Airborne polarimetric, two-color laser altimeter measurements of lake ice cover: A pathfinder for NASA's ICESat-2 spaceflight mission

Harding, David J.; Dabney, Philip W.; Valett, Susan; Yu, Anthony W.; Vasilyev, Aleksey; Kelly, A.

doi:10.1109/igarss.2011.6050002

Cited by 13 publications

(20 citation statements)

References 9 publications

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“…These are much more sensitive to low intensity signals, requiring lower powered lasers and so making the instrument lighter and cheaper. NASA's SIMPL (Dabney et al, 2010) and ICESat II (Harding et al, 2011) instruments currently in development are examples and Moussavi, Abdalati, Scambos, and Neuenschwander (2014) demonstrated that they can measure forest height. However they require repeated measurements to measure energy, either from a fixed position (Hernandez-Marin, Wallace, & Gibson, 2007;Wallace et al, 2012) or by averaging over an area (Moussavi et al, 2014), which introduces statistical errors on top of the error from inversion methods.…”

Section: System Pulsementioning

confidence: 98%

Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy

Hancock

Armston

et al. 2015

Remote Sensing of Environment

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a b s t r a c tFull waveform lidar has a unique capability to characterise vegetation in more detail than any other practical method. The reflectance, calculated from the energy of lidar returns, is a key parameter for a wide range of applications and so it is vital to extract it accurately. Fifteen separate methods have been proposed to extract return energy (the amount of light backscattered from a target), ranging from simple to mathematically complex, but the relative accuracies have not yet been assessed. This paper uses a simulator to compare all methods over a wide range of targets and lidar system parameters. For hard targets the simplest methods (windowed sum, peak and quadratic) gave the most consistent estimates. They did not have high accuracies, but low standard deviations show that they could be calibrated to give accurate energy. This may be why some commercial lidar developers use them, where the primary interest is in surveying solid objects. However, simulations showed that these methods are not appropriate over vegetation. The widely used Gaussian fitting performed well over hard targets (0.24% root mean square error, RMSE), as did the sum and spline methods (0.30% RMSE). Over vegetation, for large footprint (15 m) systems, Gaussian fitting performed the best (12.2% RMSE) followed closely by the sum and spline (both 12.7% RMSE). For smaller footprints (33 cm and 1 cm) over vegetation, the relative accuracies were reversed (0.56% RMSE for the sum and spline and 1.37% for Gaussian fitting). Gaussian fitting required heavy smoothing (convolution with an 8 m Gaussian) whereas none was needed for the sum and spline. These simpler methods were also more robust to noise and far less computationally expensive than Gaussian fitting. Therefore it was concluded that the sum and spline were the most accurate for extracting return energy from waveform lidar over vegetation, except for large footprint (15 m), where Gaussian fitting was slightly more accurate. These results suggest that small footprint (≪15 m) lidar systems that use Gaussian fitting or proprietary algorithms may report inaccurate energies, and thus reflectances, over vegetation. In addition the effect of system pulse length, sampling interval and noise on accuracy for different targets was assessed, which has implications for sensor design.

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Section: System Pulsementioning

confidence: 98%

Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy

Hancock

Armston

et al. 2015

Remote Sensing of Environment

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“…In addition, retrievals from single channel observations require high accuracy in absolute calibration. To achieve better retrievals, multi wavelength lidar observations, such as the ones made the Slope Imaging Multi-polarization Photon-counting Lidar (SIMPL) [23,24], are needed. SIMPL acquired data over a range of grain size conditions in Greenland during July and August 2015.…”

Section: Summary and Discussionmentioning

confidence: 99%

Snow grain size retrieval over the polar ice sheets with the Ice, Cloud, and land Elevation Satellite (ICESat) observations

Yang

Marshak

Han

et al. 2017

Journal of Quantitative Spectroscopy and Radiative Transfer

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Snow grain size is an important parameter for cryosphere studies. As a proof of concept, this paper presents an approach to retrieve this parameter over Greenland, East and West Antarctica ice sheets from surface reflectances observed with the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) at 1064 nm. Spaceborne lidar observations overcome many of the disadvantages in passive remote sensing, including difficulties in cloud screening and low sun angle limitations; hence tend to provide more accurate and stable retrievals. Results from the GLAS L2A campaign, which began on 25 September and lasted until 19 November, 2003, show that the mode of the grain size distribution over Greenland is the largest (~300 μm) among the three, West Antarctica is the second (~220 μm) and East Antarctica is the smallest (~190 μm). Snow grain sizes are larger over the coastal regions compared to inland the ice sheets. These results are consistent with previous studies. Applying the broadband snow surface albedo parameterization scheme developed by Garder and Sharp (2010) to the retrieved snow grain size, ice sheet surface albedo is also derived. In the future, more accurate retrievals can be achieved with multiple wavelengths lidar observations.

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“…The non-imaging airborne spectrometers and SIMPL (Dabney et al, 2010;Harding et al, 2011) were flown together on a National Aeronautics and Space Administration (NASA) Langley Research Center King Air (hereinafter UC-12B). SIMPL uses a micropulse, photon-counting, multibeam measurement like that of ATLAS but provides added information about light scattering by using co-aligned green and NIR laser pulses and a measure of pulse depolarization.…”

Section: Introductionmentioning

confidence: 99%

Radiometric calibration of a non-imaging airborne spectrometer to measure the Greenland ice sheet surface

et al. 2019

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Abstract. Methods to radiometrically calibrate a non-imaging airborne visible-to-shortwave infrared (VSWIR) spectrometer to measure the Greenland ice sheet surface are presented. Airborne VSWIR measurement performance for bright Greenland ice and dark bare rock/soil targets is compared against the MODerate resolution atmospheric TRANsmission (MODTRAN®) radiative transfer code (version 6.0), and a coincident Landsat 8 Operational Land Imager (OLI) acquisition on 29 July 2015 during an in-flight radiometric calibration experiment. Airborne remote sensing flights were carried out in northwestern Greenland in preparation for the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) laser altimeter mission. A total of nine science flights were conducted over the Greenland ice sheet, sea ice, and open-ocean water. The campaign's primary purpose was to correlate green laser pulse penetration into snow and ice with spectroscopic-derived surface properties. An experimental airborne instrument configuration that included a nadir-viewing (looking downward at the surface) non-imaging Analytical Spectral Devices (ASD) Inc. spectrometer that measured upwelling VSWIR (0.35 to 2.5 µm) spectral radiance (Wm-2sr-1µm-1) in the two-color Slope Imaging Multi-polarization Photon-Counting Lidar's (SIMPL) ground instantaneous field of view, and a zenith-viewing (looking upward at the sky) ASD spectrometer that measured VSWIR spectral irradiance (W m−2 nm−1) was flown. National Institute of Standards and Technology (NIST) traceable radiometric calibration procedures for laboratory, in-flight, and field environments are described in detail to achieve a targeted VSWIR measurement requirement of within 5 % to support calibration/validation efforts and remote sensing algorithm development. Our MODTRAN predictions for the 29 July flight line over dark and bright targets indicate that the airborne nadir-viewing spectrometer spectral radiance measurement uncertainty was between 0.6 % and 4.7 % for VSWIR wavelengths (0.4 to 2.0 µm) with atmospheric transmittance greater than 80 %. MODTRAN predictions for Landsat 8 OLI relative spectral response functions suggest that OLI is measuring 6 % to 16 % more top-of-atmosphere (TOA) spectral radiance from the Greenland ice sheet surface than was predicted using apparent reflectance spectra from the nadir-viewing spectrometer. While more investigation is required to convert airborne VSWIR spectral radiance into atmospherically corrected airborne surface reflectance, it is expected that airborne science flight data products will contribute to spectroscopic determination of Greenland ice sheet surface optical properties to improve understanding of their potential influence on ICESat-2 measurements.

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Airborne polarimetric, two-color laser altimeter measurements of lake ice cover: A pathfinder for NASA's ICESat-2 spaceflight mission

Cited by 13 publications

References 9 publications

Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy

Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy

Snow grain size retrieval over the polar ice sheets with the Ice, Cloud, and land Elevation Satellite (ICESat) observations

Radiometric calibration of a non-imaging airborne spectrometer to measure the Greenland ice sheet surface

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