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Ocean color measured by satellite-mounted optical sensors is an essential climate variable that is routinely used as a central element for assessing the health and productivity of marine ecosystems and the role of oceans in the global carbon cycle. For satellite ocean color to be reliable and used in these and other important environmental applications, the data must be trustworthy and high quality. Pre-flight and on-board calibration of satellite ocean color sensors is conducted; however, once in orbit, the data quality can only be fully assessed via independent calibration and validation activities using surface measurements. These measurements therefore need to be at least as high quality as the satellite data, which necessitates SI traceability and a full uncertainty budget. This is the basis for fiducial reference measurements (FRMs) and the FRM4SOC project, which was an European Space Agency (ESA) initiative to establish and maintain SI-traceable ground-based FRM for satellite ocean color, thus providing a fundamental contribution to the European system for monitoring the Earth (Copernicus). This Special Issue of MDPI Remote Sensing is designed to showcase this essential Earth observation work through the publication of the project’s main achievements and results accompanied by other select relevant articles.

VIIRS --- SNPP --- NOAA-20 --- DINEOF --- ocean color data --- data merging --- gap-filling --- ocean color radiometers --- radiometric calibration --- indoor intercomparison measurement --- agreement between sensors --- measurement uncertainty --- field intercomparison measurement --- Hyperspectral reflectance --- validation --- autonomous measurements --- ground-truth data --- system design --- downwelling irradiance --- satellite validation --- Fiducial Reference Measurements --- water reflectance --- satellite --- calibration --- solar diffusor --- SDSM --- desert trend --- lunar calibration --- RVS --- MODIS --- Aqua --- ocean color --- water-leaving radiance --- atmospheric correction --- Sentinel-3 OLCI --- Copernicus --- ocean colour --- system vicarious calibration --- fiducial reference measurement --- Lampedusa --- MOBY --- MarONet --- radiometry --- research infrastructure --- uncertainty budget --- satellite ocean colour --- fiducial reference measurements (FRM) --- calibration and validation --- SI traceability and uncertainty --- European Space Agency (ESA) --- Committee for Earth Observation Satellites (CEOS) --- fiducial reference measurements --- SI-traceability --- Mediterranean Sea --- BOUSSOLE --- MSEA --- hyper-temporal dataset --- optical radiometry --- coastal environment --- observation geometry --- remote sensing reflectance --- ocean colour radiometers --- TriOS RAMSES --- Seabird HyperSAS --- field intercomparison --- AERONET-OC --- Acqua Alta Oceanographic Tower --- remote sensing --- spectral irradiance comparison --- spectral radiance sources comparison

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Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic.

Technology: general issues --- History of engineering & technology --- automated driving --- scenario-based testing --- software framework --- traffic signs --- ADAS --- traffic sign recognition system --- cooperative perception --- ITS --- digital twin --- sensor fusion --- edge cloud --- autonomous drifting --- model predictive control (MPC) --- successive linearization --- adaptive control --- vehicle motion control --- varying road surfaces --- vehicle dynamics --- Mask R-CNN --- transfer learning --- inverse gamma correction --- illumination --- instance segmentation --- pedestrian custom dataset --- deep learning --- wheel loaders --- throttle prediction --- state prediction --- automation --- safety validation --- automated driving systems --- decomposition --- modular safety approval --- modular testing --- fault tree analysis --- adaptive cruise control --- informed machine learning --- physics-guided reinforcement learning --- safety --- autonomous vehicles --- autonomous conflict management --- UTM --- UAV --- UGV --- U-Space --- framework development --- lane detection --- simulation and modelling --- multi-layer perceptron --- convolutional neural network --- driver drowsiness --- ECG signal --- heart rate variability --- wavelet scalogram --- automated driving (AD) --- driving simulator --- expression of trust --- acceptance --- simulator case study --- NASA TLX --- advanced driver assistant systems (ADAS) --- system usability scale --- driving school --- virtual validation --- ground truth --- reference measurement --- calibration method --- simulation --- traffic evaluation --- simulation and modeling --- connected and automated vehicle --- driver assistance system --- virtual test and validation --- radar sensor --- physical perception model --- virtual sensor model --- automated driving --- scenario-based testing --- software framework --- traffic signs --- ADAS --- traffic sign recognition system --- cooperative perception --- ITS --- digital twin --- sensor fusion --- edge cloud --- autonomous drifting --- model predictive control (MPC) --- successive linearization --- adaptive control --- vehicle motion control --- varying road surfaces --- vehicle dynamics --- Mask R-CNN --- transfer learning --- inverse gamma correction --- illumination --- instance segmentation --- pedestrian custom dataset --- deep learning --- wheel loaders --- throttle prediction --- state prediction --- automation --- safety validation --- automated driving systems --- decomposition --- modular safety approval --- modular testing --- fault tree analysis --- adaptive cruise control --- informed machine learning --- physics-guided reinforcement learning --- safety --- autonomous vehicles --- autonomous conflict management --- UTM --- UAV --- UGV --- U-Space --- framework development --- lane detection --- simulation and modelling --- multi-layer perceptron --- convolutional neural network --- driver drowsiness --- ECG signal --- heart rate variability --- wavelet scalogram --- automated driving (AD) --- driving simulator --- expression of trust --- acceptance --- simulator case study --- NASA TLX --- advanced driver assistant systems (ADAS) --- system usability scale --- driving school --- virtual validation --- ground truth --- reference measurement --- calibration method --- simulation --- traffic evaluation --- simulation and modeling --- connected and automated vehicle --- driver assistance system --- virtual test and validation --- radar sensor --- physical perception model --- virtual sensor model