Review Article: Global Monitoring of Snow Water Equivalent Using High-Frequency Radar Remote Sensing

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth\u27s surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth\u27s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world\u27s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth\u27s cold regions\u27 ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements

Microwave models of snow characteristics for remote sensing

One of the key problems of microwave remote sensing is the development of theoretical microwave models for terrain such as soil, vegetation, snow, forest, etc., due to the complexity of modeling of microwave interaction with the terrain. In this thesis this problem is approached from the new point of view of both empirical models and rigorous theoretical models. New information concerning radar remote sensing of snow-covered terrain and permittivity of snow has been produced. A C-band semi-empirical backscattering model is presented for the forest-snow-ground system. The effective permittivity of random media such as snow, vegetation canopy, soil, etc., describes microwave propagation and attenuation in the media and is a very important parameter in modeling of microwave interaction with the terrain. Good permittivity models are needed in microwave emission and scattering models of terrain. In this thesis, the strong fluctuation theory is applied to calculate the effective permittivity of wet snow. Numerical results for the effective permittivity of wet snow are illustrated. The results are compared with the semi-empirical and the theoretical models. A comparison with experimental data at 6, 18 and 37 GHz is also presented. The results indicate that the model presented in this work gives reasonably good accuracy for calculating the effective permittivity of wet snow. Microwave emission and scattering theoretical models of wet snow are developed based on the radiative transfer and strong fluctuation theory. It is shown that the models agree with the experimental data.reviewe

Estimation of Snow Parameters Based on Passive Microwave Remote Sensing and Meteorological Information

A method to incorporate passive microwave remote sensing measurements within a spatially distributed snow hydrology model to provide estimates of the spatial distribution of Snow Water Equivalent (SWE) as a function of time is implemented. The passive microwave remote sensing measurements are at 25 km resolution. However, in mountain regions the spatial variability of SWE over a 25 km footprint is large due to topographic influences. On the other hand, the snow hydrology model has built-in topographic information and the capability to estimate SWE at a 1 km resolution. In our work, the snow hydrology SWE estimates are updated and corrected using SSM/I passive microwave remote sensing measurements. The method is applied to the Upper Rio Grande River Basin in the mountains of Colorado. The change in prediction of SWE from hydrology modeling with and without updating is compared with measurements from two SNOTEL sites in and near the basin. The results indicate that the method incorporating the remote sensing measurements into the hydrology model is able to more closely estimate the temporal evolution of the measured values of SWE as a function of time

Neural Networks Applications for the Remote Sensing of Hydrological Parameters

The main artificial neural networks (ANN)‐based retrieval algorithms developed at the Institute of Applied Physics (IFAC) are reviewed here. These algorithms aim at retrieving the main hydrological parameters, namely the soil moisture content (SMC), the plant water content (PWC) of agricultural vegetation, the woody volume of forests (WV) and the snow depth (SD) or snow water equivalent (SWE), from data collected by active (SAR/scatterometers) and passive (radiometers) microwave sensors operating from space. Taking advantage of the fast computation, ANN are able to generate output maps of the target parameter at both local and global scales, with a resolution varying from hundreds of meters to tens of kilometres, depending on the considered sensor. A peculiar strategy adopted for the training, which has been obtained by combining satellite measurements with data simulated by electromagnetic models (based on the radiative transfer theory, RTT), made these algorithms robust and site independent. The obtained results demonstrated that ANN are a powerful tool for estimating the hydrological parameters at different spatial scales, provided that they have been trained with consistent datasets, made up by both experimental and theoretical data

SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

The Snow Microwave Radiative Transfer (SMRT) thermal emission and backscatter model was developed to determine uncertainties in forward modeling through intercomparison of different model ingredients. The model differs from established models by the high degree of flexibility in switching between different electromagnetic theories, representations of snow microstructure, and other modules involved in various calculation steps. SMRT v1.0 includes the dense media radiative transfer theory (DMRT), the improved Born approximation (IBA), and independent Rayleigh scatterers to compute the intrinsic electromagnetic properties of a snow layer. In the case of IBA, five different formulations of the autocorrelation function to describe the snow microstructure characteristics are available, including the sticky hard sphere model, for which close equivalence between the IBA and DMRT theories has been shown here. Validation is demonstrated against established theories and models. SMRT was used to identify that several former studies conducting simulations with in situ measured snow properties are now comparable and moreover appear to be quantitatively nearly equivalent. This study also proves that a third parameter is needed in addition to density and specific surface area to characterize the microstructure. The paper provides a comprehensive description of the mathematical basis of SMRT and its numerical implementation in Python. Modularity supports model extensions foreseen in future versions comprising other media (e.g., sea ice, frozen lakes), different scattering theories, rough surface models, or new microstructure models.</p

Exploiting the ANN Potential in Estimating Snow Depth and Snow Water Equivalent From the Airborne SnowSAR Data at X- and Ku-Bands

Within the framework of European Space Agency (ESA) activities, several campaigns were carried out in the last decade with the purpose of exploiting the capabilities of multifrequency synthetic aperture radar (SAR) data to retrieve snow information. This article presents the results obtained from the ESA SnowSAR airborne campaigns, carried out between 2011 and 2013 on boreal forest, tundra and alpine environments, selected as representative of different snow regimes. The aim of this study was to assess the capability of X- and Ku-bands SAR in retrieving the snow parameters, namely snow depth (SD) and snow water equivalent (SWE). The retrieval was based on machine learning (ML) techniques and, in particular, of artificial neural networks (ANNs). ANNs have been selected among other ML approaches since they are capable to offer a good compromise between retrieval accuracy and computational cost. Two approaches were evaluated, the first based on the experimental data (data driven) and the second based on data simulated by the dense medium radiative transfer (DMRT). The data driven algorithm was trained on half of the SnowSAR dataset and validated on the remaining half. The validation resulted in a correlation coefficient R ≃ 0.77 between estimated and target SD, a root-mean-square error (RMSE) ≃ 13 cm, and bias = 0.03 cm. ANN algorithms specific for each test site were also implemented, obtaining more accurate results, and the robustness of the data driven approach was evaluated over time and space. The algorithm trained with DMRT simulations and tested on the experimental dataset was able to estimate the target parameter (SWE in this case) with R = 0.74, RMSE = 34.8 mm, and bias = 1.8 mm. The model driven approach had the twofold advantage of reducing the amount of in situ data required for training the algorithm and of extending the algorithm exportability to other test sites

A Unified Microwave Radiative Transfer Model with Jacobian for General Stratified Media

A unified microwave radiative transfer (UMRT) model is developed for rapid, stable and accurate level-centric calculation of the thermal radiation emitted from any geophysical medium comprised of planar layers of either densely or tenuously distributed, moderately sized spherical scatterers. The formulation includes rapid calculation of the tangent linear relationship (i.e., Jacobian) between the observed brightness temperature and any relevant radiative and geophysical layer parameters, such as the scattering and absorption coefficients, temperature, temperature lapse rate, and medium layer thickness. UMRT employs a rapid multistream scattering-based discrete ordinate eigenanalysis solution with a layer-adding algorithm stabilized by incorporating symmetrization of the discretized differential radiative transfer equations and analytical diagonalization and factorization of the resulting symmetric and positive definite matrices. It is based on the discrete ordinate tangent linear radiative transfer model of Voronovich et al. (2004), but extended to include both Mie and dense media scattering theories and employ refractive layers. Other nontrivial extensions are: 1) exact modeling of linearized temperature profiles and resulting radiation streams across medium layers, 2) compensation for refracted radiation streams using Snell\u27s law, the Fresnel reflectivity and transmissivity coefficients, and a cubic spline interpolation matrix, and 3) seamless calculation of associated Jacobians for both sparse and dense medium parameters. Details of the UMRT Jacobian formulation are presented. The entire formulation has been programmed in Matlab and validated through both energy conservation and numerical Jacobian intercomparisons. Comparisons of the upwelling brightness temperatures over dry snow and ice from simulations and field measurements are presented and discussed

Modeling microwave emission from snow covered soil

Il ciclo idrologico rappresenta l’insieme di tutti i fenomeni legati alla circolazione e alla conservazione dell’acqua sulla Terra. Il monitoraggio su scala globale dei fattori che concorrono a produrre e modificare tale ciclo (umidità del terreno, copertura vegetale, estensione e caratteristiche del manto nevoso) risulta di estrema importanza per lo studio del clima e dei cambiamenti globali. Inoltre, l’osservazione sistematica di queste grandezze è importante per prevedere condizioni di rischio da alluvioni, frane e valanghe come pure fare stime delle risorse idriche. In questo contesto Il telerilevamento da satellite gioca un ruolo fondamentale per le sue caratteristiche di osservazioni continuative di tutto globo terrestre. I sensori a microonde permettono poi di effettuare misure indipendentemente dall’illuminazione solare e anche in condizioni meteorologiche avverse. I processi idrologici, ed in particolare quelli della criosfera (la porzione di superficie terrestre in cui l’acqua è presente in forma solida), sono fra quelli che meglio si possono investigare analizzando la radiazione elettromagnetica emessa o diffusa. Mediante l’utilizzo di modelli elettromagnetici che permettono di simulare l’emissione e lo scattering da superfici naturali è possibile interpretare le misure elettromagnetiche ed effettuare l’estrazione di quelle grandezze che caratterizzano i suoli e la loro copertura. In questo lavoro di dottorato si è affrontato il problema della modellistica a microonde dei terreni coperti da neve, sia asciutta che umida. Dopo aver preso in considerazione i modelli analitici maggiormente utilizzati per simulare diffusione ed emissione a microonde dei suoli nudi e coperti da neve si è proceduto allo sviluppo e implementazione di due modelli di emissività. Il primo, basato sulla teoria delle fluttuazioni forti, è atto a descrivere il comportamento di un manto nevoso umido. Il secondo, basato sull’accoppiamento del modello di scattering superficiale AIEM (Advanced Integral Equation Method) con la teoria del trasferimento radiativo nei mezzi densi, è volto allo studio di uno strato di neve asciutta sovrastante un suolo rugoso. Tali modelli tengono conto degli effetti coerenti presenti nell’emissione del manto nevoso e non inclusi nella teoria del trasporto radiativo classico. Entrambi i codici sono stati validati con datasets numerici e sperimentali in parte derivati da archivi ed in parte ottenuti nel contesto di questo lavoro che ha previsto quindi anche una fase sperimentale. Quest’ultima è stata condotta con misure radiometriche multifrequenza su un’area di test situata sulle Alpi orientali. Le simulazioni ottenute con questi modelli e le conseguenti analisi hanno permesso di individuare la sensibilità della temperatura di brillanza ai parametri di interesse (spessore, equivalente in acqua e umidità del manto nevoso) in funzione di diverse configurazioni osservative (frequenza, polarizzazione ed angolo di incidenza). Questo ha consentito di migliorare la comprensione dei meccanismi di emissione dalle superfici innevate e di individuare le migliori condizioni osservative per un sistema di telerilevamento terrestre