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NASA NTRS2022

Chapter 10 - Remote Sensing Measurements of Aerosol Properties

Satellite instruments have proven especially capable at monitoring the quantity of airborne particles in columns of atmosphere, globally. This chapter describes the principles of satellite measurements and retrieval algorithms, and surveys current instruments and their capabilities. We outline the issues associated with retrieval algorithms, such as surface characterization and aerosol proximity to clouds, and the challenges with interpretation of the results. The relationship between measured aerosol properties and climate-relevant aerosol properties simulated in models is outlined, as well as how measurements are used to evaluate models. Most space-based aerosol instruments are passive sensors that measure reflected sunlight at multiple wavelengths, some at multiple viewing angles. A few are active sensors that send out their own laser light and measure the returned signal. Except when clouds are present, the excess amount of light scattered back to space, beyond that expected from the surface and atmospheric gas, is attributed to aerosol. Satellite measurements are used in many ways in aerosol research. They often provide the only method for monitoring hazardous phenomena such as major wildfire and volcanic eruption plumes, especially in remote areas. Stable, long-term, near-global-scale satellite data records make it possible to identify regional and global aerosol trends. Aerosol radiative effects on climate can be quantified on a near-global scale and used to estimate the strength of aerosol–radiation and aerosol–cloud interactions as well as to evaluate climate model simulations of these interactions. Aerosol-type mapping from satellite imagery is helpful for source attribution, model validation, and to constrain particle light-absorption properties that are essential for radiative forcing calculations. The range of aerosol properties retrieved from satellite observations has grown considerably since the first global estimates of aerosol optical depth (τ a) over ocean were made in the late 1970s. Methods for retrieving particle size and light-absorption properties were explored in the 1990s using multispectral, multi-angle observations, and polarization in visible and near-infrared wavelengths. Sensitivity to particle light absorption, primarily from black or brown carbon content, improved with the inclusion of UV channels, and sensitivity to very thin aerosol layers in the upper troposphere and lower stratosphere was advanced with the use of limb-sounding instruments and active sensors. There are limitations to every measurement technique, including satellite aerosol remote sensing. For wide-swath, passive instruments, aerosol retrievals near clouds can present substantial challenges as far as 15 km away due to cloud-scattered light contaminating the signal. In nearly all cases, retrievals over bright snow and ice surfaces are precluded because surface reflectance uncertainties can overwhelm the aerosol signal. Similarly, meteorological cloud is identified and masked out where possible. Data from passive sensors also lack vertical resolution except those that view toward the limb or where multi-angle imagery is acquired over plumes from wildfires, erupting volcanoes, and wind-blown dust. Yet, passive sensors provide vastly more coverage than the active instruments that mitigate these issues. Particle microphysical information is qualitative from all remote sensing techniques, relying on proxies to infer particle composition, hygroscopicity, and the amount of light-absorbing material. Further, particles smaller than about 200 nm diameter cannot be distinguished from atmospheric gas molecules with remote sensing, which hinders studies of cloud condensation nuclei and their effects on clouds. Most satellite instruments dedicated to aerosol observations are in low-Earth, near-polar, sun-synchronous orbits, which means they cross the equator at the same local time each day. Most are set on cycles that repeat approximately every 16 days, which makes it difficult to monitor aerosol evolution locally. Geostationary satellites make it possible to observe changes occurring from minutes to hours over regions up to 8000 km in size, but lack coverage of high latitudes, and often provide more limited constraints on aerosol properties. Ground-truth data are vital for satellite aerosol-retrieval validation. The AErosol RObotic NETwork (AERONET) of sun photometers was created in 1993 and has become an established global network of over 350 instruments for validating satellite measurements. The network, as well as global networks of ground-based lidars, solar flux radiometers and other sun photometers, are widely used for evaluating global satellite retrievals and model simulations. NASA's Earth Observing System (EOS) program beginning in 1999 led to improvements in reliability, spatial resolution, and spectral resolution (and hence, to improved particle size discrimination and light absorption properties). Satellite payloads include advanced broad-swath and multi-angle imagers, along with the first space-based active sensor focused largely on long-term aerosol monitoring. Since about 2002, Europe's SENTINEL and operational meteorological satellite fleets are also providing sustained aerosol observations, with planned continuation until at least 2030. Satellite remote sensing instruments offer valuable data for evaluating aerosol representations in global climate models. They have been used to assess aerosol optical and physical properties, trends and distributions, and are applied increasingly as direct model constraints in data assimilation to create global aerosol reanalysis products. Aerosol optical depth is the most common quantity adopted for routine model evaluation, including multiwavelength data to loosely constrain particle-size distributions. These evaluations of multiple models have revealed general biases in their regional aerosol amounts and seasonal patterns of transport and removal. Although satellite measurements have near-global coverage, substantial errors can be introduced into the model observation comparison unless attention is paid to spatial and temporal collocation, cloud screening, subgrid-scale variability, and measurement uncertainties that vary with retrieval conditions.

aerosol properties↗

DOE OSTI2025

Weighted FFT estimators for 1D and 3D correlations of the Lyman- α forest

Correlations in the Lyman-α (Lyα) forest, both as a function of line of sight separation (1D) and 3D separation, provide a unique window to the distribution of matter at redshifts not accessible by current galaxy surveys. While optimal quadratic estimators have been used to measure 1D correlations, they are computationally expensive and difficult to extend to 3D analyses. On the other hand, estimators based on the Fast Fourier Transform (FFT) are significantly faster, but are affected by missing data in the spectra (masked pixels) and so far have not used pixel weights to reduce the uncertainties in the measurement. In this publication we describe how to compute the window matrix that enables forward-modelling the impact of masked pixels and weights on the FFT-based estimators. Here, we use Gaussian and hydrodynamical simulations with artificially masked pixels to validate the method on the measurement of 1D correlations. Finally, we show that the formalism can be extended to model the impact on 3D correlations, in particular on the cross-spectrum, the correlation of 1D Fourier modes as a function of transverse separation. This work will enable more precise clustering measurements with the Lyα forest dataset recently collected by the Dark Energy Spectroscopic Instrument (DESI).

Lokken, Martine [Univ. Autonoma de Barcelona (Spai↗

DOE OSTI2023

Coarse-to-fine Task-driven Inpainting for Geoscience Images

The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. As far as we know, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images, and they never consider the following geoscience task when developing inpainting methods. Here, this paper aims to repair the occluded regions for a better geoscience task performance and advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with the help of designed coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we propose a MaskMix based data augmentation method, which augments inpainting masks instead of augmenting original images, to exploit the limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method. The code is available at: https://github.com/HMS97/Task-driven-Inpainting.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1992

Spacelab J: Microgravity and life sciences

Spacelab J is a joint venture between NASA and the National Space Development Agency of Japan (NASDA). Using a Spacelab pressurized long module, 43 experiments will be performed in the areas of microgravity and life sciences. These experiments benefit from the microgravity environment available on an orbiting Shuttle. Removed from the effects of gravity, scientists will seek to observe processes and phenomena impossible to study on Earth, to develop new and more uniform mixtures, to study the effects of microgravity and the space environment on living organisms, and to explore the suitability of microgravity for certain types of research. Mission planning and an overview of the experiments to be performed are presented. Orbital research appears to hold many advantages for microgravity science investigations, which on this mission include electronic materials, metals and alloys, glasses and ceramics, fluid dynamics and transport phenomena, and biotechnology. Gravity-induced effects are eliminated in microgravity. This allows the investigations on Spacelab J to help scientists develop a better understanding of how these gravity-induced phenomena affect both processing and products on Earth and to observe subtle phenomena that are masked in gravity. The data and samples from these investigations will not only allow scientists to better understand the materials but also will lead to improvements in the methods used in future experiments. Life sciences research will collect data on human adaptation to the microgravity environment, investigate ways of assisting astronauts to readapt to normal gravity, explore the effects of microgravity and radiation on living organisms, and gather data on the fertilization and development of organisms in the absence of gravity. This research will improve crew comfort and safety on future missions while helping scientists to further understand the human body.

Source record↗

NASA NTRS1995

Arctic sea ice leads from advanced very high resolution radiometer images

A large number of advanced very high resolution radiometer (AVHRR) images from throughout 1989 are analyzed to determine lead characteristics. The units of analysis are square 200-km cells, and there are 270 such cells in the data set. Clouds are masked manually. Leads determine from images of the potential open water delta, a scaled version of the surface temperature or albedo that weights thin ice by its thermal or brightness impact. The lead fraction is determined as the mean delta, the monthly mean lead fraction ranges from 0.02 in winter to 0.06 in summer in the central Arctic and is near 0.08 in the winter in the peripheral seas. A method of accounting for lead width sampling errors due to the finite sample areas is introduced. In the central Arctic the observed mean lead width for a threshold of delta = 0.1 ranges from 2 or 3 km (near the resolution of the instrument) in the winter to 6 km in the summer. In the peripheral seas it is about 5 km in the winter. Width distributions are often more heavily weighted in the tail than exponential distributions and are well approximated by a power law. The along-track, number density power law N = aw(exp -6) has a mean exponent of b = 1.60 (standard deviation 0.18) and shows some seasonal variability. Mean floe widths in the central Arctic are 40 to 50 km in the winter, dropping to about 10 km in the summer. For floes the power law has a mean exponent of 0.93 and exhibits a clearer annual cycle. Lead orientation is determined with a method based on the direction of maximum extent.

Lindsay, R. W.↗

NASA NTRS1996

ASpec: A New Spectrum and Line Analysis Package for IRAF

ASpec is a new spectrum and line analysis package developed at STScI. ASpec is designed as an add-on package for IRAF and incorporates a variety of analysis techniques for astronomical spectra. ASpec operates on spectra from a wide variety of ground-based and space-based instruments. ASpec allows simultaneous handling of spectra from different wavelength regimes. ASpec accommodates non-linear dispersion relations. ASpec provides a variety of functions, individually or in combination, with which to fit spectral features and the continuum. ASpec permits the masking of known bad data. ASpec provides a powerful, intuitive graphical user interface implemented using the new IRAF Object Manager and customized to handle: data input/output (I/O); on-line 'help'; selection of relevant features for analysis; plotting and graphical interaction; and data base management.

Hulbert, Stephen J.↗

NASA NTRS1997

The Accuracy of Saccadic and Perceptual Decisions in Visual Search

Saccadic eye movements during search for a target embedded in noise are suboptimally guided by information about target location. Our goal is to compare the spatial information used to guide the saccades with that used for the perceptual decision. Three observers were asked to determine the location of a bright disk (diameter = 21 min) in white noise (signal-to-noise ratio = 4.2) from among 10 possible locations evenly spaced at 5.9 deg eccentricity. In the first of four conditions, observers used natural eye movements. In the three remaining conditions, observers fixated a central cross at all times. The fixation conditions consisted of three different presentation times (100, 200, 300 msec), each followed by a mask. Eye-position data were collected, with a resolution of (approximately) 0.2 deg. In the natural viewing condition, we measured. the accuracy with respect to the target and the latency of the first saccade. In the fixation conditions, we discarded trials in which observers broke fixation. Perceptual performance was computed for all conditions. Averaged across observers, the first saccade was correct (closest to the target location) for 56 +/- (SD) % of trials (chance = 10 %) and occurred after a latency of 313 +/- 56 msec. Perceptual performance averaged 53 +/- 4, 63 +/- 4, 65 +/- 2 % correct at 100, 200, and 300 msec, respectively. For the signal-to-noise ratio used, at the time of initiation of the first saccade, there is little difference between the amount of information about target location available to the perceptual and saccadic systems.

Eckstein, Miguel P.↗

NASA NTRS2019

Oro-Nasal Mask Versus Two-Way Non-Rebreathing Valves for Maximal Aerobic Capacity Testing in Astronauts

Astronauts complete maximal aerobic capacity (VO2pk) testing as part of their annual fitness assessment (AFA) as well as several times once assigned to an International Space Station mission. Historically, the 2-Way T-Shape Non-Rebreathing valve with a mouthpiece and nose clip (“Mouthpiece”) has been used in these tests. The testing procedure was updated to use the oro-nasal mask (“Mask”) for the AFA starting in June 2017. Astronauts who used the mask during their AFA requested it be certified to be used for all mission associated tests. Considering the criticality of the data and the schedule constraints of astronauts, it is imperative that the requested hardware change provide data with equivalent reliability and repeatability as provided by the mouthpiece. PURPOSE: To assess the reliability and validity of mask vs. mouthpiece by comparing submaximal and VO2pkdata within subjects (approximately 1 year apart). METHODS: Each of 17 active astronauts completed a VO (sub 2pk) test with the mouthpiece (first) and the mask (second) for their AFA. The VO (sub 2pk) test was conducted on a cycle ergometer with a metabolic cart. The nominal protocol started with a 3-minute warm-up at 50 Watts (W) and increased 25W every minute until volitional fatigue (Light: 45W start; 15W increase). The VO (sub 2pk)s were compared between tests and the expected day-to-day variation (plus or minus 5 percent) was used as the threshold for determining agreement between tests. Submaximal values were plotted and evaluated visually for deviations between mask and mouthpiece. RESULTS: VO (sub 2pk) values were more than 5 percent different, despite similar test times, between mouthpiece and mask in 6 of 17 comparisons, 3 of which were higher with the mask (9.0 plus or minus 5.9 percent) while 3 were lower (minus10.8 plus or minus 2.0 percent) with the mask. The submaximal data did not indicate a leak in either apparatus during these tests. An Astronaut Strength & Conditioning Rehabilitation specialist confirmed that the measured differences in VO (sub 2pk) of these 6 astronauts was consistent with observed changes in exercise habits during the year that separated the two tests. CONCLUSION: After being presented with the results of this data mining effort the mask was accepted for use in all tests, excepting that, if a leak is detected without resolve, the test will be repeated (if schedule allows) and remaining tests will be completed with the mouthpiece.

Buxton, R.↗

NASA NTRS1984

Parametric analysis of synthetic aperture radar data acquired over truck garden vegetation

An airborne X-band SAR acquired multipolarization and multiflight pass SAR images over a truck garden vegetation area. Based on a variety of land cover and row crop direction variations, the vertical (VV) polarization data contain the highest contrast, while cross polarization contains the least. When the radar flight path is parallel to the row direction, both horizontal (HH) and VV polarization data contain very high return which masks out the specific land cover that forms the row structure. Cross polarization data are not that sensitive to row orientation. The inclusion of like and cross polarization data help delineate special surface features (e.g., row crop against non-row-oriented land cover, very-rough-surface against highly row-oriented surface).

Wu, S. T.↗

NASA NTRSDate not supplied

Machine-Learning-Based Adaptive Thinning of CrIS Radiances to Improve Global Tropical Cyclone Analysis and Forecasts

This work is focused on optimizing the assimilation of hyperspectral infrared (IR) radiances from the Cross-track Infrared Sounder (CrIS) with the goal of improving the representation of tropical cyclones (TCs) in global analyses and forecasts. Current operational assimilation systems rely on subsampling IR radiances on a regular thinning grid. A new and improved adaptive methodology based on machine learning (ML) recognizes TCs from geostationary satellite imagery and is implemented in the Goddard Earth Observing System (GEOS) model and data assimilation framework. The ML methodology is extensively trained on existing TC data sets and creates for each TC a dynamic mask, based on the evolving shape and life cycle of that specific event. Once a TC mask is created, a switch is then activated in the data assimilation system to alter the thinning, ingesting more CrIS radiances within the moving mask, thus increasing the TC sampling. After the TC dissipates, the assimilation of CrIS radiances reverts to normal data density. Results of TC segmentation provided by a state-of-the-art generative machine learning model known as the Denoising Diffusion Probabilistic Model (DDPM) are compared to the previously used U-Net model. The new approach surpasses the performance of the previously developed one. The methodology is applied to both clear-sky and cloud-cleared radiances. Benefits from the latter methodology, particularly in improving the structure of TCs and the intensity forecasts, are presented.

Oreste Reale↗

NASA NTRS1995

SeaWiFS technical report series. Volume 31: Stray light in the SeaWiFS radiometer

Some of the measurements from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) will not be useful as ocean measurements. For the ocean data set, there are procedures in place to mask the SeaWiFS measurements of clouds and ice. Land measurements will also be masked using a geographic technique based on each measurment's latitude and longitude. Each of these masks involves a source of light much brighter than the ocean. Because of stray light in the SeaWiFS radiometer, light from these bright sources can contaminate ocean measurements located a variable number of pixels away from a bright source. In this document, the sources of stray light in the sensor are examined, and a method is developed for masking measurements near bright targets for stray light effects. In addition, a procedure is proposed for reducing the effects of stray light in the flight data from SeaWiFS. This correction can also reduce the number of pixels masked for stray light. Without these corrections, local area scenes must be masked 10 pixels before and after bright targets in the along-scan direction. The addition of these corrections reduces the along-scan masks to four pixels before and after bright sources. In the along-track direction, the flight data are not corrected, and are masked two pixels before and after. Laboratory measurements have shown that stray light within the instrument changes in a direct ratio to the intensity of the bright source. The measurements have also shown that none of the bands show peculiarities in their stray light response. In other words, the instrument's response is uniform from band to band. The along-scan correction is based on each band's response to a 1 pixel wide bright sources. Since these results are based solely on preflight laboratory measurements, their successful implementation requires compliance with two additional criteria. First, since SeaWiFS has a large data volume, the correction and masking procedures must be such that they can be converted into computationally fast algorithms. Second, they must be shown to operate properly on flight data. The laboratory results, and the corrections and masking procedures that derive from them, should be considered as zeroeth order estimates of the effects that will be found on orbit.

Hooker, Stanford B.↗

NASA NTRS1976

Prediction of passenger ride quality in a multifactor environment

A model being developed, permits the understanding and prediction of passenger discomfort in a multifactor environment with particular emphasis upon combined noise and vibration. The model has general applicability to diverse transportation systems and provides a means of developing ride quality design criteria as well as a diagnostic tool for identifying the vibration and/or noise stimuli causing discomfort. Presented are: (1) a review of the basic theoretical and mathematical computations associated with the model, (2) a discussion of methodological and criteria investigations for both the vertical and roll axes of vibration, (3) a description of within-axis masking of discomfort responses for the vertical axis, thereby allowing prediction of the total discomfort due to any random vertical vibration, (4) a discussion of initial data on between-axis masking, and (5) discussion of a study directed towards extension of the vibration model to the more general case of predicting ride quality in the combined noise and vibration environments.

Dempsey, T. K.↗

NASA NTRS2018

New Neural Network Cloud Mask Algorithm Based on Radiative Transfer Simulations

Cloud detection and screening constitute critically important first steps required to derive many satellite data products. Traditional threshold-based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and they have difficulties over areas partially covered with snow/ice. Exploiting advances in machine learning techniques and radiative transfer modeling of coupled environmental systems, we have developed a new, threshold-free cloud mask algorithm based on a neural network classifier driven by extensive radiative transfer simulations. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over snow-covered areas in the mid-latitudes. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors. Comparedto threshold-based methods and previous machine-learning approaches, this new cloud mask (i) does not rely on thresholds, (ii) needs fewer satellite channels, (iii) has superior performance during winter seasons in mid-latitude areas, and (iv) can easily be applied to different sensors.

cloud mask algorithms↗

NASA NTRS1993

JAPE 91: Influence of terrain masking of the acoustic propagation of helicopter noise

The acoustic propagation in the case of a noise source masked by a small element of terrain has been investigated experimentally. These data have been measured during the 'terrain masking' experiment of the NATO JAPE 91 experimental campaign. The main objective of that experiment was to study the acoustic detection of a helicopter masked by a small hill. Microphones have been placed at different locations on the shadow zone of the hill to study the effect of the terrain obstruction on sound propagation. The results presented come from data measured by Atlas Elektronik and by ISL, and have been processed together. The terrain obstruction causes an excess attenuation of the SPL (Sound Pressure Level) for all the frequencies, but this attenuation is more effective for the high frequencies than for the low frequencies. Results typical of diffraction phenomena have been observed; the SPL is minimal at the foot of the hill and is relatively constant beyond it.

Naz, P.↗

DOE OSTI2022

NXREFINE

NXRefine implements a complete workflow for both data acquisition and reduction of single crystal diffuse x-ray scattering collected as a series of area detector frames during continuous sample rotation. The data are stored in HDF5 files, which conform to the NeXus data format standard. These files contain a comprehensive set of metadata, including the incident wavelength, powder calibration scans used to define detector distances, beam centers, and yaw, pitch, and roll corrections. Automated peak searches of the three-dimensional data arrays define a set of Bragg peaks that are used to define an orientation matrix, defined according to Busing and Levy, which is then used to transform the data from angular coordinates to reciprocal space coordinates using the CCTW program. Multiple rotations at different detector translations and offset rotation axes are combined to ensure that there are no gaps in the reconstructed data, and allow the construction of masks to eliminate detector artifacts due to Compton scattering within the sensor layers. Finally, the data can be transformed into 3D-ΔPDF maps using the punch-and-fill method after symmetrization.

OSBORN, RAYMOND↗

NASA NTRS2000

Near-Infrared Interferometric Images of the Solar System Sized Disk Surrounding the Herbig Ae/Be Star MWC 349A

We present images of the Herbig Ae/Be star MWC 349A at 1.65 and 2.27, and 3.08 micrometers, reconstructed from complex visibility data obtained with an aperture masking interferometric technique on the Keck I telescope. These images have an approximately elliptical shape, and are consistent with the expected shape of a nearly edge-on Keplerian disk. Visibility data were fitted with uniform ellipses with major axes 36 +/- 2, 47 +/- 2, and 62 +/- 1 mas, respectively. The axial ratio of the ellipses is approximately 0.5 +/- 0.1, and the major axis is at a position angle of 100 +/- 3 degrees, consistent with the position angle of the dark lane observed previously in the Very Large Array (VLA) radio continuum maps at 8 and 22 GHz, perpendicular to the symmetry axis of the bipolar lobes of H66(alpha) recombination line emission, and consistent with positions of the recombination line maser spots at 1.3 mm. At an assumed distance of 1.2 kpc, the linear sizes of the disk are 44 and 57 AU at 1.65 and 2.2 micrometers, respectively. The disk is the presumed source of ionized material in the bipolar outflow and ultracompact HII region around the star.

Danchi, W.C.↗

NASA NTRS2023

Toward Physics-informed Neural Networks for 3D Multi-layer Cloud Mask Reconstruction

Three-dimensional (3D) cloud retrievals are critical for understanding their impact on climate and other applications such as aviation safety, weather prediction, and remote sensing. However, obtaining high-resolution and accurate vertical representation of clouds remains unsolved due to the limitations imposed by satellite instrumentation, viewing conditions, and the complexity of cloud dynamics. Cloud masks are essential for comprehending various cloud vertical properties, but deriving accurate 3D cloud masks from 2D satellite imagery data is a challenging task. To tackle these challenges, we introduce a physics-informed loss function for training deep learning models that can extend 2D cloud images into 3D cloud masks. The proposed loss, called CloudMask Loss, is composed of two domain knowledge-informed loss terms: one for evaluating cloud position and thickness, and the other for measuring the number of layers. By combining these loss terms, we improve the trainability of the deep learning models for more accurate and meaningful results. We apply the proposed loss function to different neural networks and demonstrate significant improvements in multi-layer cloud mask reconstruction. Utilizing the same neural network architecture, our proposed loss outperforms standard binary crossentropy loss in terms of multi-layer cloud classification accuracy, number of layers accuracy, and thickness mean absolute error (MAE). The proposed loss function can be readily integrated into various neural network architectures, resulting in substantial performance gains in 3D cloud mask generation.

multi-layer clouds↗

NASA NTRS2022

A Novel Machine Learning-Based Gap-Filling of Fine-Resolution Remotely Sensed Snow Cover Fraction Data By Combining Downscaling and Regression

Satellite-based remotely sensed observations of snow cover fraction (SCF) can have data gaps in spatially distributed coverage from sensor and orbital limitations. We mitigate these limitations in the example fine-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data by gap-filling using auxiliary 1-km datasets that either aid in downscaling from coarser-resolution (5 km) MODIS SCF wherever not fully covered by clouds, or else by themselves via regression wherever fully cloud covered. This study’s prototype predicts a 1-km version of the 500-m MOD10A1 SCF target. Due to noncollocatedness of spatial gaps even across input and auxiliary datasets, we consider a recent gap-agnostic advancement of partial convolution in computer vision for both training and predictive gap-filling. Partial convolution accommodates spatially consistent gaps across the input images, effectively implementing a two-dimensional masking. To overcome reduced usable data from noncollocated spatial gaps across inputs, we innovate a fully generalized three-dimensional masking in this partial convolution. This enables a valid output value at a pixel even if only a single valid input variable and its value exist in the neighborhood covered by the convolutional filter zone centered around that pixel. Thus, our gap-agnostic technique can use significantly more examples for training (∼67%) and prediction (∼100%), instead of only less than 10% for the previous partial convolution. We train an example simple three-layer legacy super-resolution convolutional neural network (SRCNN) to obtain downscaling and regression component performances that are better than baseline values of either climatology or MOD10C1 SCF as relevant. Our generalized partial convolution can enable multiple Earth science applications like downscaling, regression, classification, and segmentation that were hindered by data gaps.

Soni Yatheendradas↗