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

Microwave remote sensing of snow and environment

Hemispheric snow extent and snow mass are two important parameters affecting the water cycle, carbon cycle and the radiation balance in particular at the high latitudes. In this dissertation these topics have been investigated focusing on the mapping of snow clearance day (melt-off day) and Snow Water Equivalent (SWE) by applying spaceborne microwave radiometer instruments. New algorithms have been developed and existing ones have been further advanced. Specific attention has been paid to estimate snow in boreal forests. This work has resulted in Climate Data Records (CDRs) of snow clearance day and daily values of SWE. Data are available for the entire Northern Hemisphere covering more than three decades. The developed CDRs are relevant for climate research, for example concerning the modeling of Earth System processes. CDR on snow clearance day can be used to map the CO2 balance between the biosphere and atmosphere in the case of boreal forests, which is demonstrated in the thesis. Further, methodologies to assess snow mass in terms of SWE for hemispherical and regional scales have been developed. The developed methodologies have also resulted in the establishment of new Near-Real-Time (NRT) satellite data services for hydrological end-use. In hydrology SWE data are used to enhance the performance of river discharge forecasts, which is highly important for hydropower industry and flood prevention activities

Conception d'un dispositif de caractérisation de la glace et de la neige à partir d'un radar à émission continue

Dans le but d’augmenter la sécurité des activités hivernales sur les glaces de lacs et de rivières, ce projet de maîtrise consiste à mettre en oeuvre un radar de détection de l’épaisseur de glace et à élaborer des algorithmes qui sont capables de déterminer de façon automatique la solidité de la glace à partir d’écho radar. Un radar FMCW (Frequency Modulated Continuous-Wave) a été choisi pour ses caractéristiques tel que son prix, sa précision, sa fréquence centrale et ses dimensions. Dans ce mémoire, on présente les résultats des tests effectués sur le radar lui-même, ceux en laboratoire avec des blocs de glace, ainsi que les mesures sur le terrain effectuées durant l’hiver 2015 et 2016. Grâce aux algorithmes développés, il a été possible de mesurer automatiquement l’épaisseur de la glace de plusieurs lacs, à travers la neige, avec une erreur moyenne de 2 cm. Nous croyons aussi qu’il est possible de discerner l’intersection entre la glace blanche et la glace noire sur les profils radars. La glace blanche, résultant du gel de la neige humide sur les cours d’eau est moins solide que la glace noire et influence la solidité totale de la glace. Nous avons aussi élaboré une méthode pour évaluer l’équivalent en eau de la neige (SWE) à l’aide du même radar lors de quatre campagnes de terrain au Québec et dans le nord du Canada. Ces mesures sont basées sur l’indice de réfraction de la neige qui change selon sa densité. Si la position du radar et l'épaisseur de neige sont connues, nous pouvons déduire l’indice de réfraction et la densité moyenne de la neige et ainsi calculer le SWE. Les tests effectués durant l’hiver 2016 sur des bancs de neige entre 10 et 720 mm de SWE nous donnent une erreur moyenne de 25%, ce qui est plus élevé que le capteur à rayon Gamma CS725 de Campbell Scientific Canada, habituellement utilisé pour mesurer le SWE. Mais l’analyse des résultats montre que le manque de précision serait dû aux réflexions des couches de glace présentent dans la neige qui rendent difficile la tâche de trouver la position de référence. Nous croyons qu’il serait possible d’atteindre une précision d’environ 5% si le système radar était monté dans une installation fixe et de manière à ne pas être influencé par les couches de glace. L’avantage d’un tel système est qu’il pourrait mesurer des valeurs de SWE plus importantes que le CS725 (600 mm de SWE maximum), tout en consommant beaucoup moins d’énergie.Abstract: In order to increase the safety of winter activities on the ice of lake and river, this thesis project aims to implement an ice thickness detection radar and develop algorithms that are able to determine automatically the strength of the ice from radar echo. A radar FMCW (Frequency Modulated Continuous- Wave) was chosen for its characteristics such as its price, accuracy, central frequency and size. In this thesis, we present the results of tests on the radar itself, those in the laboratory with ice blocks, and field measurements conducted during the winters 2015 and 2016. With our own algorithm developed for this project, it was possible to automatically measure the thickness of ice of several lake and rivers, through the snow, with an average error of 2 cm. We also believe that it is possible to discern the intersection between white ice and black ice on radar profiles. White ice resulting from freezing of wet snow on the river is less solid than black ice and influences the overall strength of the ice. We also developed a method to assess the snow water equivalent (SWE) using the same radar during four field campaigns in Quebec and northern Canada. These measurements are based on the refractive index of the snow that is changed by it density. By measuring the radar position above the soil and the snow depth, we can deduce the refractive index and the average density of the snow, and then calculate the SWE. Tests conducted during the winter of 2016 in a range between 10 and 720 mm of SWE give us an average error of 25%, which is higher than the Gamma ray sensor CS725 made by Campbell Scientific Canada (in collaboration in this project), usually used to measure the SWE. But the analysis shows that lack of precision is due to the layers of ice reflections present in the snow that make it difficult to find the reference position. We believe it would be possible to achieve an accuracy of about 5% if the radar system was mounted in a fixed installation and so as not to be influenced by the ice layers. The advantage of such a system is that it could measure higher snow depth that the CS725 do (600 mm maximum SWE), while consuming significantly less power

Development and evaluation of an advanced microwave radiance data assimilation system for estimating snow water storage at the continental scale

Snow cover modulates the Earth's surface energy and water fluxes, and snowmelt runoff is the principal source of water for humans and ecosystems in many of the middle to high latitudes in the Northern Hemisphere. Understanding spatial and temporal variation in snowpack is crucial for climate studies and water resource management and thus the climate and hydrological research communities have invested in improving large-scale snow estimates. This dissertation aims to develop an advanced snow radiance assimilation (RA) system to improve continental-scale snow water storage estimates. The RA system is comprised of the Community Land Model version 4 (CLM4) (for snow energy and mass balance modeling), radiative transfer models (RTMs) (for brightness temperature estimates), and the Data Assimilation Research Testbed (DART) (for ensemble-based data assimilation). Two snowpack RTMs, the Microwave Emission Model for Layered Snowpacks (MEMLS) and the Dense Media Radiative Transfer--Multi Layers model (DMRT-ML), are used to simulate T[subscript B] of a multi-layered snowpack. Through an error characterization study, this dissertation presents that the correlations between snow water equivalent (SWE) error and brightness temperature (T[subscript B]) error and subsequent RA performance in estimating snow are significantly affected by all physical properties of soil and snow involved in estimating T[subscript B]. Based on the error characterization results, it is hypothesized that the continental-scale RA performance in estimating snow water storage can be improved by simultaneously updating all model physical states and parameters determining T[subscript B] based on a rule, in which prior estimates are updated depending on their correlations with a prior T[subscript B]. The results of a series of RA experiments show that the improved continental-scale snow estimates are obtained by applying the hypothesis. This dissertation also shows that further improvement of the performance of the RA system can be achieved, especially for vegetated areas, by assimilating the best-performing frequency channels (i.e., 18.7 and 23.8 GHz) and by considering the vegetation single scattering albedo to represent the vegetation effect on T[subscript B] at the top of the atmosphere.Geological Science

Monitoring and Characterization of Arctic Sea Ice using Radar Altimetry

Department of Urban and Environmental Engineering (Environmental Science and Engineering)Launching CryoSat-2, which is a current radar altimeter mission for the monitoring of polar region enables to produce monthly based sea ice thickness since April 2010. The Sea ice thickness cannot be measured directly by satellite. Sea ice freeboard that is an elevation above sea level can be converted in to sea ice thickness by assuming hydrostatic equilibrium. Sea ice leads (e.g., linear cracks in sea ices) are regarded as sea surface tie points for the estimation of sea ice freeboard. Identifying the sea ice leads is one of the core factors to retrieve sea ice thickness. The surface elevation is estimated by the use of Threshold First maxima Retracker Algorithm (TFMRA) for a 40% threshold using CryoSat-2 L1b data and the leads are detected by machine learning approaches such as decision trees and random forest. The machine learning produces better accuracy for the sea ice thickness than previous simple thresholding approach, validating EM-31, airborne sea ice thickness observations. A novel method to overcome previous threshold based lead detection methods for identifying leads is developed, which is waveform mixture algorithm that linear mixture analysis is applied in terms of waveforms. The waveform mixture algorithm can distinguish leads without beam behavior parameters and backscatter sigma-0 but just use waveforms, which is less affected by updating baseline for CryoSat-2. In addition to the development of the algorithms, a scientific research is carried out. Causes for sea ice anomaly phenomenon in November 2016 is investigated. Eventually, sea ice the volume derived by thickness is used for the analysis of sea ice extent minimum in November 2016 and suggest a new insight of sea ice minimum phenomenon. Unlike sea ice extent, the sea ice volume is not a minimum in November 2016. However, since the base period for sea ice volume is short, it is hard to mention climatology of sea ice volume.ope

Remote sensing of snow-cover for the boreal forest zone using microwave radar

This doctoral dissertation describes the development of an operationally feasible snow monitoring methodology utilizing spaceborne synthetic aperture radar (SAR) imagery, intended for hydrological applications on the boreal forest zone. The snow-covered area (SCA) estimation methodology developed is characterized using extensive satellite-based datasets, including SAR-based estimation and optical reference data gathered during the snow-melt seasons of 1997-1998, 2000-2002 and 2004-2006 from northern Finland. The methodology applies satellite-based C-band SAR data for snow monitoring during the spring snow-melt season. The SCA information can be utilized for river discharge forecasting and flood predictions and for the optimization of hydropower production. The development efforts included 1) demonstration of a forest compensation algorithm, 2) establishing the use of wide-swath SAR data 3) development of a weather station assimilation procedure and 4) creation of an enhanced reference image selection algorithm for the SCA estimation methodology. The feasibility of a proposed, non-boreal forest specific, SAR-based SCA estimation method was evaluated for the boreal forest zone. The acquired results were compared with the characteristics determined for the boreal-forest specific methodology developed within this dissertation. These results can be used when selecting appropriate SCA estimation approaches for future snow monitoring systems whether conducted in different regions or intended for larger i.e. continental or global scale purposes. An automatic processing system for SCA estimation was developed and demonstrated as part of this work; the system has been delivered to the Finnish Environment Institute for operational use

Passive Remote Sensing of Lake Ice and Snow using Wideband Autocorrelation Radiometer (WiBAR).

Snow cover plays a vital role in providing the water supplies for domestic, industrial, and agricultural purposes. Conventionally, differential scatter darkening technique is used to detect the snow thickness. This technique is region specific and depends on the statistics of snow grain sizes. Ice formation process and ice thickness monitoring are important parameters in analyzing the overall pressure exerted to the off-shore structures such as wind farms. The traditional method for measuring the lake ice thickness is by a cumbersome drilling process through the ice. For future in-situ or remote planetary applications, the detection and analysis of ice sheets on or near the surface is one of the primary objectives of many planetary exploration missions. These applications demonstrate the requirement for an accurate remote sensing instrument, which can estimate the ice thickness without disturbing or breaking the ice. In this work, a novel microwave remote sensing technique to accurately estimate the thickness of any layered low-absorbing media including snow pack and fresh water ice using wideband autocorrelation radiometer (WiBAR) is presented. This technique relies on finding the autocorrelation response of the upwelling brightness temperature. The autocorrelation response provides enough information to estimate the microwave travel time delay of the doubly reflected thermal emission between the top and bottom interfaces an consequently the thickness of the snow or ice layer can be obtained. Several post processing techniques are developed to capture the periodicity of the ripples in the power spectral density domain. These techniques are capable of detecting very weak ripples deeply buried under noise. A compressive sensing based algorithm is also developed for detecting the thickness of ice/snow layers using 1/10 of the Nyquist rate samples. We have successfully designed, implemented, and tested a handheld ground base ice/snow thickness sensor in the frequency range of 1-3GHz or 7-10GHz under several scenarios including snow on top of undulated and vegetation covered terrain, ice over the lake water, air gap above a water surface and below a dielectric sheet, and snow cover under the forest canopy in the presence of radio frequency interference (RFI) with accuracy of within 1.5cm.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110419/1/hnejati_1.pd

Lake Icepack and Dry Snowpack Thickness Measurement Using Coherent Multipath Interference of Wideband Planck Radiation

The seasonal terrestrial snowpack is an important source of water for many parts of the globe. The global quantification of the amount of water in the snowpack reservoir has been a long term objective of most remote sensing applications. Thus far, the primary means of quantifying the amount of snow on the ground has been via the differential scatter-darkening mechanism, such as 19 and 37 GHz brightness difference. This technique is region specific and depends on the statistics of snow grain sizes. While a time series of more than 35 years of passive microwave data has been made, progress in understanding the scatter-darkening brightness signature of snow continues, especially for forested areas where vegetation scattering confounds the signature. In addition, monitoring the ice thickness is important in analyzing the pressure exerted to off-shore structures such as wind farms. It is also an essential parameter for the safety of ice fishing and ice skating activities. The current and traditional method of ice thickness measurement is by drilling holes through the ice, which is not only cumbersome but also dangerous. Hence, an accurate remote sensing technique is needed to safely and non-destructively measure the ice and snow thickness. In this work, a novel microwave radiometric technique, wideband autocorrelation radiometry (WiBAR), is introduced. The radiometer offers a direct method to remotely measure the microwave propagation time difference of multipath microwave emission from low-loss layered surfaces, such as a dry snowpack and a freshwater lake icepack. The microwave propagation time difference through the pack yields a measure of its vertical extent; thus, this technique provides a direct measurement of depth. It is also a low-power sensing method since there is no transmitter. A simple geophysical forward model for the multipath interference phenomenon is presented, and the system requirements needed to design a WiBAR instrument are derived. Three different versions of WiBAR instruments operating at L-, S-, and X-band are fabricated from commercial-off-the-shelf (COTS) components. To validate the WiBAR method, simulated laboratory measurements are first performed using a microwave scene simulator circuit. Finally, to prove the potential of this technique as an inversion algorithm, many field measurements were conducted in different winter seasons in the Upper Midwest region, Michigan and Minnesota. It is demonstrated that a WiBAR instrument operating in the frequency range of 7-10 GHz (X-band) can directly measure the icepack thicknesses from nadir to 59 degree of incidence angles. The WiBAR was able to measure the lake icepack thicknesses in the range of 22-59 cm with an accuracy of about 2 cm over this range of incidence angles.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155227/1/mousavis_1.pd

Use of satellite-derived heterogeneous surface soil moisture for numerical weather prediction, The

Summer 1996.Bibliography: pages [296]-320

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