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JIANG Yun, LI Tongwen, CHENG Qing, SHEN Huanfeng. Air Temperature Estimation in Yangtze River Economic Zone Using Geographically and Temporally Neural Networks[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210192
Citation: JIANG Yun, LI Tongwen, CHENG Qing, SHEN Huanfeng. Air Temperature Estimation in Yangtze River Economic Zone Using Geographically and Temporally Neural Networks[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210192

Air Temperature Estimation in Yangtze River Economic Zone Using Geographically and Temporally Neural Networks

doi: 10.13203/j.whugis20210192
Funds:

The National Key Research and Development Program of China (2016YFC0200900)

  • Received Date: 2020-09-23
  • Traditional remote sensing-based air temperature (Ta) estimation method usually used the global models, which ignored the effects of spatiotemporal heterogeneity, especially for the researches in large regional areas. Taking the Yangtze River Economic Zone as a typical research area, this paper introduced the geographically and temporally neural networks for high-precision Ta estimation. The influence of spatiotemporal heterogeneity was considered by establishing the local models in the generalized regression neural network. Remote sensing data, assimilation data and station data were fused in this study to obtain the spatially continuous near-surface Ta. The model performance was evaluated by the site-based ten-fold cross-validation method. The results showed that the geographically and temporally weighted neural network had effectively improved the estimation accuracy with the RMSE=1.899℃, MAE=1.310℃ and R=0.976. Compared with the multiple linear regression method and the traditional global neural network, the MAE value decreased by 1.112℃ and 0.378℃ respectively. The Ta mapping results indicated that the model used in this paper can well reflect the spatial distribution differences, which means that this study is possible to provide a new way for Ta estimation with high precision.
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Air Temperature Estimation in Yangtze River Economic Zone Using Geographically and Temporally Neural Networks

doi: 10.13203/j.whugis20210192
Funds:

The National Key Research and Development Program of China (2016YFC0200900)

Abstract: Traditional remote sensing-based air temperature (Ta) estimation method usually used the global models, which ignored the effects of spatiotemporal heterogeneity, especially for the researches in large regional areas. Taking the Yangtze River Economic Zone as a typical research area, this paper introduced the geographically and temporally neural networks for high-precision Ta estimation. The influence of spatiotemporal heterogeneity was considered by establishing the local models in the generalized regression neural network. Remote sensing data, assimilation data and station data were fused in this study to obtain the spatially continuous near-surface Ta. The model performance was evaluated by the site-based ten-fold cross-validation method. The results showed that the geographically and temporally weighted neural network had effectively improved the estimation accuracy with the RMSE=1.899℃, MAE=1.310℃ and R=0.976. Compared with the multiple linear regression method and the traditional global neural network, the MAE value decreased by 1.112℃ and 0.378℃ respectively. The Ta mapping results indicated that the model used in this paper can well reflect the spatial distribution differences, which means that this study is possible to provide a new way for Ta estimation with high precision.

JIANG Yun, LI Tongwen, CHENG Qing, SHEN Huanfeng. Air Temperature Estimation in Yangtze River Economic Zone Using Geographically and Temporally Neural Networks[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210192
Citation: JIANG Yun, LI Tongwen, CHENG Qing, SHEN Huanfeng. Air Temperature Estimation in Yangtze River Economic Zone Using Geographically and Temporally Neural Networks[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210192
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