Next Article in Journal
Particle Size Characteristics at the Top of Biomass Burning Plumes Based on Two Case Studies
Previous Article in Journal
GeoAI-Enabled Ensemble Modeling to Assess Land Use and Atmospheric Pollutant Impacts on Land Surface Temperature in the US Southwest
Previous Article in Special Issue
TPDTC-Net-DRA: Enhancing Nowcasting of Heavy Precipitation via Dynamic Region Attention
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 18
Issue 5
10.3390/rs18050748
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

An Interpretable Nonlinear Intelligent Bias Correction Method for FY-4A/GIIRS Hyperspectral Infrared Brightness Temperatures

by
Gen Wang
1,*,
Bing Xu
2,
Song Ye
1,
Xiefei Zhi
3,
Tiening Zhang
2,
Youpeng Yang
4,
Yang Liu
2,
Feng Xie
5,
Qiao Liu
5 and
Haili Zhang
1
1
School of Integrated Circuits, Chaohu University, No. 1 Bantang Road, Chaohu Economic and Technological Development Zone, Hefei 238024, China
2
Liaoning Weather Modification Office, Shenyang 110166, China
3
Key Laboratory of Meteorology Disaster, Ministry of Education (KLME), Nanjing University of Information Science and Technology, Nanjing 210044, China
4
School of Electronic Information Engineering, Chaohu University, Hefei 238024, China
5
Anhui Provincial Meteorological Observatory, Anhui Provincial Meteorological Bureau, Hefei 230031, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(5), 748; https://doi.org/10.3390/rs18050748
Submission received: 22 January 2026 / Revised: 19 February 2026 / Accepted: 27 February 2026 / Published: 1 March 2026
(This article belongs to the Special Issue Improving Meteorological Forecasting Models Using Remote Sensing Data)
Browse Figures
Versions Notes

Abstract

The hyperspectral infrared observations of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) satellite are an important data source for numerical weather prediction (NWP) assimilation. However, there are systematic differences between observed and simulated brightness temperatures (i.e., the observation increments contain predictable systematic bias components). To address the issue that traditional linear methods struggle to capture the nonlinear relationships between biases and forecast predictors, this study proposes an intelligent bias correction method that integrates ensemble learning and explainable artificial intelligence. First, the entropy reduction method is used to select 69 mid-wave channels. Then, Random Forest, XGBoost, LightGBM, Decision Tree, and Extra Tree are used as base learners to construct a weighted average ensemble model. Training and validation are conducted using high-frequency clear-sky observation data from FY-4A/GIIRS during Typhoon Lekima. The results show that: (1) the ensemble learning correction method outperforms single models and traditional offline methods, with root mean square errors of brightness temperature bias of less than 0.9209 K for the training set and 1.4447 K for the test set; (2) Shapley Additive Explanations (SHAP)-based interpretability analysis reveals the contribution and nonlinear influence mechanisms of factors such as longitude, atmospheric thickness, surface temperature, and total precipitable water on bias correction. This study provides an intelligent bias correction framework with both high precision and explainability, offering a reference for the bias correction and assimilation applications of hyperspectral satellite observations like GIIRS.
Keywords: FY-4A/GIIRS; bias correction; ensemble learning; nonlinear modeling; SHAP analysis FY-4A/GIIRS; bias correction; ensemble learning; nonlinear modeling; SHAP analysis

Share and Cite

MDPI and ACS Style

Wang, G.; Xu, B.; Ye, S.; Zhi, X.; Zhang, T.; Yang, Y.; Liu, Y.; Xie, F.; Liu, Q.; Zhang, H. An Interpretable Nonlinear Intelligent Bias Correction Method for FY-4A/GIIRS Hyperspectral Infrared Brightness Temperatures. Remote Sens. 2026, 18, 748. https://doi.org/10.3390/rs18050748

AMA Style

Wang G, Xu B, Ye S, Zhi X, Zhang T, Yang Y, Liu Y, Xie F, Liu Q, Zhang H. An Interpretable Nonlinear Intelligent Bias Correction Method for FY-4A/GIIRS Hyperspectral Infrared Brightness Temperatures. Remote Sensing. 2026; 18(5):748. https://doi.org/10.3390/rs18050748

Chicago/Turabian Style

Wang, Gen, Bing Xu, Song Ye, Xiefei Zhi, Tiening Zhang, Youpeng Yang, Yang Liu, Feng Xie, Qiao Liu, and Haili Zhang. 2026. "An Interpretable Nonlinear Intelligent Bias Correction Method for FY-4A/GIIRS Hyperspectral Infrared Brightness Temperatures" Remote Sensing 18, no. 5: 748. https://doi.org/10.3390/rs18050748

APA Style

Wang, G., Xu, B., Ye, S., Zhi, X., Zhang, T., Yang, Y., Liu, Y., Xie, F., Liu, Q., & Zhang, H. (2026). An Interpretable Nonlinear Intelligent Bias Correction Method for FY-4A/GIIRS Hyperspectral Infrared Brightness Temperatures. Remote Sensing, 18(5), 748. https://doi.org/10.3390/rs18050748

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop