unclassified pixels were reduced from 75 % to 14 %. The remaining unclassified pixels after daily merging and temporal aggregation were removed with classification learners trained with classified data, pixel location, aspect and elevation. Various snow cover characteristic metrics were derived for each pixel such as snow cover duration, first and last snow free date, deviation and dynamics of snow cover and trends during the study period. On average the first snow free date in Iceland is June 27 with a standard deviation of 19.9 days. For the study period a trend of increasing snow cover duration was observed for all months except October and November. However, statistical testing of the trends indicated that there was only a significant trend in June.

scholarly journals Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product

2019 ◽  
Vol 23 (7) ◽  
pp. 3021-3036
Author(s):  
Andri Gunnarsson ◽  
Sigurður M. Garðarsson ◽  
Óli G. B. Sveinsson
Keyword(s):  
Snow Cover ◽  
Cloud Cover ◽  
Data Availability ◽  
Statistical Testing ◽  
Optical Data ◽  
Temporal Aggregation ◽  
Landsat 7 ◽  
Snow Cover Duration ◽  
Sentinel 2

Abstract. This study presents a spatio-temporal continuous data set for snow cover in Iceland based on the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2018. Cloud cover and polar darkness are the main limiting factors for data availability of remotely sensed optical data at higher latitudes. In Iceland the average cloud cover is 75 % with some spatial variations, and polar darkness reduces data availability from the MODIS sensor from late November until mid January. In this study MODIS snow cover data were validated over Iceland with comparison to manned in situ observations and Landsat 7/8 and Sentinel 2 data. Overall a good agreement was found between in situ observed snow cover, with an average agreement of 0.925. Agreement of Landsat 7/8 and Sentinel 2 was found to be acceptable, with R2 values 0.96, 0.92 and 0.95, respectively, and in agreement with other studies. By applying daily data merging from Terra and Aqua and a temporal aggregation of 7 d, unclassified pixels were reduced from 75 % to 14 %. The remaining unclassified pixels after daily merging and temporal aggregation were removed with classification learners trained with classified data, pixel location, aspect and elevation. Various snow cover characteristic metrics were derived for each pixel such as snow cover duration, first and last snow-free dates, deviation and dynamics of snow cover and trends during the study period. On average the first snow-free date in Iceland is 27 June, with a standard deviation of 19.9 d. For the study period a trend of increasing snow cover duration was observed for all months except October and November. However, statistical testing of the trends indicated that there was only a significant trend in June.

Generation of a high-resolution snow cover dataset from Sentinel-2 images for snow model calibration 

2021 ◽  
Author(s):  
Florentin Hofmeister ◽  
Leonardo F. Arias-Rodriguez ◽  
Marco Borga ◽  
Valentina Premier ◽  
Carlo Marin ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Model Calibration ◽  
Data Availability ◽  
Cloud Detection ◽  
Snow Storage ◽  
Elevation Zone ◽  
Snow Model ◽  
Sentinel 2

<p>Modeling the runoff generation of high elevation Alpine catchments requires fundamental knowledge of the snow storage and the spatial distribution of snow cover. Since in-situ snow observations are often very scarce and represent only a point information, spatial snow information from satellite data is used since decades. However, the accuracy of snow cover maps through remote sensing products depends strongly on the cloudiness. In order to generate a spatial and temporal highly resolved dataset of snow cover maps, we applied the pixel identification processor (IdePix available in SNAP v7.0) to retrieve diverse cloud layers from Sentinel-2 Level-1C products. This makes it possible to use also high-clouded images for the snow detection, which increases significantly the data availability for the later performed snow model calibration. Cloudy areas, for which snow detection by the NDSI calculation is not possible, are set to no data. Sentinel-2 images that do not have cloud information require an extra correction based on the assumption that the snow cover has a pronounced elevation gradient. The entire NDSI dataset is subdivided into 200 m elevation zones and statistically analyzed. Thereby, the cloud-influenced images clearly stand out as outliers in the elevation zones >3000 m. If an elevation zone is detected as an outlier, the corresponding elevation zone is set to no data as well. After the comprehensive cloud detection, a pixel wise comparison with in-situ snow depth observation of four different sites allows us a first validation of the snow detection quality. In a second step, the generated snow maps are compared with the snow and cloud detection algorithm developed by Eurac Research. The final snow cover maps are used together with the in-situ snow depth observations to calibrate two different snowmelt approaches of the hydrological model WaSiM - the T-index and the energy balance-based approach (including gravitational snow redistribution) - over a mountainous basin in the Eastern Italian Alps.</p>

scholarly journals A snow cover climatology for the Pyrenees from MODIS snow products

2014 ◽  
Vol 11 (11) ◽  
pp. 12531-12571 ◽  
Author(s):  
S. Gascoin ◽  
O. Hagolle ◽  
M. Huc ◽  
L. Jarlan ◽  
J.-F. Dejoux ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Landsat Images ◽  
Forested Areas ◽  
Land Cover Map ◽  
Hydropower Production ◽  
Snow Cover Duration

Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (we) and 105 mm respectively, for both MOD10A1 and MYD10A1. Kappa coefficients are within 0.74 and 0.92 depending on the product and the variable. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both datasets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decreases over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gapfilling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band. We finally analyze the snow patterns for the atypical winter 2011–2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

scholarly journals Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco

2018 ◽  
Author(s):  
Mohamed Wassim Baba ◽  
Simon Gascoin ◽  
Lahoucine Hanich
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Snow Cover Area ◽  
High Atlas ◽  
Data Assimilation Scheme ◽  
Snow Cover Extent ◽  
Assimilation Scheme ◽  
Sentinel 2

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.

scholarly journals The S2M meteorological and snow cover reanalysis over the French mountainous areas, description and evaluation (1958–2020)

2021 ◽  
Author(s):  
Matthieu Vernay ◽  
Matthieu Lafaysse ◽  
Diego Monteiro ◽  
Pascal Hagenmuller ◽  
Rafife Nheili ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Scientific Publications ◽  
Mountain Areas ◽  
Data Set ◽  
Meteorological Observations ◽  
Surface Scheme

Abstract. This work introduces the S2M (SAFRAN - SURFEX/ISBA-Crocus - MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2020. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2020) and the best possible set of available in-situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground, and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends), and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in-situ snow depth observations. Further, we describe the technical handling of this open access data set, available at this address: http://dx.doi.org/10.25326/37#v2020.2. Scientific publications using this dataset must mention in the acknowledgments: "The S2M data are provided by Météo-France - CNRS, CNRM Centre d’Etudes de la Neige, through AERIS" and refer to it as Vernay et al. (2020).

scholarly journals Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and -2 Data Fusion

2020 ◽  
Vol 12 (2) ◽  
pp. 302 ◽  
Author(s):  
Kai Heckel ◽  
Marcel Urban ◽  
Patrick Schratz ◽  
Miguel Mahecha ◽  
Christiane Schmullius
Keyword(s):  
South Africa ◽  
Time Series ◽  
Forest Cover ◽  
Short Wave ◽  
Optical Data ◽  
Biophysical Parameters ◽  
Data Set ◽  
Area Index ◽  
The Individual ◽  
Sentinel 2

The fusion of microwave and optical data sets is expected to provide great potential for the derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating in twin mode, they can provide an unprecedented data source to build dense spatial and temporal high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation (CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of 87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover. In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover predictions in open savanna-like environments with heterogeneous regional features. The presented approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high spatial resolution.

Detection of irrigated and rainfed crop fields in temperate areas using Sentinel-1, Sentinel-2 and rainfall time series

2021 ◽  
Author(s):  
Yann Pageot ◽  
Frédéric Baup ◽  
Jordi Inglada ◽  
Valérie Demarez
Keyword(s):  
Time Series ◽  
Adaptation Strategies ◽  
Optical Data ◽  
Spectral Information ◽  
Agricultural Activity ◽  
Reference Scenario ◽  
Data Set ◽  
Radar Rainfall ◽  
Ground Truth Data ◽  
Sentinel 2

<p><span>Human activities have an impact on the different components of the hydrosphere and 80 % of the world's population is now facing water shortages that will worsen with global warming. Faced with this emergency situation, it is necessary to develop adaptation strategies to monitor and manage water resources for the entire population and to maintain agricultural activity. One of the adaptation strategies that has been favoured is the management of crop irrigation to optimize the use of scarce water ressources. </span></p><p><span>To meet this objective, it is necessary to have explicit information on irrigated areas. However, up to now, this information is missing or imprecise at the field scale (it is only produced as aggregated statistics or maps at the regional or national scales). In this work, we propose a method for detecting irrigated and rainfed plots in a temperate areas (Adour-Amont watershed of 1500 km² located in south-western France) jointly using optical (Sentinel-2), radar (Sentinel-1) and rainfall (SAFRAN) time series, through the random forest classification algorithm. This spectral information was synthesized in the form of cumulative monthly indices corresponding to the sum of the spectral information for each element (optical, radar, rainfall). This cumulative approach makes it possible to reduce the redundancy of the spectral information and the calculation time of the classification process.</span></p><p><span>The summer crops studied were maize, soybean and sunflower, representing respectively 82%, 9% and 8% of the crops cultivated of the studied area, but only part of these crops were irrigated. In order to make the distinction for the same crop, we assume that the speed and amplitude of canopy development differs between irrigated and rainfed crop. Five scenarios were used to evaluate the performance of classification models. They have been built according to the different spatialized data, i.e (Optic; Radar; Optic & Radar; Optic, Radar & Rainfall and 10-day images, which is reference scenario without the cumulative monthly indices). Finally, generated classification maps were evaluated using ground truth data collected during 2 years with contrasted meteorological conditions. </span></p><p> <span>The use of separate radar and optical data gives low results (Overall Accuracy (OA) < 0.5) compared to the combined classifications of the cumulated data set (optical & radar), which gives good results (OA ± 0.7). The use of the monthly cumulated rainfall allows a significant improvement of the Fscore of the irrigated and rainfed crop classes. Our study also reveals that the use of cumulative monthly indices leads to performances similar to those of the use of 10-day images while considerably reducing computational resources.</span></p>

Satellite-derived shorelines reveal fascinating dynamics for the last three decades on Danube Delta coast

2021 ◽  
Author(s):  
Florin Tatui ◽  
Georgiana Anghelin ◽  
Sorin Constantin
Keyword(s):  
Satellite Images ◽  
High Energy ◽  
Data Availability ◽  
Danube Delta ◽  
Dune System ◽  
Temporal Scales ◽  
Wide Range ◽  
The Impact ◽  
Sentinel 2

<p>Shoreline, as the interface between the upper shoreface and the beach-dune system, is sensitive to all changes from both the underwater and sub-aerial parts of the beach at a wide range of temporal scales (seconds to decades), making it a good indicator for coastal health. While more traditional techniques of shoreline monitoring present some shortcomings (low temporal resolution for photointerpretation, reduced spatial extension for video-based techniques, high costs for DGPS in-situ data acquisition), freely available satellite images can provide information for large areas (tens/hundreds of km) at very good temporal scales (days).</p><p>We employed a shoreline detection workflow for the dynamic environment of the Danube Delta coast (Black Sea). We focused on an index-based approach using the Automated Water Extraction Index (AWEI). A fully automated procedure was deployed for data processing and the waterline was estimated at sub-pixel level with an adapted image thresholding technique. For validation purposes, 5 Sentinel-2 and 5 Landsat based results were compared with both in-situ (D)GPS measurements and manually digitized shoreline positions from very high-resolution satellite images (Pleiades – 0.5 m and Spot 7 – 1.5 m). The overall accuracy of the methodology, expressed as mean absolute error, was found to be of approximately 7.5 m for Sentinel-2 and 4.7 m for Landsat data, respectively.</p><p>More than 200 Landsat (5 and 8) and Sentinel-2 images were processed and the corresponding satellite-derived shorelines between 1990 and 2020 were analysed for the whole Romanian Danube Delta coast (130 km). This high number of shorelines allowed us the discrimination of different patterns of coastline dynamic and behaviour which could not have been possible using usual surveying techniques: the extent of accumulation areas induced by the 2005-2006 historical river floods, the impact of different high-energy storms and the subsequent beach recovery after these events, the alongshore movement of erosional processes in accordance with the dominant direction of longshore sediment transport, multi-annual differences in both erosional and accumulation trends. Moreover, a very important result of our analysis is the zonation of Danube Delta coast based on multi-annual trends of shoreline dynamics at finer alongshore spatial resolution than before. This has significant implications for future studies dealing with different scenarios of Danube Delta response to projected sea level rise and increased storminess.</p><p>The presented approach and resulting products offer optimal combination of data availability, accuracy and frequency necessary to meet the monitoring and management needs of the increasing number of stakeholders involved in the coastal zone protection activities.</p>

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>

Sign in / Sign up

Export Citation Format

Share Document