scholarly journals TerraSAR-X Time Series Fill a Gap in Spaceborne Snowmelt Monitoring of Small Arctic Catchments—A Case Study on Qikiqtaruk (Herschel Island), Canada

2018 ◽  
Vol 10 (7) ◽  
pp. 1155 ◽  
Author(s):  
Samuel Stettner ◽  
Hugues Lantuit ◽  
Birgit Heim ◽  
Jayson Eppler ◽  
Achim Roth ◽  
...  
Keyword(s):  
Snow Cover ◽  
Spatiotemporal Variability ◽  
Accurate Estimation ◽  
Landsat 8 ◽  
Snowmelt Period ◽  
X Band ◽  
Data Gaps ◽  
Wet Snow ◽  
Positive Threshold

The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE) and fractional snow cover (FSC) on the small catchment scale. We investigate the performance of multi-polarized and multi-pass TSX X-Band SAR data in monitoring SCE and FSC in small Arctic tundra catchments of Qikiqtaruk (Herschel Island) off the Yukon Coast in the Western Canadian Arctic. We applied a threshold based segmentation on ratio images between TSX images with wet snow and a dry snow reference, and tested the performance of two different thresholds. We quantitatively compared TSX- and Landsat 8-derived SCE maps using confusion matrices and analyzed the spatiotemporal dynamics of snowmelt from 2015 to 2017 using TSX, Landsat 8 and in situ time lapse data. Our data showed that the quality of SCE maps from TSX X-Band data is strongly influenced by polarization and to a lesser degree by incidence angle. VH polarized TSX data performed best in deriving SCE when compared to Landsat 8. TSX derived SCE maps from VH polarization detected late lying snow patches that were not detected by Landsat 8. Results of a local assessment of TSX FSC against the in situ data showed that TSX FSC accurately captured the temporal dynamics of different snow melt regimes that were related to topographic characteristics of the studied catchments. Both in situ and TSX FSC showed a longer snowmelt period in a catchment with higher contributions of steep valleys and a shorter snowmelt period in a catchment with higher contributions of upland terrain. Landsat 8 had fundamental data gaps during the snowmelt period in all 3 years due to cloud cover. The results also revealed that by choosing a positive threshold of 1 dB, detection of ice layers due to diurnal temperature variations resulted in a more accurate estimation of snow cover than a negative threshold that detects wet snow alone. We find that TSX X-Band data in VH polarization performs at a comparable quality to Landsat 8 in deriving SCE maps when a positive threshold is used. We conclude that TSX data polarization can be used to accurately monitor snowmelt events at high temporal and spatial resolution, overcoming limitations of Landsat 8, which due to cloud related data gaps generally only indicated the onset and end of snowmelt.

Wet Snow Cover Mapping Algorithm Based on Multitemporal COSMO-SkyMed X-Band SAR Images

2012 ◽  
Vol 5 (3) ◽  
pp. 1045-1053 ◽  
Author(s):  
Thomas Schellenberger ◽  
Bartolomeo Ventura ◽  
Marc Zebisch ◽  
Claudia Notarnicola
Keyword(s):  
Snow Cover ◽  
Sar Images ◽  
Mapping Algorithm ◽  
X Band ◽  
Wet Snow

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.0)

2015 ◽  
Vol 8 (10) ◽  
pp. 8481-8518
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed e.g. in case of the frequently used Normalised-Difference Snow Index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user but not by reproducible independent information. PRACTISE V.2.0 addresses this important aspect and does show additional improvements. The Matlab based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilised as in-situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results have shown that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

scholarly journals Theia Snow collection: high resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

2018 ◽  
Author(s):  
Simon Gascoin ◽  
Manuel Grizonnet ◽  
Marine Bouchet ◽  
Germain Salgues ◽  
Olivier Hagolle
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Snow Depth ◽  
Western Europe ◽  
Landsat 8 ◽  
Mountain Regions ◽  
Snow Cover Area ◽  
Level 2 ◽  
Sentinel 2

Abstract. The Theia Snow collection routinely provides high resolution maps of the snow cover area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide including the main mountain regions in Western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Snow collection contains four classes: snow, no-snow, cloud and no-data. We present the algorithm to generate the snow products and provide an evaluation of their accuracy using in situ snow depth measurements, higher resolution snow maps, and visual control. The results suggest that the snow is accurately detected in the Theia snow collection, and that the snow detection is more accurate than the sen2cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds. The snow maps are currently produced and freely distributed in average 5 days after the image acquisition as raster and vector files via the Theia portal (http://doi.org/10.24400/329360/F7Q52MNK).

A Hydrometeor Classification Method for X-Band Polarimetric Radar: Construction and Validation Focusing on Solid Hydrometeors under Moist Environments

2015 ◽  
Vol 32 (11) ◽  
pp. 2052-2074 ◽  
Author(s):  
Takeharu Kouketsu ◽  
Hiroshi Uyeda ◽  
Tadayasu Ohigashi ◽  
Mariko Oue ◽  
Hiroto Takeuchi ◽  
...  
Keyword(s):  
Imaging System ◽  
Ice Crystals ◽  
Polarimetric Radar ◽  
X Band ◽  
Wet Snow ◽  
Moist Environment ◽  
Sampling Volume ◽  
In Situ Observations ◽  
Differential Reflectivity

AbstractA fuzzy-logic-based hydrometeor classification (HC) method for X-band polarimetric radar (X-pol), which is suitable for observation of solid hydrometeors under moist environments producing little or no hail, is constructed and validated. This HC method identifies the most likely hydrometeor at each radar sampling volume from eight categories: 1) drizzle, 2) rain, 3) wet snow aggregates, 4) dry snow aggregates, 5) ice crystals, 6) dry graupel, 7) wet graupel, and 8) rain–hail mixture. Membership functions are defined on the basis of previous studies. The HC method uses radar reflectivity Zh, differential reflectivity Zdr, specific differential phase Kdp, and correlation coefficient ρhv as its main inputs, and temperature with some consideration of relative humidity as supplemental information. The method is validated against ground and in situ observations of solid hydrometeors (dry graupel, dry snow aggregates, and ice crystals) under a moist environment. Observational data from a ground-based imaging system are used to validate the HC method for dry graupel and dry snow aggregates. For dry snow aggregates and ice crystals, the HC method is validated using simultaneous observations from a balloonborne instrument [hydrometeor videosonde (HYVIS)] and an X-pol range–height indicator directed toward the HYVIS. The HC method distinguishes effectively between dry graupel, dry snow aggregates, and ice crystals, and is therefore valid for HC under moist environments.

Exploitation of X-band SAR images and ground data for SWE retrieval through a machine learning technique

2020 ◽  
Author(s):  
Ludovica De Gregorio ◽  
Francesca Cigna ◽  
Giovanni Cuozzo ◽  
Alexander Jacob ◽  
Simonetta Paloscia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Snow Cover ◽  
Microwave Remote Sensing ◽  
Accurate Estimation ◽  
Mountainous Area ◽  
Sar Images ◽  
The Earth ◽  
X Band ◽  
Dense Media

<p>Snow cover is a critical geophysical parameter for Earth climate and hydrological systems. It contributes to regulate the Earth surface temperature and represents an important water storage that is slowly released during the melting season and contributes to the river discharge.</p><p>The parameter that characterizes the hydrological importance of snow cover is the snow water equivalent (SWE). An accurate estimation of the spatial and temporal distribution of SWE in mountain environments is still a relevant challenge for the scientific community, due to the complex topography that causes a high spatial heterogeneity in snow distribution, by reducing the representativeness of traditional pointwise in situ measurements.</p><p>Several efforts have been done to develop new methods for estimating snow-related parameters. In particular, the large-scale monitoring of the Earth’s surface from space-borne sensors has proven to be very effective, by improving the spatialization of land surface parameters. In the last decades, scientists have extensively investigated the potential of Synthetic Aperture Radar (SAR) data for deriving SWE. Unlikely to visible sensors, microwave sensors do not depend on the presence of sunlight and are not affected by the presence of clouds.</p><p>In this context, the main objective of this work is to exploit the already demonstrated sensitivity of the X-band SAR to snow [1] for estimating the SWE in the mountainous area of South Tyrol, in north-eastern Italy. For this purpose, the information derived from X-band SAR imagery acquired by the Italian Space Agency (ASI)’s COSMO-SkyMed constellation in StripMap HIMAGE mode at 3 m ground resolution is exploited together with ground measurements of SWE, which have been chosen by selecting the dates corresponding to the satellite acquisitions in the study period (2013-2015). In order to increase the training dataset, further backscattering coefficients have been simulated by using an implementation of the Dense Media Radiative Transfer (DMRT) theory, based on the Quasi-Crystalline Approximation (QCA) of Mie scattering of densely packed Sticky spheres [2]. Moreover, to optimize the satellite acquisition and use as much corresponding SWE data as possible, we integrated the ground dataset with other SWE values obtained as explained in [3] by means of a data fusion approach involving the snow model AMUNDSEN.</p><p>This work is carried out by EURAC, CNR/IFAC and ASI in the framework of the 2019-2021 project ‘Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone’, funded by ASI under grant agreement n.2018-37-HH.0.</p><p> </p><p>References</p><p>[1] Pettinato, S. et al. (2012). The potential of COSMO-SkyMed SAR images in monitoring snow cover characteristics. IEEE Geoscience and Remote Sensing Letters, 10(1), 9-13.</p><p>[2] Tsang, L. et al. (2007). Modeling active microwave remote sensing of snow using dense media radiative transfer (DMRT) theory with multiple-scattering effects. IEEE Transactions on Geoscience and Remote Sensing, 45(4), 990-1004.</p><p>[3] De Gregorio, L. et al. (2019). Improving SWE Estimation by Fusion of Snow Models with Topographic and Remotely Sensed Data. Remote Sensing, 11(17), 2033.</p><p> </p>

scholarly journals Investigating the Drivers of Total Suspended Sediment Regime in the Senegal River Basin Using Landsat 8 Satellite Images

2020 ◽  
Vol 13 (1-2) ◽  
pp. 31-42
Author(s):  
Cheikh Faye ◽  
Manuela Grippa ◽  
Laurent Kergoat ◽  
Elodie Robert
Keyword(s):  
Temporal Dynamics ◽  
Time Lag ◽  
Suspended Sediments ◽  
Spatiotemporal Variability ◽  
Landsat 8 ◽  
Senegal River ◽  
In Situ Data ◽  
Spatio Temporal ◽  
Solid Transport

AbstractBecause total suspended sediments (TSS) influence the penetration of light into the water column and are likely to carry pollutants and nutrients, their study is essential for understanding the functioning of African river ecosystems. If the estimation of solid transport is important in the context of hydro-agricultural developments, its quantification often poses a problem. In addition, in situ data for these areas are rare and, as a result, the environmental factors responsible for the variability of TSS can hardly be understood. This work aims to evaluate the spatiotemporal variability of TSS in the surface waters of the Senegal River using satellite data over the 2014-2018 period. The spatio-temporal dynamics of TSS is reconstructed using a relationship established on several West African sites between in situ data from TSS and satellite reflectances from Landsat 8. These data allow analyzing the relationship between TSS and factors such as rainfall and discharge. We found that the TSS peaks in Bakel coincide with the arrival of the first rains and are followed by peaks in discharge with a lag of 2 months. A time lag between TSS and discharge peaks is also observed on its tributaries like the River Falémé. Concerning the spatial variability, TSS generally increase from the river upstream to the downstream and decrease in the Senegal delta after the Diama dam. The analysis of the TSS upstream and downstream of the Manantali dam, in the upstream area, confirms the relatively low sediment deposits in the dam lake.

scholarly journals Development of the Statistical Model for Monitoring Salinization in the Mekong Delta of Vietnam Using Remote Sensing Data and In-Situ Measurements

Proceedings ◽  
2018 ◽  
Vol 2 (10) ◽  
pp. 565
Author(s):  
Nguyen Nguyen Vu ◽  
Le Van Trung ◽  
Tran Thi Van
Keyword(s):  
Statistical Model ◽  
Mekong Delta ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Measured Data ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Component Band

This article presents the methodology for developing a statistical model for monitoring salinity intrusion in the Mekong Delta based on the integration of satellite imagery and in-situ measurements. We used Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat- 8 OLI and TIRS) satellite data to establish the relationship between the planetary reflectance and the ground measured data in the dry season during 2014. The three spectral bands (blue, green, red) and the principal component band were used to obtain the most suitable models. The selected model showed a good correlation with the exponential function of the principal component band and the ground measured data (R2 > 0.8). Simulation of the salinity distribution along the river shows the intrusion of a 4 g/L salt boundary from the estuary to the inner field of more than 50 km. The developed model will be an active contribution, providing managers with adaptation and response solutions suitable for intrusion in the estuary as well as the inner field of the Mekong Delta.

scholarly journals Application of an improved surface energy balance model to two large valley glaciers in the St. Elias Mountains, Yukon

2020 ◽  
pp. 1-16
Author(s):  
Tim Hill ◽  
Christine F. Dow ◽  
Eleanor A. Bash ◽  
Luke Copland
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Shortwave Radiation ◽  
Balance Model ◽  
Landsat 8 ◽  
Glacier Surface ◽  
Ice Dynamics ◽  
Surface Melt

Abstract Glacier surficial melt rates are commonly modelled using surface energy balance (SEB) models, with outputs applied to extend point-based mass-balance measurements to regional scales, assess water resource availability, examine supraglacial hydrology and to investigate the relationship between surface melt and ice dynamics. We present an improved SEB model that addresses the primary limitations of existing models by: (1) deriving high-resolution (30 m) surface albedo from Landsat 8 imagery, (2) calculating shadows cast onto the glacier surface by high-relief topography to model incident shortwave radiation, (3) developing an algorithm to map debris sufficiently thick to insulate the glacier surface and (4) presenting a formulation of the SEB model coupled to a subsurface heat conduction model. We drive the model with 6 years of in situ meteorological data from Kaskawulsh Glacier and Nàłùdäy (Lowell) Glacier in the St. Elias Mountains, Yukon, Canada, and validate outputs against in situ measurements. Modelled seasonal melt agrees with observations within 9% across a range of elevations on both glaciers in years with high-quality in situ observations. We recommend applying the model to investigate the impacts of surface melt for individual glaciers when sufficient input data are available.

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