Classification of North Africa for Use as an Extended Pseudo Invariant Calibration Sites (Epics) for Radiometric Calibration and Stability Monitoring of Optical Satellite Sensors

An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to help the understanding of the Earth’s complex systems and to monitor significant changes to them. The quality of data recorded by these sensors is a primary concern, as it critically depends on accurate radiometric calibration for each sensor. Pseudo Invariant Calibration Sites (PICS) have been extensively used for radiometric calibration and temporal stability monitoring of optical satellite sensors. Due to limited knowledge about the radiometric stability of North Africa, only a limited number of sites in the region are used for this purpose. This work presents an automated approach to classify North Africa for its potential use as an extended PICS (EPICS) covering vast portions of the continent. An unsupervised classification algorithm identified 19 “clusters” representing distinct land surface types; three clusters were identified with spatial uncertainties within approximately 5% in the shorter wavelength bands and 3% in the longer wavelength bands. A key advantage of the cluster approach is that large numbers of pixels are aggregated into contiguous homogeneous regions sufficiently distributed across the continent to allow multiple imaging opportunities per day, as opposed to imaging a typical PICS once during the sensor’s revisit period. In addition, this work proposes a technique to generate a representative hyperspectral profile for these clusters, as the hyperspectral profile of these identified clusters are mandatory in order to utilize them for performing cross-calibration of optical satellite sensors. 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. Furthermore, 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 crosscalibration. Eventually, North Africa can be used a continental scale PICS

A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest

Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way

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

Vicarious Methodologies to Assess and Improve the Quality of the Optical Remote Sensing Images: A Critical Review

Over the past decade, number of optical Earth observing satellites performing remote sensing has increased substantially, dramatically increasing the capability to monitor the Earth. The quantity of remote sensing satellite increase is primarily driven by improved technology, miniaturization of components, reduced manufacturing, and launch cost. These satellites often lack on-board calibrators that a large satellite utilizes to ensure high quality (e.g., radiometric, geometric, spatial quality, etc.) scientific measurement. To address this issue, this work presents “best” vicarious image quality assessment and improvement techniques for those kinds of optical satellites which lacks on-board calibration system. In this article, image quality categories have been explored, and essential quality parameters (e.g., absolute and relative calibration, aliasing, etc.) have been identified. For each of the parameters, appropriate characterization methods are identified along with its specifications or requirements. In cases of multiple methods, recommendation has been made based-on the strengths and weaknesses of each method. Furthermore, processing steps have been presented, including examples. Essentially, this paper provides a comprehensive study of the criteria that needs to be assessed to evaluate remote sensing satellite data quality, and best vicarious methodologies to evaluate identified quality parameters such as coherent noise, ground sample distance, etc