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Harmonizing surface reflectance between Landsat-7 ETM + , Landsat-8 OLI, and Sentinel-2 MSI over China

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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. In particular, the Landsat and Sentinel series provide a rich source of data for Earth observations. National Aeronautics and Space Administration (NASA) scientists proposed a method that creates global fixed per-band transformation coefficients to reduce the reflectance difference between Landsat-8 and Sentinel-2 for the harmonized Landsat and Sentinel-2 (HLS) surface reflectance product. However, the coefficient has yet to be further validated in the target study area and the coefficient can only be used for Landsat-8 and Sentinel-2, and is not useful for other sensors. The purpose of this study is to evaluate the potential of integrating surface reflectance data from Landsat-7, Landsat-8, and Sentinel-2. 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 results show that the model adjusted the Sentinel-2 surface reflectance to match Landsat-7 or Landsat-8. The maximum MRE of the adjusted sensor for surface reflectance was reduced from 17.96 to 12.15%. Differences in reflectance produce corresponding differences in estimates of biophysical quantities, such as NDVI, with MRE as high as 18.33%. However, adjusting the Sentinel-2 sensor was able to reduce this part of the discrepancy to about 12.56%. The 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.

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Data availability

Data derived from public domain resources. The data that support the findings of this study are available were derived from the following resources available in the public domain: https://glovis.usgs.gov/ (for Landsat data), https://scihub.copernicus.eu/ (for Sentinel-2 data), https://earthengine.google.com/ (You can also access and process the research data for this study on the platform).

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Acknowledgements

The European Space Agency and Copernicus program and the USGS Landsat program are thanked for the free provision of the Sentinel-2A, Landsat-7, and Landsat-8 data respectively. The National Geomatics Center of China is thanked for providing the GlobalLand30 global surface coverage dataset. We would like to thank the Google Earth Engine cloud platform for support in developing the Earth Engine scripts. A team of data scientists from Ljubljana, Slovenia, are thanked for sharing the s2cloudless Python package. The anonymous reviewers are thanked for their comments which helped to improve this paper.

Funding

The authors would like to thank the financial support provided by the Fundamental Research Funds for the Central Universities, CHD (300102352901) and Key Research and Development Program in Shaanxi Province (2022ZDLSF07-05).

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Authors and Affiliations

  1. College of Geological Engineering and Geomatics, Chang’an University, Xi’an, China

    Hongye Cao & Liangzhi Li

  2. School of Land Engineering, Chang’an University, Xi’an , 710064, China

    Ling Han

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  1. Hongye Cao
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  2. Ling Han
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Hongye Cao: conceptualization, methodology, software, validation and visualization; Ling Han: supervision; Langzhi Li: software and formal analysis.

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Correspondence to Ling Han.

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Cao, H., Han, L. & Li, L. Harmonizing surface reflectance between Landsat-7 ETM + , Landsat-8 OLI, and Sentinel-2 MSI over China. Environ Sci Pollut Res 29, 70882–70898 (2022). https://doi.org/10.1007/s11356-022-20771-4

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