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This Algorithm Theoretical Basis (ATB) document describes the algorithms used to derive the parameters which make up the level 1 Ancillary Geographic Product (AGP).
This Algorithm Theoretical Basis (ATB) document describes the algorithms used to generate the parameters of certain ancillary products and datasets used during Level 2 processing of Multi-angle Imaging SpectroRadiometer (MIST) data.
This Algorithm Theoretical Basis Document (ATBD) describes the theoretical background of the TROPICS native radiance calibration algorithms. It also includes TROPICS payload characteristics and the algorithm’s ancillary data (i.e., data coming from sources other than the TROPICS Space Vehicle). Details of the native radiance (i.e., Level-1a antenna temperatures and Level-1b brightness temperatures) data product format can be found in the TROPICS Data User’s Guide. This ATBD information on the pre-launch testing completed to verify the algorithm. The TROPICS Data User’s Guide will contain the post-launch radiance validation.
This Algorithm Theoretical Basis Document (ATBD) describes the theoretical background of the TROPICS Atmospheric Vertical Temperature and Moisture Profile (AVTP/AVMP) retrieval algorithms. It also includes TROPICS payload characteristics and the algorithm’s ancillary data (i.e., data coming from sources other than the TROPICS Space Vehicle). Details of the AVTP and AVMP, data product format can be found in the TROPICS Data User’s Guide. This ATBD will also contain the pre-launch testing completed to verify the algorithm. The TROPICS Data User’s Guide will contain the post-launch validation.
This Algorithm Theoretical Basis Document (ATBD) describes the theoretical background of the TROPICS Level-2A Unified Resolution Radiometric product (L2A-URRP). It also includes TROPICS payload characteristics and the algorithm’s ancillary data (i.e., data coming from sources other than the TROPICS Space Vehicle). Details of the L2A-URRP data product format can be found in the TROPICS Data User’s Guide. This ATBD will also contain the pre-launch testing completed to verify the algorithm. The TROPICS Data User’s Guide will contain the post-launch validation.
We co-design a family of quantum eigenvalue transformation oracles that can be efficiently implemented on hybrid discrete- or continuous-variable (qubit or qumode) hardware. To illustrate the oracle’s representation-theoretic power and near-term experimental accessibility, we encode a Gaussian imaginary time-evolution spectral filter. As a result, we define a continuous linear combination of unitaries block encoding. Due to the ancillary qumode’s infinite-dimensional nature, continuous-variable qumodes constitute a powerful compilation tool for encoding continuous spectral functions without discretization errors while minimizing resource requirements. We then focus on the ubiquitous task of preparing eigenstates in quantum spin models. For completeness, we provide an end-to-end compilation which expresses high-level oracles in terms of an experimentally realizable instruction set architecture in both 1D and 2D. Finally, we examine the leading-order effects of physical errors and highlight open research directions. Our algorithms scale linearly with the spatial extent of the target system and are applicable to both near-term and large-scale quantum processors.
The general objectives of this project were to improve understanding of the directional emittance properties of land surfaces in the thermal infrared (TIR) region of the electro-magnetic spectrum. To accomplish these objectives our research emphasized a combination of theoretical model development and empirical studies designed to improve land surface temperature (LST) retrievals from space-borne remote sensing instruments. Following the proposal, the main tasks for this project were to: (1) Participate in field campaigns; (2) Acquire and process field, aircraft, and ancillary data; (3) Develop and refine models of LST emission; (4) Develop algorithms for LST retrieval; and (5) Explore LST retrieval methods for use in energy balance models. In general all of these objectives were addressed, and for the most part achieved. The main results from this project are described in the publications arising from this effort. We summarize our efforts related to each of the objectives.
The objectives of the study are: (1) Analysis of SIR-C/X-SAR response to soil moisture, vegetation and surface roughness and development of an algorithm to retrieve these parameters; (2) Combination of the visible and near-infrared data and the SIR-C/X-SAR data to improve the range and accuracy of vegetation classification; (3) Testing of theoretical models for microwave propagation with SIR-C/X-SAR and microwave radiometric measurements over rough surfaces; and (4) Evaluation of a water balance model using SIR-C/X-SAR derived soil moisture values and other ancillary data. Progress, significant results and future plans are presented.
This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.