Snow stratigraphic heterogeneity within ground-based passive microwave radiometer footprints: implications for emission modeling

Two-dimensional measurements of snowpack properties (stratigraphic layering, density, grain size and temperature) were used as inputs to the multi-layer Helsinki University of Technology (HUT) microwave emission model at a centimeter-scale horizontal resolution, across a 4.5 m transect of ground-based passive microwave radiometer footprints near Churchill, Manitoba, Canada. Snowpack stratigraphy was complex (between six and eight layers) with only three layers extending continuously throughout the length of the transect. Distributions of one-dimensional simulations, accurately representing complex stratigraphic layering, were evaluated using measured brightness temperatures. Large biases (36 to 68 K) between simulated and measured brightness temperatures were minimized (-0.5 to 0.6 K), within measurement accuracy, through application of grain scaling factors (2.6 to 5.3) at different combinations of frequencies, polarizations and model extinction coefficients. Grain scaling factors compensated for uncertainty relating optical SSA to HUT effective grain size inputs and quantified relative differences in scattering and absorption properties of various extinction coefficients. The HUT model required accurate representation of ice lenses, particularly at horizontal polarization, and large grain scaling factors highlighted the need to consider microstructure beyond the size of individual grains. As variability of extinction coefficients was strongly influenced by the proportion of large (hoar) grains in a vertical profile, it is important to consider simulations from distributions of one-dimensional profiles rather than single profiles, especially in sub-Arctic snowpacks where stratigraphic variability can be high. Model sensitivity experiments suggested the level of error in field measurements and the new methodological framework used to apply them in a snow emission model were satisfactory. Layer amalgamation showed a three-layer representation of snowpack stratigraphy reduced the bias of a one-layer representation by about 50%

Global snow mass measurements and the effect of stratigraphic detail on inversion of microwave brightness temperatures

Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as Snow Water Equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment (NASA CLPX) and the Helsinki University of Technology (HUT) microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 GHz and 37 GHz vertically polarised microwaves are consistent with Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Special Sensor Microwave Imager (SSM/I) retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10 cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method then it is equivalent to ±13 mm SWE (7% of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model

Monitoring spatial and temporal variations of surface albedo on Saint Sorlin Glacier (French Alps) using terrestrial photography

Accurate knowledge of temperate glacier mass balance is essential to understand the relationship between glacier and climate. Defined as the reflected fraction of incident radiation over the whole solar spectrum, the surface broadband albedo is one of the most important variable in a glacier's mass balance. This study presents a new method to retrieve the albedo of frozen surfaces from terrestrial photography at visible and near infrared wavelengths. This method accounts for the anisotropic reflectance of snow and ice surfaces and uses a radiative transfer model for narrow-to-broadband conversion. The accuracy of the method was assessed using concomitant measurements of albedo during the summers 2008 and 2009 on Saint Sorlin Glacier (Grandes Rousses, France). These albedo measurements are performed at two locations on the glacier, one in the ablation area and the other in the accumulation zone, with a net radiometer Kipp and Zonen CNR1. The main sources of uncertainty are associated with the presence of high clouds and the georeferencing of the photographs

Downscaling Coarse Resolution Satellite Passive Microwave SWE Estimates

The spatio-temporal heterogeneity of seasonal snow and its impact on socio-economic and environmental functionality make accurate, real-time estimates of snow water equivalent (SWE) important for hydrological and climatological predictions. Passive microwave remote sensing offers a cost effective, temporally and spatially consistent approach to SWE monitoring at the global to regional scale. However, local scale estimates are subject to large errors given the coarse spatial resolution of passive microwave observations (25 x 25 km). Regression downscaling techniques can be implemented to increase the spatial resolution of gridded datasets with the use of related auxiliary datasets at a finer spatial resolution. These techniques have been successfully implemented to remote sensing datasets such as soil moisture estimates, however, limited work has applied such techniques to snow-related datasets. This thesis focuses on assessing the feasibility of using regression downscaling to increase the spatial resolution of the European Space Agency’s (ESA) Globsnow SWE product in the Red River basin, an agriculturally important region of the northern United States that is widely recognized as a suitable location for passive microwave remote sensing research. Multiple Linear (MLR), Random Forest (RFR) and Geographically Weighted (GWR) regression downscaling techniques were assessed in a closed loop experiment using Snow Data Assimilation System (SNODAS) SWE estimates at a 1 x 1 km spatial resolution. SNODAS SWE data for a 5-year period between 2013-2018 was aggregated to a 25 x 25 km spatial resolution to match Globsnow. The three regression techniques were applied using correlative datasets to downscale the aggregated SNODAS data back to the original 1 x 1 km spatial resolution. By comparing the downscaled SNODAS estimates to the original SNODAS data, it was found that RFR downscaling produced much less variation in downscaled results, and lower RMSE values throughout the study period when compared to MLR and GWR downscaling techniques, indicating it was the optimal downscaling method. RFR downscaling was then implemented on daily Globsnow SWE estimates for the same time period. The downscaled SWE results were evaluated using SNODAS SWE as well as in situ derived SWE estimates from weather stations within the study region. Spatial and temporal errors were assessed using both the SNODAS and in situ reference datasets and overall RMSEs of 21 mm and 37 mm were found, respectively. It was observed that the southern regions of the basin and seasons with higher downscaled SWE estimates were associated with higher errors with overestimation being the most common bias throughout the region. A major contribution of this study is the illustration that RFR downscaling of Globsnow SWE estimates is a feasible approach to understanding the seasonal dynamics of SWE in the Red River basin. This is extremely beneficial for local communities within the basin for flood management and mitigation and water resource management

Detecting Archaeological Features with Airborne Laser Scanning in the Alpine Tundra of Sapmi, Northern Finland

Open access airborne laser scanning (ALS) data have been available in Finland for over a decade and have been actively applied by the Finnish archaeologists in that time. The low resolution of this laser scanning 2008-2019 dataset (0.5 points/m(2)), however, has hindered its usability for archaeological prospection. In the summer of 2020, the situation changed markedly, when the Finnish National Land Survey started a new countrywide ALS survey with a higher resolution of 5 points/m(2). In this paper we present the first results of applying this newly available ALS material for archaeological studies. Finnish LIDARK consortium has initiated the development of semi-automated approaches for visualizing, detecting, and analyzing archaeological features with this new dataset. Our first case studies are situated in the Alpine tundra environment of Sapmi in northern Finland, and the assessed archaeological features range from prehistoric sites to indigenous Sami reindeer herding features and Second Word War-era German military structures. Already the initial analyses of the new ALS-5p data show their huge potential for locating, mapping, and assessing archaeological material. These results also suggest an imminent burst in the number of known archaeological sites, especially in the poorly accessible and little studied northern wilderness areas, when more data become available.Peer reviewe

Detecting Archaeological Features with Airborne Laser Scanning in the Alpine Tundra of Sápmi, Northern Finland

Open access airborne laser scanning (ALS) data have been available in Finland for over a decade and have been actively applied by the Finnish archaeologists in that time. The low resolution of this laser scanning 2008–2019 dataset (0.5 points/m2), however, has hindered its usability for archaeological prospection. In the summer of 2020, the situation changed markedly, when the Finnish National Land Survey started a new countrywide ALS survey with a higher resolution of 5 points/m2. In this paper we present the first results of applying this newly available ALS material for archaeological studies. Finnish LIDARK consortium has initiated the development of semi-automated approaches for visualizing, detecting, and analyzing archaeological features with this new dataset. Our first case studies are situated in the Alpine tundra environment of Sápmi in northern Finland, and the assessed archaeological features range from prehistoric sites to indigenous Sámi reindeer herding features and Second Word War-era German military structures. Already the initial analyses of the new ALS-5p data show their huge potential for locating, mapping, and assessing archaeological material. These results also suggest an imminent burst in the number of known archaeological sites, especially in the poorly accessible and little studied northern wilderness areas, when more data become available

Geofysikaalisten parametrien estimointi kaukokartoitushavainnoista tilastollista inversiota käyttäen

Tässä työssä on tutkittu tilastollisen inversion käyttöä geofysikaalisten suureiden estimoinnissa kaukokartoitushavainnoista. Menetelmä on varsin yleispätevä, työssä demonstroitiin menetelmää metsän runkotilavuuden sekä lumipeitteen ominaisuuksien - paksuuden, vesiarvon ja peittoalan - estimoinnissa. Yksi tilastollisen inversion eduista on eri lähteistä tulevien havaintojen yhdistäminen optimaalisesti, koska menetelmä painottaa eri lähteistä tulevia havaintoja niiden tilastollisen tarkkuuden mukaan. Menetelmä pystyy myös tuottamaan estimoimilleen suureille tarkkuusarvion, joka pohjautuu käytettyjen havaintojen sekä mallien tarkkuuteen. Menetelmää on tässä työssä käytetty koko Euraasian laajuisten lumensyvyyskarttojen luomiseen mikroaaltoradiometrihavainnoista, lumen peittoalan estimoimiseen tutka- ja optisista havainnoista, metsän runkotilavuuden estimoimiseen ERS INSAR-havainnoista sekä operatiivisen vesistömallin (WSFS) virtaamaennusteiden parantamiseen tutkahavaintojen avulla. Menetelmä käyttää hyväkseen kaukokartoitusmalleja. Kaukokartoitusmalleina käytettiin Teknillisessä korkeakoulussa kehitettyä lumen mikroaaltoemissiomallia ja metsän mikroaaltosirontamallia sekä Suomen ympäristökeskuksessa kehitettyä lumen reflektanssimallia. Näiden kaukokartoitusmallien lisäksi käytettiin dynaamista ympäristömallia (WSFS), johon assimiloitiin tutkahavaintoja. Edellä mainittujen sovellusten lisäksi työssä kehitettiin myös kaksi ohjelmistoa. Ensimmäinen simuloi monikanavaisen radiometrin havainnoimia kirkkauslämpötiloja testatakseen erilaisia tunnettuja inversioalgoritmeja sekä tilastollista inversiota. Toinen kehitetty ohjelma on yleiskäyttöinen työkalu suureiden estimointiin kaukokartoitushavainnoista tilastollisella inversiolla. Ohjelmaa voi käyttää sellaisenaan tai jonkin kuvankäsittelyjärjestelmän osana.In this thesis the use of statistical inversion method for retrieving geophysical parameters from different remote sensing data was studied. The statistical inversion method is rather universal. In this work it was demonstrated by retrieving the following snow and forest parameters: snow depth, snow water equivalent, snow-covered area and forest stem volume. One of the benefits of the statistical inversion method is that it can combine data from different sources based on their statistical accuracy. The method can also estimate the accuracy of the estimation result based on the accuracy of the input data and the models used. In this work the statistical inversion method was demonstrated by retrieving snow depth of Eurasia from microwave radiometer data, snow covered area from microwave and optical data, forest stem volume from ERS INSAR data, and enhancing the accuracy of the discharge forecasts of the operational watershed simulation and forecasting system (WSFS) using SAR data. The statistical inversion method utilises remote sensing models. The Helsinki University of Technology (HUT) microwave snow emission model, the HUT forest backscattering model, and the optical reflectance model developed at the Finnish Environment Institute were used as such. In addition to these remote sensing models, a dynamic environmental model (WSFS) was used to assimilate SAR measurements to it. In addition to the studies mentioned above, two software applications were developed. The first one was developed to simulate brightness temperatures observed by a multichannel microwave radiometer and to test the performance of the available inversion algorithms and the statistical inversion method. The second software application developed is a general purpose statistical inversion tool that can be used either independently or as a part of an image processing system

Mapping methods and observations of surficial snow/ice cover at Redoubt and Pavlof volcanoes, Alaska using optical satellite imagery

Thesis (M.S.) University of Alaska Fairbanks, 2014Alaska is a natural laboratory for the study of how active volcanism interacts with underlying seasonal snow, perennial snow, and glacial ice cover. While over half of the historically active volcanoes in Alaska have some degree of perennial snow or glacial ice, all Alaskan volcanoes have a covering of seasonal snow for a period of time throughout the year. Previous research has centered on how volcanic deposits erode away the underlying snow/ice cover during an eruption, producing volcanic mudflows called lahars. Less emphasis has been placed on how variations in the snow/ice cover substrate effect the efficiency of meltwater generation during a volcanic eruption. Glacial ice, perennial snow, and seasonal snow can all contribute significantly to meltwater, and therefore the variations in the types of snow/ice cover present at Alaskan volcanoes must be analyzed. By examining the changing spatial extent of seasonal snow present at a volcano during multiple Alaskan summers, the approximate boundaries of perennial snow and ice can be mapped as the snow/ice cover consistently present at the end of each ablation season. In this study, two methods of snow/ice cover mapping for Redoubt and Pavlof volcanoes are analyzed for efficiency and accuracy. Identification of the best method allows for mapping of the snow/ice cover consistently present during each Alaskan summer month over at least two different years. These maps can serve as approximations for the snow/ice cover likely to be present at both volcanoes during each summer month. Volcanic deposits produced during the 2009 Redoubt and 2013 Pavlof eruptions are spatially linked to these snow/ice cover maps so that future research can focus on the interaction between deposits and type of snow/ice substrate. Additional observations and conclusions are made regarding how the visible snow/ice cover varies during and after each eruption.Chapter 1. Introduction -- 1.1. Background -- 1.2. Comparison of snow/ice cover mapping methods for Alaskan volcanoes -- 1.3. Mapping snow/ice on Redoubt and Pavlof during quiescence and eruption -- 1.4. Summary of final outcomes -- 1.5. References -- Chapter 2. Methods for snow/ice cover mapping of Redoubt and Pavlof volcanoes using optical satellite imagery -- 2.1. Introduction -- 2.1.1. Satellite remote sensing of glaciers and snow cover in Alaska -- 2.1.2. Previous work and methods for studying snow/ice on volcanoes -- 2.1.3. Challenges of mapping snow/ice cover at Alaskan volcanoes -- 2.2. Setting of Redoubt volcano -- 2.2.1. Basic setting of Redoubt volcano -- 2.3. Setting of Pavlof volcano -- 2.3.1. Basic setting of Pavlof volcano -- 2.4. Methods -- 2.4.1. Previous work in snow/ice cover mapping using satellite imagery -- 2.4.2. Sensors used for snow/ice cover mapping -- 2.4.3. Pre-processing of satellite imagery -- 2.4.4. Methods used to map snow/ice cover at Redoubt and Pavlof -- 2.4.5. Technique 1: band ratios -- 2.4.6. Technique 2: principal component analysis -- 2.4.7. Technique 3: linear spectral unmixing -- 2.5. Results and discussion -- 2.5.1. Snow/ice cover mapping using threshold method -- 2.5.2. Snow/ice cover mapping using linear spectral unmixing method -- 2.5.3. Improvements to linear spectral unmixing method for snow/ice cover mapping -- 2.5.4. Validation of results -- 2.6. Conclusion -- 2.7. Figures -- 2.8. Tables -- 2.9. References -- Chapter 3. Observations of surficial snow/ice cover changes due to seasonal and eruptive influences on Redoubt and Pavlof volcanoes, Alaska using optical remote sensing -- 3.1. Introduction -- 3.1.1. Alaskan volcanoes -- 3.2. Volcano-snow/ice interactions -- 3.2.1. Short term interactions -- 3.2.2. Long term interactions -- 3.2.3. Lahar formation and hazards -- 3.2.4. Influence of snow/ice substrate type on lahar generation -- 3.3. Background on Redoubt volcano -- 3.3.1. Setting of Redoubt volcano -- 3.3.2. Recent eruptions at Redoubt volcano -- 3.3.3. Eruption effects on Drift Glacier -- 3.3.4. Lahar hazards at Redoubt volcano -- 3.4. Background on Pavlof volcano -- 3.4.1. Setting of Pavlof volcano -- 3.4.2. Recent eruptions at Pavlof volcano -- 3.4.3. Lahar hazards at Pavlof volcano -- 3.5. Methods -- 3.5.1. Sensors used to create Products 1, 2, and 3 -- 3.5.2. Methods used to produce Product 1: individual snow/ice cover maps -- 3.5.3. Methods used to produce Product 2: snow/ice cover summary maps -- 3.5.4. Methods used to produce Product 3: composite maps of eruptive deposits and snow/ice cover -- 3.6. Results and discussion -- 3.6.1. Product 1: individual snow/ice cover maps of Redoubt subset -- 3.6.2. Product 2: snow/ice cover summary maps of Redoubt subset -- 3.6.3. Product 3: composite maps of eruptive deposits and snow/ice cover of Redoubt subset -- 3.6.4. Product 1: individual snow/ice cover maps of Pavlof subset -- 3.6.5. Product 2: snow/ice cover maps of Pavlof subset -- 3.6.6. Product 3: composite maps of eruptive deposits and snow/ice cover of Pavlof subset -- 3.7. Conclusion -- 3.8. Figures -- 3.9. Tables -- 3.10. References -- Chapter 4. Conclusion -- 4.1. Comparison of snow/ice cover mapping methods for Alaskan volcanoes -- 4.2. Mapping snow/ice on Redoubt and Pavlof during quiescence and eruption -- 4.3. Limitations and future work -- 4.4. References

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

Snow is an important component of the terrestrial freshwater budget in high mountainAsia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despitethe importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (1Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASALand Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMsserve as a nonlinear map between the geophysical space (modeled in Noah-MP) andthe observation space (1Tb as measured by the radiometer). Advanced MicrowaveScanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm