Pseudo Invariant Calibration Sites (PICS) have been increasingly used as an independent data source for on-orbit radiometric calibration and stability monitoring of optical satellite sensors. Generally, this would be a small region of land that is extremely stable in time and space, predominantly found in North Africa. Use of these small regions, referred to as traditional PICS, can be limited by: (i) the spatial extent of an individual Region of Interest (ROI) and/or site; (ii) and the frequency of how often the site can be acquired, based on orbital patterns and cloud cover at the site, both impacting the time required to construct a richly populated temporal dataset. This paper uses a new class of continental scaled PICS clusters (also known as Extended PICS or EPICS), to demonstrate their capability in increasing temporal frequency of the calibration time series which ultimately allows calibration and stability assessment at a much finer scale compared to the traditional PICS-based method while also reducing any single location’s potential impact to the overall assessment. The use of EPICS as a calibration site was evaluated using data from Landsat-8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Sentinel-2A&B Multispectral Instrument (MSI) images at their full spatial resolutions. Initial analysis suggests that EPICS, at its full potential and with nominal cloud consideration, can significantly decrease the temporal revisit interval of moderate resolution sensors to as much as of 0.33 day (3 collects/day). A traditional PICS is expected to have a temporal uncertainty (defined as the ratio of temporal standard deviation and temporal mean) of 2–5% for TOA reflectance. Over the same time period EPICS produced a temporal uncertainty of 3%. But the advantage to be leveraged is the ability to detect sensor change quicker due to the denser dataset and reduce the impact of any potential ‘local’ changes. Moreover, this approach can be extended to any on-orbit sensor. An initial attempt to quantify the minimum detectable change (a threshold slope value which must be exceeded by the reflectance trend to be considered statistically significant) suggests that the use of EPICS can decrease the time period up to approximately half of that found using traditional PICS-based approach.

An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to aid the understanding of the Earth’s complex systems and to monitor significant changes to them. To make full use of the data from these sensors, it is mandatory to bring them to a common radiometric scale through a cross-calibration approach. Commonly, cross-calibration data were acquired from selected pseudo-invariant calibration sites (PICS), located primarily throughout the Saharan desert in North Africa, determined to be temporally, spatially, and spectrally stable. The major limitation to this approach is that long periods of time are required to assemble sufficiently sampled cloud-free cross-calibration datasets. Recently, Shrestha et al. identified extended, cluster-based sites potentially suitable for PICS-based cross-calibration and estimated representative hyperspectral profiles for them. This work investigates the performance of extended pseudo-invariant calibration sites (EPICS) in cross-calibration for one of Shrestha’s clusters, Cluster 13, by comparing its results to those obtained from a traditional PICS-based cross-calibration. The use of EPICS clusters can significantly increase the number of cross-calibration opportunities within a much shorter time period. The cross-calibration gain ratio estimated using a cluster-based approach had a similar accuracy to the cross-calibration gain derived from region of interest (ROI)-based approaches. The cluster-based cross-calibration gain ratio is consistent within approximately 2% of the ROI-based cross-calibration gain ratio for all bands except for the coastal and shortwave-infrared (SWIR) 2 bands. These results show that image data from any region within Cluster 13 can be used for sensor cross-calibration.

Landsat 9 (L9) was launched on 27 September 2021. This spacecraft contained two instruments, the Operational Land Imager-2 (OLI-2) and Thermal Infrared Sensor-2 (TIRS-2), that allow for a continuation of the Landsat program and the mission to acquire multi-spectral observations of the globe on a moderate scale. Following a period of commissioning, during which time the spacecraft and instruments were initialized and set up for operations, with the initial calibration performed, the mission moved to an operational mode This operational mode involved the same cadence and methods that were performed for the Landsat 8 (L8) spacecraft and the two instruments onboard, the Operational Land Imager-1 (OLI-1) and Thermal Infrared Sensor-1 (TIRS-1), with respect to calibration, characterization, and validation. This paper discusses the geometric operational aspects of the L9 instruments during the first year of the mission and post-commissioning, and compares these same geometric activities performed for L8 during the same time frame. During this time, optical axes of the two sensors, OLI-1 and OLI-2, were adjusted to stay aligned with the spacecraft’s Attitude Control System (ACS), and the TIRS-1 and TIRS-2 instruments were adjusted to stay aligned with the OLI-1 and OLI-2 instruments, respectively. In this paper, the L9 operational adjustments are compared to the same operational aspects of L8 during this same time frame. The comparisons shown in this paper will demonstrate that both instruments aboard L8 and L9 performed very similar geometric qualities while fully meeting the expected requirements. This paper describes the geometric differences between the L9 imagery that was made available to the public prior to the reprocessing campaign that was performed using the new calibration updates to the sensor and to ACS and TIRS-to-OLI alignment parameters. This reprocessing campaign of L9 products involved data acquired from the launch of the spacecraft up to early 2023.

Reference of Earth-observing satellite sensor data to a common, consistent radiometric scale is an increasingly critical issue as more of these sensors are launched; such consistency can be achieved through radiometric cross-calibration of the sensors. A common cross-calibration approach uses a small set of regions of interest (ROIs) in established Pseudo-Invariant Calibration Sites (PICS) mainly located throughout North Africa. The number of available cloud-free coincident scene pairs available for these regions limits the usefulness of this approach; furthermore, the temporal stability of most regions throughout North Africa is not known, and limited hyperspectral information exists for these regions. As a result, it takes more time to construct an appropriate cross-calibration dataset. In a previous work, Shrestha et al. presented an analysis identifying 19 distinct “clusters” of spectrally similar surface cover that are widely distributed across North Africa, with the potential to provide near-daily cloud-free imaging for most sensors. This paper proposes a technique to generate a representative hyperspectral profile for these clusters. The technique was used to generate the profile for the cluster containing the largest number of aggregated pixels. The resulting profile was found to have temporal uncertainties within 5% across all the spectral regions. Overall, this technique shows great potential for generation of representative hyperspectral profiles for any North African cluster, which could allow the use of the entire North Africa Saharan region as an extended PICS (EPICS) dataset for sensor cross-calibration. This should result in the increased temporal resolution of cross-calibration datasets and should help to achieve a cross-calibration quality similar to that of individual PICS in a significantly shorter time interval. It also facilitates the development of an EPICS based absolute calibration model, which can improve the accuracy and consistency in simulating any sensor’s top of atmosphere (TOA) reflectance.