Engineering topics

Tan, Bin

Publications and source records attributed to Tan, Bin.

At least 19 records

NASA NTRS2019

Joint 3D-Wind Retrievals with Stereoscopic Views from MODIS and GOES

Atmospheric motion vectors (AMVs), derived by tracking patterns, represent the winds in a layer characteristic of the pattern. AMV height (or pressure), important for applications in atmospheric research and operational meteorology, is usually assigned using observed IR brightness temperatures with a modeled atmosphere and can be inaccurate. Stereoscopic tracking provides a direct geometric height measurement of the pattern that an AMV represents. We extend our previous work with multi-angle imaging spectroradiometer (MISR) and GOES to moderate resolution imaging spectroradiometer (MODIS) and the GOES-R series advanced baseline imager (ABI). MISR is a unique satellite instrument for stereoscopy with nine angular views along track, but its images have a narrow (380 km) swath and no thermal IR channels. MODIS provides a much wider (2330 km) swath and eight thermal IR channels that pair well with all but two ABI channels, o e ring a rich set of potential applications. Given the similarities between MODIS and VIIRS, our methods should also yield similar performance with VIIRS. Our methods, as enabled by advanced sensors like MODIS and ABI, require high-accuracy geographic registration in both systems but no synchronization of observations. AMVs are retrieved jointly with their heights from the disparities between triplets of ABI scenes and the paired MODIS granule. We validate our retrievals against MISR-GOES retrievals, operational GOES wind products, and by tracking clear-sky terrain. We demonstrate that the 3D-wind algorithm can produce high-quality AMV and height measurements for applications from the planetary boundary layer (PBL) to the upper troposphere, including cold-air outbreaks, wildfire smoke plumes, and hurricanes.

Carr, James L.↗

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 NTRS2019

Overview of JPSS VIIRS Geometric Calibration and Validation

The presentation stressed the importance of a pre-launch test plan and a post-launch CalVal plan in the geometric aspects of instrument focal length, spatial responses, band-to-band registration, pointing, geolocation and long-term monitoring.

VIIRS↗

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

Thirty-Six Combined Years of MODIS Geolocation Trending

Two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been in operations for more than 19 and 17 years (thus 36 combined years) as part of NASA's Earth Observing System (EOS) on the Terra platform that was launched in December 1999 and on the Aqua platform that was launched in May 2002, respectively. Accurate geolocation is a critical element needed for accurate retrieval of global biogeophysical parameters. In this paper, we describe the latest trends in the continuously improved MODIS geolocation accuracy in Collection-5 (C5), C6 and C6.1 re-processing and forward-processing data streams. We improved geolocation accuracy in the re-processed data and corrected for geolocation biases found in forward-processed data, including those caused by operations such as the stop-go-stop status of the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) instrument on the Aqua platform. We discuss scan-toscan underlaps near nadir over the equator regions that was discovered in checking the non-underlapping requirement in the Visible Infrared Imaging Radiometer Suite (VIIRS) based on trending parameters from the actual Suomi National Polar-orbiting Partnership (S-NPP) satellite orbit. The underlaps are closely tied to instrument effective focal length that is measured from on-orbit data using a technique we recently developed. We also discuss potential improvements for the upcoming C7 re-processing.

ground control point↗

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 NTRS2018

JPSS-1/NOAA-20 VIIRS Early On-Orbit Geometric Performance

The first NOAA/NASA Join Polar Satellite System (JPSS-1) satellite was successfully launched on November 18, 2017,becoming NOAA-20. Instruments on-board the NOAA-20 satellite include the Visible Infrared Imaging RadiometerSuite (VIIRS). This instrument is the second build of VIIRS, with the first flight instrument on-board NASA/NOAASuomi National Polar-orbiting Partnership (SNPP) satellite operating since October 2011. The purpose of these VIIRSinstruments is to continue the long-term measurements of biogeophysical variables for multiple applications includingweather forecasting, rapid response and climate research. The geometric performance of VIIRS is essential to retrievingaccurate biogeophysical variables. This paper describes the early on-orbit geometric performance of the JPSS-1/NOAA-20 VIIRS. It first discusses the on-orbit orbit and attitude performance, a key input needed for accurate geolocation. Itthen discusses the on-orbit geometric characterization and calibration of VIIRS and an initial assessment of thegeometric accuracy. It follows with a discussion of an improvement in the instrument geometric model that correctssmall geometrical artifacts that appear in the along-scan direction. Finally, this paper discusses on-orbit measurements ofthe focal length and the impact of this on the scan-to-scan underlap/overlap.

Lin, Guoqing↗

NASA NTRS2017

HOTEX: An Approach for Global Mapping of Human Built-Up and Settlement Extent

Understanding the impacts of urbanization requires accurate and updatable urban extent maps. Here we present an algorithm for mapping urban extent at global scale using Landsat data. An innovative hierarchical object-based texture (HOTex) classification approach was designed to overcome spectral confusion between urban and nonurban land cover types. VIIRS nightlights data and MODIS vegetation index datasets are integrated as high-level features under an object-based framework. We applied the HOTex method to the GLS-2010 Landsat images to produce a global map of human built-up and settlement extent. As shown by visual assessments, our method could effectively map urban extent and generate consistent results using images with inconsistent acquisition time and vegetation phenology. Using scene-level cross validation for results in Europe, we assessed the performance of HOTex and achieved a kappa coefficient of 0.91, compared to 0.74 from a baseline method using per-pixel classification using spectral information.

Wang, Panshi↗

NASA NTRS2016

Cross Correlation versus Normalized Mutual Information on Image Registration

This is the first study to quantitatively assess and compare cross correlation and normalized mutual information methods used to register images in subpixel scale. The study shows that the normalized mutual information method is less sensitive to unaligned edges due to the spectral response differences than is cross correlation. This characteristic makes the normalized image resolution a better candidate for band to band registration. Improved band-to-band registration in the data from satellite-borne instruments will result in improved retrievals of key science measurements such as cloud properties, vegetation, snow and fire.

band-to-band↗

NASA NTRS2016

Avoiding Stair-Step Artifacts in Image Registration for GOES-R Navigation and Registration Assessment

In developing software for independent verification and validation (IVV) of the Image Navigation and Registration (INR) capability for the Geostationary Operational Environmental Satellite R Series (GOES-R) Advanced Baseline Imager (ABI), we have encountered an image registration artifact which limits the accuracy of image offset estimation at the subpixel scale using image correlation. Where the two images to be registered have the same pixel size, subpixel image registration preferentially selects registration values where the image pixel boundaries are close to lined up. Because of the shape of a curve plotting input displacement to estimated offset, we call this a stair-step artifact. When one image is at a higher resolution than the other, the stair-step artifact is minimized by correlating at the higher resolution. For validating ABI image navigation, GOES-R images are correlated with Landsat-based ground truth maps. To create the ground truth map, the Landsat image is first transformed to the perspective seen from the GOES-R satellite, and then is scaled to an appropriate pixel size. Minimizing processing time motivates choosing the map pixels to be the same size as the GOES-R pixels. At this pixel size image processing of the shift estimate is efficient, but the stair-step artifact is present. If the map pixel is very small, stair-step is not a problem, but image correlation is computation-intensive. This paper describes simulation-based selection of the scale for truth maps for registering GOES-R ABI images.

stair-step artifact↗

NASA NTRS2016

Avoiding Stair-Step Artifacts in Image Registration for GOES-R Navigation and Registration Assessment

image registration↗

NASA NTRS2016

Measurement of the Band-to-Band Registration of the SNPP VIIRS Imaging System from On-Orbit Data

No abstract available

band-to-band↗

NASA NTRS2014

Estimation of Crop Gross Primary Production (GPP): I. Impact of MODIS Observation Footprint and Impact of Vegetation BRDF Characteristics

Accurate estimation of gross primary production (GPP) is essential for carbon cycle and climate change studies. Three AmeriFlux crop sites of maize and soybean were selected for this study. Two of the sites were irrigated and the other one was rainfed. The normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the green band chlorophyll index (CIgreen), and the green band wide dynamic range vegetation index (WDRVIgreen) were computed from the moderate resolution imaging spectroradiometer (MODIS) surface reflectance data. We examined the impacts of the MODIS observation footprint and the vegetation bidirectional reflectance distribution function (BRDF) on crop daily GPP estimation with the four spectral vegetation indices (VIs - NDVI, EVI, WDRVIgreen and CIgreen) where GPP was predicted with two linear models, with and without offset: GPP = a × VI × PAR and GPP = a × VI × PAR + b. Model performance was evaluated with coefficient of determination (R2), root mean square error (RMSE), and coefficient of variation (CV). The MODIS data were filtered into four categories and four experiments were conducted to assess the impacts. The first experiment included all observations. The second experiment only included observations with view zenith angle (VZA) ≤ 35◦ to constrain growth of the footprint size,which achieved a better grid cell match with the agricultural fields. The third experiment included only forward scatter observations with VZA ≤ 35◦. The fourth experiment included only backscatter observations with VZA ≤ 35◦. Overall, the EVI yielded the most consistently strong relationships to daily GPP under all examined conditions. The model GPP = a × VI × PAR + b had better performance than the model GPP = a × VI × PAR, and the offset was significant for most cases. Better performance was obtained for the irrigated field than its counterpart rainfed field. Comparison of experiment 2 vs. experiment 1 was used to examine the observation footprint impact whereas comparison of experiment 4 vs. experiment 3 was used to examine the BRDF impact. Changes in R2, RMSE,CV and changes in model coefficients "a" and "b" (experiment 2 vs. experiment 1; and experiment 4 vs. experiment 3) were indicators of the impacts. The second experiment produced better performance than the first experiment, increasing R2 (↑0.13) and reducing RMSE (↓0.68 g C m−2 d−1) and CV (↓9%). For each VI, the slope of GPP = a × VI × PAR in the second experiment for each crop type changed little while the slope and intercept of GPP = a × VI × PAR + b varied field by field. The CIgreen was least affected by the MODIS observation footprint in estimating crop daily GPP (R2, ↑0.08; RMSE, ↓0.42 g C m−2 d−1; and CV, ↓7%). Footprint most affected the NDVI (R2, ↑0.15; CV, ↓10%) and the EVI (RMSE, ↓0.84 g C m−2 d−1). The vegetation BRDF impact also caused variation of model performance and change of model coefficients. Significantly different slopes were obtained for forward vs. backscatter observations, especially for the CIgreen and the NDVI. Both the footprint impact and the BRDF impact varied with crop types, irrigation options, model options and VI options.

Chlorophyll↗

NASA NTRS2014

Interannual Variations and Trends in Global Land Surface Phenology Derived from Enhanced Vegetation Index During 1982-2010

Land swiace phenology is widely retrieved from satellite observations at regional and global scales, and its long-term record has been demonstmted to be a valuable tool for reconstructing past climate variations, monitoring the dynamics of terrestrial ecosystems in response to climate impacts, and predicting biological responses to future climate scenarios. This srudy detected global land surface phenology from the advanced very high resolution radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 1982 to 2010. Based on daily enhanced vegetation index at a spatial resolution of 0.05 degrees, we simulated the seasonal vegetative trajectory for each individual pixel using piecewise logistic models, which was then used to detect the onset of greenness increase (OGI) and the length of vegetation growing season (GSL). Further, both overall interannual variations and pixel-based trends were examIned across Koeppen's climate regions for the periods of 1982-1999 and 2000-2010, respectively. The results show that OGI and OSL varied considerably during 1982-2010 across the globe. Generally, the interarmual variation could be more than a month in precipitation-controlled tropical and dry climates while it was mainly less than 15 days in temperature-controlled temperate, cold, and polar climates. OGI, overall, shifted early, and GSL was prolonged from 1982 to 2010 in most climate regions in North America and Asia while the consistently significant trends only occurred in cold climate and polar climate in North America. The overall trends in Europe were generally insignificant. Over South America, late OGI was consistent (particularly from 1982 to 1999) while either positive or negative OSL trends in a climate region were mostly reversed between the periods of 1982-1999 and 2000-2010. In the Northern Hemisphere of Africa, OGI trends were mostly insignificant, but prolonged GSL was evident over individual climate regions during the last 3 decades. OGI mainly showed late trends in the Southern Hemisphere of Africa while GSL was reversed from reduced GSL trends (1982-1999) to prolonged trends (2000-2010). In Australia, GSL exhibited considerable interannual variation, but the consistent trend lacked presence in most regions. Finally, the proportion of pixels with significant trends was less than I% in most of climate regions although it could be as large as 10%.

remote sensing↗

NASA NTRS2014

Regional Distribution Models with Lack of Proximate Predictors: Africanized Honeybees Expanding North

Species distribution models have often been hampered by poor local species data, reliance on coarse-scale climate predictors and the assumption that species-environment relationships, even with non-proximate predictors, are consistent across geographical space. Yet locally accurate maps of invasive species, such as the Africanized honeybee (AHB) in North America, are needed to support conservation efforts. Current AHB range maps are relatively coarse and are inconsistent with observed data. Our aim was to improve distribution maps using more proximate predictors (phenology) and using regional models rather than one across the entire range of interest to explore potential differences in drivers.

PRedictors↗

NASA NTRS2013

Mapping Impervious Surfaces Globally at 30m Resolution Using Global Land Survey Data

Impervious surfaces, mainly artificial structures and roads, cover less than 1% of the world's land surface (1.3% over USA). Regardless of the relatively small coverage, impervious surfaces have a significant impact on the environment. They are the main source of the urban heat island effect, and affect not only the energy balance, but also hydrology and carbon cycling, and both land and aquatic ecosystem services. In the last several decades, the pace of converting natural land surface to impervious surfaces has increased. Quantitatively monitoring the growth of impervious surface expansion and associated urbanization has become a priority topic across both the physical and social sciences. The recent availability of consistent, global scale data sets at 30m resolution such as the Global Land Survey from the Landsat satellites provides an unprecedented opportunity to map global impervious cover and urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such a buildings, roads and parking lots. With long term GLS data now available for the 1975, 1990, 2000, 2005 and 2010 time periods, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. In the Global Land Survey - Imperviousness Mapping Project (GLS-IMP), we are producing the first global 30 m spatial resolution impervious cover data set. We have processed the GLS 2010 data set to surface reflectance (8500+ TM and ETM+ scenes) and are using a supervised classification method using a regression tree to produce continental scale impervious cover data sets. A very large set of accurate training samples is the key to the supervised classifications and is being derived through the interpretation of high spatial resolution (approx. 2 m or less) commercial satellite data (Quickbird and Worldview2) available to us through the unclassified archive of the National Geospatial Intelligence Agency (NGA). For each continental area several million training pixels are derived by analysts using image segmentation algorithms and tools and then aggregated to the 30m resolution of Landsat. Here we will discuss the production/testing of this massive data set for Europe, North and South America and Africa, including assessments of the 2010 surface reflectance data. This type of analysis is only possible because of the availability of long term 30m data sets from GLS and shows much promise for integration of Landsat 8 data in the future.

global land survey↗

NASA NTRS2012

Vegetation Phenology Metrics Derived from Temporally Smoothed and Gap-filled MODIS Data

Smoothed and gap-filled VI provides a good base for estimating vegetation phenology metrics. The TIMESAT software was improved by incorporating the ancillary information from MODIS products. A simple assessment of the association between retrieved greenup dates and ground observations indicates satisfactory result from improved TIMESAT software. One application example shows that mapping Nectar Flow Phenology is tractable on a continental scale using hive weight and satellite vegetation data. The phenology data product is supporting more researches in ecology, climate change fields.

Tan, Bin↗

NASA NTRS2012

Global Characterization and Monitoring of Forest Cover Using Landsat Data: Opportunities and Challenges

The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth's land cover at Landsat resolutions of 30 m. In this article, we describe the methods to create global products of forest cover and cover change at Landsat resolutions. Nevertheless, there are many challenges in ensuring the creation of high-quality products. And we propose various ways in which the challenges can be overcome. Among the challenges are the need for atmospheric correction, incorrect calibration coefficients in some of the data-sets, the different phenologies between compilations, the need for terrain correction, the lack of consistent reference data for training and accuracy assessment, and the need for highly automated characterization and change detection. We propose and evaluate the creation and use of surface reflectance products, improved selection of scenes to reduce phenological differences, terrain illumination correction, automated training selection, and the use of information extraction procedures robust to errors in training data along with several other issues. At several stages we use Moderate Resolution Spectroradiometer data and products to assist our analysis. A global working prototype product of forest cover and forest cover change is included.

Global↗