Purpose

Radiometric calibration is a method that estimates the reflection of the target from the measured input radiation. The purpose of this study is to radiometrically calibrate three spectral bands of Sentinel-2A, including green, red and infrared. For this purpose, Landsat-8 OLI data are used. Because they have bands with the same wavelength range and they have the same structure. As a result, Landsat-8 OLI is appropriate for relative radiometric calibration.

Design/methodology/approach

The method used in this study is radiometric calibration uncorrected data from a sensor with corrected data from another sensor. Also, another aim of this study is a comparison between radiometric correction data and data that, in addition to radiometric correction, has been sharpened with panchromatic data. In this method, both of them have been used for radiometric calibration. Calibration coefficients have been obtained using the first-order polynomial equation.

Findings

This study showed that the corrected data has more valid answers than corrected and sharpened data. This method studied three land-cover types, including soil, water and vegetation, which it obtained the most accurate coefficients of calibration for soil class because R-square in all three bands was above 88%, and the root mean square error in all three bands was below 0.01. In the case of water and vegetation classes, only results of red and infrared bands were suitable.

Originality/value

For validating this method, the radiometric correction module of SNAP software was used. According to the results, the coefficient of radiometric calibration of the Landsat-8 sensor was very close to the coefficients obtained from the corrected data by SNAP.

中文翻译：

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Sentinel-2卫星图像快速辐射校正方法

目的

辐射校准是一种从测量的输入辐射估计目标反射的方法。本研究的目的是对 Sentinel-2A 的三个光谱带进行辐射校准，包括绿色、红色和红外线。为此，使用了 Landsat-8 OLI 数据。因为它们具有相同波长范围的波段并且它们具有相同的结构。因此，Landsat-8 OLI 适用于相对辐射校准。

设计/方法/方法

本研究中使用的方法是将来自一个传感器的辐射校准未校正数据与来自另一个传感器的校正数据。此外，本研究的另一个目的是比较辐射校正数据和除辐射校正外还用全色数据锐化的数据。在这种方法中，它们都被用于辐射校准。校准系数已使用一阶多项式方程获得。

发现

这项研究表明，更正后的数据比更正和锐化的数据具有更多的有效答案。该方法研究了土壤、水和植被三种土地覆盖类型，得到了最准确的土壤类校准系数，因为三个波段的R平方都在88%以上，并且所有三个波段的均方根误差带低于 0.01。在水和植被类别的情况下，只有红色和红外波段的结果是合适的。

原创性/价值

为了验证该方法，使用了 SNAP 软件的辐射校正模块。结果表明，Landsat-8 传感器的辐射定标系数与 SNAP 校正数据得到的系数非常接近。