scholarly journals Spatio-temporal influence of tundra snow properties on Ku-band (17.2 GHz) backscatter

2015 ◽  
Vol 61 (226) ◽  
pp. 267-279 ◽  
Author(s):  
Joshua King ◽  
Richard Kelly ◽  
Andrew Kasurak ◽  
Claude Duguay ◽  
Grant Gunn ◽  
...  
Keyword(s):  
Near Infrared ◽  
Snow Water Equivalent ◽  
Sampling Procedure ◽  
Field Of View ◽  
Boreal Winter ◽  
Snow Properties ◽  
Snow Sampling ◽  
Temporal Influence ◽  
Spatio Temporal ◽  
Ku Band

AbstractDuring the 2010/11 boreal winter, a distributed set of backscatter measurements was collected using a ground-based Ku-band (17.2 GHz) scatterometer system at 26 open tundra sites. A standard snow-sampling procedure was completed after each scan to evaluate local variability in snow layering, depth, density and water equivalent (SWE) within the scatterometer field of view. The shallow depths and large basal depth hoar encountered presented an opportunity to evaluate backscatter under a set of previously untested conditions. Strong Ku-band response was found with increasing snow depth and snow water equivalent (SWE). In particular, co-polarized vertical backscatter increased by 0.82 dB for every 1 cm increase in SWE (R2 = 0.62). While the result indicated strong potential for Ku-band retrieval of shallow snow properties, it did not characterize the influence of sub-scan variability. An enhanced snow-sampling procedure was introduced to generate detailed characterizations of stratigraphy within the scatterometer field of view using near-infrared photography along the length of a 5 m trench. Changes in snow properties along the trench were used to discuss variations in the collocated backscatter response. A pair of contrasting observation sites was used to highlight uncertainties in backscatter response related to short length scale spatial variability in the observed tundra environment.

scholarly journals CrocO_v1.0 : a Particle Filter to assimilate snowpack observations in a spatialised framework

2020 ◽  
Author(s):  
Bertrand Cluzet ◽  
Matthieu Lafaysse ◽  
Emmanuel Cosme ◽  
Clément Albergel ◽  
Louis-François Meunier ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Near Infrared ◽  
Snow Water Equivalent ◽  
Sources Of Information ◽  
Average Improvement ◽  
Modelling Uncertainties ◽  
Snow Water ◽  
Spatio Temporal ◽  
Snowpack Properties

Abstract. Monitoring the evolution of the snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources management. In-situ and remotely sensed observations provide precious information on the snowpack but usually offer a limited spatio-temporal coverage of bulk or surface variables only. In particular, visible-near infrared (VIS-NIR) reflectance observations can inform on the snowpack surface properties but are limited by terrain shading and clouds. Snowpack modelling enables to estimate any physical variable, virtually anywhere, but is affected by large errors and uncertainties. Data assimilation offers a way to combine both sources of information, and to propagate information from observed areas to non observed areas. Here, we present CrocO, (Crocus-Observations) an ensemble data assimilation system able to ingest any snowpack observation (applied as a first step to the height of snow (HS) and VIS-NIR reflectances) in a spatialised geometry. CrocO uses an ensemble of snowpack simulations to represent modelling uncertainties, and a Particle Filter (PF) to reduce them. The PF is known to collapse when assimilating a too large number of observations. Two variants of the PF were specifically implemented to ensure that observations information is propagated in space while tackling this issue. The global algorithm ingests all available observations with an iterative inflation of observation errors, while the klocal algorithm is a localised approach performing a selection of the observations to assimilate based on background correlation patterns. Experiments are carried out in a twin experiment setup, with synthetic observations of HS and VIS-NIR reflectances available in only a 1/6th of the simulation domain. Results show that compared against runs without assimilation, analyses exhibit an average improvement of snow water equivalent Continuous Rank Probability Score (CRPS) of 60 % when assimilating HS with a 40-member ensemble, and an average 20 % CRPS improvement when assimilating reflectance with a 160-member ensemble. Significant improvements are also obtained outside the observation domain. These promising results open a way for the assimilation of real observations of reflectance, or of any snowpack observations in a spatialised context.

scholarly journals CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework

2021 ◽  
Vol 14 (3) ◽  
pp. 1595-1614
Author(s):  
Bertrand Cluzet ◽  
Matthieu Lafaysse ◽  
Emmanuel Cosme ◽  
Clément Albergel ◽  
Louis-François Meunier ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Near Infrared ◽  
Snow Water Equivalent ◽  
Sources Of Information ◽  
Average Improvement ◽  
Modelling Uncertainties ◽  
Snow Water ◽  
Spatio Temporal ◽  
Snowpack Properties

Abstract. Monitoring the evolution of snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources management. In situ and remotely sensed observations provide precious information on the state of the snowpack but usually offer limited spatio-temporal coverage of bulk or surface variables only. In particular, visible–near-infrared (Vis–NIR) reflectance observations can provide information about the snowpack surface properties but are limited by terrain shading and clouds. Snowpack modelling enables the estimation of any physical variable virtually anywhere, but it is affected by large errors and uncertainties. Data assimilation offers a way to combine both sources of information and to propagate information from observed areas to non-observed areas. Here, we present CrocO (Crocus-Observations), an ensemble data assimilation system able to ingest any snowpack observation (applied as a first step to the height of snow (HS) and Vis–NIR reflectances) in a spatialised geometry. CrocO uses an ensemble of snowpack simulations to represent modelling uncertainties and a particle filter (PF) to reduce them. The PF is prone to collapse when assimilating too many observations. Two variants of the PF were specifically implemented to ensure that observational information is propagated in space while tackling this issue. The global algorithm ingests all available observations with an iterative inflation of observation errors, while the klocal algorithm is a localised approach performing a selection of the observations to assimilate based on background correlation patterns. Feasibility testing experiments are carried out in an identical twin experiment setup, with synthetic observations of HS and Vis–NIR reflectances available in only one-sixth of the simulation domain. Results show that compared against runs without assimilation, analyses exhibit an average improvement of the snow water equivalent continuous rank probability score (CRPS) of 60 % when assimilating HS with a 40-member ensemble and an average 20 % CRPS improvement when assimilating reflectance with a 160-member ensemble. Significant improvements are also obtained outside the observation domain. These promising results open a possibility for the assimilation of real observations of reflectance or of any snowpack observations in a spatialised context.

scholarly journals Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

2021 ◽  
Vol 13 (15) ◽  
pp. 2922
Author(s):  
Yang Song ◽  
Patrick D. Broxton ◽  
Mohammad Reza Ehsani ◽  
Ali Behrangi
Keyword(s):  
Snow Water Equivalent ◽  
Stage Iv ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
Correction Factors ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Snow Properties ◽  
Precipitation Estimates

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

scholarly journals Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification

2021 ◽  
Vol 13 (13) ◽  
pp. 2604
Author(s):  
Patrick Osei Darko ◽  
Margaret Kalacska ◽  
J. Pablo Arroyo-Mora ◽  
Matthew E. Fagan
Keyword(s):  
Statistical Physics ◽  
Near Infrared ◽  
Information Gain ◽  
Hyperspectral Imagery ◽  
Full Range ◽  
Surface Reflectance ◽  
Mangrove Species ◽  
New Approach ◽  
Spatio Temporal ◽  
Shortwave Infrared

Hyperspectral remote sensing across multiple spatio-temporal scales allows for mapping and monitoring mangrove habitats to support urgent conservation efforts. The use of hyperspectral imagery for assessing mangroves is less common than for terrestrial forest ecosystems. In this study, two well-known measures in statistical physics, Mean Information Gain (MIG) and Marginal Entropy (ME), have been adapted to high spatial resolution (2.5 m) full range (Visible-Shortwave-Infrared) airborne hyperspectral imagery. These two spectral complexity metrics describe the spatial heterogeneity and the aspatial heterogeneity of the reflectance. In this study, we compare MIG and ME with surface reflectance for mapping mangrove extent and species composition in the Sierpe mangroves in Costa Rica. The highest accuracy for separating mangroves from forest was achieved with visible-near infrared (VNIR) reflectance (98.8% overall accuracy), following by shortwave infrared (SWIR) MIG and ME (98%). Our results also show that MIG and ME can discriminate dominant mangrove species with higher accuracy than surface reflectance alone (e.g., MIG–VNIR = 93.6% vs. VNIR Reflectance = 89.7%).

Optimisation of Magnification Levels for near Infrared Chemical Imaging of Blending of Pharmaceutical Powders

2007 ◽  
Vol 15 (3) ◽  
pp. 137-151 ◽  
Author(s):  
Hua Ma ◽  
Carl A. Anderson
Keyword(s):  
Near Infrared ◽  
Image Data ◽  
Principal Component ◽  
Chemical Imaging ◽  
Field Of View ◽  
Pharmaceutical Dosage Forms ◽  
Component Distribution ◽  
Circular Field ◽  
Pharmaceutical Powder ◽  
Magnification Level

A critical parameter in the evaluation of pharmaceutical dosage forms by hyperspectral imaging is the level of magnification. If the magnification (as set by the optical objective) is inadequate to resolve the relevant features, then the value of the imaging is diminished; if the magnification level is greater than is required, then the field of view is unnecessarily reduced. The purpose of this study was to determine an optimum magnification level for the study of powder mixing. Relevant features in this system include distribution of individual components within samples and the overall content of a given sample. In the present study, three magnification levels of near infrared (NIR) chemical imaging objectives were evaluated for their effects on imaging a blend of pharmaceutical materials (powders). High, medium and low objective magnification levels were investigated by comparing the resulting blend surface images of a two-component (salicylic acid and lactose) pharmaceutical powder mixture. Multiple images from high and medium magnification were concatenated so that an equivalent field of view was obtained for all magnification levels. Univariate images, principal component analysis score images, partial least squares predicted images and spectra extracted from different intensity regions in the area images were analysed qualitatively and quantitatively for comparison. A series of images spanning a strip across the centre of the circular field were collected at each magnification level and compared with respect to surface features elucidated and area of blend surface imaged. Analyses of images indicate that the three magnification levels delineate the component distribution for this particular powder system similarly. Images obtained at the low magnification level demonstrated adequate resolution and provided the broadest view of the blend surface. It is concluded that the low optical magnification level was adequate for the system being studied and is the preferred mode for pharmaceutical powder blend image data collection for this system.

scholarly journals Validation of nine years of MOPITT V5 NIR using MOZAIC/IAGOS measurements: biases and long-term stability

2014 ◽  
Vol 7 (11) ◽  
pp. 3783-3799 ◽  
Author(s):  
A. T. J. de Laat ◽  
I. Aben ◽  
M. Deeter ◽  
P. Nédélec ◽  
H. Eskes ◽  
...  
Keyword(s):  
Near Infrared ◽  
Transport Model ◽  
A Priori ◽  
Validation Data ◽  
Chemistry Transport Model ◽  
Large Variability ◽  
Long Term Stability ◽  
Spatio Temporal ◽  
Good Agreement

Abstract. Validation results from a comparison between Measurement Of Pollution In The Troposphere (MOPITT) V5 Near InfraRed (NIR) carbon monoxide (CO) total column measurements and Measurement of Ozone and Water Vapour on Airbus in-service Aircraft (MOZAIC)/In-Service Aircraft for a Global Observing System (IAGOS) aircraft measurements are presented. A good agreement is found between MOPITT and MOZAIC/IAGOS measurements, consistent with results from earlier studies using different validation data and despite large variability in MOPITT CO total columns along the spatial footprint of the MOZAIC/IAGOS measurements. Validation results improve when taking the large spatial footprint of the MOZAIC/IAGOS data into account. No statistically significant drift was detected in the validation results over the period 2002–2010 at global, continental and local (airport) scales. Furthermore, for those situations where MOZAIC/IAGOS measurements differed from the MOPITT a priori, the MOPITT measurements clearly outperformed the MOPITT a priori data, indicating that MOPITT NIR retrievals add value to the MOPITT a priori. Results from a high spatial resolution simulation of the chemistry-transport model MOCAGE (MOdèle de Chimie Atmosphérique à Grande Echelle) showed that the most likely explanation for the large MOPITT variability along the MOZAIC-IAGOS profile flight path is related to spatio-temporal CO variability, which should be kept in mind when using MOZAIC/IAGOS profile measurements for validating satellite nadir observations.

scholarly journals Sodankylä manual snow survey program

2016 ◽  
Vol 5 (1) ◽  
pp. 163-179 ◽  
Author(s):  
Leena Leppänen ◽  
Anna Kontu ◽  
Henna-Reetta Hannula ◽  
Heidi Sjöblom ◽  
Jouni Pulliainen
Keyword(s):  
Snow Water Equivalent ◽  
Measurement Techniques ◽  
Liquid Water Content ◽  
The Arctic ◽  
Research Centre ◽  
Future Research ◽  
Forest Zone ◽  
Finnish Meteorological Institute ◽  
Snow Properties ◽  
Snow Water

Abstract. The manual snow survey program of the Arctic Research Centre of the Finnish Meteorological Institute (FMI-ARC) consists of numerous observations of natural seasonal taiga snowpack in Sodankylä, northern Finland. The easily accessible measurement areas represent the typical forest and soil types in the boreal forest zone. Systematic snow measurements began in 1909 with snow depth (HS) and snow water equivalent (SWE). In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from regular snow pits at several sites using both traditional and novel measurement techniques. Present-day snow pit measurements include observations of HS, SWE, temperature, density, stratigraphy, grain size, specific surface area (SSA) and liquid water content (LWC). Regular snow pit measurements are performed weekly during the snow season. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack. This snow survey program is an excellent base for the future research of snow properties.

scholarly journals Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss

2016 ◽  
Vol 8 (6) ◽  
pp. 505 ◽  
Author(s):  
Yurong Cui ◽  
Chuan Xiong ◽  
Juha Lemmetyinen ◽  
Jiancheng Shi ◽  
Lingmei Jiang ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Absorption Loss ◽  
Snow Water ◽  
Ku Band

scholarly journals Space-Time Cube and Mixed Reality – New Approaches to Support Understanding Historical Landscape Changes

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Mathias Jahnke ◽  
Edyta P. Bogucka ◽  
Maria Turchenko
Keyword(s):  
Cultural Landscape ◽  
Mixed Reality ◽  
New Technology ◽  
Space Time ◽  
Field Of View ◽  
Landscape Changes ◽  
Huge Amount ◽  
Visual Variables ◽  
Spatio Temporal ◽  
Microsoft Hololens

<p><strong>Abstract.</strong> Mixed reality is a rather new technology but came to its nowadays success through the availability of devices like Microsoft HoloLens which easily support the users and developers to use such devices. Therefore, visualization specialists like cartographers paid attention due to interaction possibilities such devices provide. In particular, to utilize the huge amount of opportunities such device gave. The applicability within the cartographic domain needs to be further investigated.</p><p>The main goal of this contribution is to evaluate the applicability of a mixed reality device in the domain of spatio-temporal representations on the example of the space-time cube to show cultural landscape changes. The hologram of the space-time cube provides the changes of the Royal Castle in Warsaw and their surrounding elements. The hologram therefore incorporated the different buildings of the castle, space-time prisms and space-time links to connect building elements over the years. The visual variables colour hue, colour value and transparency are mainly used to feature distinguishable space-time prisms and to show the space-time links. Different colour schemes are developed which features the characteristics of a mixed reality device. The possibilities of input actions are ranging from gaze/head movement, to gesture and voice.</p><p>The usability evaluation of the mixed reality hologram showed the overall comfort of interactions, perception of the visual components of the space-time cube and determines advantageous features and limitations of the technology. Most of the found limitations are connected to current devices, like e.g. resolution or field of view. An important aspect which came out is, that the experience the user has which such devices/technology plays an important role in successfully use and knowledge discovery from such applications.</p>

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