ALBERT

Description: We investigated snow cover dynamics using time series of moderate (MODIS) to high (SPOT-4/5, Landsat-8) spatial resolution satellite imagery in a 3700 km2 region of the southwestern French Alps. Our study was carried out in the context of the SPOT (Take 5) Experiment initiated by the Centre National d’Etudes Spatiales (CNES), with the aim of exploring the utility of high spatial and temporal resolution multispectral satellite imagery for snow cover mapping and applications in alpine ecology. Our three objectives were: (i) to validate remote sensing observations of first snow free day derived from the Normalized Difference Snow Index (NDSI) relative to ground-based measurements; (ii) to generate regional-scale maps of first snow free day and peak standing biomass derived from the Normalized Difference Vegetation Index (NDVI); and (iii) to examine the usefulness of these maps for habitat mapping of herbaceous vegetation communities above the tree line. Imagery showed strong agreement with ground-based measurements of snow melt-out date, although R2 was higher for SPOT and Landsat time series (0.92) than for MODIS (0.79). Uncertainty surrounding estimates of first snow free day was lower in the case of MODIS, however (±3 days as compared to ±9 days for SPOT and Landsat), emphasizing the importance of high temporal as well as high spatial resolution for capturing local differences in snow cover duration. The main floristic differences between plant communities were clearly visible in a two-dimensional habitat template defined by the first snow free day and NDVI at peak standing biomass, and these differences were accentuated when axes were derived from high spatial resolution imagery. Our work demonstrates the enhanced potential of high spatial and temporal resolution multispectral imagery for quantifying snow cover duration and plant phenology in temperate mountain regions, and opens new avenues to examine to what extent plant community diversity and functioning are controlled by snow cover duration.

Description: Remote Sensing, Vol. 10, Pages 619: An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data Remote Sensing doi: 10.3390/rs10040619 Authors: Théo Masson Marie Dumont Mauro Dalla Mura Pascal Sirguey Simon Gascoin Jean-Pierre Dedieu Jocelyn Chanussot 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.

Description: Glaciers are one of the terrestrial essential climate variables (ECVs) as they respond very sensitively to climate change. A key driver of their response is the glacier surface mass balance that is typically derived from field measurements. It deserves to be quantified over long time scales to better understand the accumulation and ablation processes at the glacier surface and their relationships with inter-annual changes in meteorological conditions and long-term climate changes. Glaciers with in situ monitoring of surface mass balance are scarce at the global scale, and satellite remote sensing provides a powerful tool to increase the number of monitored glaciers. In this study, we present a review of three optical remote sensing methods developed to quantify seasonal and annual glacier surface mass balances. These methodologies rely on the multitemporal monitoring of the end-of-summer snow line for the equilibrium-line altitude (ELA) method, the annual cycle of glacier surface albedo for the albedo method and the mapping of the regional snow cover at the seasonal scale for the snow-map method. Together with a presentation of each method, an application is illustrated. The ELA method shows promising results to quantify annual surface mass balance and to reconstruct multi-decadal time series. The other two methods currently need a calibration on the basis of existing in situ data; however, a generalization of these methods (without calibration) could be achieved. The two latter methods show satisfying results at the annual and seasonal scales, particularly for the summer surface mass balance in the case of the albedo method and for the winter surface mass balance in the case of the snow-map method. The limits of each method (e.g., cloud coverage, debris-covered glaciers, monsoon-regime and cold glaciers), their complementarities and the future challenges (e.g., automating of the satellite images processing, generalization of the methods needing calibration) are also discussed.

Description: In the central part of the Hindu Kush Himalayan region, snowmelt is one of the main inputs that ensures the availability of surface water outside the monsoon period. A common approach for snowpack modeling is based on the degree day factor (DDF) method to represent the snowmelt rate. However, the important seasonal variability of the snow processes is usually not represented when using a DDF method, which can lead to large uncertainties for snowpack simulation. The SPOT-VGT and the MODIS-Terra sensors provide valuable information for snow detection over several years. The aim of this work was to use those data to parametrize the seasonal variability of the snow processes in the hydrological distributed snow model (HDSM), based on a DDF method. The satellite products were corrected and combined in order to implement a database of 8 day snow cover area (SCA) maps over the northern part of the Dudh Koshi watershed (Nepal) for the period 1998–2017. A revisited version of the snow module of the HDSM model was implemented so as to split it into two parameterizations depending on the seasonality. Corrected 8 day SCA maps retrieved from MODIS-Terra were used to calibrate the seasonal parameterization, through a stochastic method, over the period of study (2013–2016). The results demonstrate that the seasonal parameterization reduces the error in the simulated SCA and increases the correlation with the MODIS SCA. The two-set version of the model improved the yearly RMSE from 5.9% to 7.7% depending on the basin, compared to the one-set version. The correlation between the model and MODIS passes from 0.73 to 0.79 in winter for the larger basin, Phakding. This study shows that the use of a remote sensing product can improve the parameterization of the seasonal dynamics of snow processes in a model based on a DDF method.

Description: Les destins de l'Espagne et de la France paraissent opposés aux XVIe et XVIIe siècles. La première, la Reconquête achevée, se trouve propulsée au premier rang grâce aux Découvertes et à une politique matrimoniale chanceuse. La seconde, de construcrion plus ancienne, de maturation plus lente, paraît être frappée en son cœur par une grave crise dynasrique et religieuse lorsque sa rivale est au sommet. La France l'emporte cependant au XVIIe siècle. Le pouvoir royal s'affermit, un long règne éclatant fait de la monarchie française un modèle alors que des unions matrimoniales consanguines épuisent la lignée des Habsbourg d'Espagne. L'étude conjointe de ces destins croisés permet une plus juste appréciation des deux monarchies dont les relations sont longuement conflictuelles. La décadence espagnole n'est pas aussi prononcée qu'on l'avait affirmé. Les monarchies espagnole et française au temps de leur affrontement offre une synthèse à jour des problématiques les plus récentes ainsi que de nombreux documents, indispensables pour donner la profondeur qui sied à cette histoire complexe. Autant qu'une claire mise au point, cet ouvrage constituera aussi un bon instrument de travail.

Description: L’Inquisition, un tribunal parmi d’autres, le Saint-Office, enfin replacé dans l’ensemble des institutions judiciaires ; une remise en contexte qui révèle sa véritable originalité et détruit bien des légendes. L’Inquisition de Castille : une institution au service de l’Église et du Roi, un instrument de la Contre-Réforme, qui poursuit conversos et morisques, de même que les vieux-chrétiens, et dont toute l’action répressive constitue une pédagogie de la foi.

Description: Due to recent climate change conditions, i.e. increasing temperatures and changing precipitation patterns, arctic snow cover dynamics exhibit strong changes in terms of extent and duration. Arctic amplification processes and impacts are well documented expected to strengthen in coming decades. In this context, innovative observation methods are helpful for a better comprehension of the spatial variability of snow properties relevant for climate research and hydrological applications. Microwave remote sensing provides exceptional spatial and temporal performance in terms of all-weather application and target penetration. Time-series of Synthetic Active Radar images (SAR) are becoming more accessible at different frequencies and polarimetry has demonstrated a significant advantage for detecting changes in different media. Concerning arctic snow monitoring, SAR sensors can offer continuous time-series during the polar night and with cloud cover, providing a consequent advantage in regard of optical sensors. The aim of this study is dedicated to the spatial/temporal variability of snow in the Ny-Ålesund area on the Br∅gger peninsula, Svalbard (N 78°55’ / E 11° 55’). The TerraSAR-X satellite (DLR, Germany) operated at X-band (3.1 cm, 9.6 GHz) with dual co-pol mode (HH/VV) at 5-m spatial resolution, and with high incidence angles (36° to 39°) poviding a better snow penetration and reducing topographic constraints. A dataset of 92 images (ascending and descending) is available since 2017, together with a high resolution DEM (NPI 5-m) and consistent in-situ measurements of meteorological data and snow profiles including glaciers sites. Polarimetric processing is based on the Kennaugh matrix decomposition, copolar phase coherence (CCOH) and copolar phase difference (CPD). The Kennaugh matrix elements K0, K3, K4, and K7 are, respectively, the total intensity, phase ratio, intensity ratio, and shift between HH and VV phase center. Their interpretation allows analysing the structure of the snowpack linked to the near real time of in-situ measurements (snow profiles). The X-band signal is strongly influenced by the snow stratigraphy: internal ice layers reduce or block the penetration of the signal into the snow pack. The best R2 correlation performances between estimated and measured snow heights are ranging from 0.50 to 0.70 for dry snow conditions. Therefore, the use of the X-band for regular snow height estimations remains limited under these conditions. Conversely, this study shows the benefit of TerraSAR-X thanks to the Kennaugh matrix elements analysis. A focus is set on the Copolar Phase Difference (CPD, Leinss 2016) between VV and HH polarization: Φ CPD = Φ VV - Φ HH. Our results indicate that the CPD values are related to the snow metamorphism: positive values correspond to dry snow (horizontal structures), negative values indicate recrystallization processes (vertical structures). Backscattering evolution in time offer a good proxy for meteorological events detection, impacting on snow metamorphism. Fresh snowfalls or melting processes can then be retrieved at the regional scale and linked to air temperature or precipitation measurements at local scale. Polarimetric SAR time series is therefore of interest to complement satellite-based precipitation measurements in the Arctic.

Description: Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability is one of the primary constraints in model development. In terms of spatial resolution, active microwaves (synthetic aperture radar – SAR) can address the issue and outperform methods based on passive microwaves. Thus, high-spatial-resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow. The overall objective of this study is to evaluate the potential of the TerraSAR-X (TSX) SAR sensor and the wave co-polar phase difference (CPD) method for characterizing snow cover at high spatial resolution. Consequently, we first (1) investigate SD and depth hoar fraction (DHF) variability between different vegetation classes in the Ice Creek catchment (Qikiqtaruk/Herschel Island, Yukon, Canada) using in situ measurements collected over the course of a field campaign in 2019; (2) evaluate linkages between snow characteristics and CPD distribution over the 2019 dataset; and (3) determine CPD seasonality considering meteorological data over the 2015–2019 period. SD could be extracted using the CPD when certain conditions are met. A high incidence angle () with a high topographic wetness index (TWI) (〉7.0) showed correlation between SD and CPD (R2 up to 0.72). Further, future work should address a threshold of sensitivity to TWI and incidence angle to map snow depth in such environments and assess the potential of using interpolation tools to fill in gaps in SD information on drier vegetation types.