Harmonizing Surface Reflectance Between Landsat-7 ETM+, Landsat-8 OLI, and Sentinel-2 MSI

Abstract

Remote sensing dynamic monitoring methods often benefit from a dense time series of observations. To enhance these time series, it is sometimes necessary to integrate data from multiple satellite systems. For more than 40 years, Landsat has provided the longest time record of space-based land surface observations, and the successful launch of the Landsat-8 Operational Land Imager (OLI) sensor in 2013 continues this tradition. However, the 16-day observation period of Landsat images has challenged the ability to measure subtle and transient changes like never before. The European Space Agency (ESA) launched the Sentinel-2A satellite in 2015. The satellite carries a Multispectral Instrument (MSI) sensor that provides a 10-20m spatial resolution data source providing an opportunity to complement the Landsat data record. The collection of Sentinel-2A MSI, Landsat-7 ETM+, and Landsat-8 OLI data provide multispectral global coverage from 10m to 30m with further reduced data revisit intervals. There are many differences between sensor data that need to be taken into account to use these data together reliably. The purpose of this study is to evaluate the potential of integrating surface reflectance data from Landsat-7, Landsat-8 and Sentinel-2 archived in the Google Earth Engine (GEE) cloud platform. To test and quantify the differences between these sensors, hundreds of thousands of surface reflectance data from sensor pairs were collected over China. In this study, some differences in the surface reflectance of the sensor pairs were identified, based upon which a cross-sensor conversion model was proposed, i.e., a suitable adjustment equation was fitted using an ordinary least squares (OLS) linear regression method to convert the Sentinel-2 reflectance values closer to the Landsat-7 or Landsat-8 values. The regression results show that the Sentinel MSI data are spectrally comparable to both types of Landsat image data, just as the Landsat sensors are comparable to each other. The root mean square error (RMSE) values between MSI and Landsat spectral values before coordinating the sensors ranged from 0.014 to 0.037, and the RMSE values between OLI and ETM + ranged from 0.019 to 0.039. After coordination, RMSE values between MSI and Landsat spectral values ranged from 0.011 to 0.026, and RMSD values between OLI and ETM + ranged from 0.013 to 0.034. The fitted adjustment equations were also compared to the HLS (Harmonized Landsat-8 Sentinel-2) global fitted equations (Sentinel-2 to Landsat-8) published by the National Aeronautics and Space Administration (NASA) and were found to be significantly different, increasing the likelihood that such adjustments would need to be fitted on a regional basis. This study believes that despite the differences in these datasets, it appears feasible to integrate these datasets by applying a linear regression correction between the bands.

Study on Radiometric Consistency of Landsat Images using Multi-band Joint Regression Model

“…The continuous increase in the number of remote sensing satellites and the open access to remote sensing data with different spatial and temporal resolutions have promoted the comprehensive application of remote sensing data [1]. The time-series observation of multi satellite sensors sensor and multi temporal remote sensing images has become an effective means to quickly and efficiently obtain spatiotemporal distribution and change characteristics such as surface land cover changes, ecological environment monitoring, and quantitative remote sensing inversion [2][3][4][5]. However, due to the differences in the observation angle, reflective-band, spectral response ability, atmospheric absorption and scattering, solar illumination geometry and other conditions of different satellite sensors at the observation time, the radiation information of the same ground objects in the same region is inconsistent, resulting in "false changes" on the ground [2,[6][7][8], which is very unfavorable to the use of time series remote sensing data for change detection.…”