Next Article in Journal
Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries
Next Article in Special Issue
Using LANDSAT 8 and VENµS Data to Study the Effect of Geodiversity on Soil Moisture Dynamics in a Semiarid Shrubland
Previous Article in Journal
Modulation Effect of Mesoscale Eddies on Sequential Typhoon-Induced Oceanic Responses in the South China Sea
Previous Article in Special Issue
Bayesian Calibration of the Aquacrop-OS Model for Durum Wheat by Assimilation of Canopy Cover Retrieved from VENµS Satellite Data
Open AccessLetter

Evaluation of Methods for Mapping the Snow Cover Area at High Spatio-Temporal Resolution with VENμS

1
CESBIO, Université de Toulouse, CNES/CNRS/IRD/INRA/UPS, 31400 Toulouse, France
2
Center for Remote Sensing Application (CRSA), University Mohammed VI Polytechnic (UM6P), Benguerir 43150, Morocco
3
CNES, 31400 Toulouse, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 3058; https://doi.org/10.3390/rs12183058
Received: 16 June 2020 / Revised: 10 September 2020 / Accepted: 14 September 2020 / Published: 18 September 2020
(This article belongs to the Special Issue VENµS Image Processing Techniques and Applications)
The VENμS mission launched in 2017 provides multispectral optical images of the land surface with a 2-day revisit time at 5 m resolution for over 100 selected sites. A few sites are subject to seasonal snow accumulation, which gives the opportunity to monitor the variations of the snow cover area at unprecedented spatial and temporal resolution. However, the 12 spectral bands of VENμS only cover the visible and near-infrared region of the spectra while existing snow detection algorithms typically make use of a shortwave infrared band to determine the presence of snow. Here, we evaluate two alternative snow detection algorithms. The first one is based on a normalized difference index between the near-infrared and the visible bands, and the second one is based on a machine learning approach using the Theia Sentinel-2 snow products as training data. Both approaches are tested using Sentinel-2 data (as surrogate of VENμS data) as well as actual VENμS in the Pyrenees and the High Atlas. The results confirm the possibility of retrieving snow cover without SWIR with a slight loss in performance. As expected, the results confirm that the machine learning method provides better results than the index-based approach (e.g., an RMSE equal to the learning method 1.35% and for the index-based method 10.80% in the High Atlas.). The improvement is more evident in the Pyrenees probably due to the presence of vegetation which complicates the spectral signature of the snow cover area in VENμS images. View Full-Text
Keywords: snow; snow cover area; Venus; Atlas; Pyrenees; NDSI; Sentinel-2; machine learning snow; snow cover area; Venus; Atlas; Pyrenees; NDSI; Sentinel-2; machine learning
Show Figures

Graphical abstract

MDPI and ACS Style

Baba, M.W.; Gascoin, S.; Hagolle, O.; Bourgeois, E.; Desjardins, C.; Dedieu, G. Evaluation of Methods for Mapping the Snow Cover Area at High Spatio-Temporal Resolution with VENμS. Remote Sens. 2020, 12, 3058. https://doi.org/10.3390/rs12183058

AMA Style

Baba MW, Gascoin S, Hagolle O, Bourgeois E, Desjardins C, Dedieu G. Evaluation of Methods for Mapping the Snow Cover Area at High Spatio-Temporal Resolution with VENμS. Remote Sensing. 2020; 12(18):3058. https://doi.org/10.3390/rs12183058

Chicago/Turabian Style

Baba, Mohamed W.; Gascoin, Simon; Hagolle, Olivier; Bourgeois, Elsa; Desjardins, Camille; Dedieu, Gérard. 2020. "Evaluation of Methods for Mapping the Snow Cover Area at High Spatio-Temporal Resolution with VENμS" Remote Sens. 12, no. 18: 3058. https://doi.org/10.3390/rs12183058

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop