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Related Experiment Video

Updated: May 7, 2026

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An Integrated Pixel-Level Reflectance Adjustment (IPRA) for Harmonizing GF-1/6 WFV and Sentinel-2 MSI Data.

Jianli Shi1,2, Xingfa Gu1,2,3, Yan Liu3

  • 1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

Sensors (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

This study introduces an integrated pixel-level reflectance adjustment (IPRA) method to fix radiometric issues in GF-1/6 WFV satellite imagery. IPRA improves radiometric consistency, making satellite data more reliable for cross-sensor applications.

Keywords:
GF-1/6 WFVSentinel-2 MSIharmonizingpixel-level

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Area of Science:

  • Remote Sensing
  • Earth Observation
  • Image Processing

Background:

  • Radiometric discrepancies in satellite imagery, particularly from GF-1/6 WFV sensors, hinder accurate analysis.
  • Sensor decay and geometric distortions are primary causes of these radiometric inconsistencies.
  • Existing methods like IRMAD, HM, and TRA have limitations in addressing these issues comprehensively.

Purpose of the Study:

  • To propose and validate an integrated pixel-level reflectance adjustment (IPRA) method for normalizing GF-1/6 WFV imagery.
  • To address radiometric discrepancies caused by sensor decay and geometric distortions using Sentinel-2 MSI as a reference.
  • To enhance the radiometric consistency and cross-sensor applicability of GF-1/6 WFV data.

Main Methods:

  • Developed an integrated pixel-level reflectance adjustment (IPRA) method utilizing Sentinel-2 MSI as a reference.
  • Employed time-series data and a spatial heterogeneity detection mechanism to mitigate geometric distortions.
  • Incorporated a weighted linear regression (WLR) model to account for pixel temporal decay characteristics.

Main Results:

  • The IPRA method demonstrated superior radiometric consistency compared to IRMAD, HM, and TRA.
  • Normalized Difference Atmospheric Effect (NAE) decreased by 42.9%, and Root Mean Square Error (RMSE) decreased by 37.3%.
  • Peak Signal-to-Noise Ratio (PSNR) improved significantly, and the Structural Similarity (SC) index approached the ideal value of 1.

Conclusions:

  • The proposed IPRA method offers a robust solution for normalizing GF-1/6 WFV imagery.
  • IPRA effectively addresses radiometric discrepancies and geometric distortions, improving data quality.
  • The normalized imagery facilitates reliable cross-sensor applications in Earth observation.