太阳成8722(中国)有限公司-GREEN NO.1

标题: Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5

作者: Li, TW (Li, Tongwen); Shen, HF (Shen, Huanfeng); Yuan, QQ (Yuan, Qiangqiang); Zhang, LP (Zhang, Liangpei)

来源出版物: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING  卷: 167  页: 178-188  DOI: 10.1016/j.isprsjprs.2020.06.019  出版年: SEP 2020  

摘要: The integration of satellite-derived aerosol optical depth (AOD) and station-measured PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 mu m) provides a promising approach for the monitoring of PM2.5. Previous models have generally only considered either the spatiotemporal heterogeneities of the AOD-PM2.5 relationship or the nonlinear relationship between AOD and PM2 5. In this paper, to simultaneously allow for the nonlinearity and spatiotemporal heterogeneities of the AOD-PM2.5 relationship, the geographically and temporally weighted neural network (GTWNN) model is proposed for the satellite-based estimation of ground-level PM2.5. The GTWNN model represents the nonlinear AOD-PM2.5 relationship via a generalized regression neural network, and is separately established for individual locations and times, to address the spatiotemporal heterogeneities of the AOD-PM2.5 relationship. Meanwhile, a spatiotemporal weighting scheme is incorporated in the GTWNN model to capture the local relations of samples for the training of the AOD-PM2.5 relationship. By the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD product, meteorological data, and MODIS normalized difference vegetation index (NDVI) data as input, the GTWNN model was verified using ground station PM2.5 measurements from China in 2015. The GTWNN model achieved sample-based cross-validation (CV) and site-based CV R-2 values of 0.80 and 0.79, respectively, and it outperformed the geographically and temporally weighted regression model (CV R-2: 0.75 and 0.73) and the daily geographically weighted regression model (CV R-2: 0.72 and 0.72). The proposed model implements the combination of geographical law and a neural network, and will be of great use for remote sensing retrieval of environmental variables.

入藏号: WOS:000561346200012

语言: English

文献类型: Article

作者关键词: Geographically and temporally weighted neural network; Generalized regression neural network; PM2.5; AOD; Satellite remote sensing

KeyWords Plus: AEROSOL OPTICAL DEPTH; PARTICULATE MATTER; CHINA; REGRESSION; RESOLUTION; MODEL; POLLUTION; REGION; LAND; PREDICTION

地址: [Li, Tongwen; Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Shen, Huanfeng; Yuan, Qiangqiang; Zhang, Liangpei] Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China.

[Shen, Huanfeng] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China.

[Yuan, Qiangqiang] Wuhan Univ, Sch Geodesy & Geomat, Wuhan 430079, Peoples R China.

[Zhang, Liangpei] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.

通讯作者地址: Shen, HF (corresponding author)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: shenhf@whu.edu.cn

影响因子：7.319