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Snow-Mantle Remote Sensing from Spaceborne Sar Interferometry Using a Model-Based Synergetic Retrieval Approach in Central Apennines
Italy is a territory characterized by complex topography with the Apennines mountain range crossing the entire peninsula and its highest peaks in central Italy. Using the latter as our area of interest and the snow seasons 2018/19, 2019/20 and 2020/21, the goal of this study is to investigate the ability of a simple single-layer and a more sophisticated multi-layer snow cover numerical model to reproduce the observed snow height, snow water equivalent and snow extent in the central Apennines, using for both models the same forecast weather data as meteorological forcing. We here consider two well-known ground surface and soil models: (i) Noah LSM, an Eulerian model which simulates the snowpack as a bulk single layer, and (ii) Alpine3D, a multi-layer Lagrangian model which simulates the snowpack stratification. We adopt the Weather Research and Forecasting (WRF) model to produce the meteorological data to drive both Noah LSM and Alpine3D at a regional scale with a spatial resolution of 3 km. While Noah LSM is already online-coupled with the WRF model, we develop here a dedicated offline coupling between WRF and Alpine3D. We validate the WRF simulations of surface meteorological variables in central Italy using a dense network of automatic weather stations, obtaining correlation coefficients higher than 0.68, except for wind speed, which suffered from the model underestimation of the real elevation. The performances of both WRF–Noah and WRF–Alpine3D are evaluated by comparing simulated and measured snow height, snow height variation and snow water equivalent, provided by a quality-controlled network of automatic and manual snow stations located in the central Apennines. We find that WRF–Alpine3D can predict better than WRF–Noah the snow height and the snow water equivalent, showing a correlation coefficient with the observations of 0.9 for the former and 0.7 for the latter. Both models show similar performances in reproducing the observed daily snow height variation; nevertheless WRF–Noah is slightly better at predicting large positive variations, while WRF–Alpine3D can slightly better simulate large negative variations. Finally we investigate the abilities of the models in simulating the snow cover area fraction, and we show that WRF–Noah and WRF–Alpine3D have almost equal skills, with both models overestimating it. The equal skills are also confirmed by Jaccard and the average symmetric surface distance indices.
Italy is a territory characterized by complex topography with the Apennines mountain range crossing the entire peninsula with its highest peaks in central Italy. Using the latter as area of interest and the winter season during 2018–2019, the goal of this study is to investigate the ability of snow cover models to reproduce the observed snow height, using forecast weather data as meteorological forcing. We here consider two well-known ground surface and soil models: i) Noah LSM, a single-layer Eulerian model; ii) Alpine3D, a multi-layer Lagrangian model. We adopt the Weather Research and Forecasting (WRF) model to produce the meteorological data to drive both Noah LSM and Alpine3D at regional scale with a spatial resolution of 3 km. While Noah LSM is already online coupled with the WRF model, we develop here a dedicated offline coupling between WRF and Alpine3D LSM. We validate the WRF simulations of surface meteorological variables in central Italy using a dense network of automatic weather stations, obtaining correlation coefficients of 0.84, 0.58, 0.4, 0.77 and 0.66 for air temperature, relative humidity, wind speed, incoming shortwave radiation and daily precipitation, respectively. The performances of both WRF-Noah and WRF-Alpine3D, are evaluated by comparing simulated and measured snow heights, provided by a quality-controlled network of snow stations located in Central Apennines. We find that WRF-Noah and WRF-Alpine3D models present similar correlation coefficients equal to 0.77 and 0.71, respectively, but the WRF-Alpine3D model produces a lower bias (about 2.2 cm) compared to the WRF-Noah model (about −8.0 cm) in the snow height estimation. For the estimation of daily snow height variation WRF-Noah and WRF-Alpine3D present similar results with correlation coefficients of 0.72 and 0.71, respectively, but again WRF-Alpine3D showed a bias lower than WRF-Noah, about 0.09 cm and −0.22 cm respectively. The WRF-Noah model is slightly better than WRF-Alpine3D to reproduce the snow cover area observed with respect to the Moderate Resolution Imaging Spectroradiometer (MODIS) with the Jaccard spatial correlation index of 0.38 and 0.36 (optimal value equal 1), respectively, and Average Symmetric Surface Distance (ASSD) of 2.0 and 2.2 (optimal value equal 0), respectively, even though both models tend to overestimate it. We finally show that snow settlement rate in WRF-Alpine3D is mainly driven by densification, whereas in WRF-Noah there is also an important contribution of snow melting especially at high elevation. As a general conclusion, the snow cover extension and height in central Italy at moderate spatial resolution (3 km) are well reproduced by both WRF-Noah and WRF-Alpine3D, but with the latter exhibiting a lower bias likely due to its multi-layer more sophisticated numerical scheme.
The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments.
Large scale mapping of snow water equivalent (SWE) is a long-lasting request in many scientific and economical fields. Active and passive microwave remote sensing methods are explored, as local methods cannot be generalized due to the spatial inhomogeneity of the snow pack. Microwaves interact with snow by absorption, scattering, and refraction. For dry snow of a few meters depth and frequencies below 20 GHz, absorption and scattering in the snow volume are negligible compared with the backscattered energy from the underlying ground. The signal delay caused by refraction can be measured with differential radar interferometry, but phase wrapping errors and temporal decorrelation must be considered. We demonstrate that large can be accurately determined from dense time series of differential interferograms at X- and Ku-band by temporal integration. Lost phase cycles are reconstructed with a two-frequency approach. Temporal decorrelation is minimized by a temporal resolution of 4h. A linear function between and phase difference is derived, which deviates only a few percent from the exact solution and which depends negligibly on snow density and stratigraphy. retrieved from observations of the SnowScat instrument (SSI) were validated against observed SWE from different reference instruments, installed at a test site near the town of Sodankylä, Finland. An accuracy below SWE was achieved at frequencies of 10 and 16 GHz for up to 200 mm of SWE. An exceptionally high temporal coherence was observed for up to 3- days for dry snow, whereas for wet snow it decayed within hours.
The cryosphere is the frozen part of the Earth’s system. Snow and ice are the main constituents of the cryosphere and may be found in different states, such as snow, freshwater ice, sea ice, permafrost, and continental ice masses in the form of glaciers and ice sheets. The present review mainly deals with state-of-the-art applications of synthetic aperture radar (SAR) with a special emphasize on cryospheric information extraction. SAR is the most important active microwave remote sensing (RS) instrument for ice monitoring, which provides high-resolution images of the Earth’s surface. SAR is an ideal sensor in RS technology, which works in all-weather and day and night conditions to provide useful unprecedented information, especially in the cryospheric regions which are almost inaccessible areas on Earth. This paper addresses the technological evolution of SAR and its applications in studying the various components of the cryosphere. The arrival of SAR radically changed the capabilities of information extraction related to ice type, new ice formation, and ice thickness. SAR applications can be divided into two broad classes-polarimetric applications and interferometric applications. Polarimetric SAR has been effectively used for mapping calving fronts, crevasses, surface structures, sea ice, detection of icebergs, etc. The paper also summarizes both the operational and climate change research by using SAR for sea ice parameter detection. Digital elevation model (DEM) generation and glacier velocity mapping are the two most important applications used in cryosphere using SAR interferometry or interferometric SAR (InSAR). Spaceborne InSAR techniques for measuring ice flow velocity and topography have developed rapidly over the last decade. InSAR is capable of measuring ice motion that has radically changed the science of glaciers and ice sheets. Measurement of temperate glacier velocities and surface characteristics by using airborne and space-borne interferometric satellite images have been the significant application in glaciology and cryospheric studies. Space-borne InSAR has contributed to
Snow cover over the Northern Hemisphere plays a crucial role in the Earth’s hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation.Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow.
An algorithm has been developed for mapping wet snow in
mountainous terrain using repeat pass synthetic aperture radar (SAR)
images. As a basis for algorithm development, backscattering properties
of snow-free and snow-covered alpine surfaces were investigated using
ERS SAR data and field measurements at test sites in the Austrian Alps.
The incidence angle dependence of backscattering derived from SAR data
is compared with simulations for snow-free surfaces and for surfaces
covered by dry snow and wet snow. Significant seasonal changes of
backscattering are observed, which are mainly caused by variations of
the snow liquid water content and of the surface roughness. The
importance of surface roughness for backscattering of wet snow is
demonstrated by a surface roughness experiment. The algorithm for
mapping wet snow applies change detection using ratios of wet snow
versus snow-free or dry snow surfaces. The main steps include
coregistration, speckle reduction, thresholding of ratio images,
geocoding, and, optionally, combination of crossing passes to reduce the
loss of information due to layover. A threshold of -3 dB was found to be
appropriate for both Radarsat and ERS SAR to separate wet snow from
other surfaces. Postprocessing steps, based on historic snow maps or
topographic information, are used to correct for dry snow areas at high
elevations. Effects of imaging geometry are investigated by comparing
ERS SAR images with a look angle of 19° and Radarsat SAR Beam Mode
S7 images with a look angle of 40°. The comparison of snow maps from
SAR and Landsat-5 Thematic Mapper images shows good agreement in areas
of closed snow cover, whereas near the snow line/SAR tends to slightly
underestimate the snow extent
For mapping Earth surface movements at larger scale and smaller amplitudes, many new synthetic aperture radar instruments (Sentinel-1A/B, Gaofen-3, ALOS-2) have been developed and launched from 2014–2017, and this trend is set to continue with Sentinel-1C/D, Gaofen-3B/C, RADARSAT Constellation planned for launch during 2018–2025. This poses more challenges for correcting interferograms for atmospheric effects since the spatial-temporal variations of tropospheric delay may dominate over large scales and completely mask the actual displacements due to tectonic or volcanic deformation. To overcome this, we have developed a generic interferometric synthetic aperture radar atmospheric correction model whose notable features comprise (i) global coverage, (ii) all-weather, all-time useability, (iii) correction maps available in near real time, and (iv) indicators to assess the correction performance and feasibility. The model integrates operational high-resolution European Centre for Medium-Range Weather Forecasts (ECMWF) data (0.125° grid, 137 vertical levels, and 6-hr interval) and continuous GPS tropospheric delay estimates (every 5 min) using an iterative tropospheric decomposition model. The model's performance was tested using eight globally distributed Sentinel-1 interferograms, encompassing both flat and mountainous topographies, midlatitude and near polar regions, and monsoon and oceanic climate systems, achieving a phase standard deviation and displacement root-mean-square (RMS) of ~1 cm against GPS over wide regions (250 by 250 km). Indicators describing the model's performance including (i) GPS network and ECMWF cross RMS, (ii) phase versus estimated atmospheric delay correlations, (iii) ECMWF time differences, and (iv) topography variations were developed to provide quality control for subsequent automatic processing and provide insights of the confidence level with which the generated atmospheric correction maps may be applied.
The capability of mapping wet snowcovers by means of Synthetic Aperture Radar (SAR) is well known. However, using only the backscattering intensity at C-band, there still remain some incertitudes regarding the effect of either rough snow surfaces or very small liquid water contents for measurements performed at small incidence angles. Since the European remote sensing (ERS) satellites usually operate at 23 incidence angle, it is important to consider these effects. This paper shows that these difficulties can be overcome using repeatpass SAR interferometry. ERS data over a part of the Netherlands and a part of Switzerland are used as illustrative examples. In the situations examined the occurrence of wet snow could not be directly observed by means of the backscattering intensity, but the use of the degree of coherence allowed an easy discrimination. The results are validated with in situ measurements.
This paper describes the theoretical relation between
interferometric phase and changes in snow water equivalent (SWE) and
show results from experiments using ERS-½ tandem data. The main
scattering contribution from a dry snow cover is from the snow-ground
interface. However, the radar wave will be refracted in the snow. Thus,
only small changes in the snow properties between two interferometric
synthetic aperture radar (SAR) images will change the interferometric
phase. This phase change is shown to introduce a significantly increase
in the digital elevation model (DEM) height error, although no effects
are observed on the degree of coherence. The phase change is also shown
to affect the differential interferometric results and may wrongly be
interpreted as range displacement. The presented theory and results
implies that light snowfall and/or small changes in snow properties
between interferometric SAR (InSAR) image acquisitions, may introduce
significant height errors in DEM derived from glaciers, ice sheets, or
bare ground, even in the case of high degree of coherence. Thus,
meteorological observations in addition to degree of coherence must be
considered when generating DEM in areas covered with snow or where snow
fall is likely to have occurred
Snow cover prediction in the Italian Central Apennines using weather forecast and snowpack numerical models, The Cryosphere Discuss.
- E Raparelli
- P Tuccella
- V Colaiuda
- F S Marzano