Landsat-8 Sensor Characterization and Calibration

Landsat-8 was launched on 11 February 2013 with two new Earth Imaging sensors to provide a continued data record with the previous Landsats. For Landsat-8, pushbroom technology was adopted, and the reflective bands and thermal bands were split into two instruments. The Operational Land Imager (OLI) is the reflective band sensor and the Thermal Infrared Sensor (TIRS), the thermal. In addition to these fundamental changes, bands were added, spectral bandpasses were refined, dynamic range and data quantization were improved, and numerous other enhancements were implemented. As in previous Landsat missions, the National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS) cooperated in the development, launch and operation of the Landsat- 8 mission. One key aspect of this cooperation was in the characterization and calibration of the instruments and their data. This Special Issue documents the efforts of the joint USGS and NASA calibration team and affiliates to characterize the new sensors and their data for the benefit of the scientific and application users of the Landsat archive. A key scientific use of Landsat data is to assess changes in the land-use and land cover of the Earth's surface over the now 43-year record. In order to perform these analyses and avoid confusing sensor changes with Earth surface changes, a solid understanding of the sensors' performance, consistent geolocation and radiometry are essential. Particularly with the significant changes in the Landsat-8 sensors relative to previous Landsat missions, this characterization becomes all the more important

Investigation and Detection of the dieback on Juniperus Phoenicea trees in al-Jabal Al-Akhdar

The dieback on the Juniperus Phoenicea in Al-Jabal Al-Akhdar north east of Libya was reported without proof of disease causative in 2014 and 2016. This investigation carried out to reveal the cause of the disease. The spatial statistics method was used to determine the relationship between disease and other environmental variables (Land surface temperature, elevation, slope, rainfall, and the distance from coast). Random sampling of the trees branches were taken from most of the forests of the region. The study indicated that the isolated microorganism of the dieback on the Phoenician Juniper recognized a fungal infection of Diplodia Africana by analyzing culture, morphological, and PCR of genomic TSI and 1-alpha gene. The result shows that the most infected sit is that the closer to the coast which was confirmed with strong negative correlation (-0.583), furthermore the relation with the elevation showed same direction with (-0.604), while the LST presented positive correlations (0.367) thus confirming the significant effect of temperature in increasing these diseases

Sub-Pixel Technique for Time Series Analysis of Shoreline Changes Based on Multispectral Satellite Imagery

The measurement and monitoring of shoreline changes are of great interest to coastal managers and engineers. Shoreline change information can be crucial for the assessment of coastal disasters, design of coastal infrastructure and protection of coastal environment. This chapter presents shoreline change monitoring based on multispectral satellite imagery and sub-pixel technique. Firstly, a brief introduction of shoreline definitions and indicators is given. Sub-pixel techniques for shoreline mapping on multispectral satellite images are then introduced. Following that, a brief review of existing research studies of long-term shoreline change monitoring based on multispectral imagery is given. Subsequently, a case study of sub-pixel shoreline change monitoring at the northern Gold Coast on the east coast of Australia is presented. By comparing the longshore averaged beach widths at seven representative transects from Landsat with those from Argus imaging data, the RMSEs range from 9.1 to 12.3 m and the correlations are all no less than 0.7. Annual means and variabilities of beach widths were estimated without significant differences from the reference data for most of the results. Finally, conclusions and recommendations for future work are given

Cross Calibration and Validation of Landsat 8 OLI and Sentinel 2A MSI

This work describes a proposed radiometric cross calibration between the Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multispectral Instrument (MSI) sensors. The cross calibration procedure involves i) correction of the MSI data to account for spectral band differences with the OLI; and ii) correction of BRDF effects in the data from both sensors using a new model accounting for the view zenith/azimuth angles in addition to the solar zenith/view angles. Following application of the spectral and BRDF corrections, standard least-squares linear regression is used to determine the cross calibration gain and offset in each band. Uncertainties related to each step in the proposed process are determined, as is the overall uncertainty associated with the complete processing sequence. Validation of the proposed cross calibration gains and offsets is performed on image data acquired over the Algodones Dunes site. In general, the estimated cross calibration offsets in all bands were small, on the order of 0.0075 or less in magnitude. The cross calibration gains generally varied less than 1.0% from unity; for the Blue and Red bands, the gains varied by approximately -2.5% and - 1.4% from unity, respectively. For a forced zero offset, the estimated gain in all but the Blue band changed little; the Blue band gain varied by approximately 1.86% from unity. Consequently, cross calibration of the Blue band requires both the gain and nonzero offset. To maintain processing consistency, it is recommended to use the gain and (nonzero) offset in all bands. Overall, the net uncertainty in the proposed process was estimated to be on the order of 6.76%, with the largest uncertainty component due to each sensor’s calibration uncertainty, on the order of 5% and 3% for the MSI and OLI, respectively. Other significant contributions to the uncertainty include: seasonal changes in solar zenith and azimuth angles, on the order of 2.27%; target site non-uniformity, on the order of 1.8%; variability in atmospheric water vapor and/or aerosol concentration, on the order of 1.29%; and potential shifts in each sensor’s spectral filter central wavelength and/or bandwidth, on the order of 0.82% and 0.28%, respectively

A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring

Combination of different satellite data will provide increased opportunities for more frequent cloud-free surface observations due to variable cloud cover at the different satellite overpass times and dates. Satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors offer 10 m to 30 m multi-spectral global coverage. Together, they advance the virtual constellation paradigm for mid-resolution land imaging. In this study, a global analysis of Landsat-8, Sentinel-2A and Sentinel-2B metadata obtained from the committee on Earth Observation Satellite (CEOS) Visualization Environment (COVE) tool for 2016 is presented. A global equal area projection grid defined every 0.05° is used considering each sensor and combined together. Histograms, maps and global summary statistics of the temporal revisit intervals (minimum, mean, and maximum) and the number of observations are reported. The temporal observation frequency improvements afforded by sensor combination are shown to be significant. In particular, considering Landsat-8, Sentinel-2A, and Sentinel-2B together will provide a global median average revisit interval of 2.9 days, and, over a year, a global median minimum revisit interval of 14 min (±1 min) and maximum revisit interval of 7.0 days

Empirical Absolute Calibration Model for Multiple Pseudo-Invariant Calibration Sites

This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models at predicting sensor top-of-atmosphere (TOA) reflectance. It attempts to generate absolute calibration models for these PICS that have an accuracy and precision comparable to or better than the current Libya 4 model, with the intent of providing additional opportunities for sensor calibration. In addition, this work attempts to validate the general applicability of the model to other sites. The method uses Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as the reference radiometer and Earth Observing-1 (EO-1) Hyperion image data to provide a representative hyperspectral reflectance profile of the PICS. Data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the PICS are used for developing the model. The developed models were used to simulate observations of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Landsat 8 (L8) Operational Land Imager (OLI), Sentinel 2A (S2A) Multispectral Instrument (MSI) and Sentinel 2B (S2B) Multispectral Instrument (MSI) from their respective launch date through 2018

Classification and Evaluation of Extended PICS (EPICS) on a Global Scale for Calibration and Stability Monitoring of Optical Satellite Sensors

As targets for the calibration and monitoring of optical satellite sensors, historically stable areas across North Africa have been used, known as Pseudo Invariant Calibration Sites PICS. However, two major drawbacks exist for these sites; first is the dependency on a single location to be always invariant, and second is the limited amount of observation achieved using these sites. As a result, longer time periods are needed to construct a dense data set to assess the radiometric performance of on-orbit optical sensors, and be convinced that the change detected is sensor-specific rather than site-specific. This work presents a global land cover classification to obtain an Extended Pseudo Invariant Calibration Site (EPICS) on a global scale using Landsat 8 Operational Land Imager (OLI) data. This technique provides multiple calibration sites across the globe, giving the possibility to build richer data sets in a shorter time frame compared to the traditional approach (PICS), with the advantage of assessing the calibration and stability of the sensors faster, detecting possible changes quicker and correcting them accordingly. This work identified 23 World Reference System two (WRS-2) Path/Row(s) locations around the globe as part of the global EPICS. This EPICS has the advantage of achieving multiple observations per day, with similar spectral characteristics compared to traditional PICS, while still producing temporal coefficient of variation (ratio of temporal standard deviation and temporal mean) less than 4% for all bands, with some as low as 2.7%

Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)

This study evaluates the radiometric consistency between Landsat-8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) using cross calibration techniques. Two approaches are used, one based on cross calibration between the two sensors using simultaneous image pairs, acquired during an underfly event on 29–30 March 2013. The other approach is based on using time series of image statistics acquired by these two sensors over the Libya 4 pseudo invariant calibration site (PICS) (+28.55°N, +23.39°E). Analyses from these approaches show that the reflectance calibration of OLI is generally within ±3% of the ETM+ radiance calibration for all the reflective bands from visible to short wave infrared regions when the ChKur solar spectrum is used to convert the ETM+ radiance to reflectance. Similar results are obtained comparing the OLI radiance calibration directly with the ETM+ radiance calibration and the results in these two different physical units (radiance and reflectance) agree to within ±2% for all the analogous bands. These results will also be useful to tie all the Landsat heritage sensors from Landsat 1 MultiSpectral Scanner (MSS) through Landsat-8 OLI to a consistent radiometric scale

Detection of Change Points in Pseudo-Invariant Calibration Sites Time Series Using Multi-Sensor Satellite Imagery

The remote sensing community has extensively used Pseudo-Invariant Calibration Sites (PICS) to monitor the long-term in-flight radiometric calibration of Earth-observing satellites. The use of the PICS has an underlying assumption that these sites are invariant over time. However, the site’s temporal stability has not been assured in the past. This work evaluates the temporal stability of PICS by not only detecting the trend but also locating significant shifts (change points) lying behind the time series. A single time series was formed using the virtual constellation approach in which multiple sensors data were combined for each site to achieve denser temporal coverage and overcome the limitation of dependence related to a specific sensor. The sensors used for this work were selected based on radiometric calibration uncertainty and availability of the data: operational land imager (Landsat-8), enhanced thematic mapper (Landsat-7), moderate resolution imaging spectroradiometer (Terra and Aqua), and multispectral instrument (Sentinel-2A). An inverse variance weighting method was applied to the Top-of- Atmosphere (TOA) reflectance time series to reveal the underlying trend. The sequential Mann–Kendall test was employed upon the weighted TOA reflectance time-series recorded over 20 years to detect abrupt changes for six reflective bands. Statistically significant trends and abrupt changes have been detected for all sites, but the magnitude of the trends (maximum of 0.215% change in TOA reflectance per year) suggest that these sites are not changing substantially over time. Hence, it can be stated that despite minor changes in all evaluated PICS, they can be used for radiometric calibration of optical remote sensing sensors. The new approach provides useful results by revealing underlying trends and providing a better understanding of PICS\u27 stability