Next updates of atmospheric correction processor Sen2Cor

Abstract

The Sentinel-2 mission is dedicated to land monitoring, emergency management and security. It serves for monitoring of land-cover change and biophysical variables related to agriculture and forestry. The mission is also used to monitor coastal and inland waters and is useful for risk and disaster mapping. The Sentinel-2 mission is fully operating since June 2017 with a constellation of two polar orbiting satellite units. Both Sentinel-2A and Sentinel-2B are equipped with an optical imaging sensor MSI (Multi-Spectral Instrument) which acquires optical data products with spatial resolution up to 10 m.Accurate atmospheric correction of satellite observations is a precondition for the development and delivery of high quality applications. Therefore the atmospheric correction processor Sen2Cor was developed with the objective of delivering land surface reflectance products. Sen2Cor is designed to process monotemporal single tile Level-1C products, providing Level-2A surface (Bottom-of-Atmosphere) reflectance product together with Aerosol Optical Thickness (AOT), Water Vapour (WV) estimation maps and a Scene Classification (SCL) map for further processing.The paper will give an overview of the Level-2A product content and up-to-date information about the data quality of the Level-2A products generated with Sen2Cor 2.8 in terms of Cloud Screening and Atmospheric Correction. In addition the paper gives an outlook on the next updates of Sen2Cor and their impact on Level-2A Data Quality.

A multivariate linear regression model for estimating chlorophyll-a concentration in Quan Son Reservoir (Hanoi, Vietnam) using Sentinel-2B Imagery

“…The S2 level 2 provides bottom-of-atmosphere (BOA) reflectance, also known as surface reflectance, using the Sentinel-2 Correction (Sen2Cor) atmosphere correction processor. The Sen2Cor performs comprehensive atmospheric, terrain, and cirrus corrections on the Top-Of-Atmosphere Level-1C data to generate a Level-2A BOA product (Louis et al, 2016;Pflug et al, 2020). It is worth noting that while Sen2Cor was initially designed for land applications, it demonstrated reasonable accuracy for water retrievals in inland waters (Al-Kharusi et al, 2020;Grendaitė and Stonevičius, 2022;Martins et al, 2017).…”

Comparative Analysis Between Sentinal 2 , OLI Sensors and Field Mesurements to Estimate Leaf Area Index (LAI )

“…The inclusion of three spectral bands (705 nm, 740 nm, and 783 nm) within the red-edge domain is motivated by their perceived significance in the characterization of green vegetation, specifically in the evaluation of vegetation quality and health (Kamenova and Dimitrov, 2021). According to Phiri et al (2020), the utilization of Sentinel-2 Satellite data enables the acquisition of imagery characterized by a spatial resolution ranging from 10 to 20 m. This advancement in technology presents novel opportunities for the monitoring of agricultural activities at a regional to global scale.The presence of freely accessible, highresolution satellite datasets obtained from spacebased sensors such as Sentinel-2, along with the Sentinel Application Platform (SNAP) and other powerful open-source applications, and the increasing availability of analysis-ready data (ARD), collectively contribute to the promising prospects of achieving precise, reliable, and practical Leaf Area Index (LAI) measurements. Proba-v (Baret et al 2013)., have certain limitations.…”

“…This frequency is generally adequate for agricultural monitoring purposes, assuming cloud-free conditions. The SNAP application provides users with the capability to carry out atmospheric correction using the tool of Sen2Cor (Pflug et al, 2020). Additionally, it enables the estimation of biophysical parameters through the utilization of a physically-based radiative transfer model called PROSAIL, combined with a robust machine learning technique known as Neural Networks (Wolanin et al, 2019).…”