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NASA NTRS2016

Exploiting Measurement Uncertainty Estimation in Evaluation of GOES-R ABI Image Navigation Accuracy Using Image Registration Techniques

In evaluating GOES-R Advanced Baseline Imager (ABI) image navigation quality, upsampled sub-images of ABI images are translated against downsampled Landsat 8 images of localized, high contrast earth scenes to determine the translations in the East-West and North-South directions that provide maximum correlation. The native Landsat resolution is much finer than that of ABI, and Landsat navigation accuracy is much better than ABI required navigation accuracy and expected performance. Therefore, Landsat images are considered to provide ground truth for comparison with ABI images, and the translations of ABI sub-images that produce maximum correlation with Landsat localized images are interpreted as ABI navigation errors. The measured local navigation errors from registration of numerous sub-images with the Landsat images are averaged to provide a statistically reliable measurement of the overall navigation error of the ABI image. The dispersion of the local navigation errors is also of great interest, since ABI navigation requirements are specified as bounds on the 99.73rd percentile of the magnitudes of per pixel navigation errors. However, the measurement uncertainty inherent in the use of image registration techniques tends to broaden the dispersion in measured local navigation errors, masking the true navigation performance of the ABI system. We have devised a novel and simple method for estimating the magnitude of the measurement uncertainty in registration error for any pair of images of the same earth scene. We use these measurement uncertainty estimates to filter out the higher quality measurements of local navigation error for inclusion in statistics. In so doing, we substantially reduce the dispersion in measured local navigation errors, thereby better approximating the true navigation performance of the ABI system.

navigation performance↗

NASA NTRS2019

GOES-16 ABI Solar Reflective Channel Validation for Earth Science Application

This paper presents the validation results of GOES-16 Satellite's Advanced Baseline Imager (ABI) obtained from a reflectance-based field campaign undertaken at the Salar de Uyuni in Bolivia in June 2017. In situ ground measurements are used to characterize the surface reflectance and the atmosphere in order to constrain the radiative transfer code and predict at-sensor reflectance (also referred to as top-of-atmosphere (TOA) reflectance) to compare with concurrent GOES-16 ABI measurements. The five-day field campaign provided repeated TOA reflectance estimates, allowing assessment not only of the calibration accuracy of the ABI reflective channels 1, 2, 3, 5 and 6, but also of its stability over the duration of the campaign. The results show that the accuracy of the ABI reflective channels calibration is within specification for channels 1, 3, 5, and 6 - average biases within 2%; for channel 2 the bias is 5%. The estimated uncertainty on the derived biases is 2–2.4%. Some calibration stability issues were present in the ABI calibration at the time of the campaign: (i) a jump on the order of 2% in channels 1 and 6, coincident with an ABI solar calibration event, reflects an instability of the ABI gains in these channels, and (ii) short-term variability in channels 1 and 2 is due to striping (ABI detector-todetector calibration differences). Continued validation and subsequent reprocessing of ABI reflectance imagery would allow Earth scientists to fully benefit from the high spatial and spectral fidelity of the GOES-16 ABI diurnal measurements at the continental scale.

Joel McCorkel↗

NASA NTRS2017

A Closer Look at the ABI on the GOES-R Series

The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) is America's next-generation geostationary advanced imager. GOES-R launched on 19 November 2016. The ABI is a state-of-the-art 16-band radiometer, with spectral bands covering the visible, near-infrared, and infrared portions of the electromagnetic spectrum. Many attributes of the ABI-such as spectral, spatial, and temporal resolution; radiometrics; and image navigation/registration-are much improved from the current series of GOES imagers. This paper highlights and discusses the expected improvements of each of these attributes. From ABI data many higher-level-derived products can be generated and used in a large number of environmental applications. The ABI's design allows rapid-scan and contiguous U.S. imaging automatically interleaved with full-disk scanning. In this paper the expected instrument attributes are covered, as they relate to signal-to-noise ratio, image navigation and registration, the various ABI scan modes, and other parameters. There will be several methods for users to acquire GOES-R imagery and products depending on their needs. These include direct reception of the imagery via the satellite downlink and an online-accessible archive. The information from the ABI on the GOES-R series will be used for many applications related to severe weather, tropical cyclones and hurricanes, aviation, natural hazards, the atmosphere, the ocean, and the cryosphere.

GOES-R↗

NASA NTRSDate not supplied

Detecting Short Term Drought Impact in the Southwest US Using GOES-16 ABI Data

Satellite optical remote sensing has been often used for monitoring broad-region vegetation change, for example, phenology observations and the year-to-year leaf area index (LAI) responses to climate oscillations. However, rapid responses of vegetation to day-to-day weather perturbations are difficult to detect using available optical remote sensing satellites because of the low frequency of the observations. Sun-synchronous optical sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR), can observe a target area once a day. Daily observations cannot distinguish whether short-term changes in Normalized Differential Vegetation Index (NDVI) are actual LAI change or cloud contamination. NOAA’s GOES satellites make observations every 10 to 15 minutes using the Advanced Baseline Imager (ABI). In this study, we used the Geostationary-NASA Earth Exchange (GeoNEX) L1G Top-of-Atmosphere (TOA) ABI data to detect drought impact on NDVI time series in the Southwest US. We used an empirical method to cancel the BRDF effect of varying solar zenith angle. The ABI was able to detect short term drought impacts as well as an NDVI decrease in the dry season. Increased NDVI right after a rainfall followed by an immediate decrease was observed. These ABI NDVI changes were correlated with the RGB time series from PhenoCam Network data. Results indicated that the ABI can be used for short-term analysis of LAI and can detect small LAI changes caused by drought in an arid area, suggesting the potential for its use in near-real time drought monitoring applications.

ABI↗

NASA NTRS2021

Tracking Smoke from a Prescribed Fire and its Impacts on Local Air Quality using Temporally Resolved GOES-16 ABI Aerosol Optical Depth (AOD)

Aerosol optical depth (AOD) retrieved from the GOES-16 Advanced Baseline Imager (ABI) was used to track a smoke plume from a prescribed fire in northeastern Virginia on 8 March 2020. Weather and atmospheric conditions created a favorable environment to transport the plume through the Washington, D.C., and Baltimore, Maryland, metro areas in the afternoon and concentrate smoke near the surface, degrading air quality for several hours. ABI AOD with 5-min temporal resolution and 2-km spatial resolution definitively identified the timing and geographic extent of the plume during daylight hours. Comparison to AERONET AOD indicates that ABI AOD captured the relative change in AOD due to passage of the smoke, with a mean absolute error of 0.047. Ground-based measurements of fine particulate matter (PM2.5) confirm deteriorations in air quality coincident with the progression of the smoke. Ceilometer aerosol backscatter profiles verify plume transport timing and indicate that smoke aerosols were well mixed in a shallow boundary layer. This event illustrates the advantages of using multiple datasets to analyze the impacts of aerosols on ambient air quality. Given the quickly evolving nature of the event over several hours, ABI AOD provided information for the public and decision-makers that was not available from any other source, including polar-orbiting satellite sensors. This study suggests that PM2.5 concentrations estimated from ABI AOD can be used to fill in the gaps in nationwide regulatory PM2.5 monitor networks and may be a valuable addition to EPA’s PM2.5 NowCast of current air quality conditions.

satellite observations↗

NASA NTRSDate not supplied

Validation of GOES-17 ABI Reflective Channels Performance: Salar De Uyuni 2018 Field Campaign Results

Validation results from a reflectance-based field campaign at the Salar de Uyuni in Bolivia (September 2018) are presented for GOES-17 and GOES-16 Advanced Baseline Imagers (ABI) reflective channels. The in situ measurements were used to characterize the surface reflectance and the atmosphere in order to constrain a radiative transfer model and predict the reflectance at the top of the atmosphere (TOA), which was then compared to the ABI measurements. The field campaign provides TOA reflectance estimates over several days, allowing assessment of the calibration accuracy and stability of channels 1, 2, 3, 5 and 6 for GOES-17 andGOES-16 ABI. Channel 1 of GOES-17 ABI shows -5.5% bias in comparison to the ground-based predicted TOA. Over 6% bias in GOES-17 B2 was confirmed. A comparison to NOAA-20 VIIRS was also carried on during a near nadir overpass.

validation↗

NASA NTRS2018

GOES-16 ABI Navigation Assessment

The US Geostationary Operational Environmental Satellite â€“ R Series (GOES-R) was launched on November 19, 2016and was designated GOES-16 upon reaching geostationary orbit ten days later. After checkout and calibration, GOES-16 was relocated to its operational location of 75.2 degrees west and officially became GOES East on December 18, 2017. The Advanced Baseline Imager (ABI) is the primary instrument on the GOES-R series for imaging Earth's surface and atmosphere to significantly improve the detection and observation of severe environmental phenomena. A team supporting the GOES-R Flight Project at NASA's Goddard Space Flight Center developed algorithms and software for independent verification of ABI Image Navigation and Registration (INR), which became known as the INR Performance Assessment Tool Set (IPATS). In this paper, we will briefly describe IPATS on top concept level, and then introduce the Landsat chips, chip registration algorithms, and how IPATS measurements are filtered. We present GOES-16 navigation (NAV) errors from flight data from January 2017 to May 2018. The results show a) IPATS characterized INR variations throughout the post-launch test phase; and b) ABI INR has improved over time as post-launch tests were performed and corrections applied. Finally, we will describe how estimated NAV errors have been used to assess and understand satellite attitude anomalies and scale errors etc. This paper shows that IPATS is an effective tool for assessing and improving GOES-16 ABI INR and is also useful for INR long-term monitoring.

GOES-R↗

NASA NTRS2019

GOES-16 and GOES-17 ABI INR Assessment

The first two satellites of the US Geostationary Operational Environmental Satellite R-Series (GOES-R) were launched on November 19, 2016 and March 1, 2018 respectively. GOES-16 officially became GOES East on December 18, 2017, and the designation of GOES-17 as GOES West occurred on February 12 2019. The Advanced Baseline Imager (ABI) is the primary instrument on GOES-16 and GOES-17 for imaging Earth’s surface and atmosphere to significantly improve the detection and observation of severe environmental phenomena. The Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) was developed to assess INR performance of GOES-R series ABI images. In this paper, we first describe the assessment of IPATS algorithm accuracy. Next, we present the relationship between view zenith angle (VZA) and the quality of the IPATS measurements. Lastly, we present GOES-16 and GOES-17 navigation (NAV) assessments results from flight data spanning from the start of INR assessment to June 2019. The results show a) IPATS “stair step” measurement error is less or equal to 0.06 ABI pixel with IPATS baseline configuration; b) VZA is an effective filter to exclude outliers of the measurements; and c) ABI INR for both satellites has improved over time as post-launch tests (PLT) were performed and corrections applied. This paper also shows that the post-launch INR tuning of GOES-17 was much shorter than GOES-16.

GOES-17↗

NASA NTRS2019

GOES-16 and GOES-17 ABI INR Assessment

The Image navigation and registration (INR) Performance Assessment Tool Set (IPATS) measurement accuracy, within 0.06 pixels, is sufficient for INR assessment. IPATS is not a static system. Additional filters and/or sub-procedures were developed when the demand emerged, e.g. the development of Short Term AbNormal Dectection (STAND) and View Zenith Angle (VZA) filters in post-launch test (PLT) of GOES-16 and GOES-17 respectively. NAV INR accuracy improved with updates and tuning in PLT. Currently, NAV errors are about 1-2 urad for all assessed channels of both ABIs. IPATS NAV assessments will continue to provide feedback for tuning the navigation algorithms and parameters in future updates and future GOES-R series ABIs.

GOES-17↗

NASA NTRSDate not supplied

Hourly Carbon Fluxes Estimation Using the GOES Advanced Baseline Imager (ABI) Data Over the Conterminous USA

Tremendous efforts by Fluxnet scientists over the past few decades have made thousands of site-years of carbon flux observations available for advancing our understanding of carbon cycling in terrestrial ecosystems. One of key Fluxnet measurements is net ecosystem exchange (NEE) as it is directly related to carbon budget of terrestrial ecosystems. However, carbon flux estimation studies using satellite remote sensing have focused mainly on daily Gross Primary Production (GPP). The satellite based carbon flux estimation used the polar orbiting satellite sensors (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)), which allow us to observe target regions only once during the day. Because daily NEE is close to zero value, the carbon flux models using the polar orbiting satellite data have not been well used for NEE estimation. The new generation of geostationary satellite sensors (e.g., GOES Advanced Baesline Imager (ABI) and Himawari Advanced Himawari Imager (AHI)) provide frequent observations, often less than every 10 minutes. Here, we use GOES ABI data to estimate hourly NEE over the conterminous USA. We used the Terrestrial Observation Prediction System (TOPS) model for estimating hourly NEE. TOPS is a diagnostic ecosystem process model that simulates the fluxes of carbon and water through vegetation in response to climate variability. For the climate input, we developed hourly climate data using the same algorithm with NASA Earth Exchange Gridded Daily Meteorology (NEX-GDM) datasets based on machine learning techniques. The hourly climate data includes precipitation, maximum temperature, minimum temperature, dew point temperature, and, in particular, solar radiation that is directly derived from the Geostationary observations. The spatial patterns of ecosystem parameters used in TOPS are optimized using satellite Solar Induced Fluorescence (SIF) data. The high frequency GPP estimations from geostationary satellite sensors make it comparable to the instantaneous SIF data than daily GPP. We also used Fluxnet data for optimization of model parameters and the validation of the output. The derived data addresses the diurnal dynamics of carbon cycling at large scales and should help in reducing the uncertainties in carbon budget studies.

Geostationary satellite↗

NASA NTRSDate not supplied

Development of Carbon Flux Model Using ABI Data Over the Conterminous US

The satellite-driven carbon flux estimation has been playing important role to estimate continental-scale carbon budget. One of the biggest recent advances in the satellite-driven carbon flux modeling is utilization of high-frequent geostationary satellites to estimate diurnal cycle in carbon fluxes. The satellite based carbon flux estimation used the polar orbiting satellite sensors (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)), which allow us to observe target regions only once during the day. The new generation of geostationary satellite sensors provide frequent observations, often less than every 10 minutes. Here, we use GOES Advanced Baseline Imager (ABI) data to estimate hourly NEE over the conterminous US. We used the Terrestrial Observation Prediction System (TOPS) model for estimating hourly NEE. TOPS is a diagnostic ecosystem process model that simulates the fluxes of carbon and water through vegetation in response to climate variability. For the climate input, we developed hourly climate data using the same algorithm with NASA Earth Exchange Gridded Daily Meteorology (NEX-GDM) datasets based on machine learning techniques. The hourly climate data includes precipitation, maximum temperature, minimum temperature, dew point temperature, and solar radiation were derived from the Geostationary observations. The spatial patterns of ecosystem parameters used in TOPS are optimized using satellite Solar Induced Fluorescence (SIF) data. The high frequency GPP estimations from geostationary satellite sensors make it comparable to the instantaneous SIF data than daily GPP. We also used Ameriflux data for optimization of model parameters and the validation of the output. The derived data addresses the diurnal dynamics of carbon cycling at large scales and should help in reducing the uncertainties in carbon budget studies.

geostationary satellite↗

NASA NTRS1974

Influence of ultrasounds on some mechanical properties of fir wood (Abies alba Mill). Microscopic sections

It was found that as a result of ultrasonic treatment fir wood (Abies alba Mill) shows the effects of homogenization, and the modulus of elasticity for static flexure drops on an average by 50% for 45 min of treatment. The drop is more pronounced for test pieces with 8 to 12 annual rings. Study of microscopic sections disclosed that early wood with one annual ring undergoes the most powerful effect.

Parpala, V.↗

NASA NTRS1986

Thigmomorphogenesis: the role of ethylene in the response of Pinus taeda and Abies fraseri to mechanical perturbation

Ethylene production was monitored for 48 h in two half-sibs of Pinus taeda L. grown in the greenhouse and given mechanical perturbation (MP) by flexing; and for 22 h in Abies fraseri (Pursh) Poir. grown in the field and exposed to wind-mediated MP. Both species produced a peak of ethylene 18 h after MP. Seedlings of P. taeda exposed to MP for the duration of the growing season (preconditioned) produced less ethylene compared to non-MP controls, with a peak production at 8 h. One half-sib which responded to MP by an increase in radial growth produced 16 times more ethylene than another half-sib which had no significant change in radial growth. Preconditioned A. fraseri produced no significant quantities of ethylene after MP. The production of wound ethylene appears to be different from MP-induced ethylene. When an ethylene-generating solution was applied to P. taeda seedlings, it mimicked many of the morphological and mechanical characteristics of MP seedlings. The putative role of ethylene in the thigmomorphogenetic response is addressed.

Non-NASA Center↗

NASA NTRS1986

Thigmomorphogenesis: field and laboratory studies of Abies fraseri in response to wind or mechanical perturbation

Field- and greenhouse-grown Abies fraseri (Pursh) Poir. (Fraser fir) were analyzed for wind- or mechanically-induced flexure changes. These changes included inhibition of stem and needle elongation, reinforcement of branch bases around the stem, and increased radial growth in the direction of the mechanical perturbation (MP). Mature trees exposed to high wind conditions were severely flag-formed. These modified tree crowns had a lower drag than crowns of non-flag formed trees in wind-tunnel tests. In both field-grown and greenhouse-grown A. fraseri, MP induced a decrease in flexibility and increased elasticity of the stems. The increased radial growth of the stems overrode the increase in elasticity, resulting in the overall decrease in flexibility. The increase in radial growth caused by wind or mechanical flexure was due to greater cell divisions of the vascular cambium, resulting in increased numbers of tracheids. The decrease in stem elongation in these trees was due, at least in part, to a decrease in tracheid length. The potential biological and mechanical significance of these induced growth changes in trees are addressed. The data support the thigmomorphogenetic theory, which states that plants respond to wind and other mechanical perturbations in a way that is favorable to the plant for continued survival in windy environments.

NASA Discipline Plant Biology↗

NASA NTRS2017

Image Navigation and Registration Performance Assessment Evaluation Tools for GOES-R ABI and GLM

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. This paper describes the software design and implementation of IPATS and provides preliminary test results.

Image registration↗

NASA NTRS2017

Image Navigation and Registration Performance Assessment Evaluation Tools for GOES-R ABI and GLM

image registration↗

NASA NTRS2020

SEASONAL VARIATION IN THE MEASUREMENT OF GOES-16 ABI CHANNEL-TO-CHANNEL REGISTRATION

An Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) was developed to assess the US Geostationary Operational Environmental Satellite R-series (GOES-R) Advanced Baseline Imager (ABI) and Geostationary Lighting Mapper (GLM) INR performance.Channel-to-channel registration (CCR) is one of the five INR performance metricsproduced by IPATS. A seasonal variationis observed in the CCR assessment in north-south direction when one or both channels are reflective. However, indirect CCR, calculated as the difference of NAV measurements between two channels, does not presentthe similar seasonal variation. The phenomenon of the seasonal variationcoincides with the annual change of the subsolar point location. The amplitude of the seasonal variation is related tothe length and the direction of the shadow. DirectCCR, measured by IPATSdirectly,performs better than indirect CCR when both channels are visible wavelengthsor emissivechannels. For all other channel paircombinations, the assessment of indirect CCR is more accurate.

Bin Tan↗

NASA NTRSDate not supplied

Imbalanced Multi-layer Cloud Classification with Advanced Baseline Imager (ABI) and CloudSat/CALIPSO Data

Clouds at different altitudes play different roles in Earth’s climate. Comprehensive understanding of overlapping clouds is important for climate and weather prediction. The East Pacific region is where El Ni˜no and La Ni˜na originate and where multi-layer clouds frequently occur. The overlap of clouds at different altitudes in this region increases the classification complexity for cloud-based climatological studies. Unlike prior work in cloud layer classification that assumes single layer or two-layer of clouds, in this work, we consider multi-layer cloud classification with 8 cloud-level classes (clear-sky, high, middle, low, high+middle, high+low, middle+low, high+middle+low). We develop and analyze machine learning models on features extracted from satellite images from the East Pacific regions collected by GOES Advanced Baseline Imager (ABI). These are used to classify CloudSat/CALIPSO observed multi-layer clouds. Due to the imbalanced nature of the data, we investigate the adoption of conventional resampling methods, as well as deep learning methods with data augmentation. In our experiments, we utilize the random forest classifier and Multilayer perceptron classifier with data augmentation methods to reduce the class imbalance during training. With these approaches, we achieve a classification accuracy of 83.6% without exploiting any ancillary information.

machine learning↗