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Exploring Global Land Coarse-Mode Aerosol Changes from 2001-2021 Using a New Spatiotemporal Coaction Deep-Learning Model.
Zang, Zhou; Zhang, Yue; Zuo, Chen; Chen, Jiayi; He, Bin; Luo, Nana; Zou, Junxiao; Zhao, Wenji; Shi, Wenzhong; Yan, Xing.
Afiliación
  • Zang Z; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • Zhang Y; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • Zuo C; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • Chen J; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • He B; State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • Luo N; School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102612, China.
  • Zou J; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • Zhao W; College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China.
  • Shi W; Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Yan X; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Environ Sci Technol ; 57(48): 19881-19890, 2023 Dec 05.
Article en En | MEDLINE | ID: mdl-37962866
Coarse-mode aerosol optical depths (cAODs) are critical for understanding the impact of coarse particle sizes, especially dust aerosols, on climate. Currently, the limited data length and high uncertainty of satellite products diminish the applicability of cAOD for climate research. Here, we propose a spatiotemporal coaction deep-learning model (SCAM) for the retrieval of global land cAOD (500 nm) from 2001-2021. In contrast to conventional deep-learning models, the SCAM considers the impacts of spatiotemporal feature interactions and can simultaneously describe linear and nonlinear relationships for retrievals. Based on these unique characteristics, the SCAM considerably improved global daily cAOD accuracies and coverages (R = 0.82, root-mean-square error [RMSE] = 0.04). Compared to official products from the multiangle imaging spectroradiometer (MISR), the moderate resolution imaging spectroradiometer (MODIS), and the polarization and directionality of Earth's reflectances (POLDER) instrument, as well as the physical-deep learning (Phy-DL) derived cAOD, the SCAM cAOD improved the monthly R from 0.44 to 0.88 and more accurately captured over the desert regions. Based on the SCAM cAOD, daily dust cases decreased over the Sahara, Thar Desert, Gobi Desert, and Middle East during 2001-2021 (>3 × 10-3/year). The SCAM-retrieved cAOD can contribute considerably to resolving the climate change uncertainty related to coarse-mode aerosols. Our proposed method is highly valuable for reducing uncertainties regarding coarse aerosols and climate interactions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos / Aprendizaje Profundo Idioma: En Revista: Environ Sci Technol Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
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Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos / Aprendizaje Profundo Idioma: En Revista: Environ Sci Technol Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos