Publications
1. Journal Papers
Li, T., Wang, Y., Wu, J., 2024. Deriving PM2.5 from satellite observations with spatiotemporally weighted tree-based algorithms: enhancing modeling accuracy and interpretability. npj Climate and Atmospheric Science, 7, 138. https://doi.org/10.1038/s41612-024-00692-4 (SCI, First author)
Wu, J., Li, T., Lin, L., Zeng, C., 2024. Progressive gap-filling in optical remote sensing imagery through a cascade of temporal and spatial reconstruction models. Remote Sensing of Environment, 311, 114245. https://doi.org/10.1016/j.rse.2024.114245 (SCI, TOP, Corresponding author)
Jin, S., Wang, T., Huang, H., Zheng, X., Li, T., Guo, Z., 2024. A self-adaptive wildfire detection algorithm by fusing physical and deep learning schemes. International Journal of Applied Earth Observation and Geoinformation, 127, 103671. https://doi.org/10.1016/j.jag.2024.103671 (SCI)
Chen, J., Hu, R., Chen, L., Liao, Z., Che, L., Li, T., 2024. Multi-sensor integrated mapping of global XCO2 from 2015 to 2021 with a local random forest model. ISPRS Journal of Photogrammetry and Remote Sensing, 208, 107-120. https://doi.org/10.1016/j.isprsjprs.2024.01.009 (SCI, TOP, Corresponding author)
Yang, H., Li, T., Wu, J., Zhang, L., 2023. Inter-comparison and evaluation of global satellite XCO2 products. Geo-spatial Information Science, 1-14. https://doi.org/10.1080/10095020.2023.2252017 (SCI, Corresponding author)
Wang, Y., Yuan, Q., Li, T., Yang, Y., Zhou, S., Zhang, L., 2023. Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method.Earth System Science Data, 15, 3597-3622. https://doi.org/10.5194/essd-15-3597-2023 (SCI)
Jin, C., Yuan, Q., Li, T., Wang, Y., Zhang, L., 2023. An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters.Geoscientific Model Development, 16, 4137-4154. https://doi.org/10.5194/gmd-16-4137-2023 (SCI, Corresponding author)
Zhang, L., Li, T., Wu, J., Yang, H., 2023. Global estimates of gap-free and fine-scale CO2 concentrations during 2014–2020 from satellite and reanalysis data.Environment International, 178, 108057. https://doi.org/10.1016/j.envint.2023.108057 (SCI, TOP, Corresponding author)
Li, T., Wu, J., Wang, T., 2023. Generating daily high-resolution and full-coverage XCO2 across China from 2015 to 2020 based on OCO-2 and CAMS data.Science of The Total Environment, 893, 164921. https://doi.org/10.1016/j.scitotenv.2023.164921 (SCI, TOP, First author)
Yang, Q., Yuan, Q., Gao, M., Li, T., 2023. A new perspective to satellite-based retrieval of ground-level air pollution: Simultaneous estimation of multiple pollutants based on physics-informed multi-task learning.Science of The Total Environment, 857, 159542. https://doi.org/10.1016/j.scitotenv.2022.159542 (SCI, TOP)
Wu, J., Lin, L., Zhang, C., Li, T., Cheng, X., Nan, F., 2023. Generating Sentinel-2 all-band 10-m data by sharpening 20/60-m bands: A hierarchical fusion network.ISPRS Journal of Photogrammetry and Remote Sensing, 196, 16-31. https://doi.org/10.1016/j.isprsjprs.2022.12.017 (SCI, TOP, Corresponding author)
Li, T., Yang, Q., Wang, Y., Wu, J., 2023. Joint estimation of PM2.5 and O3 over China using a knowledge-informed neural network.Geoscience Frontiers, 101499. https://doi.org/10.1016/j.gsf.2022.101499 (SCI,TOP, First and Corresponding author)
Zhang, L., Li, T., Wu, J., 2022. Deriving gapless CO2 concentrations using a geographically weighted neural network: China, 2014–2020.International Journal of Applied Earth Observation and Geoinformation, 114, 103063. https://doi.org/10.1016/j.jag.2022.103063 (SCI,TOP, Corresponding author)
Yin, S., Li, T., Cheng, X., Wu, J., 2022. Remote sensing estimation of surface PM2.5 concentrations using a deep learning model improved by data augmentation and a particle size constraint.Atmospheric Environment, 287, 119282. https://doi.org/10.1016/j.atmosenv.2022.119282 (SCI,TOP, Corresponding author)
Yang, Q., Yuan, Q., Li, T., 2022. Ultrahigh-resolution PM2.5 estimation from top-of-atmosphere reflectance with machine learning: Theories, methods, and applications.Environmental Pollution, 306, 119347. https://doi.org/10.1016/j.envpol.2022.119347 (SCI)
Wu, J., Lin, L., Li, T., Cheng, Q., Zhang, C., Shen, H., 2022. Fusing Landsat 8 and Sentinel-2 data for 10-m dense time-series imagery using a degradation-term constrained deep network.International Journal of Applied Earth Observation and Geoinformation, 108, 102738. https://doi.org/10.1016/j.jag.2022.102738 (SCI,TOP)
Tan, S., Wang, Y., Yuan, Q., Zheng, L., Li, T., Shen, H., Zhang, L., 2022. Reconstructing global PM2.5 monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM.Environmental Research Letters, 17, 034014. https://doi.org/10.1088/1748-9326/ac52c9 (SCI)
Li, T., Wu, J., Chen, J., Shen, H., 2022. An Enhanced Geographically and Temporally Weighted Neural Network for Remote Sensing Estimation of Surface Ozone. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-13. https://doi.org/10.1109/TGRS.2022.3187095 (SCI,TOP, First author)
Li, S., Jing, H., Yuan, Q., Yue, L., Li, T., 2022. Investigating the spatio-temporal variation of vegetation water content in the western United States by blending GNSS-IR, AMSR-E, and AMSR2 observables using machine learning methods. Science of Remote Sensing, 6, 100061. https://doi.org/10.1016/j.srs.2022.100061 (SCI)
Jing, Y., Lin, L., Li, X., Li, T., Shen, H., 2022. Cascaded Downscaling–Calibration Networks for Satellite Precipitation Estimation. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. https://doi.org/10.1109/LGRS.2022.3214083 (SCI)
Jing, Y., Lin, L., Li, X., Li, T., Shen, H., 2022. An attention mechanism based convolutional network for satellite precipitation downscaling over China. Journal of Hydrology, 128388. https://doi.org/10.1016/j.jhydrol.2022.128388 (SCI)
Chen, J., Shen, H., Li, X., Li, T., Wei, Y., 2022. Ground-level ozone estimation based on geo-intelligent machine learning by fusing in-situ observations, remote sensing data, and model simulation data. International Journal of Applied Earth Observation and Geoinformation, 112, 102955. https://doi.org/10.1016/j.jag.2022.102955 (SCI)
Wang, Y., Yuan, Q., Li, T., Zhu, L., 2022. Global spatiotemporal estimation of daily high-resolution surface carbon monoxide concentrations using Deep Forest. Journal of Cleaner Production, 350, 131500. https://doi.org/10.1016/j.jclepro.2022.131500 (SCI)
Hu, Y., Zeng, C., Li, T., Shen, H., 2022. Performance comparison of Fengyun-4A and Himawari-8 in PM2.5 estimation in China. Atmospheric Environment, 271, 118898. https://doi.org/10.1016/j.atmosenv.2021.118898 (SCI)
Jin, C., Wang, Y., Li, T., Yuan, Q., 2022. Global validation and hybrid calibration of CAMS and MERRA-2 PM2.5 reanalysis products based on OpenAQ platform. Atmospheric Environment, 274, 118972. https://doi.org/10.1016/j.atmosenv.2022.118972 (SCI)
Li, T., Shen, H., Yuan, Q., Zhang, L., 2022. A Locally Weighted Neural Network Constrained by Global Training for Remote Sensing Estimation of PM2.5. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2021.3074569 (SCI, TOP, First author)
Wu, J., Li, T., Zhang, C., Cheng, Q., Shen, H., 2021. Hourly PM2.5 Concentration Monitoring With Spatiotemporal Continuity by the Fusion of Satellite and Station Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/jstars.2021.3103020 (SCI)
Li, T., Cheng, X., 2021. Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach. International Journal of Applied Earth Observation and Geoinformation, 101, 102356. https://doi.org/10.1016/j.jag.2021.102356 (SCI Q1, TOP, First author)
Yang, Q., Wang, B., Wang, Y., Yuan, Q., Jin, C., Wang, J., Li, S., Li, M., Li, T., Liu, S., Shen, H., Zhang, L., 2021. Global air quality change during COVID-19: a synthetic analysis of satellite, reanalysis and ground station data. Environmental Research Letters, 16, 074052. https://doi.org/10.1088/1748-9326/ac1012 (SCI, TOP)
Wang, Y., Yuan, Q., Li, T., Tan, S., Zhang, L., 2021. Full-coverage spatiotemporal mapping of ambient PM2.5 and PM10 over China from Sentinel-5P and assimilated datasets: Considering the precursors and chemical compositions. Science of The Total Environment, 793, 148535. https://doi.org/10.1016/j.scitotenv.2021.148535 (SCI, TOP)
Wang, Y., Yuan, Q., Li T., Zhu, L., Zhang, L., 2021. Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 311-325. https://doi.org/10.1016/j.isprsjprs.2021.03.018 (SCI Q1, TOP)
Wang, B., Yuan, Q., Yang, Q., Zhu, L., Li T., Zhang, L., 2021. Estimate hourly PM2.5 concentrations from Himawari-8 TOA reflectance directly using geo-intelligent long short-term memory network. Environmental Pollution, 271, 116327. https://doi.org/10.1016/j.envpol.2020.116327 (SCI, TOP)
Zhang, X., Shen, H., Li T., Zhang, L., 2020, The Effects of Fireworks Discharge on Atmospheric PM2.5 Concentration in the Chinese Lunar New Year. International Journal of Environmental Research and Public Health, 17. https://doi.org/10.3390/ijerph17249333 (SCI)
Yang, Q., Yuan, Q., Li T., Yue, L., 2020. Mapping PM2.5 concentration at high resolution using a cascade random forest based downscaling model: Evaluation and application. Journal of Cleaner Production, 277, 123887. https://doi.org/10.1016/j.jclepro.2020.123887 (SCI Q1, TOP)
Li T., Wang, Y., Yuan, Q., 2020. Remote Sensing Estimation of Regional NO2 via Space-Time Neural Networks. Remote Sensing, 12. https://doi.org/10.3390/rs12162514 (SCI, First author)
Li T., Shen, H., Yuan, Q., Zhang, L., 2020. Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 178-188. https://doi.org/10.1016/j.isprsjprs.2020.06.019 (SCI Q1，TOP, First author)
Li T., Shen, H., Zeng, C., Yuan, Q., 2020. A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1312-1321. https://doi.org/10.1109/JSTARS.2020.2977668 (SCI, First author)
Yang, Q., Yuan, Q., Yue, L., Li T., Shen, H., Zhang, L., 2020. Mapping PM2.5 concentration at a sub-km level resolution: A dual-scale retrieval approach. ISPRS Journal of Photogrammetry and Remote Sensing, 165, 140-151. https://doi.org/10.1016/j.isprsjprs.2020.05.018 (SCI Q1，TOP, Co-corresponding author)
Wang, J., Yuan, Q., Shen, H., Liu, T., Li T., Yue, L., Shi, X., Zhang, L., 2020. Estimating snow depth by combining satellite data and ground-based observations over Alaska: A deep learning approach. Journal of Hydrology, 585, 124828. https://doi.org/10.1016/j.jhydrol.2020.124828 (SCI, TOP)
Yuan, Q., Shen, H., Li T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., Gao, J., Zhang, L., 2020. Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716. https://doi.org/10.1016/j.rse.2020.111716 (SCI Q1,TOP, ESI Highly Cited Paper, ESI Hot Paper)
Shen, H., Jiang, Y., Li T., Cheng, Q., Zeng, C., Zhang, L., 2020. Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data. Remote Sensing of Environment, 240, 111692. https://doi.org/10.1016/j.rse.2020.111692 (SCI Q1,TOP)
Yang, Q., Yuan, Q., Yue, L., Li T., 2020. Investigation of the spatially varying relationships of PM2. 5 with meteorology, topography, and emissions in China in 2015 by using modified geographically weighted regression. Environmental Pollution, 114257. https://doi.org/10.1016/j.envpol.2020.114257 (SCI, TOP)
Yuan, Q., Xu, H., Li T., Shen, H., Zhang, L., 2020. Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental U.S. Journal of Hydrology, 580, 124351. https://doi.org/10.1016/j.jhydrol.2019.124351 (SCI, TOP)
Wang, Y., Yuan, Q., Li T., Shen, H., Zheng, L., Zhang, L., 2019. Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation. ISPRS Journal of Photogrammetry and Remote Sensing, 157, 1-12. https://doi.org/10.1016/j.isprsjprs.2019.08.017 (SCI Q1, TOP, Co-corresponding author)
Yuan, M., Song, Y., Huang, Y., Shen, H., Li T., 2019. Exploring the association between the built environment and remotely sensed PM2.5 concentrations in urban areas. Journal of Cleaner Production, 220, 1014-1023. https://doi.org/10.1016/j.jclepro.2019.02.236 (SCI Q1, TOP)
Yang, Q., Yuan, Q., Yue, L., Li T., Shen, H., Zhang, L., 2019. The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations. Environmental Pollution, 248, 526-535. https://doi.org/10.1016/j.envpol.2019.02.071 (SCI, TOP)
Guo, L., Luo, J., Yuan, M., Huang, Y., Shen, H., Li T., 2019. The influence of urban planning factors on PM2.5 pollution exposure and implications: A case study in China based on remote sensing, LBS, and GIS data. Science of The Total Environment, 659, 1585-1596. https://doi.org/10.1016/j.scitotenv.2018.12.448 (SCI, TOP)
Wang, Y., Yuan, Q., Li T., Shen, H., Zheng, L., Zhang, L., 2019. Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces. Atmospheric Environment, 200, 280-301. https://doi.org/10.1016/j.atmosenv.2018.12.023 (SCI, TOP, ESI Highly Cited Paper)
Chen, J., Shen, H., Li T., Peng, X., Cheng, H., Ma, C., 2019. Temporal and Spatial Features of the Correlation between PM2. 5 and O3 Concentrations in China. International Journal of Environmental Research and Public Health, 16, 4824. https://doi.org/10.3390/ijerph16234824 (SCI)
Shen, H., Zhou, M., Li T., Zeng, C., 2019. Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping. International Journal of Environmental Research and Public Health, 16. https://doi.org/10.3390/ijerph16214102 (SCI)
Yuan, Q., Li, S., Yue, L., Li T., Shen, H., Zhang, L., 2019. Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point–Surface Fusion of MODIS Products and GNSS-IR Observations. Remote Sensing, 11. https://doi.org/10.3390/rs11121440 (SCI)
Shen, H., Li T., Yuan, Q., Zhang, L., 2018. Estimating Regional Ground-Level PM2.5 Directly From Satellite Top-Of-Atmosphere Reflectance Using Deep Belief Networks. Journal of Geophysical Research: Atmospheres, 123, 13,875-813,886. https://doi.org/10.1029/2018JD028759 (SCI, TOP)
Xu, H., Yuan, Q., Li T., Shen, H., Zhang, L., Jiang, H., 2018. Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S. Remote Sensing, 10. https://doi.org/10.3390/rs10091351 (SCI)
Yuan, M., Huang, Y., Shen, H., Li T., 2018. Effects of urban form on haze pollution in China: Spatial regression analysis based on PM2.5 remote sensing data. Applied Geography, 98, 215-223. https://doi.org/10.1016/j.apgeog.2018.07.018 (SSCI, ESI Highly Cited Paper)
Li T., Shen, H., Yuan, Q., Zhang, X., Zhang, L., 2017. Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach. Geophysical Research Letters, 44, 11,985-11,993. https://doi.org/10.1002/2017gl075710 (SCI Q1, TOP, First author, ESI Highly Cited Paper)
Yang, Q., Yuan, Q., Li T., Shen, H., Zhang, L., 2017. The Relationships between PM2.5 and Meteorological Factors in China: Seasonal and Regional Variations. International Journal of Environmental Research and Public Health, 14. https://doi.org/10.3390/ijerph14121510 (SCI)
Li T., Shen, H., Zeng, C., Yuan, Q., Zhang, L., 2017. Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment. Atmospheric Environment, 152, 477-489. http://dx.doi.org/10.1016/j.atmosenv.2017.01.004 (SCI, TOP, First author, ESI Highly Cited Paper)
陈戴荣，崔玉祥，苏悦侬，吴金橄，李同文, 2013. 考虑周期性的深度学习臭氧预测模型研究. 环境监控与预警. http://www.hjjkyyj.com/html/hjjkyyj/2023/3/A20220916001.htm (Corresponding author)(in Chinese)
杨倩倩, 靳才溢, 李同文, 袁强强, 沈焕锋, 张良培, 2021. 数据驱动的定量遥感研究进展与挑战. 遥感学报. http://www.ygxb.ac.cn/thesisDetails#10.11834/jrs.20211410 (EI)(in Chinese)
江芸, 李同文, 程青, 沈焕锋, 2021. 利用时空神经网络模型的长江经济带气温反演. 武汉大学学报(信息科学版), 1-10. http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20210192 (EI)(in Chinese)
沈焕锋, 李同文, 2019. 大气PM2.5遥感制图研究进展. 测绘学报, 48, 1624. http://xb.sinomaps.com/CN/10.11947/j.AGCS.2019.20190456 (EI, Corresponding author)(in Chinese)
2. Conference Paper
Wang, Y., Yuan, Q., Xiao, R., Li T., Zhang, L., 2020. Recovery of the Carbon Monoxide Product from S5P-TROPOMI by Fusing Multiple Datasets: A Case Study in Hubei Province, China. 2020 IEEE International Geoscience and Remote Sensing Symposium. pp. 5529-5532. Waikoloa, HI, USA. http://dx.doi.org/10.1109/IGARSS39084.2020.9323092
Xu, H., Yuan, Q., Li T., Shen, H., Zhang, L., 2019. Estimating Surface Soil Moisture from Satellite Observations Using Machine Learning Trained on In Situ Measurements in the Continental U.S. 2019 IEEE International Geoscience and Remote Sensing Symposium. pp. 6166-6169. Yokohama, Japan. http://dx.doi.org/10.1109/IGARSS.2019.8900101
Wang, Y., Yuan, Q., Wang, H., Li T., Shen, H., Zhang, L., 2019. Validation of MODIS 1-Km MAIAC Aerosol Products with AERONET in China During 2008-2016. 2019 IEEE International Geoscience and Remote Sensing Symposium. pp. 7610-7613. Yokohama, Japan. http://dx.doi.org/10.1109/IGARSS.2019.8898248
Wang, J., Yuan, Q., Li T., Shen, H., Zhang, L., 2019. Estimating Snow-Depth by Fusing Satellite and Station Observations: A Deep Learning Approach. 2019 IEEE International Geoscience and Remote Sensing Symposium. pp. 4109-4112. Yokohama, Japan. http://dx.doi.org/10.1109/IGARSS.2019.8900518
Li, S., Yuan, Q., Yue, L., Li T., Shen, H., Zhang, L., 2019. Downscaling GNSS-R Based Vegetation Water Content Product Using Random Forest Model. 2019 IEEE International Geoscience and Remote Sensing Symposium. pp. 6720-6723. Yokohama, Japan. http://dx.doi.org/10.1109/IGARSS.2019.8900472
Li T., Zhang, C., Shen, H., Yuan, Q., Zhang, L., 2018. REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM2.5 USING SATELLITE REMOTE SENSING. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. pp. 143-147. Beijing, China. http://dx.doi.org/10.5194/isprs-annals-IV-3-143-2018
Li T., Shen, H., Yuan, Q., Zhang, L., 2018. Deep Learning for Ground-Level PM2.5 Prediction from Satellite Remote Sensing Data. 2018 IEEE International Geoscience and Remote Sensing Symposium. pp. 7581-7584. Valencia, Spain. http://dx.doi.org/10.1109/IGARSS.2018.8519036
Zhang, X., Shen, H., Li T., 2016. Effect characteristics of Chinese New Year fireworks/firecrackers on PM2.5 concentration at large space and time scales. 2016 4th International Workshop on Earth Observation and Remote Sensing Applications. pp. 179-182. Guangzhou, China. http://dx.doi.org/10.1109/EORSA.2016.7552792
Li T., Shen, H., Zhang, L., 2016. Mapping PM2.5 distribution in China by fusing station measurements and satellite observation. 2016 IEEE International Geoscience and Remote Sensing Symposium. pp. 5761-5764. Beijing, China. http://dx.doi.org/10.1109/IGARSS.2016.7730505
3. Patent (in Chinese)
沈焕锋，李同文. 一种结合卫星和站点观测反演时空连续PM2.5浓度的方法，授权号：ZL201510849327.0. 授权时间：2018.01
沈焕锋，周曼，李同文，袁强强. 一种结合遥感数据与社会感知数据的PM2.5深度学习反演方法. 申请号：201910451339.6.
李同文，阴顺超，程晓，吴金橄，王天星. 一种粒子直径约束的PM2.5深度学习遥感估算方法. 申请号：202111340135.9.
李同文，张岭峰，吴金橄. 一种全局-局部建模结合的卫星CO2浓度缺失信息重建方法. 申请号：202211258374.4.
4. Software Copyright (in Chinese)
沈焕锋，李同文，徐少良，袁强强. 大气PM2.5实时无缝监测发布系统，软件著作权登记号：2019SR0535101.