Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements

2021 ◽  
Author(s):  
Niklas Bohn ◽  
David Thompson ◽  
Nimrod Carmon ◽  
Jouni Susiluoto ◽  
Michael Turmon ◽  
...  
Keyword(s):  
Liquid Water ◽  
Imaging Spectroscopy ◽  
Greenland Ice Sheet ◽  
Optimal Estimation ◽  
Surface Reflectance ◽  
Mixing Ratio ◽  
Surface Parameters ◽  
Ice Surface ◽  
Orbital Imaging ◽  
Snow And Ice

<p>Snow and ice melt processes are key variables in Earth energy-balance and hydrological modeling. Their quantification facilitates predictions of meltwater runoff and distribution and availability of fresh water. Furthermore, they are indicators of climate change and control the balance of the Earth's ice sheets. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), making imaging spectroscopy a powerful tool to measure and map this phenomenon. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and space borne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in the inversion and also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. An additional validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet yields promising results. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach’s potential and suitability for upcoming orbital imaging spectroscopy missions.</p>

scholarly journals Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements

2021 ◽  
Author(s):  
Niklas Bohn ◽  
Thomas Painter ◽  
David Thompson ◽  
Nimrod Carmon ◽  
Jouni Susiluoto ◽  
...  
Keyword(s):  
Imaging Spectroscopy ◽  
Optimal Estimation ◽  
Surface Parameters ◽  
Ice Surface ◽  
Snow And Ice

Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements

2021 ◽  
Vol 264 ◽  
pp. 112613
Author(s):  
Niklas Bohn ◽  
Thomas H. Painter ◽  
David R. Thompson ◽  
Nimrod Carmon ◽  
Jouni Susiluoto ◽  
...  
Keyword(s):  
Imaging Spectroscopy ◽  
Optimal Estimation ◽  
Surface Parameters ◽  
Ice Surface ◽  
Snow And Ice

scholarly journals Optimal Estimation of Snow and Ice Surface Parameters from Imaging Spectroscopy Measurements

2021 ◽  
Author(s):  
Urs Niklas Bohn ◽  
David Thompson ◽  
Nimrod Carmon ◽  
Jouni Susiluoto ◽  
Michael Turmon ◽  
...  
Keyword(s):  
Imaging Spectroscopy ◽  
Optimal Estimation ◽  
Surface Parameters ◽  
Ice Surface ◽  
Snow And Ice

scholarly journals CO2 Retrieval over Clouds from the OCO Mission: Model Simulations and Error Analysis

2009 ◽  
Vol 26 (6) ◽  
pp. 1090-1104 ◽  
Author(s):  
Jérôme Vidot ◽  
Ralf Bennartz ◽  
Christopher W. O’Dell ◽  
René Preusker ◽  
Rasmus Lindstrot ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Absorption Band ◽  
Liquid Water ◽  
Spectral Characteristics ◽  
Optimal Estimation ◽  
High Spectral Resolution ◽  
Oxygen Absorption ◽  
Co2 Absorption ◽  
Mixing Ratio ◽  
Cloud Properties

Abstract Spectral characteristics of the future Orbiting Carbon Observatory (OCO) sensor, which will be launched in January 2009, were used to infer the carbon dioxide column-averaged mixing ratio over liquid water clouds over ocean by means of radiative transfer simulations and an inversion process based on optimal estimation theory. Before retrieving the carbon dioxide column-averaged mixing ratio over clouds, cloud properties such as cloud optical depth, cloud effective radius, and cloud-top pressure must be known. Cloud properties were not included in the prior in the inversion but are retrieved within the algorithm. The high spectral resolution of the OCO bands in the oxygen absorption spectral region around 0.76 μm, the weak CO2 absorption band around 1.61 μm, and the strong CO2 absorption band around 2.06 μm were used. The retrieval of all parameters relied on an optimal estimation technique that allows an objective selection of the channels needed to reach OCO’s requirement accuracy. The errors due to the radiometric noise, uncertainties in temperature profile, surface pressure, spectral shift, and presence of cirrus above the liquid water clouds were quantified. Cirrus clouds and spectral shifts are the major sources of errors in the retrieval. An accurate spectral characterization of the OCO bands and an effective mask for pixels contaminated by cirrus would mostly eliminate these errors.

scholarly journals Temporal Variability of Surface Reflectance Supersedes Spatial Resolution in Defining Greenland’s Bare-Ice Albedo

2021 ◽  
Vol 14 (1) ◽  
pp. 62
Author(s):  
Tristram D. L. Irvine-Fynn ◽  
Pete Bunting ◽  
Joseph M. Cook ◽  
Alun Hubbard ◽  
Nicholas E. Barrand ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Temporal Dynamics ◽  
Model Performance ◽  
Greenland Ice Sheet ◽  
Surface Reflectance ◽  
Primary Control ◽  
Local Ablation ◽  
Ice Surface ◽  
Moderate Resolution ◽  
Reflectance Data

Ice surface albedo is a primary modulator of melt and runoff, yet our understanding of how reflectance varies over time across the Greenland Ice Sheet remains poor. This is due to a disconnect between point or transect scale albedo sampling and the coarser spatial, spectral and/or temporal resolutions of available satellite products. Here, we present time-series of bare-ice surface reflectance data that span a range of length scales, from the 500 m for Moderate Resolution Imaging Spectrometer’s MOD10A1 product, to 10 m for Sentinel-2 imagery, 0.1 m spot measurements from ground-based field spectrometry, and 2.5 cm from uncrewed aerial drone imagery. Our results reveal broad similarities in seasonal patterns in bare-ice reflectance, but further analysis identifies short-term dynamics in reflectance distribution that are unique to each dataset. Using these distributions, we demonstrate that areal mean reflectance is the primary control on local ablation rates, and that the spatial distribution of specific ice types and impurities is secondary. Given the rapid changes in mean reflectance observed in the datasets presented, we propose that albedo parameterizations can be improved by (i) quantitative assessment of the representativeness of time-averaged reflectance data products, and, (ii) using temporally-resolved functions to describe the variability in impurity distribution at daily time-scales. We conclude that the regional melt model performance may not be optimally improved by increased spatial resolution and the incorporation of sub-pixel heterogeneity, but instead, should focus on the temporal dynamics of bare-ice albedo.

scholarly journals Glacier ice surface properties in South-West Greenland Ice Sheet: first estimates from PRISMA imaging spectroscopy data

2021 ◽  
Author(s):  
Niklas Bohn ◽  
Biagio Di Mauro ◽  
Roberto Colombo ◽  
David Ray Thompson ◽  
Jouni Susiluoto ◽  
...  
Keyword(s):  
Surface Properties ◽  
Imaging Spectroscopy ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Spectroscopy Data ◽  
West Greenland ◽  
Ice Surface ◽  
South West ◽  
Glacier Ice

Does optimal estimation perform adequately for hyperspectral surface reflectance retrieval?

2020 ◽  
Author(s):  
Jouni Susiluoto ◽  
Michael Turmon ◽  
Nimrod Carmon ◽  
David Thompson
Keyword(s):  
Imaging Spectroscopy ◽  
Optimal Estimation ◽  
Full Potential ◽  
Surface Reflectance ◽  
Atmospheric Conditions ◽  
Bayesian Techniques ◽  
Posterior Mean ◽  
A Posteriori Estimate ◽  
Spectral Bands ◽  
Wide Range

<p>The current and coming imaging spectroscopy missions (EMIT, ECOSTRESS, AVIRIS-NG), and observables for potential future missions studying Surface Biology and Geology (SBG) observe a wide range of spectral bands, which can be used to infer about surface properties. The current state of the art approach for performing the retrieval of surface reflectance is optimal estimation (OE), which amounts to finding the maximum a posteriori estimate of the surface reflectance, after which the posterior covariance is approximated by linearizing the forward model (Rodgers, 2001). While this method has a principled basis and often performs well, with challenging atmospheres the optimization may fall into local minima, or the estimated posterior mean and covariance may be wrong.  Addressing these failures under realistic observing conditions is particularly important to realize the full potential of upcoming global observations.                                                                                                                                                                        </p><p><br>As a preparation to improving the quality of future retrievals, we evaluate the performance of OE against posteriors generated with advanced Bayesian techniques.  We present results from comparing the OE posterior mean and covariance to the true posterior, as computed by MCMC, for moderately challenging atmospheric conditions, and an instrument configuration consistent with AVIRIS-NG. </p>

scholarly journals Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1865
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-François Léon
Keyword(s):  
Matched Filter ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Sequential Method ◽  
Modal Radius ◽  
Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

scholarly journals Ice sheet mass loss caused by dust and black carbon accumulation

2015 ◽  
Vol 9 (2) ◽  
pp. 2563-2596
Author(s):  
T. Goelles ◽  
C. E. Bøggild ◽  
R. Greve
Keyword(s):  
Mass Loss ◽  
Black Carbon ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Carbon Accumulation ◽  
Model Framework ◽  
Ice Dynamics ◽  
Ablation Area ◽  
Ice Surface ◽  
Surface Melt

Abstract. Albedo is the dominating factor governing surface melt variability in the ablation area of ice sheets and glaciers. Aerosols such as mineral dust and black carbon (soot) accumulate on the ice surface and cause a darker surface and therefore a lower albedo. The dominant source of these aerosols in the ablation area is melt-out of englacial material which has been transported via ice flow. The darkening effect on the ice surface is currently not included in sea level projections, and the effect is unknown. We present a model framework which includes ice dynamics, aerosol transport, aerosol accumulation and the darkening effect on ice albedo and its consequences for surface melt. The model is applied to a simplified geometry resembling the conditions of the Greenland ice sheet, and it is forced by several temperature scenarios to quantify the darkening effect of aerosols on future mass loss. The effect of aerosols depends non-linearly on the temperature rise due to the feedback between aerosol accumulation and surface melt. The effect of aerosols in the year 3000 is up to 12% of additional ice sheet volume loss in the warmest scenario.

scholarly journals A depolarisation lidar based method for the determination of liquid-cloud microphysical properties

2014 ◽  
Vol 7 (9) ◽  
pp. 9917-9992 ◽  
Author(s):  
D. P. Donovan ◽  
H. Klein Baltink ◽  
J. S. Henzing ◽  
S. R. de Roode ◽  
A. P. Siebesma
Keyword(s):  
Water Content ◽  
Multiple Scattering ◽  
Liquid Water ◽  
Cloud Droplet ◽  
Optimal Estimation ◽  
Liquid Water Content ◽  
Cloud Base ◽  
Base Region ◽  
Large Eddy Simulation Model ◽  
Inversion Procedure

Abstract. The fact that polarisation lidars measure a depolarisation signal in liquid clouds due to the occurrence of multiple-scattering is well-known. The degree of measured depolarisation depends on the lidar characteristics (e.g. wavelength and receiver field-of-view) as well as the cloud macrophysical (e.g. liquid water content) and microphysical (e.g. effective radius) properties. Efforts seeking to use depolarisation information in a quantitative manner to retrieve cloud properties have been undertaken with, arguably, limited practical success. In this work we present a retrieval procedure applicable to clouds with (quasi-)linear liquid water content (LWC) profiles and (quasi-)constant cloud droplet number density in the cloud base region. Thus limiting the applicability of the procedure allows us to reduce the cloud variables to two parameters (namely the derivative of the liquid water content with height and the extinction at a fixed distance above cloud-base). This simplification, in turn, allows us to employ a fast and robust optimal-estimation inversion using pre-computed look-up-tables produced using extensive lidar Monte-Carlo multiple-scattering simulations. In this paper, we describe the theory behind the inversion procedure and successfully apply it to simulated observations based on large-eddy simulation model output. The inversion procedure is then applied to actual depolarisation lidar data corresponding to a range of cases taken from the Cabauw measurement site in the central Netherlands. The lidar results were then used to predict the corresponding cloud-base region radar reflectivities. In non-drizzling condition, it was found that the lidar inversion results can be used to predict the observed radar reflectivities with an accuracy within the radar calibration uncertainty (2–3 dBZ). This result strongly supports the accuracy of the lidar inversion results. Results of a comparison between ground-based aerosol number concentration and lidar-derived cloud droplet number densities are also presented and discussed. The observed relationship between the two quantities is seen to be consistent with the results of previous studies based on aircraft-based in situ measurements.

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