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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

STRV RADMON: An integrated high-energy particle detector

The RADMON (Radiation Monitor) was developed as a compact device with a 4-kbit SRAM particle detector and two p-FET total dose monitors. Thus it can be used as a spacecraft radiation alarm and in situ total dose monitor. This paper discusses the design and calibration of the SRAM for proton, alpha, and heavy ion detection. Upset rates for the RADMON, based on a newly developed space particle flux algorithm, are shown to vary over eight orders of magnitude. On the STRV (Space Technology Research Vehicle) the RADMON's SRAM will be used to detect trapped protons, solar flares, and cosmic rays and to evaluate our ability to predict space results from ground tests.

Buehler, Martin↗
NASA NTRS2004

Above-Cloud Precipitable Water Retrievals using the MODIS 0.94 micron Band with Applications for Multi-Layer Cloud Detection

In order to perform satellite retrievals of cloud properties, it is important to account for the effect of the above-cloud atmosphere on the observations. The solar bands used in the operational MODIS Terra and Aqua cloud optical and microphysical algorithms (visible, NIR, and SWIR spectral windows) are primarily affected by water vapor, and to a lesser extent by well-mixed gases. For water vapor, the above-cloud column amount, or precipitable water, provides adequate information for an atmospheric correction; details of the vertical vapor distribution are not typically necessary for the level of correction required. Cloud-top pressure has a secondary effect due to pressure broadening influences. For well- mixed gases, cloud-top pressure is also required for estimates of above-cloud abundances. We present a method for obtaining above-cloud precipitable water over dark Ocean surfaces using the MODIS 0.94 pm vapor absorption band. The retrieval includes an iterative procedure for establishing cloud-top temperature and pressure, and is useful for both single layer water and ice clouds. Knowledge of cloud thermodynamic phase is fundamental in retrieving cloud optical and microphysical properties. However, in cases of optically thin cirrus overlapping lower water clouds, the concept of a single unique phase is ill- defined and depends, at least, on the spectral region of interest. We will present a method for multi-layer and multi-phase cloud detection which uses above-cloud precipitable water retrievals along with several existing MODIS operational cloud products (cloud-top pressure derived from a C02 slicing algorithm, IR and SWIR phase retrievals). Results are catagorized by whether the radiative signature in the MODIS solar bands is primarily that of a water cloud with ice cloud contamination, or visa-versa. Examples in polar and mid-latitude regions will be shown.

Platnick, S.↗
DOE OSTI2021

VERIFICATION OF TRISO FUEL BURNUP USING MACHINE LEARNING ALGORITHMS

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134Cs, 137Cs, 154Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Verification of Triso Fuel Burnup Using Machine Learning Algorithms

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134 Cs, 137 Cs, 154 Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Extinction Monitoring of Pulsed Proton Beams Using FPGA-Based Peak Detection

The Mu2e experiment at Fermilab imposes stringent requirements on the elimination of out-of-time beam in its pulsed proton beam - a requirement known as "extinction". We present a method to measure the out-of-time particle rates to calculate the level of extinction in the inter-pulse gaps. The proposed method utilizes an array of quartz Cherenkov radiators and photomultiplier tubes to detect particles scattered from a vacuum chamber in the M4 transfer beamline at Fermilab.The measurement will employ a new μTCA-based FPGA system for data acquisition and signal processing, utilizing real-time peak detection algorithms to count scattered beam particles. By integrating data over many transfers, the time profile of the out-of-time beam will be resolved to fractional levels relative to that of the in-time beam. These results are compared with G4beamline simulations to validate models of beam transport, dynamics, and extinction, providing critical input for optimizing beam delivery to Mu2e.

Hensley, Ryan [UC, Davis]↗
NASA NTRS1993

Airborne full polarization radiometry using the MSFC Advanced Microwave Precipitation Radiometer (AMPR)

The applications of vertically and horizontally polarized brightness temperatures in both atmospheric and surface remote sensing have been long recognized by many investigators, particularly those studying SMMR and SSM/I data. Here, the large contrast between the first two Stokes' parameters (T(sub V) and T(sub H)) can be used for detection of sea ice, measurement of ocean surface wind speed, and measurement of cloud and water vapor opacity. High-resolution aircraft data from instruments such as the NASA/MSFC AMPR is crucial for verifying radiative transfer models and developing retrieval algorithms. Currently, the AMPR is outfitted with single-polarization channels at 10, 18, 37 and 85 GHz. To increase its utility, it is proposed that additional orthogonal linearly polarized channels be added to the AMPR. Since the AMPR's feedhorns are already configured for dual orthogonal linearly polarized modes, this would require only a duplication of the currently existing receivers. To circumvent the resulting polarization basis skew caused by the cross-track scanning mechanism, the technique of Electronic Polarization Basis Rotation is proposed to be implemented. Implementation of EPBR requires precise measurement of the third Stokes parameter and will eliminate polarization skew by allowing the feedhorn basis skew angle to be corrected in software. In addition to upgrading AMPR to dual polarization capability (without skew), the modifications will provide an opportunity to demonstrate EPBR on an airborne platform. This is a highly desirable intermediate step prior to satellite implementation.

Gasiewski, Al J.↗
NASA NTRSDate not supplied

Detection of Drizzle and Light Snowfall Over the Southern Ocean Using the GPM Combined Algorithm

Following the 2014 launch of the Global Precipitation Measurement Mission (GPM), an unprecedented combination of coincident active and passive microwave observations are available for state of the art precipitation retrieval. The GPM Combined Algorithm forms the backbone of this effort, optimizing geophysical variables for agreement with the multi-spectral information content. Combined retrievals are then utilized, along with a radiative transfer model, as a database applied for retrievals across a constellation of passive microwave radiometers of varying frequencies and viewing geometries. By keeping such retrievals related through the transfer standard of the combined algorithm, level 3 products such as the Integrated Multi-satellitE Retrievals for GPM (IMERG) are able to provide consistent global products for users at the higher temporal resolution required for hydrological applications on the global scale. In the current version of the combined product, precipitation retrievals are carried out only in the presence of a signal from the active radar. As a result, light precipitation and drizzle below the threshold of radar sensitivity are not included in any of the products down the chain from the constellation to IMERG. In this work, the effects of enhancing the retrievals with an optimal estimation-type (OE) water vapor and cloud retrieval using the passive observations are explored over the Southern Ocean on a regional scale. Non-convergence of the OE in areas with no detectable radar signal is used to identify areas with high probability of light precipitation and drizzle. Microphysical scale characterization of the light precipitation will be explored using information derived from the higher sensitivity CloudSat mission along with model information to identify and associate the related atmospheric state and dynamics. This is a physically-based approach requiring radiometric consistency with all available multi-spectral observations. Retrieved drizzle can then be included in the constellation databases continuing across all scales through to the level 3 products. The technique, successfully demonstrated for the Southern Ocean, can be easily adapted for light precipitation retrieval over other areas and surfaces leading to a global climatology of light precipitation.

Sarah Rinegerud↗
DOE OSTI2026

Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network

Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications. Current detection methods operate offline, introducing latency incompatible with in-the-loop qubit control. In this paper, an online detector of charge jumps for superconducting qubits, based on a dilated causal convolutional neural network (DCCNN) designed for in-the-loop deployment on the Quantum Instrumentation Control Kit (QICK) platform, is presented. The network is trained on synthetic Ramsey tomography scans generated from qubit templates measured at the Northwestern Experimental Underground Site (NEXUS) at Fermilab, and translated to FPGA firmware via hls4ml with ap_fixed$\langle 16,6 \rangle$ quantization, reaching a per-inference latency of $6.19 μ$s on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline $χ^2$ algorithm ($0.843 \pm 0.022$ vs. $0.866 \pm 0.020$ on $|Δq| \in [0.1, 0.5] e$ at matched false-positive rate), while requiring no per-qubit hyperparameter tuning. This shifts charge-jump detection from a post-hoc diagnostic to a control-loop primitive, enabling adaptive protocols that respond to radiation-induced events in situ, with applications to quantum-computing error mitigation and to the use of superconducting qubits as particle detectors.

Gaytan-Villarreal, Daniel [Carnegie Mellon U.]↗
NASA NTRS1998

The Design and Evaluation of the Lighting Imaging Sensor Data Applications Display (LISDAD)

The design and evaluation of the Lightning Imaging Sensor Data Applications Display (LISDAD). The ultimate goal of the LISDAD system is to quantify the utility of total lightning information in short-term, severe-weather forecasting operations. To this end, scientists from NASA, NWS, and MIT organized an effort to study the relationship of lightning and severe-weather on a storm-by-storm, and even cell-by-cell basis for as many storms as possible near Melbourne, Florida. Melbourne was chosen as it offers a unique combination of high probability of severe weather and proximity to major relevant sensors - specifically: NASA's total lightning mapping system at Kennedy Space Center (the LDAR system at KSC); a NWS/NEXRAD radar (at Melbourne); and a prototype Integrated Terminal Weather System (ITWS, at Orlando), which obtains cloud-to-ground lightning Information from the National Lightning Detection Network (NLDN), and also uses NSSL's Severe Storm Algorithm (NSSL/SSAP) to obtain information about various storm-cell parameters. To assist in realizing this project's goal, an interactive, real-time data processing system (the LISDAD system) has been developed that supports both operational short-term weather forecasting and post facto severe-storm research. Suggestions have been drawn from the operational users (NWS/Melbourne) in the design of the data display and its salient behavior. The initial concept for the users Graphical Situation Display (GSD) was simply to overlay radar data with lightning data, but as the association between rapid upward trends in the total lightning rate and severe weather became evident, the display was significantly redesigned. The focus changed to support the display of time series of storm-parameter data and the automatic recognition of cells that display rapid changes in the total-lightning flash rate. The latter is calculated by grouping discrete LDAR radiation sources into lightning flashes using a time-space association algorithm. Specifically, the GSD presents the user with the Composite Maximum Reflectivity obtained from the NWS/NEXRAD. Superimposed upon this background image are placed small black circles indicating the locations of storm cells identified by the NSSL/SSA. The circles become cyan if lightning is detected within the storm-cell; if the cell has lightning rates indicative of a severe-storm, the circle turns red. This paper will: (1) review the design of LISDAD system; (2) present some examples of its data display; and shown results of the lightning based severe-weather prediction algorithm.

Boldi, B.↗
NASA NTRS2009

Performance of MODIS Thermal Emissive Bands On-orbit Calibration Algorithms

Two nearly identical copies of the Moderate Resolution Imaging Spectroradiometer (MODIS) are currently operated on-board the Terra and Aqua spacecrafts, launched in December 1999 and May 2002, respectively. Together, they have produced an unprecedented amount of science data products, which are widely used for the studies of changes in the Earth's system of land, oceans, and atmosphere. MODIS is a cross-track scanning radiometer, which uses a two-sided scan mirror and collects data continuously over a wide scan angle range (+/-55 degree relative to the instrument nadir) each scan of 1.47 seconds. It has 36 spectral bands with wavelengths ranging from visible (VIS) to long-wave infrared (LWIR). MODIS bands 1-19 and 26 are the reflective solar bands (RSB) and bands 20-25 and 27-36 are the thermal emissive bands (TEB). MODIS was developed and designed with improvements made over its heritage sensors (such as AVHRR and Landsat) and, in particular, with more stringent calibration requirements. Because of this, MODIS was built with a set of state-of-art on-board calibrators (OBC), which include a solar diffuser (SD), a solar diffuser stability monitor (SDSM), a blackbody (BB), a spectroradiometric calibration assembly (SRCA), and a space view (SV) port. With the exception of view angle differences, MODIS OBC measurements and the Earth View (EV) observations are made via the same optical path. MODIS TEB have a total of 160 individual TEB detectors (10 per band), which are located on two cold focal plane assemblies (CFPA). For nominal on-orbit operation, the CFPA temperature is controlled at 83K via a passive radiative cooler. For the TEB, the calibration requirements at specified typical scene radiances are less than or equal to 1% with an exception for the fire detection (low gain) band. MODIS TEB on-orbit calibration is performed on a scan-by-scan basis using a quadratic calibration algorithm, and data collected from sensor responses to the onboard BB and SV. The BB serves as the thermal calibration source and the SV provides measurements for the sensor's background and offsets. MODIS on-board BB is a v-grooved plate with its temperature measured using 12 platinum resistive thermistors (PRT) uniformly embedded in the BB substrate. All the BB thermistors were characterized pre-launch with reference to the NIST temperature standards. Unlike typical BB operations in many heritage sensors, which have no temperature control capability, the MODIS on-board BB can be operated at any temperatures between instrument ambient (about 270K) and 315K and can also be varied continuously within this range. This feature has significantly enhanced the MODIS' capability of tracking and updating the TEB nonlinear calibration coefficients over its entire mission. Following a brief description of MODIS TEB on-orbit calibration methodologies and its onboard BB operational activities, this paper provides a comprehensive performance assessment of MODIS TEB quadratic calibration algorithm. It examines the scan-by-scan, orbit-by-orbit, daily, and seasonal variations of detector responses and associated impact due changes in the CFPA and instrument temperatures. Specifically, this paper will analyze the contribution by each individual thermal emissive source term (BB, scan cavity, and scan mirror), the impact on the Level 1 B data product quality due to pre-launch and on-orbit calibration uncertainties. A comparison of Terra and Aqua TEB on-orbit performance, lessons learned, and suggestions for future improvements will also be made.

Xiong, Xiaoxiong↗
DOE OSTI2022

An Ensemble Approach to Computationally Efficient Radiological Anomaly Detection and Isotope Identification

Radiological source search is a challenging task involving detection and identification of weak sources in a constantly changing radiological background. As of now, many radiological source detection algorithms have been proposed; however, their computational complexity, and hence reliance on power intensive processing units inhibit low-power applications of radiological source search systems. In this work, we introduce the anomaly filter (AF) algorithm; a computationally light, yet effective time-series source detection algorithm based on exponential weighted moving average (EWMA) and Poisson deviance statistics. Then, we demonstrate that the proposed algorithm can be used in ensemble with other more computationally intensive source detection and identification algorithms to achieve both increased detection performance and reduced power consumption. The proposed AF algorithm and the ensemble algorithms were thoroughly benchmarked against several existing source detection and identification algorithms. The results show that the AF algorithm outperforms existing conventional source detection algorithms, and the ensemble approach improves the overall performance of existing source detection and isotope identification algorithms. Furthermore, the AF algorithm and the Non-negative Matrix Factorization approach based source identification (NMF-ID) algorithm were combined and implemented on a singleboard microcontroller and the power consumption was measured. This ensemble algorithm reduced the power consumption of the NMF-ID algorithm almost by a factor of 100, while improving the detection performance of the overall system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2003

An algorithm for neurite outgrowth reconstruction

We present a numerical method which provides the ability to analyze digitized microscope images of retinal explants and quantify neurite outgrowth. Few parameters are required as input and limited user interaction is necessary to process an entire experiment of images. This eliminates fatigue related errors and user-related bias common to manual analysis. The method does not rely on stained images and handles images of variable quality. The algorithm is used to determine time and dose dependent, in vitro, neurotoxic effects of 1 GeV per nucleon iron particles in retinal explants. No neurotoxic effects are detected until 72 h after exposure; at 72 h, significant reductions of neurite outgrowth occurred at doses higher than 10 cGy.

NASA Program Biomedical Research and Countermeasur↗
NASA NTRS1986

Atmospheric, climatic and environmental research

Research conducted during the past year in the climate and atmospheric modeling programs was focused on the development of appropriate atmospheric and upper ocean models, and preliminary applications of these models. Prinicpal models are a one-dimensional radiative-convection model, a three-dimensional global climate model, and an upper ocean model. Principal application is the study of the impact of CO2, aerosols and the solar constant on climate. Also the performance of the International Satellite Cloud Climatology Project cloud detection algorithm was evaluated, concentrating initially on its application to geosynchronous data, with an eventual switch of the developed methodologies to data from polar orbiting satellites. In the process, a number of improvements were made, in particular: an improved technique for tracking small scale day to day variability in clear sky continental temperatures; a number of techniques for the statistical assessment of cloud detection uncertainties due to cloud types which are spatially and temporally invariant; and a method used to detect those cloudy regions which have long term spatial and temporal stability.

Broecker, W. S.↗
DOE OSTI2020

Confirming the absence of nuclear warheads via passive gamma-ray measurements

Arms-control agreements between the United States and Russia negotiated after the end of the Cold War have imposed limits on the number of deployed strategic nuclear weapons. Verification of these agreements has relied on onsite inspections, sometimes supported by radiation detection techniques to confirm the absence of a nuclear warhead when ambiguities arise. So far, these measurements have sought to detect neutron emissions associated with the presence of plutonium, but they would be inadequate for uranium devices. In an effort to offer instruments that could be used to confirm the absence of both plutonium and uranium weapons, here we propose an inspection system that uses only passive gamma radiation detection techniques. Such a system would be particularly valuable for next-generation arms-control agreements that limit total numbers of weapons and would involve containerized items in storage. We conducted extensive Monte Carlo simulations to support the development of a verification protocol and detection algorithm. Here, we demonstrate the viability of the technique using standard laboratory check sources and MCNP simulations for simplified configurations of special nuclear material.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2005

Infrared On-Orbit Inspection of Shuttle Orbiter Reinforced Carbon-Carbon Using Solar Heating

Thermographic nondestructive inspection techniques have been shown to provide quantitative, large area damage detection capabilities for the ground inspection of the reinforced carbon-carbon (RCC) used for the wing leading edge of the Shuttle orbiter. The method is non-contacting and able to inspect large areas in a relatively short inspection time. Thermal nondestructive evaluation (NDE) inspections have been shown to be applicable for several applications to the Shuttle in preparation for return to flight, including for inspection of RCC panels during impact testing, and for between-flight orbiter inspections. The focus of this work is to expand the capabilities of the thermal NDE methodology to enable inspection by an astronaut during orbital conditions. The significant limitations of available resources, such as weight and power, and the impact of these limitations on the inspection technique are discussed, as well as the resultant impact on data analysis and processing algorithms. Of particular interest is the impact to the inspection technique resulting from the use of solar energy as a heat source, the effect on the measurements due to working in the vacuum of space, and the effect of changes in boundary conditions, such as radiation losses seen by the material, on the response of the RCC. The resultant effects on detectability limits are discussed. Keywords: Nondestructive Evaluation, Shuttle, on-orbit inspection, thermography, infrared

Howell, P. A.↗
NASA NTRS1983

LANDSAT-D investigations in snow hydrology

Progress on the registration of TM data to digital topographic data; on comparison of TM, MSS and NOAA meteorological satellite data for snowcover mapping; and on radiative transfer models for atmospheric correction is reported. Some methods for analyzing spatial contiguity of snow within the snow covered area were selected. The methods are based on a two-channel version of the grey level co-occurence matrix, combined with edge detection derived from an algorithm for computing slopes and exposures from digital terrain data.

Dozier, J.↗
DOE OSTI2024

Wintertime extreme warming events in the high Arctic: characteristics, drivers, trends, and the role of atmospheric rivers

Abstract. An extreme warming event near the North Pole, with 2 m temperature rising above 0 °C, was observed in late December 2015. This specific event has been attributed to cyclones and their associated moisture intrusions. However, little is known about the characteristics and drivers of similar events in the historical record. Here, using data from European Centre for Medium-Range Weather Forecasts Reanalysis, version 5 (ERA5), we study these winter extreme warming events with 2 m temperature over a grid point above 0 °C over the high Arctic (poleward of 80° N) that occurred during 1980–2021. In ERA5, such wintertime extreme warming events can only be found over the Atlantic sector. They occur rarely over many grid points, with a total absence during some winters. Furthermore, even when occurring, they tend to be short-lived, with the majority of the events lasting for less than a day. By examining their surface energy budget, we found that these events transition with increasing latitude from a regime dominated by turbulent heat flux into the one dominated by downward longwave radiation. Positive sea level pressure anomalies which resemble blocking over northern Eurasia are identified as a key ingredient in driving these events, as they can effectively deflect the eastward propagating cyclones poleward, leading to intense moisture and heat intrusions into the high Arctic. Using an atmospheric river (AR) detection algorithm, the roles of ARs in contributing to the occurrence of these extreme warming events defined at the grid-point scale are explicitly quantified. The importance of ARs in inducing these events increases with latitude. Poleward of about 83° N, 100 % of these events occurred under AR conditions, corroborating that ARs were essential in contributing to the occurrence of these events. Over the past 4 decades, both the frequency, duration, and magnitude of these events have been increasing significantly. As the Arctic continues to warm, these events are likely to increase in both frequency, duration, and magnitude, with great implications for the local sea ice, hydrological cycle, and ecosystem.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Enhancing a Simple MODIS Cloud Mask Algorithm for the Landsat Data Continuity Mission

The presence of clouds in images acquired by the Landsat series of satellites is usually an undesirable, but generally unavoidable fact. With the emphasis of the program being on land imaging, the suspended liquid/ice particles of which clouds are made of fully or partially obscure the desired observational target. Knowing the amount and location of clouds in a Landsat scene is therefore valuable information for scene selection, for making clear-sky composites from multiple scenes, and for scheduling future acquisitions. The two instruments in the upcoming Landsat Data Continuity Mission (LDCM) will include new channels that will enhance our ability to detect high clouds which are often also thin in the sense that a large fraction of solar radiation can pass through them. This work studies the potential impact of these new channels on enhancing LDCM's cloud detection capabilities compared to previous Landsat missions. We revisit a previously published scheme for cloud detection and add new tests to capture more of the thin clouds that are harder to detect with the more limited arsenal channels. Since there are no Landsat data yet that include the new LDCM channels, we resort to data from another instrument, MODIS, which has these bands, as well as the other bands of LDCM, to test the capabilities of our new algorithm. By comparing our revised scheme's performance against the performance of the official MODIS cloud detection scheme, we conclude that the new scheme performs better than the earlier scheme which was not very good at thin cloud detection.

Wilson, Michael J.↗