An iterative approach to multisensor sea ice classification
Characterizing the variability in sea ice in the polar regions is fundamental to an understanding of global climate and the geophysical processes governing climate changes.
Aperature↗
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UAVSAR Program: Initial Results from New Instrument Capabilities
UAVSAR is an imaging radar instrument suite that serves as NASA's airborne facility instrument to acquire scientific data for Principal Investigators as well as a radar test-bed for new radar observation techniques and radar technology demonstration. Since commencing operational science observations in January 2009, the compact, reconfigurable, pod-based radar has been acquiring L-band fully polarimetric SAR (POLSAR) data with repeat-pass interferometric (RPI) observations underneath NASA Dryden's Gulfstream-III jet to provide measurements for science investigations in solid earth and cryospheric studies, vegetation mapping and land use classification, archaeological research, soil moisture mapping, geology and cold land processes. In the past year, we have made significant upgrades to add new instrument capabilities and new platform options to accommodate the increasing demand for UAVSAR to support scientific campaigns to measure subsurface soil moisture, acquire data in the polar regions, and for algorithm development, verification, and cross-calibration with other airborne/spaceborne instruments.
P-band polarimetry↗
Digital exploitation of synthetic aperture radar
A digital processing and analysis scheme for use with digitized synthetic aperture radar data was developed. Using data from a four channel system, the imagery is preprocessed using specially designed software and then analyzed using preexisting facilities originally intended for use with MSS type data. Geometric and radiometric correction may be performed if desired, as well as classification analysis, Fast Fourier transform, filtering and level slice and display functions. The system provides low cost output in real time, permitting interactive imagery analysis. System information flow diagrams as well as sample output products are shown.
Wagner, H. L.↗
Introducing Multisensor Satellite Radiance-Based Evaluation for Regional Earth System Modeling
Earth System modeling has become more complex, and its evaluation using satellite data has also become more difficult due to model and data diversity. Therefore, the fundamental methodology of using satellite direct measurements with instrumental simulators should be addressed especially for modeling community members lacking a solid background of radiative transfer and scattering theory. This manuscript introduces principles of multisatellite, multisensor radiance-based evaluation methods for a fully coupled regional Earth System model: NASA-Unified Weather Research and Forecasting (NU-WRF) model. We use a NU-WRF case study simulation over West Africa as an example of evaluating aerosol-cloud-precipitation-land processes with various satellite observations. NU-WRF-simulated geophysical parameters are converted to the satellite-observable raw radiance and backscatter under nearly consistent physics assumptions via the multisensor satellite simulator, the Goddard Satellite Data Simulator Unit. We present varied examples of simple yet robust methods that characterize forecast errors and model physics biases through the spatial and statistical interpretation of various satellite raw signals: infrared brightness temperature (Tb) for surface skin temperature and cloud top temperature, microwave Tb for precipitation ice and surface flooding, and radar and lidar backscatter for aerosol-cloud profiling simultaneously. Because raw satellite signals integrate many sources of geophysical information, we demonstrate user-defined thresholds and a simple statistical process to facilitate evaluations, including the infrared-microwave-based cloud types and lidar/radar-based profile classifications.
Planetary Boundary Layer↗
Marine Boundary Layer Cloud Boundaries and Phase Estimation Using Airborne Radar and In Situ Measurements During the SOCRATES Campaign over Southern Ocean
The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (15 January–26 February 2018) that deployed in situ probes and remote sensors to investigate low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud boundaries and classify cloud phases in single-layer, low-level marine boundary layer (MBL) clouds below 3 km using the HIAPER Cloud Radar (HCR) and in situ measurements. The cloud base and top heights derived from HCR reflectivity, Doppler velocity, and spectrum width measurements agreed well with corresponding lidar-based and in situ estimates of cloud boundaries, with mean differences below 100 m. A liquid water content–reflectivity (LWC-Z) relationship, LWC = 0.70Z0.29, was derived to retrieve the LWC and liquid water path (LWP) from HCR profiles. The cloud phase was classified using HCR measurements, temperature, and LWP, yielding 40.6% liquid, 18.3% mixed-phase, and 5.1% ice samples, along with drizzle (29.1%), rain (3.2%), and snow (3.7%) for drizzling cloud cases. The classification algorithm demonstrates good consistency with established methods. This study provides a framework for the boundary and phase detection of MBL clouds, offering insights into SO cloud microphysics and supporting future efforts in satellite retrievals and climate model evaluation.
MBL clouds over Southern Ocean↗
Low Latency DESDynI Data Products for Disaster Response, Resource Management and Other Applications
We are developing onboard processor technology targeted at the L-band SAR instrument onboard the planned DESDynI mission to enable formation of SAR images onboard opening possibilities for near-real-time data products to augment full data streams. Several image processing and/or interpretation techniques are being explored as possible direct-broadcast products for use by agencies in need of low-latency data, responsible for disaster mitigation and assessment, resource management, agricultural development, shipping, etc. Data collected through UAVSAR (L-band) serves as surrogate to the future DESDynI instrument. We have explored surface water extent as a tool for flooding response, and disturbance images on polarimetric backscatter of repeat pass imagery potentially useful for structural collapse (earthquake), mud/land/debris-slides etc. We have also explored building vegetation and snow/ice classifiers, via support vector machines utilizing quad-pol backscatter, cross-pol phase, and a number of derivatives (radar vegetation index, dielectric estimates, etc.). We share our qualitative and quantitative results thus far.
applications↗
Snowpack ground truth: Radar test site, Steamboat Springs, Colorado, 8-16 April 1976
Ground-truth data taken at Steamboat Springs, Colorado is presented. Data taken during the period April 8, 1976 - April 16, 1976 included the following: (1) snow depths and densities at selected locations (using a Mount Rose snow tube); (2) snow pits for temperature, density, and liquid water determinations using the freezing calorimetry technique and vertical layer classification; (3) snow walls were also constructed of various cross sections and documented with respect to sizes and snow characteristics; (4) soil moisture at selected locations; and (5) appropriate air temperature and weather data.
Howell, S.↗
Classifying thermodynamic cloud phase using machine learning models
Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.
ARM Aerial Facility↗
Polarization effects and multipolarization SAR
Imaging radar polarimeters are usually implemented using a Synthetic Aperture Radar (SAR) approach to give a high resolution image in two dimensions: range and azimuth. For each pixel in the image a polarimetric SAR gives sufficient information to characterize the polarimetric scattering properties of the imaged area (or target) as seen by the radar. Using a polarimetric SAR system as opposed to a single-polarization SAR system provides significantly more information about the target scattering mechanisms and allows better discrimination between different types of surfaces. In these notes a brief overview of SAR polarimetry is offered. The notes are intended as a text to accompany a lecture on SAR polarimetry as part of an AGARD-NATO course. Covered in the notes are the following: the polarization properties of electromagnetic waves; the concepts of radar scattering and measuring radar backscatter with a SAR; polarization synthesis; scattering matrix, Stokes matrix, and covariance matrix representations of polarimetric SAR data; polarization signature plots; design and calibration of polarimetric SAR systems; polarization filtering for target detection; fitting a simple model to polarimetric SAR measurements of naturally occurring features; and supervised classification of polarimetric SAR data.
Freeman, Anthony↗
Analysis of Synthetic Aperture Radar data acquired over a variety of land cover
An analysis has been conducted of two-look-angle, multipolarization X-band SAR results. On the basis of the variety of land covers studied, the vertical-vertical polarization (VV) data is judged to contain the highest degree of contrast, while the horizontal-vertical (HV) polarization contained the least. VV polarization data is accordingly recommended for forest vegetation classification in those cases where only one data channel is available. The inclusion of horizontal-horizontal polarization data, however, is noted to be capable of delineating special surface features.
Wu, S. T.↗
A review of future remote sensing satellite capabilities
Existing, planned and future NASA capabilities in the field of remote sensing satellites are reviewed in relation to the use of remote sensing techniques for the identification of irrigated lands. The status of the currently operational Landsat 2 and 3 satellites is indicated, and it is noted that Landsat D is scheduled to be in operation in two years. The orbital configuration and instrumentation of Landsat D are discussed, with particular attention given to the thematic mapper, which is expected to improve capabilities for small field identification and crop discrimination and classification. Future possibilities are then considered, including a multi-spectral resource sampler supplying high spatial and temporal resolution data possibly based on push-broom scanning, Shuttle-maintained Landsat follow-on missions, a satellite to obtain high-resolution stereoscopic data, further satellites providing all-weather radar capability and the Large Format Camera.
Calabrese, M. A.↗
Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network
Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.
Passive Remote Sensing↗
Automatic Estimation of Volcanic Ash Plume Height using WorldView-2 Imagery
We explore the use of machine learning, computer vision, and pattern recognition techniques to automatically identify volcanic ash plumes and plume shadows, in WorldView-2 imagery. Using information of the relative position of the sun and spacecraft and terrain information in the form of a digital elevation map, classification, the height of the ash plume can also be inferred. We present the results from applying this approach to six scenes acquired on two separate days in April and May of 2010 of the Eyjafjallajokull eruption in Iceland. These results show rough agreement with ash plume height estimates from visual and radar based measurements.
pattern recognition↗
Blade Vibration Measurement System
The Phase I project successfully demonstrated that an advanced noncontacting stress measurement system (NSMS) could improve classification of blade vibration response in terms of mistuning and closely spaced modes. The Phase II work confirmed the microwave sensor design process, modified the sensor so it is compatible as an upgrade to existing NSMS, and improved and finalized the NSMS software. The result will be stand-alone radar/tip timing radar signal conditioning for current conventional NSMS users (as an upgrade) and new users. The hybrid system will use frequency data and relative mode vibration levels from the radar sensor to provide substantially superior capabilities over current blade-vibration measurement technology. This frequency data, coupled with a reduced number of tip timing probes, will result in a system capable of detecting complex blade vibrations that would confound traditional NSMS systems. The hardware and software package was validated on a compressor rig at Mechanical Solutions, Inc. (MSI). Finally, the hybrid radar/tip timing NSMS software package and associated sensor hardware will be installed for use in the NASA Glenn spin pit test facility.
Platt, Michael J.↗
Comparison of edges detected at different polarisations in MAESTRO data
Edge detection would appear to be a crucial tool for analyzing multi-polarized, multi-frequency, and multi-temporal Synthetic Aperture Radar (SAR) images. Edge structure provides a simple means for comparing different polarizations and frequencies, and for detecting changes over time. Due to the fact that edges and segments (homogeneous regions) are dual concepts, edge detection has an important role to play in identifying segments within which mean backscatter measurements for use in image classification can be made. As part of a general investigation into edge detection in SAR imagery, an initial investigation was carried out into the detectability and nature of edges in multi-polarized and multi-frequency SAR images. The contrast ratio (CR) operator was used to detect edges. This operator was previously shown to perform well at detecting edges in single-polarized and single-frequency SAR images.
Caves, Ronald G.↗
Imaging radar polarimetry - A status report
The development of imaging radar polarimetry at JPL is considered. Advances in the correlation of polarimetric SAR and in the synthesis of arbitrary polarization are discussed. The capabilities of the polarization workstation are described. Various applications for polarimetric SAR data include: the identification of scatter, the evaluation of scattering theories, the measurement of the roughness of geologic targets, the classification of forest stands, and the characterization of trees within a particular stand. The development and testing of the DC-8 SAR and the SIR-C system are examined.
Held, Daniel N.↗
PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies
Accurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.
54 ENVIRONMENTAL SCIENCES↗
Arctic coastal polynya observations with ERS-1 SAR and DMSP SSM/I
Work to improve the characterization of the distribution of new and young sea ice types and open water amount within Arctic coastal polynyas through the combined use of ERS-1 SAR (Synthetic Aperture Radar) and DMSP SSM/I (Defense Meteorological Satellite Program Special Sensor Microwave/Imager) data is described. Two St. Lawrence Island polynya events are studied using low resolution, geocoded SAR images and coincident SSM/I data. The SAR images are analyzed in terms of polarization and spectral gradient ratios. Results of the combined analysis show that the SAR ice type classification is consistent with that from SSM/I and that the combined use of SAR and SSM/I can improve the characterization of thin ice better than either data set can do alone.
Cavalieri, D. J.↗