数字农科院2.0

An Operational Split-Window Algorithm for Land Surface Temperature Estimation From Chinese FY-3C VIRR Data

文献类型： 外文期刊

作者： Li, Jia-Hao;Li, Zhao-Liang;Fan, Jinlong;Liu, Niantang

作者机构：

关键词： Land surface temperature;Land surface;Satellite broadcasting;MODIS;Atmospheric measurements;Spatial resolution;Radiometry;Evaluation;Fengyun-3C (FY-3C) Visible and Infrared Radiometer (VIRR);land surface emissivity (LSE);land surface temperature (LST);retrieval;split window (SW) algorithm

期刊名称： IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

ISSN： 0196-2892

年卷期： 2024 年 62 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： Land surface temperature (LST) is a critical parameter in global long-term meteorological and climatological studies. The Visible and Infrared Radiometer (VIRR) sensor aboard the Chinese Fengyun-3 (FY-3) series satellites provides a continuous collection of thermal infrared (TIR) data, facilitating the generation of global long-term LST products. Notably, the FY-3C VIRR has served as a key instrument in collecting global TIR data since 2013. In this study, we proposed an operational split-window (SW) algorithm for retrieving LST from FY-3C VIRR TIR data. Initially, the Thermodynamic Initial Guess Retrieval 2000 atmospheric profile library, the atmospheric transfer model MODTRAN, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral library were employed to construct a simulation database for fitting the algorithm coefficients. To enhance the accuracy of the SW algorithm, LST, atmospheric water vapor content (WVC), and average emissivity were segmented into various subranges. Subsequently, land surface emissivity (LSE) was dynamically estimated by combining the ASTER Global Emissivity Database (GED) with the normalized difference vegetation index threshold approach. Finally, in situ measurements from the Surface Radiation Budget (SURFRAD) network, along with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (MOD11A1 and MOD21A1), were utilized to evaluate the accuracy of the retrieved LSTs. The results indicate: 1) the retrieved LSTs showed a high correlation with the in situ LSTs, with a coefficient of determination of 0.94, a root-mean-square error (RMSE) of 2.6 K, and a bias of 0.3 K; 2) the retrieved LSTs were consistent with MODIS LST products, showing a root-mean-square difference (RMSD) of approximately 2.4 K; and 3) compared to the result of MOD11A1, MOD21A1 exhibited a significantly smaller bias. These results indicated that the proposed algorithm is effectively capable of estimating global LST from FY-3C VIRR TIR data with reasonable accuracy.

分类号：

  • 相关文献

[1]Estimating All-Weather Land Surface Temperature: A Method Considering Cloud Fraction and Energy Balance. Yu, Wenping,Deng, Xiangyi,Xiao, Yao,Huang, Yajun,Zhou, Wei,Liu, Xiangyang. 2025

[2]Evaluating Spatial Representativeness Across Multiple Scales for a Comprehensive Ground Validation Network Using Landsat Land Surface Temperature Data and Random Forest. He, Xuanwei,Liu, Xiangyang,Ru, Chen,Deng, Xiangyi,Zhao, Ruoyi,Yu, Wenping. 2025

[3]A normal form for synchronous land surface temperature and emissivity retrieval using deep learning coupled physical and statistical methods. Han Wang,Kebiao Mao,Jiancheng Shi,Sayed M. Bateni,Dorjsuren Altantuya,Bayarsaikhan Sainbuyan,Yuhai Bao. 2024

[4]Spatio-Temporal Distribution Characteristics of Global Annual Maximum Land Surface Temperature Derived from MODIS Thermal Infrared Data From 2003 to 2019. Duan, Si-Bo,Huang, Cheng,Liu, Xiangyang,Liu, Meng,Sun, Yingwei,Gao, Caixia. 2022

[5]Cross-satellite comparison of operational land surface temperature products derived from MODIS and ASTER data over bare soil surfaces. Duan, Si-Bo,Li, Zhao-Liang,Leng, Pei,Li, Zhao-Liang,Cheng, Jie.

[6]Optimizing Latent Heat Flux Calculation via Composited Thermal Infrared Temperatures. Jiang, Yazhen,Zhao, Jianing,Wu, Anqi,Si, Menglin,Bian, Zunjian,Tang, Ronglin,Li, Zhao-Liang. 2025

[7]An Algorithm for Retrieving Land Surface Temperatures Using VIIRS Data in Combination with Multi-Sensors. Xia, Lang,Mao, Kebiao,Ma, Ying,Zhao, Fen,Qin, Zhihao,Jiang, Lipeng,Shen, Xinyi. 2014

[8]An Efficient Approach for Pixel Decomposition to Increase the Spatial Resolution of Land Surface Temperature Images from MODIS Thermal Infrared Band Data. Wang, Fei,Song, Caiying,Zhao, Shuhe,Qin, Zhihao,Li, Wenjuan,Karnieli, Arnon,Zhao, Shuhe. 2015

[9]Crop Mapping Based on Temporal and Spatial Sample Migrations: A Case Study Over Three Counties in Heilongjiang Province, Northeast China. Zuo, Hao-Nan,Leng, Pei,Li, Yu-Xuan,Song, Qian,Li, Zhao-Liang. 2024

[10]An Improved Algorithm for Retrieving Land Surface Emissivity and Temperature From MSG-2/SEVIRI Data. Gao, Caixia,Jiang, Xiaoguang,Gao, Caixia,Li, Zhao-Liang,Li, Zhao-Liang,Qiu, Shi,Tang, Bohui,Wu, Hua.

[11]A Novel Fully Coupled Physical–Statistical–Deep Learning Method for Retrieving Near-Surface Air Temperature from Multisource Data. Baoyu Du,Kebiao Mao,Sayed M. Bateni,Fei Meng,Xu Ming Wang,Zhonghua Guo,Changhyun Jun,Guoming Du. 2022

[12]A robust framework for accurate land surface temperature retrieval: Integrating split-window into knowledge-guided machine learning approach. Yuanliang Cheng,Hua Wu,Zhao Liang Li,Frank M. Göttsche,Xingxing Zhang,Xiujuan Li,Huanyu Zhang,Yitao Li. 2025

[13]A Data-Driven Method for Direct Estimation of Global 8-Day 500-m Ecosystem Water Use Efficiency. Huang, Lingxiao,Sun, Yifei,Yao, Na,Liu, Meng. 2025

[14]Evaluation and Intercomparison of MODIS and GEOV1 Global Leaf Area Index Products over Four Sites in North China. Li, Zhenwang,Tang, Huan,Zhang, Baohui,Yang, Guixia,Xin, Xiaoping. 2015

[15]Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement. Jing Li,Zhao Liang Li,Hua Wu,Nanshan You. 2022

[16]Performances of six reanalysis profile products in the atmospheric correction of passive microwave data for estimating land surface temperature under cloudy-sky conditions. Xin Ming Zhu,Xiao Ning Song,Pei Leng,Fang Cheng Zhou,Liang Gao,Da Guo. 2022

[17]Estimation of Diurnal Cycle of Land Surface Temperature at High Temporal and Spatial Resolution from Clear-Sky MODIS Data. Duan, Si-Bo,Tang, Bo-Hui,Wu, Hua,Tang, Ronglin,Duan, Si-Bo,Li, Zhao-Liang,Bi, Yuyun,Li, Zhao-Liang,Zhou, Guoqing. 2014

[18]A Physical-Based Method for Pixel-by-Pixel Quantifying Uncertainty of Land Surface Temperature Retrieval From Satellite Thermal Infrared Data Using the Generalized Split-Window Algorithm. Yang Gui,Si Bo Duan,Zhao Liang Li,Cheng Huang,Meng Liu,Xiangyang Liu,Caixia Gao. 2023

[19]Land Surface Temperature Retrieval From Landsat 8 Thermal Infrared Data Over Urban Areas Considering Geometry Effect: Method and Application. Ru, Chen,Duan, Si-Bo,Jiang, Xiao-Guang,Li, Zhao-Liang,Jiang, Yazhen,Ren, Huazhong,Leng, Pei,Gao, Maofang. 2021

[20]Generation of an all-weather land surface temperature product from MODIS and AMSR-E data. Duan, Si-Bo,Li, Zhao-Liang,Leng, Pei,Han, Xiao-Jing,Chen, Yuanyuan,Li, Zhao-Liang. 2015

作者其他论文 更多>>