A global daily AVHRR true-color image record since 1981 derived from the XBAER-RGB reconstruction framework

True-color images synthesized from red, green, and blue (RGB) bands are essential for monitoring the Earth’s surface and atmosphere. However, the Advanced Very High-Resolution Radiometer (AVHRR), despite four decades of observations, lacks green and blue bands, limiting natural-color representation and feature discrimination. Cross-sensor spectral inconsistencies between AVHRR and the Moderate Resolution Imaging Spectroradiometer (MODIS), together with radiometric variations caused by atmospheric scattering, aerosol effects, and calibration drift, complicate true-color reconstruction. To address this limitation, we propose XBAER-RGB, a reconstruction framework within the eXtensiBle Atmospheric and surfacE Retrieval (XBAER) algorithm family. The framework removes Rayleigh scattering and ozone absorption while preserving aerosol-related signals, and reconstructs missing bands using an eXtreme Gradient Boosting (XGBoost) model trained on corrected MODIS corrected reflectance. XBAER-RGB was validated on MODIS by comparing reconstructed and observed green/blue bands, and then applied to spectrally adjusted AVHRR data. The reconstructed MODIS green/blue bands achieved peak signal-to-noise ratio/structural similarity index (PSNR/SSIM) values of 37.49 dB/0.9869 and 30.08 dB/0.9544, while AVHRR achieved 33.66 dB/0.8480 and 32.51 dB/0.8440. A global AVHRR true color record since 1981 was generated, capturing clouds, wildfires, dust storms, and land-ocean features, while supporting cloud detection, environmental monitoring, and long-term climate analysis.

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Publication Details

Journal
International Journal of Digital Earth
Published
2026-09-19
DOI
https://doi.org/10.1080/17538947.2026.2721042
Primary Topic
Remote Sensing in Agriculture
Type
article
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A global daily AVHRR true-color image record since 1981 derived from the XBAER-RGB reconstruction framework

Rui Xu, Hang Li, Xinhong Zhang, Ruilai Nie et al.
International Journal of Digital Earth
Remote Sensing in Agriculture
article

A global daily AVHRR true-color image record since 1981 derived from the XBAER-RGB reconstruction framework

Rui Xu, Hang Li, Xinhong Zhang, Ruilai Nie, Zhibin Lai, Feiyang Wang, Kai Ji, Linlu Mei, Yongliang Yang, Yuhan Zhao
article en

Abstract

True-color images synthesized from red, green, and blue (RGB) bands are essential for monitoring the Earth’s surface and atmosphere. However, the Advanced Very High-Resolution Radiometer (AVHRR), despite four decades of observations, lacks green and blue bands, limiting natural-color representation and feature discrimination. Cross-sensor spectral inconsistencies between AVHRR and the Moderate Resolution Imaging Spectroradiometer (MODIS), together with radiometric variations caused by atmospheric scattering, aerosol effects, and calibration drift, complicate true-color reconstruction. To address this limitation, we propose XBAER-RGB, a reconstruction framework within the eXtensiBle Atmospheric and surfacE Retrieval (XBAER) algorithm family. The framework removes Rayleigh scattering and ozone absorption while preserving aerosol-related signals, and reconstructs missing bands using an eXtreme Gradient Boosting (XGBoost) model trained on corrected MODIS corrected reflectance. XBAER-RGB was validated on MODIS by comparing reconstructed and observed green/blue bands, and then applied to spectrally adjusted AVHRR data. The reconstructed MODIS green/blue bands achieved peak signal-to-noise ratio/structural similarity index (PSNR/SSIM) values of 37.49 dB/0.9869 and 30.08 dB/0.9544, while AVHRR achieved 33.66 dB/0.8480 and 32.51 dB/0.8440. A global AVHRR true color record since 1981 was generated, capturing clouds, wildfires, dust storms, and land-ocean features, while supporting cloud detection, environmental monitoring, and long-term climate analysis.

International Journal of Digital EarthVol. 19(2)
Chinese Academy of Sciences (CN), Aerospace Information Research Institute (CN), University of Chinese Academy of Sciences (CN), International Research Center of Big Data for Sustainable Development Goals (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 11%
Remote Sensing in Agriculture
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