Marie Dumont

Small-scale horizontal variability of snow, sea-ice thickness and freeboard in the first-year ice region north of Svalbard

Abstract Variability of sea-ice and snow conditions on the scale of a few hundred meters is examined using in situ measurements collected in first-year pack ice in the European Arctic north of Svalbard. Snow thickness and surface elevation measurements were performed in the standard manner using a snow stick and a rotating laser. Altogether, 4109 m of measurement lines were surveyed. The snow loading was large, and in many locations the ice freeboard was negative (38.8% of snowline measurements), although the modal ice and snow thickness was 1.8 m. The mean of all the snow thickness measurements was 36 cm, with a standard deviation of 26 cm. The mean freeboard was only 3 cm, with a standard deviation of 23 cm. There were noticeable differences in snow thickness among the measurement sites. Over the undeformed ice areas, the mean snow thickness and freeboard were 23 and 2.4 cm, respectively. Over the ridged ice areas, the mean freeboard was only –0.3 cm due to snow accumulation on the sails of ridges (average thickness 54 cm). These findings imply that retrieval algorithms for converting freeboard to ice thickness should take account of spatial variability of snow cover.

Recent glacier decline in the Kerguelen Islands (49°S, 69°E) derived from modeling, field observations, and satellite data

The retreat of glaciers in the Kerguelen Islands (49°S, 69°E) and their associated climatic causes have been analyzed using field data and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images to validate a positive degree-day (PDD) model forced by data from local meteorological stations. Mass balance measurements made during recent field campaigns on the largest glacier of the Cook Ice Cap were compared to data from the early 1970s, providing a 40 year view of the differences in the spatial distribution of surface mass balance (SMB). To obtain additional regional data for the validation of our models, we analyzed MODIS images (2000–2012) to determine if our model was capable of reproducing variations in the transient snow line. The PDD model correctly simulated the variations in the snow line, the spatial variations in the SMB, and its trend with elevation. Yet current SMB values diverge from their classic linear representation with elevation, and stake data at high altitudes now display more negative SMB values than expected. By analyzing MODIS albedo, we observed that these values are caused by the disappearance of snow and associated feedback on melt rates. In addition, certain parts of Ampere Glacier could not be reproduced by the surface energy balance model because of overaccumulation due to wind deposition. Finally, the MODIS data, field data, and our models suggest that the acceleration of glacier wastage in Kerguelen is due to reduced net accumulation and an associated rise in the snow line since the 1970s.

HYPXIM: A second generation high spatial resolution hyperspectral satellite for dual applications

This paper synthesizes the mission requirements defined by a group of French scientists and defence users expert in the field of hyperspectral remote sensing to design a spaceborne mission composed of a second generation imaging spectrometer (8m) coupled with a panchromatic camera (2m). Its technical characteristics open the way for new applications in several topics: geosciences and solid Earth science, urban ecosystems, vegetation biodiversity, coastal and inland ecosystems, atmospheric sciences, cryosphere and defence. For each of them, the improvements brought by such a mission are summarized.

Improved subpixel monitoring of seasonal snow cover: A case study in the Alps

The snow coverage area (SCA) is one of the most important parameters for cryospheric studies. The use of remote sensing imagery can complement field measurements by providing means to derive SCA with a high temporal frequency and covering large areas. Images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) are perhaps the most widely used data to retrieve SCA maps. Some MODIS derived algorithms are available for subpixel SCA estimation, as MODSCAG and MODImLab. Both algorithms make use of spectral unmixing techniques using a fixed set of snow, rocks and other materials spectra (endmembers). We aim to improve the performance of a modified version of MODIm-Lab algorithm by exploring advanced spectral unmixing techniques. Furthermore, we make use of endmember induction algorithms to obtain the endmembers from the data itself instead of using a fixed spectral library. We validate the proposed approach on a case study in the mountainous region of the Alps.

Multilayer snowpack backscattering model and assimilation of TerraSAR-X satellite data

The advantages of the new generation of radar systems with high resolution image, short revisit time provide the possibility of characterization and monitoring the evolution of the cryoshpere. In this paper, we propose an adaptation of the multilayer snow backscattering model based on radiative transfer theory in order to estimate the total backscattering coefficient of high frequency (X-band) electromagnetic wave on snowcover area. Next, from the physical model, we develop the adjoint operator and implement a variational assimilation scheme in order to constrain the snow stratigraphy profiles calculated by CROCUS, a snow metamorphism model used by MeteoFrance. Some tests are carried out with TerraSAR-X image data. The results show that the snow stratigraphy profiles obtained after the data assimilation process have good agreement with the measured profiles, and therefore show the high potential of this method in constraining the snowpack profiles of CROCUS.

The VIS/NIR Land and Snow BRDF Atlas for RTTOV: Comparison between MODIS MCD43C1 C5 and C6

A monthly mean land and snow Bidirectional Reflectance Distribution Function (BRDF) atlas for visible and near infrared parts of the spectrum has been developed for Radiative Transfer for Television Infrared Observation Satellite (TIROS) Operational Vertical sounder (TOVS) (RTTOV). The atlas follows the methodology of the RTTOV University of Wisconsin infrared land surface emissivity (UWIREMIS) atlas, i.e., it combines satellite retrievals and a principal component analysis on a dataset of hyper-spectral surface hemispherical reflectance or albedo. The current version of the BRDF atlas is based on the Collection 5 of the Moderate Resolution Imaging (MODIS) MCD43C1 Climate Modeling Grid BRDF kernel-driven model parameters product. The MCD43C1 product combines both Terra and Aqua satellites over a 16-day period of acquisition and is provided globally at 0.05° of spatial resolution. We have improved the RTTOV land surface BRDF atlas by using the last Collection 6 of MODIS product MCD43C1. We firstly found that the MODIS C6 product improved the quality index of the BRDF model as compared with that of C5. When compared with clear-sky top of atmosphere (TOA) reflectance of Spinning Enhanced Visible and InfraRed Imagers (SEVIRI) solar channels over snow-free land surfaces, we showed that the reflectances are simulated with an absolute accuracy of 3% to 5% (i.e., 0.03–0.05 in reflectance units) when either the satellite zenith angle or the solar zenith angle is below 70°, regardless of the MODIS collection. For snow-covered surfaces, we showed that the comparison with in situ snow spectral albedo is improved with C6 with an underestimation of 0.05 in the near infrared.

Multi-Criteria Evaluation of Snowpack Simulations in Complex Alpine Terrain Using Satellite and In Situ Observations

This work presents an extensive evaluation of the Crocus snowpack model over a rugged and highly glacierized mountain catchment (Arve valley, Western Alps, France) from 1989 to 2015. The simulations were compared and evaluated using in-situ point snow depth measurements, in-situ seasonal and annual glacier surface mass balance, snow covered area evolution based on optical satellite imagery at 250 m resolution (MODIS sensor), and the annual equilibrium-line altitude of glaciers, derived from satellite images (Landsat, SPOT, and ASTER). The snowpack simulations were obtained using the Crocus snowpack model driven by the same, originally semi-distributed, meteorological forcing (SAFRAN) reanalysis using the native semi-distributed configuration, but also a fully distributed configuration. The semi-distributed approach addresses land surface simulations for discrete topographic classes characterized by elevation range, aspect, and slope. The distributed approach operates on a 250-m grid, enabling inclusion of terrain shadowing effects, based on the same original meteorological dataset. Despite the fact that the two simulations use the same snowpack model, being potentially subjected to same potential deviation from the parametrization of certain physical processes, the results showed that both approaches accurately reproduced the snowpack distribution over the study period. Slightly (although statistically significantly) better results were obtained by using the distributed approach. The evaluation of the snow cover area with MODIS sensor has shown, on average, a reduction of the Root Mean Squared Error (RMSE) from 15.2% with the semi-distributed approach to 12.6% with the distributed one. Similarly, surface glacier mass balance RMSE decreased from 1.475 m of water equivalent (W.E.) for the semi-distributed simulation to 1.375 m W.E. for the distribution. The improvement, observed with a much higher computational time, does not justify the recommendation of this approach for all applications; however, for simulations that require a precise representation of snowpack distribution, the distributed approach is suggested.

An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data

The characterization of snow extent is critical for a wide range of applications. Since 1966, snow maps at different spatial resolutions have been produced using various satellite sensor images. Nowadays, the most widely used products are likely those derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) data, which cover the whole Earth at a near-daily frequency. There are a variety of snow mapping methods for MODIS data, based on different methodologies and applied at different spatial resolutions. Up to now, all these products have been tested and evaluated separately. This study aims to compare the methods currently available for retrieving snow from MODIS data. The focus is on fractional snow cover, which represents the snow cover area at the subpixel level. We examine the two main approaches available for generating such products from MODIS data; namely, linear regression of the Normalized Difference Snow Index (NDSI) and spectral unmixing (SU). These two approaches have resulted in several methods, such as MOD10A1 (the NSIDC MODIS snow product) for NDSI regression, and MODImLAB for SU. The assessment of these approaches was carried out using higher resolution binary snow maps (i.e., showing the presence or absence of snow) at spatial resolutions of 10, 20, and 30 m, produced by SPOT 4, SPOT 5, and LANDSAT-8, respectively. Three areas were selected in order to provide landscape diversity: the French Alps (117 dates), the Pyrenees (30 dates), and the Moroccan Atlas (24 dates). This study investigates the impact of reference maps on accuracy assessments, and it is suggested that NDSI-based high spatial resolution reference maps advantage NDSI medium-resolution snow maps. For MODIS snow maps, the results show that applying an NDSI approach to accurate surface reflectance corrected for topographic and atmospheric effects generally outperforms other methods for the global retrieval of snow cover area. The improvements to the newer version of MOD10A1 (Collection 6) compared to the older version (Collection 5) are significant. Products based on SU provide a good alternative and more accurate retrieval of the snow fraction where wider ranges of land covers are concerned. The fusion process and its resulting 250 m spatial resolution product improve snow line retrieval. False detection in mixed pixels, probably due to the spectral variability associated with the various materials in the spectral mixture, has been identified as an area that will require improvement.

Using time series to improve endmembers estimation on multispectral images for snow monitoring

We propose to use the temporal coherence of a time series to extract using Vertex Component Analysis (VCA) the suitable set of endmembers for each scene. The reconstruction error computed on the two previous scenes for each date is used to constrain the selection of the set of endmembers produced by VCA. Snow cover estimation is considered as application. We tested different approaches for abundance estimation (FCLSU, SUnSAL, ELMM) over the French Alps from Moderate Resolution Imaging Spectroradiometer (MODIS) images. Results shows a decrease of the false positive rate with the proposed approach.

Snow cover estimation based on spectral unmixing

Spectral Unmixing is the most recent method used to recover the Snow Cover Fraction of an area, but it depends particularly on the relevance of the set of endmembers. This communication investigates different strategies for defining set of endmembers for retrieving snow cover fraction with spectral unmixing. Endmembers can be estimated from on site measurements or estimated directly on the image. In this work we propose a set of endmembers associating semantics of field data for snow endmembers with the extraction of a set in a date without snow for other materials. A heterogeneous area in the Alps was considered in the experiment. Considering reference maps of snow available for several dates, Precision and Mean Absolute Error were computed for evaluating the estimated Snow Cover Fractions. Results obtained confirm the soundness of the proposed approach for low snow fraction.