Geolocation Accuracy Research Articles

GEOMETRIC QUALITY ASSESSMENT OF LIDAR DATA BASED ON SWATH OVERLAP

Abstract. This paper provides guidelines on quantifying the relative horizontal and vertical errors observed between conjugate features in the overlapping regions of lidar data. The quantification of these errors is important because their presence quantifies the geometric quality of the data. A data set can be said to have good geometric quality if measurements of identical features, regardless of their position or orientation, yield identical results. Good geometric quality indicates that the data are produced using sensor models that are working as they are mathematically designed, and data acquisition processes are not introducing any unforeseen distortion in the data. High geometric quality also leads to high geolocation accuracy of the data when the data acquisition process includes coupling the sensor with geopositioning systems. Current specifications (e.g. Heidemann 2014) do not provide adequate means to quantitatively measure these errors, even though they are required to be reported. Current accuracy measurement and reporting practices followed in the industry and as recommended by data specification documents also potentially underestimate the inter-swath errors, including the presence of systematic errors in lidar data. Hence they pose a risk to the user in terms of data acceptance (i.e. a higher potential for Type II error indicating risk of accepting potentially unsuitable data). For example, if the overlap area is too small or if the sampled locations are close to the center of overlap, or if the errors are sampled in flat regions when there are residual pitch errors in the data, the resultant Root Mean Square Differences (RMSD) can still be small. To avoid this, the following are suggested to be used as criteria for defining the inter-swath quality of data: a) Median Discrepancy Angle b) Mean and RMSD of Horizontal Errors using DQM measured on sloping surfaces c) RMSD for sampled locations from flat areas (defined as areas with less than 5 degrees of slope) It is suggested that 4000-5000 points are uniformly sampled in the overlapping regions of the point cloud, and depending on the surface roughness, to measure the discrepancy between swaths. Care must be taken to sample only areas of single return points only. Point-to-Plane distance based data quality measures are determined for each sample point. These measurements are used to determine the above mentioned parameters. This paper details the measurements and analysis of measurements required to determine these metrics, i.e. Discrepancy Angle, Mean and RMSD of errors in flat regions and horizontal errors obtained using measurements extracted from sloping regions (slope greater than 10 degrees). The research is a result of an ad-hoc joint working group of the US Geological Survey and the American Society for Photogrammetry and Remote Sensing (ASPRS) Airborne Lidar Committee.

Characterization of geolocation accuracy of Suomi NPP Advanced Technology Microwave Sounder measurements

AbstractThe Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar‐orbiting Partnership satellite has 22 channels at frequencies ranging from 23 to 183 GHz for probing the atmospheric temperature and moisture under all weather conditions. As part of the ATMS calibration and validation activities, the geolocation accuracy of ATMS data must be well characterized and documented. In this study, the coastline crossing method (CCM) and the land‐sea fraction method (LFM) are utilized to characterize and quantify the ATMS geolocation accuracy. The CCM is based on the inflection points of the ATMS window channel measurements across the coastlines, whereas the LFM collocates the ATMS window channel data with high‐resolution land‐sea mask data sets. Since the ATMS measurements provide five pairs of latitude and longitude data for K, Ka, V, W, and G bands, respectively, the window channels 1, 2, 3, 16, and 17 from each of these five bands are chosen for assessing the overall geolocation accuracy. ATMS geolocation errors estimated from both methods are generally consistent from 40 cases in June 2014. The ATMS along‐track (cross‐track) errors at nadir are within ±4.2 km (±1.2 km) for K/Ka, ±2.6 km (±2.7 km) for V bands, and ±1.2 km (±0.6 km) at W and G bands, respectively. At the W band, the geolocation errors derived from both algorithms are probably less reliable due to a reduced contrast of brightness temperatures in coastal areas. These estimated ATMS along‐track and cross‐track geolocation errors are well within the uncertainty requirements for all bands.

LiCHy: The CAF’s LiDAR, CCD and Hyperspectral Integrated Airborne Observation System

We describe the design, implementation and performance of a novel airborne system, which integrates commercial waveform LiDAR, CCD (Charge-Coupled Device) camera and hyperspectral sensors into a common platform system. CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System is a unique system that permits simultaneous measurements of vegetation vertical structure, horizontal pattern, and foliar spectra from different view angles at very high spatial resolution (~1 m) on a wide range of airborne platforms. The horizontal geo-location accuracy of LiDAR and CCD is about 0.5 m, with LiDAR vertical resolution and accuracy 0.15 m and 0.3 m, respectively. The geo-location accuracy of hyperspectral image is within 2 pixels for nadir view observations and 5–7 pixels for large off-nadir observations of 55° with multi-angle modular when comparing to LiDAR product. The complementary nature of LiCHy’s sensors makes it an effective and comprehensive system for forest inventory, change detection, biodiversity monitoring, carbon accounting and ecosystem service evaluation. The LiCHy system has acquired more than 8000 km2 of data over typical forests across China. These data are being used to investigate potential LiDAR and optical remote sensing applications in forest management, forest carbon accounting, biodiversity evaluation, and to aid in the development of similar satellite configurations. This paper describes the integration of the LiCHy system, the instrument performance and data processing workflow. We also demonstrate LiCHy’s data characteristics, current coverage, and potential vegetation applications.

Adaptive Air-to-Ground Secure Communication System Based on ADS-B and Wide-Area Multilateration

A novel air-to-ground (ATG) communication system, which is based on adaptive modulation and beamforming enabled by automatic dependent surveillance-broadcast (ADS-B) and multilateration techniques, is presented in this paper. From an aircraft geolocation perspective, the proposed multilateration technique uses the time-difference-of-arrival (TDOA), angle-of-arrival (AOA), and frequency-difference-of-arrival (FDOA) features within the ADS-B signal to implement the hybrid geolocation mechanism. Moreover, this hybrid mechanism aims for the optimal selection of multilateration sensors to provide a precise aircraft geolocation estimate by minimizing the geometric dilution-of-precision (GDOP) metric and imparts significant resilience to the current ADS-B-based geolocation framework to withstand any form of attack involving aircraft impersonation and ADS-B message infringement. From an ATG communication perspective, the ground base stations can use this hybrid aircraft geolocation estimate to dynamically adapt their modulation parameters and transmission beampattern in an effort to provide a high-data-rate secure ATG communication link. Additionally, we develop a hardware prototype that is highly accurate in estimating AOA data and facilitating TDOA and FDOA extraction associated with the received ADS-B signal. This hardware setup for the ADS-B-based ATG system is analytically established and validated with commercially available universal software-defined radio peripheral units. This hardware setup displays 1.5° AOA estimation accuracy, whereas the simulated geolocation accuracy is approximately 30 m over 100 nautical miles for a typical aircraft trajectory. The adaptive modulation and beamforming approach assisted by the proposed GDOP-minimization-based multilateration strategy achieves significant enhancement in throughput and reduction in packet error rate.

An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery

We adapted the automated, open source NASA Ames Stereo Pipeline (ASP) to generate digital elevation models (DEMs) and orthoimages from very-high-resolution (VHR) commercial imagery of the Earth. These modifications include support for rigorous and rational polynomial coefficient (RPC) sensor models, sensor geometry correction, bundle adjustment, point cloud co-registration, and significant improvements to the ASP code base. We outline a processing workflow for ∼0.5m ground sample distance (GSD) DigitalGlobe WorldView-1 and WorldView-2 along-track stereo image data, with an overview of ASP capabilities, an evaluation of ASP correlator options, benchmark test results, and two case studies of DEM accuracy. Output DEM products are posted at ∼2m with direct geolocation accuracy of <5.0m CE90/LE90. An automated iterative closest-point (ICP) co-registration tool reduces absolute vertical and horizontal error to <0.5m where appropriate ground-control data are available, with observed standard deviation of ∼0.1–0.5m for overlapping, co-registered DEMs (n=14,17). While ASP can be used to process individual stereo pairs on a local workstation, the methods presented here were developed for large-scale batch processing in a high-performance computing environment. We are leveraging these resources to produce dense time series and regional mosaics for the Earth’s polar regions.

Improved MUSIC-Based SMOS RFI Source Detection and Geolocation Algorithm

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission has been providing L-band brightness temperature (BT) using its instrument, the Microwave Imaging Radiometer using Aperture Synthesis. In the measurements, the negative effect of radio frequency interference (RFI) is clearly present, deteriorating the quality of geophysical parameter retrieval. Detection and geolocation of RFI sources are essential to remove or at least mitigate the RFI impacts and ultimately improve the performance of parameter retrieval. This paper discusses a new approach to SMOS RFI source detection, based on the MUltiple SIgnal Classification (MUSIC) algorithm. Recently, the feasibility of MUSIC direction-of-arrival estimation has been shown for the RFI source detection of the synthetic aperture interferometric radiometer. This paper refines the MUSIC RFI source detection algorithm and tailors it to the SMOS scenario. To consolidate the RFI source detection procedure, several required steps are devised, including the rank estimation of the covariance matrix, local peak detection and thresholds, and multiple-snapshot processing. The developed method is tested using a number of SMOS visibility samples. In the test results, the MUSIC method shows an improvement on the accuracy and precision of the RFI source geolocation, compared with a simple detection method based on the local peaks of BT images. The MUSIC results especially outperform the SMOS BT image on the spatial resolution.

Estimation and Correction of Geolocation Errors in FengYun-3C Microwave Radiation Imager Data

Microwave Radiation Imager (MWRI) onboard the FengYun (FY)-3C satellite provides measurements of the Earth's atmosphere and surface at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with dual polarization. While FY MWRI data have been widely distributed to the user community, their geolocation accuracy has not been documented. In this paper, the coastline inflection method is used to estimate MWRI geolocation errors. Three coastal regions where MWRI brightness temperature exhibits a large contrast are selected for the geolocation analysis. A total of 720 MWRI data points are identified that cross the coastlines. The latitudes and longitudes at these data points are compared with the fine-resolution database of the Global Self-consistent, Hierarchical, High-resolution Shoreline (GSHHS). It is found that the mean geolocation errors in along- and cross-track directions are approximately 5–6 km at 89 GHz. This magnitude of errors is more than 30% of the field-of-view size at 89 GHz. Such a geolocation error must be corrected so that the MWRI data can be more useful for quantitative remote sensing. Thus, the mean geolocation errors are further utilized to adjust the satellite attitude angles (e.g., pitch, roll, and raw). After the attitude angle correction, the MWRI geolocation is very accurate at 89 GHz, and errors in other MWRI channels may be corrected through their co-registration relationships to the 89-GHz channel.

THE RATIONAL POLYNOMIAL COEFFICIENTS MODIFICATION USING DIGITAL ELEVATION MODELS

Abstract. The high-resolution satellite imageries (HRSI) are as primary dataset for different applications such as DEM generation, 3D city mapping, change detection, monitoring, and deformation detection. The geo-location information of HRSI are stored in metadata called Rational Polynomial Coefficients (RPCs). There are many methods to improve and modify the RPCs in order to have a precise mapping. In this paper, an automatic approach is presented for the RPC modification using global Digital Elevation Models. The main steps of this approach are: relative digital elevation model generation, shift parameters calculation, sparse point cloud generation and shift correction, and rational polynomial fitting. Using some ground control points, the accuracy of the proposed method is evaluated based on statistical descriptors in which the results show that the geo-location accuracy of HRSI can be improved without using Ground Control Points (GCPs).

Area-based quality control of airborne laser scanning 3D models for different land classes using terrestrial laser scanning: sample survey in Houston, USA

Airborne laser scanning (ALS) is a remote-sensing technique that provides scale-accurate 3D models consisting of dense point clouds with x, y planimetric coordinates and altitude z. Using ALS, very high-resolution (VHR) digital surface models (DSMs) have been widely used for commercial and scientific applications since the early 1990s. Although there is widespread usage, there has been little comprehensive investigation of quality control for ALS DSMs in the literature, as most studies have been limited to assessing point-based vertical accuracy. This article is dedicated to investigating the quality of ALS DSMs for different land classes using statistical and visual approaches based on absolute and relative vertical accuracy metrics. Rather than a limited number of ground control points (GCP), the model-to-model-based approach is applied and DSMs derived from terrestrial laser scanning (TLS) point clouds that have around 5 mm absolute and 3 mm relative geolocation accuracy were used as the reference data for comparison. The results demonstrate that in open, grass, and building land classes, the ALS DSMs reached both standard deviation (σ) and normalized median absolute deviation (NMAD) of 3–5 cm after the elimination of any systematic biases. This result sufficiently satisfies the vertical accuracy requirements for 1/1000-scale topographic maps determined by National Digital Elevation Program (NDEP) specifications. In tall vegetation, a higher number of discrepancies larger than 0.5 m exist, reversing the relation between σ and NMAD. These vegetation errors also do not appear to be normally distributed. As an additional investigation, the performance of ALS DEMs under dense high-vegetation areas was assessed. These under-canopy ALS DEMs, created using only classified ground returns, offer both σ and NMAD of 12–14 cm, a performance level that is difficult to achieve under-canopy using photogrammetric techniques.

Ocean Heat Content Reveals Secrets of Fish Migrations

For centuries, the mechanisms surrounding spatially complex animal migrations have intrigued scientists and the public. We present a new methodology using ocean heat content (OHC), a habitat metric that is normally a fundamental part of hurricane intensity forecasting, to estimate movements and migration of satellite-tagged marine fishes. Previous satellite-tagging research of fishes using archival depth, temperature and light data for geolocations have been too coarse to resolve detailed ocean habitat utilization. We combined tag data with OHC estimated from ocean circulation and transport models in an optimization framework that substantially improved geolocation accuracy over SST-based tracks. The OHC-based movement track provided the first quantitative evidence that many of the tagged highly migratory fishes displayed affinities for ocean fronts and eddies. The OHC method provides a new quantitative tool for studying dynamic use of ocean habitats, migration processes and responses to environmental changes by fishes, and further, improves ocean animal tracking and extends satellite-based animal tracking data for other potential physical, ecological, and fisheries applications.

REMOTE SPECTRAL IMAGING USING A LOW COST UAV SYSTEM

Abstract. The purpose of this scientific survey is to support the research being conducted at York University in the field of spectroscopy and nanosatellites using Argus 1000 micro- spectrometer and low cost unmanned aerial vehicle (UAV) system. On the CanX-2 mission, the Argus spectrometer observes reflected infrared solar radiation emitted by Earth surface targets as small as 1.5 km within the 0.9-1.7 μm range. However, limitations in the volume of data due to onboard power constraints and a lack of an onboard camera system make it very difficult to verify these objectives using ground truth. In the last five years that Argus has been in operation, we have made over 200 observations over a series of land and ocean targets. We have recently examined algorithms to improve the geolocation accuracy of the spectrometer payload and began to conduct an analysis of soil health content using Argus spectral data. A field campaign is used to obtain data to assess geolocation accuracy using coastline crossing detection and to obtain airborne bare soil spectra in ground truth form. The payload system used for the field campaign consists of an Argus spectrometer, optical camera, GPS, and attitude sensors, integrated into a low-cost, unmanned aerial vehicle (UAV), which will be presented along with the experimental procedure and field campaign results.

The georeferencing of RASAT satellite imagery and some practical approaches to increase the georeferencing accuracy

RASAT Earth Observation Satellite is the second remote sensing satellite of The Scientific and Technological Research Council of Turkey (TUBITAK) Space Technologies Search Institute (TUBITAK Space). Generally, the first step to utilize the satellite imagery in GIS applications is the accurate geometric correction of the imagery. But, the geometric correction process of RASAT images is somewhat difficult due to insufficient orbit data and lack of satellite imagery processing software with RASAT model. Although the geolocation of RASAT images is investigated in some recent studies, subpixel accuracies cannot be achieved. In this study, different geometric correction methods and combination of them are tested with a more interactive workflow that uses the results of other approaches. Results show that these approaches can be used for the geometric correction of RASAT like pushbroom satellite images with insufficient orbit data and better geolocation accuracies can be achieved by different geometric correction approaches.

Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry

Abstract. Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers to entry and now allow repeat mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these advancements to the measurement of snow depth from manned aircraft. Our main airborne hardware consists of a consumer-grade digital camera directly coupled to a dual-frequency GPS; no inertial motion unit (IMU) or on-board computer is required, such that system hardware and software costs less than USD 30 000, exclusive of aircraft. The photogrammetric processing is done using a commercially available implementation of the structure from motion (SfM) algorithm. The system is simple enough that it can be operated by the pilot without additional assistance and the technique creates directly georeferenced maps without ground control, further reducing overall costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (real-world geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by a similar photogrammetric system. We empirically determined that our DEMs have a geolocation accuracy of ±30 cm and a repeatability of ±8 cm (both 95 % confidence). We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 separate test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and were statistically identical at 95 % confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (&lt; 20 cm). The ability to directly measure such small changes over entire landscapes eliminates the need to extrapolate limited field measurements. The fact that this mapping can be done at substantially lower costs than current methods may transform the way we approach studying change in the cryosphere.

Sentinel-1A Product Geolocation Accuracy: Commissioning Phase Results

Sentinel-1A (S1A) is an Earth observation satellite carrying a state-of-the-art Synthetic Aperture Radar (SAR) imaging instrument. It was launched by the European Space Agency (ESA) on 3 April 2014. With the end of the in-orbit commissioning phase having been completed at the end of September 2014, S1A data products are already consistently providing highly accurate geolocation. StripMap (SM) mode products were acquired regularly and tested for geolocation accuracy and consistency during dedicated corner reflector (CR) campaigns. At the completion of this phase, small geometric inconsistencies had been understood and mitigated, with the high quality of the final product geolocation estimates reflecting the mission’s success thus far. This paper describes the measurement campaign, the methods used during geolocation estimation, and presents best estimates of the product Absolute Location Error (ALE) available at the beginning of S1A’s operational phase.

Multistrip Bundle Block Adjustment of ZY-3 Satellite Imagery by Rigorous Sensor Model Without Ground Control Point

Extensive applications of Zi-Yuan 3 (ZY-3) satellite imagery of China have commenced since its on-orbit test was finished. Most of the data are processed scene by scene with a few ground control points (GCPs) for each scene; this conventional method is mature and widely used all over the world. However, very little work has focused on its application to super large area blocks. This letter aims to study mapping applications without GCPs for a super large area, which is defined as a range of interprovincial or even nationwide areas. The automatic matching and bundle block adjustment (BBA) software developed by our research team are applied to deal with two blocks of ZY-3 three-line camera imagery which covers most of the provinces in eastern China. Our comparison analysis of different data processing methods and the geolocation accuracies of the overlapped areas between adjacent strips are presented in this letter, as well as the possibility of nationwide BBA. The preliminary test results show that multistrip BBA without GCPs can achieve accuracies of about 13-15 m in both planimetry and height, which means that nationwide BBA is considered practical and feasible.

An Optimization Method for the Geolocation Databases of Internet Hosts Based on Machine Learning

In order to improve the accuracy and robustness of geolocation (geographic location) databases, a method based on machine learning called GeoCop (Geolocation Cop) is proposed for optimizing the geolocation databases of Internet hosts. In addition to network measurement, which is always used by the existing geolocation methods, our geolocation model for Internet hosts is also derived by both routing policy and machine learning. After optimization with the GeoCop method, the geolocation databases of Internet hosts are less prone to imperfect measurement and irregular routing. In addition to three frequently used geolocation databases (IP138, QQWry, and IPcn), we obtain two other geolocation databases by implementing two well-known geolocation methods (the constraint-based geolocation method and the topology-based geolocation method) for constructing the optimized objects. Finally, we give a comprehensive analysis on the performance of our method. On one hand, we use typical benchmarks to compare the performance of these databases after optimization; on the other hand, we also perform statistical tests to display the improvement of the GeoCop method. As presented in the comparison tables, the GeoCop method not only achieves improved performance in both accuracy and robustness but also enjoys less measurements and calculation overheads.

The Geolocation Accuracy of Lidar Footprint

This paper describes the geometric geolocation accuracy of LiDAR footprint, utilizing an aircraft configuration that supports a sensor designed to scan the surface of the Earth, a DGPS and INS/IMU system. It is presented a review of LiDAR’s footprints accuracy as a relationship between the input parameters, which include errors of the orbital state, attitude information of the aircraft and the look vector errors of the active sensor (LiDAR scanner), that give us the coordinates of the point of intersection of the line of sight scanning system and the Earth's surface as a function of: terrestrial ellipsoid surface, aircraft position, aircraft velocity, aircraft attitude (spatial situation) and the orientation of the LiDAR scanner. Using the derived error formulas, based on the accuracy of the navigation solution, the boresight misalignment angles, the ranging and scan angle accuracy, and laser beam divergence, the achievable point positioning accuracy can be computed for any given LiDAR system which operates at different flying heights between 70 m - 6,000 m.

Quantitative Analysis of VIIRS DNB Nightlight Point Source for Light Power Estimation and Stability Monitoring

The high sensitivity and advanced onboard calibration on the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables accurate measurements of low light radiances which leads to enhanced quantitative applications at night. The finer spatial resolution of DNB also allows users to examine social economic activities at urban scales. Given the growing interest in the use of the DNB data, there is a pressing need for better understanding of the calibration stability and absolute accuracy of the DNB at low radiances. The low light calibration accuracy was previously estimated at a moderate 15% using extended sources while the long-term stability has yet to be characterized. There are also several science related questions to be answered, for example, how the Earth’s atmosphere and surface variability contribute to the stability of the DNB measured radiances; how to separate them from instrument calibration stability; whether or not SI (International System of Units) traceable active light sources can be designed and installed at selected sites to monitor the calibration stability, radiometric and geolocation accuracy, and point spread functions of the DNB; furthermore, whether or not such active light sources can be used for detecting environmental changes, such as aerosols. This paper explores the quantitative analysis of nightlight point sources, such as those from fishing vessels, bridges, and cities, using fundamental radiometry and radiative transfer, which would be useful for a number of applications including search and rescue in severe weather events, as well as calibration/validation of the DNB. Time series of the bridge light data are used to assess the stability of the light measurements and the calibration of VIIRS DNB. It was found that the light radiant power computed from the VIIRS DNB data matched relatively well with independent assessments based on the in situ light installations, although estimates have to be made due to limited ground truth data and lack of suitable radiative transfer models. Results from time series analysis are encouraging in potentially being able to detect anomalies in the DNB calibration. The study also suggests that accurate ground based active lights, when properly designed and installed, can be used to monitor the stability of the VIIRS DNB calibration at near the specified minimum radiances (3 nW∙cm−2∙sr−1), and potentially can be used to monitor the environmental changes as well.

Cellphone geolocation via magnetic mapping

We develop novel algorithms based on interval analysis with theoretical guarantees, to augment the accuracy of cell phone geolocation by taking advantage of local variations of magnetic intensity. Thus, the sources of disturbances to magnetometer readings caused indoors are effectively used as beacons for localization. We construct a magnetic intensity map for an indoor environment by collecting magnetic field data over each floor tile. We then test the algorithms without position initialization and obtain indoor geolocation to within 2 m while slowly walking over a complex path of 80 m. The geolocation errors are smaller in the vicinity of large magnetic disturbances. Finally, we fuse the magnetometer measurement with inertial measurements on the cell phone to yield even smaller geolocation errors of under 50 cm for a moving user. Our theoretical results connect geolocation accuracy to combinations of sensor and map properties.

Landsat 8 Operational Land Imager On-Orbit Geometric Calibration and Performance

The Landsat 8 spacecraft was launched on 11 February 2013 carrying the Operational Land Imager (OLI) payload for moderate resolution imaging in the visible, near infrared (NIR), and short-wave infrared (SWIR) spectral bands. During the 90-day commissioning period following launch, several on-orbit geometric calibration activities were performed to refine the prelaunch calibration parameters. The results of these calibration activities were subsequently used to measure geometric performance characteristics in order to verify the OLI geometric requirements. Three types of geometric calibrations were performed including: (1) updating the OLI-to-spacecraft alignment knowledge; (2) refining the alignment of the sub-images from the multiple OLI sensor chips; and (3) refining the alignment of the OLI spectral bands. The aspects of geometric performance that were measured and verified included: (1) geolocation accuracy with terrain correction, but without ground control (L1Gt); (2) Level 1 product accuracy with terrain correction and ground control (L1T); (3) band-to-band registration accuracy; and (4) multi-temporal image-to-image registration accuracy. Using the results of the on-orbit calibration update, all aspects of geometric performance were shown to meet or exceed system requirements.