A Virtual Geostationary Ocean Color Sensor to Analyze the Coastal Optical Variability

Abstract

In the coastal environment the optical properties can vary on temporal scales that are shorter than the near-polar orbiting satellite temporal resolution (~1 image per day), which does not allow capturing most of the coastal optical variability. The objective of this work is to fill the gap between the near-polar orbiting and geostationary sensor temporal resolutions, as the latter sensors provide multiple images of the same basin during the same day. To do that, a Level 3 hyper-temporal analysis-ready Ocean Color (OC) dataset, named Virtual Geostationary Ocean Color Sensor (VGOCS), has been created. This dataset contains the observations acquired over the North Adriatic Sea by the currently functioning near-polar orbiting sensors, allowing approaching the geostationary sensor temporal resolution. The problem in using data from different sensors is that they are characterized by different uncertainty sources that can introduce artifacts between different satellite images. Hence, the sensors have different spatial and spectral resolutions, their calibration procedures can have different accuracies, and their Level 2 data can be retrieved using different processing chains. Such differences were reduced here by adjusting the satellite data with a multi-linear regression algorithm that exploits the Fiducial Reference Measurements data stream of the AERONET-OC water-leaving radiance acquired at the Acqua Alta Oceanographic Tower, located in the Gulf of Venice. This work aims to prove the suitability of VGOCS in analyzing the coastal optical variability, presenting the improvement brought by the adjustment on the quality of the satellite data, the VGOCS spatial and temporal coverage, and the inter-sensor differences. Hence, the adjustment will strongly increase the agreement between the satellite and in situ data and between data from different near-polar orbiting OC imagers; moreover, the adjustment will make available data traditionally masked in the standard processing chains, increasing the VGOCS spatial and temporal coverage, fundamental to analyze the coastal optical variability. Finally, the fulfillment by VGOCS of the three conditions for a hyper-temporal dataset will be demonstrated in this work.

First Evaluation of PRISMA Level 1 Data for Water Applications

“…For the match-up analysis, the TOA radiances are derived from imagery data by computing the average value and standard deviation of PRISMA and MSI data corresponding to a square box of 3 × 3 pixels defined over the in situ measurements stations. Common descriptive statistical metrics (e.g., [62]) such as root mean square difference (RMSD), mean absolute difference (MAD), spectral angle (SA), and the square of the coefficient of correlation (R 2 ) ( Table 3) and scatterplots are used for the comparison. A good agreement between the datasets is achieved when R 2 is close to 1, and bias and the other parameters are close to 0.…”

The Ground to Space CALibration Experiment (G-SCALE): Simultaneous Validation of UAV, Airborne, and Satellite Imagers for Earth Observation Using Specular Targets

“…Each of these platforms may be fitted with multiple EO sensor payloads [6,7], with advantages and disadvantages in terms of spatial, radiometric, and temporal coverage and performance. As such, a combination of data sources may be capable of enhancing the quality and type of remote sensing products that can be produced [8][9][10][11][12][13][14][15][16][17][18][19].…”