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Kirk D Knobelspiesse

Publications and source records attributed to Kirk D Knobelspiesse.

10 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

3D Cloud Masking Across A Broad Swath Using Multi-Angle Polarimetry and Deep Learning

Understanding the 3-dimensional structure of clouds is of crucial importance to modeling our changing climate. Active sensors, such as radar and lidar, provide accurate vertical cloud profiles, but are mostly restricted to along-track sampling. Passive sensors can capture a wide swath, but struggle to see beneath cloud tops. In essence, both types of products are restricted to two dimensions: as a cross-section in the active case, and an image in the passive case. However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information can be challenging, requiring computationally expensive radiative transfer techniques. Machine learning, as an alternative, may be able to capture some of the complexity of a full 3D radiative transfer solution with significantly less computational expense. In this work, we make three contributions towards understanding 3D cloud structure from multi-angle polarimetry. First, we introduce a large-scale, publicly available dataset that fuses existing cloud products into a format more amenable to machine learning. This dataset treats multi-angle polarimetry as an input, and radar-based vertical cloud profiles as an output. Second, we describe and evaluate strong baseline machine learning models based that predict these profiles from the passive imagery. Notably, these models are trained only on center-swath labels, but can predict cloud profiles over the entire passive imagery swath. Third, we leverage the information-theoretic nature of machine learning to draw conclusions about the relative utility of various sensor configurations, including spectral channels, viewing angles, and polarimetry. These findings have implications for Earth-observing missions such as NASA’s Plankton, Aerosol, Cloud-ocean Ecosystem (PACE) and Atmosphere Observing System (AOS) missions, as well as in informing future applications of computer vision to atmospheric remote sensing.

clouds↗
NASA NTRS2023

Unifying Radiative Transfer Models in Computer Graphics and Remote Sensing, Part II: A Differentiable, Polarimetric Forward Model and Validation

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 2 of this work, we demonstrate that Monte Carlo forward models in computer graphics are capable of sufficient accuracy for remote sensing by extending Mitsuba 3, a forward and inverse modeling framework recently developed in the computer graphics community, to simulate simple atmosphere-ocean systems and show that our framework is capable of achieving error on par with codes currently used by the remote sensing community on benchmark results.

Radiative transfer↗
NASA NTRS2023

Unifying radiative transfer models in computer graphics and remote sensing, Part I: A Survey

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 1 of this work, we examine the evolution of radiative transfer models in computer graphics and highlight recent advancements that have the potential to push forward models in remote sensing beyond their current periphery of realism.

Radiative transfer↗
NASA NTRS2023

PACE Technical Report Series, Volume 11: The PACE Postlaunch Airborne eXperiment (PACE-PAX)

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE; https://pace.gsfc.nasa.gov) mission is NASA's latest and greatest effort to study the ocean-atmosphere system and build upon the legacy of previous satellite missions that have revolutionized our understanding of our home planet. By using advanced radiometric and polarimetric instruments to measure ocean color and key atmospheric variables, PACE aims to provide new insights into the complex interactions between the ocean, atmosphere, and biosphere. This information will be critical for improving our ability to model and predict changes in the Earth's climate, as well as to manage and conserve our planet's valuable natural resources. Performance assessments and science data product validation is a critical process for any Earth Science space-borne mission, as it involves assessing and verifying the quality of the data products generated by the mission while it is in orbit, as well as assigning uncertainties to these retrievals. More specifically, validation efforts are essential for assessing the accuracy of instrument data and the derived science products across varied spatial and temporal scales and ensuring their reliability and consistency with the mission's scientific goals and objectives. The overall plan for validation of PACE data is described in “PACE Science Data Product Validation Plan” (see, e.g., https://pace.gsfc.nasa.gov), and among range of post-launch activities, it describes the need and requirements for a post-launch airborne field campaign in support of PACE validation. This volume outlines the plans and requirements of the PACE Postlaunch Airborne eXperiment (PACE-PAX). The main goal of PACE-PAX is to gather validation and assessment data for the PACE mission, using a variety of airborne and ground-based assets. These assets will be coordinated to obtain complementary measurements of key ocean and atmospheric variables, such as ocean color, aerosols, and clouds. By collecting data from multiple sources, PACE-PAX will help to validate and refine the data products generated by the PACE mission, as well as to identify any potential errors or biases in the measurements. This will be essential for ensuring the accuracy and reliability of the PACE data products, and for maximizing the scientific value of the mission.

Kirk D Knobelspiesse↗
NASA NTRS2022

Circular Polarization in Atmospheric Aerosols

Recent technological advances have demonstrated the feasibility of deploying spaceborne optical detectors with full polarimetric capabilities. The measurement of all four Stokes coefficients opens significant new opportunities for atmospheric aerosol studies and applications. While considerable amounts of attention have been dedicated to sensors with sensitivity to the total intensity and linear polarization (represented by Stokes coefficients I, U, Q), there has been less attention to the additional information brought by measuring circular polarization (coefficient V). This report fills this gap in knowledge by providing an overview of aerosol sources of circular polarization in the atmosphere and discusses possible remote sensing signatures. In this manuscript, circularly polarized radiation that results from the interaction of incident unpolarized radiation are considered in three physical settings: optical activity originating in biogenic aerosols, alignment of non-spherical particles in the presence of electrical fields (such as dust, smoke, and volcanic ash) and aerosol multiple scattering effects. Observational and theoretical evidence of, and the settings and conditions for non-zero aerosol circular polarization generated from incident unpolarized radiation are here gathered and discussed. In addition, novel radiative transfer simulations are shown to illustrate notable spectral and other features where circular polarization may provide additional information that is possibly independent from total intensity and linear polarization only observations. Current techniques for detection of aerosol composition (also referred as aerosol type) from space provide limited information. Remote identification of aerosols such as smoke, volcanic ash, and dust particles can only be accomplished with some degree of confidence for moderate to high concentrations. When the same aerosols are found at lower concentrations (but still high enough to be of importance for air quality and cloud formation), these methods often produce ambiguous results. The circular polarization of aerosols is rarely utilized, and we explore its value for improved determination aerosol composition. This study is presented as an overview with a goal to provide a new perspective on an overlooked optical property and to trigger interest in further exploration of this subject.

Aerosol type↗
NASA NTRS2019

The Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Mission: Status, Science, Advances

The PACE mission represents NASA’s next investment in ocean color, cloud, and aerosol data records to enable continued and advanced insight into oceanographic and atmospheric responses to Earth’s changing climate. The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA’s next investment in satellite ocean color and the study of Earth’s ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, and ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles, food web processes, and ecosystem responses to a changing climate, and improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and air and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument is a spectrometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-km at nadir. This payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere-land system. Here, we present an overview of the PACE mission, including its developmental history, science objectives, instrument payload, observatory characteristics, and data products.

P Jeremy Werdell↗
NASA NTRS2019

SPEX Airborne Spectropolarimeter Calibration and Performance

To improve our understanding of the complex role of aerosols in the climate system and on air quality, measurements are needed of optical and microphysical aerosol. From many studies, it has become evident that a satellite-based multiangle, multiwavelength polarimeter will be essential to provide such measurements. Here, high accuracy (∼0.003) on the degree of linear polarization (DoLP) measurements is important to retrieve aerosol properties with an accuracy needed to advance our understanding of the aerosol effect on climate. SPEX airborne, a multiangle hyperspectral polarimeter, has been developed for observing and characterizing aerosols from NASA’s high-altitude research aircraft ER-2. It delivers measurements of radiance and DoLP at visual wavelengths with a spectral resolution of 3 and 7–30 nm, respectively, for radiance and polarization, at nine fixed equidistant viewing angles from −56° to +56° oriented along the ground track, and a swath of 7° oriented across-track. SPEX airborne uses spectral polarization modulation to determine the state of linear polarization of scattered sunlight. This technique has been developed in the Netherlands and has been demonstrated with ground-based instruments. SPEX airborne serves as a demonstrator for a family of space-based SPEX instruments that have the ability to measure and characterize atmospheric aerosol by multiangle hyperspectral polarimetric imaging remotely from a satellite platform. SPEX airborne was calibrated radiometrically and polarimetrically using Jet Propulsion Laboratory (JPL) facilities including the Polarization Stage Generator-2 (PSG-2), which is designed for polarimetric calibration and validation of the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI). Using the PSG-2, the accuracy of the SPEX airborne DoLP measurements in the laboratory setup is found to be 0.002–0.004. Radiometric calibration is realized with an estimated accuracy of 4%. In 2017, SPEX airborne took part in the “Aerosol Characterization from Polarimeters and Lidar” campaign on the ER-2 that included four polarimeters and two lidars. Polarization measurements of SPEX airborne and the coflying Research Scanning Polarimeter (RSP), recorded during the campaign, were compared and display root-mean-square (RMS) differences ranging from 0.004 (at 555 nm) up to 0.02 (at 410 nm). For radiance measurements, excellent agreement between SPEX airborne and RSP is obtained with an RMS difference of ∼4%. The lab- and flight-performance values for polarization are similar to those recently published for AirMSPI, where also an intercomparison with RSP was made using data from field campaigns in 2013. The intercomparison of radiometric and polarimetric data both display negligible bias. The in-flight comparison results provide verification of SPEX airborne’s capability to deliver high-quality data.

Aerosols↗
NASA NTRSDate not supplied

Validation of Modis Cloud Liquid Water Path to Prepare for Pace Evaluation Efforts

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will launch in January 2024, extending and improving NASA’s global satellite observations in its eponymous domains. PACE’s hyperspectral Ocean Color Instrument (OCI) will offer daily near-global spatial coverage with a 1.2 km horizontal pixel size at the subsatellite point. PACE will also have two multi-angle polarimeters (HARP2 and SPEXone) capable of advanced atmospheric characterization. Validation of cloud retrievals is challenging. Here we evaluate liquid water path (LWP) from MODIS from the standard (MOD06) product. The same cloud optical properties retrieval algorithm will be applied to OCI data. This will allow us to understand the expected performance of this algorithm and to develop the processing and analysis code needed to evaluate PACE data. Please tell us what you think and if we should be doing something differently!

MODIS↗
NASA NTRSDate not supplied

ARM Data as A Resource for Validation of NASA PACE Cloud Retrievals

NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will launch in January 2024 and continue and improve upon satellite data records in its eponymous domains. PACE will carry a broad-swath hyperspectral imager, OCI, which will provide MODIS/VIIRS-type cloud data products (i.e. a cloud mask, top height, visible optical thickness, droplet effective radius, phase, and derived water path). It will also carry two multi-angle polarimeters (HARP2 and SPEXone) which will not only provide the above but also enable retrievals of additional cloud properties (e.g. droplet effective variance, ice crystal asymmetry parameter). Validating satellite-based cloud retrievals is challenging. We plan to use several ARM data streams to evaluate PACE cloud data products and are prototyping our analyses using retrievals from MODIS on the Aqua satellite and OLCI on the Sentinel-3A satellite. This poster shows how we plan to use ARM data to evaluate liquid water path (via MWRRET) and cloud top height retrievals (via KARZASRCL), with example results from these proxy sensors and ARM data from the SGP, ENA, and NSA sites. We seek comments from and collaborations with the ARM community to get the most out of our respective data streams.

ARM↗