With the launch of Landsat 9 in September 2021, an optimal opportunity for in-flight cross-calibration occurred when Landsat 9 flew underneath Landsat 8 while being moved into its final orbit. Since the two instruments host nearly identical imaging systems, the underfly event offered ideal cross-calibration conditions. The purpose of this work was to use the underfly imagery collected by the instruments to estimate cross-calibration parameters for Landsat 9 for a calibration update scheduled at the end of the on-orbit initial verification (OIV) period. Three types of uncertainty were considered: geometric, spectral, and angular (bidirectional reflectance distribution function—BRDF). Differences caused by geometric uncertainty were found to be negligible for this application. Spectral uncertainty was found to be minimal except for the green band when viewing vegetative targets. BRDF models derived from the MODIS BRDF product indicated substantial error could occur and required development of a mitigating methodology. With these three contributions of uncertainty properly addressed, it was estimated that the total cross-calibration uncertainty for underfly data could be kept under 1%. The data collected during the underfly were filtered to remove outliers based on uncertainty analysis. These data were used to calculate the TOA reflectance and radiance cross-calibration values for each spectral band by taking the ratio of Landsat 8 average pixel values to Landsat 9. Initial results of this approach indicated the cross-calibration may be as accurate as 0.5% in reflectance space and 1.0% in radiance space. The initial results developed in this study were used to refine the cross-calibration of Landsat 9 to Landsat 8 at the end of the OIV period.

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 OLI and (ii) normalization of Bidirectional Reflectance Distribution Function (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 normalization, 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. The results of this work indicate that the blue band has the most significant offset, requiring use of the estimated cross-calibration offset in addition to the estimated gain. The highest difference was observed in the blue and red bands, which are 2.6% and 1.4%, respectively, while other bands shows no significant difference. 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, target site nonuniformity, variability in atmospheric water vapor, and/or aerosol concentration.

Atmospherically corrected surface reflectance (SR) products are used for reliable monitoring of land surfaces and are the standard products of Landsat sensors. Due to increased demand for SR products, a need exists to verify that the L2C2 (Level-2 Collection-2) SR products are precise and accurate. The Level-2 Collection 2 (L2C2) SR Product has processed satellite imagery data that correct for atmospheric effects such as absorption and scattering, providing a more accurate representation of Earth’s surface. The validation of SR products using ground truth measurement is essential. This study aims to develop and evaluate a validation methodology for satellite SR products. Thus, the Empirical Line Method (ELM) is used here for atmospheric validation of remotely sensed data. Validation is performed using the SR derived from ELM tied to ground truth measurement. Absolute surface reflectance models of Algodones Dunes and the Salton Sea located in North America Sonoran Desert are developed to extend the temporally limited ground truth measurements. This model can give ground truth reflectance in any time frame independent of time constraints. The result of the absolute surface reflectance model of Algodones Dunes indicates that the model predicts the response of Algodones Dunes with an average accuracy of 0.0041 and precision of 0.0063 and gives ground measurements across all multispectral between 350 and 2500 nm. For the Salton Sea, the model predicts the response of the Salton Sea with mean absolute error (MAE) of 0.0035 and gives ground measurements across all multispectral between 350 and 2500 nm. The ELM generates atmospheric coefficients (gain and bias), which are applied to an image to obtain SR. Validation results indicated that for L9-OLI-2, L8-OLI, and L5-TM-SR products, the RMSE range is 0.0019 to 0.0106, 0.0019 to 0.0148 and 0.0026 to 0.0045 reflectance unit, respectively, and accuracy is within 0.0038, 0.0022, and 0.0055 reflectance unit across all spectral bands of L9, L8, and L5, respectively. On average, the validation result showed a strong linear relation between the L2C2 SR products and ELM SR within 0.5 to 1 reflectance units. These results demonstrate the high accuracy and reliability of the L2C2 SR product, providing valuable information for a wide range of remote sensing applications, including land cover and land use mapping, vegetation monitoring, and climate change studies.

Historically stable areas across North Africa, known as pseudo invariant calibration sites (PICS), have been used as targets for the calibration and monitoring of optical satellite sensors. 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 necessary to construct a dense dataset to assess the radiometric performance of on-orbit optical sensors and confirm 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, allowing for the building of richer datasets 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 sooner and correcting them accordingly. This work identified 23 World Reference System two (WRS-2) path/row locations around the globe as part of the global EPICS. These EPICS have the advantage of achieving multiple observations per day, with similar spectral characteristics compared to traditional PICS, while still producing a 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%.

Satellite sensors have been extremely useful and are in massive demand in the understanding of the Earth’s surface and monitoring of changes. For quantitative analysis and acquiring consistent measurements, absolute radiometric calibration is necessary. The most common vicarious approach of radiometric calibration is cross-calibration, which helps to tie all the sensors to a common radiometric scale for consistent measurement. One of the traditional methods of cross-calibration is performed using temporally and spectrally stable pseudo-invariant calibration sites (PICS). This technique is limited by adequate cloud-free acquisitions for cross-calibration which would require a longer time to study the differences in sensor measurements. To address the limitation of traditional PICS-based approaches and to increase the cross-calibration opportunity for quickly achieving high-quality results, the approach presented here is based on using extended pseudo invariant calibration sites (EPICS) over North Africa. With the EPICS-based approach, the area of extent of the cross-calibration site covers a large portion of the North African continent. With targets this large, many sensors should image some portion of EPICS nearlydaily, allowing for evaluation of performance with much greater frequency. By using these near-daily measurements, trends of the sensor’s performance are then used to evaluate sensor-to-sensor daily cross-calibration. With the use of the proposed methodology, the dataset for cross-calibration is increased by an order of magnitude compared to traditional approaches, resulting in the differences between any two sensors being detected within a much shorter time. Using this new trend in trend cross-calibration approaches, gains were evaluated for Landsat 7/8 and Sentinel 2A/B, with the results showing that the sensors are calibrated within 2.5% (within less than 8% uncertainty) or better for all sensor pairs, which is consistent with what the traditional PICS-based approach detects. The proposed cross-calibration technique is useful to cross-calibrate any two sensors without the requirement of any coincident or near-coincident scene pairs, while still achieving results similar to traditional approaches in a short time.

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. The models developed for the Egypt 1, Libya 1 and Sudan 1 PICS have an estimated accuracy of approximately 3% and precision of approximately 2% for the sensors used in the study, comparable to the current Libya 4 model. The models developed for the Niger 1 and Niger 2 sites are significantly less accurate with similar precision.