NPP estimation by fusing geodetector and deep spatio-temporal networks

NPP estimation by fusing geodetector and deep spatio-temporal networks

In recent years, deep learning has demonstrated significant potential in net primary productivity (NPP) estimation but the existing methods fall short in fully exploiting the spatio-temporal dependencies inherent in remote sensing data for modeling NPP. To address this limitation, we propose a novel...

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Bibliographic Details
Main Authors: Xiaohui He, Chenqiao Yuan, Panle Li, Xijie Cheng, Mengjia Qiao, Xiaoyu He, Nan Yang, Guangsheng Zhou, Jiandong Shang
Format: Article
Language:English
Published: Taylor & Francis Group 2025-08-01
Series:International Journal of Digital Earth
Subjects:
Net primary productivity
geodetector
geographic mechanism
deep learning
Tibetan plateau
Online Access:https://www.tandfonline.com/doi/10.1080/17538947.2025.2515265
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Summary:In recent years, deep learning has demonstrated significant potential in net primary productivity (NPP) estimation but the existing methods fall short in fully exploiting the spatio-temporal dependencies inherent in remote sensing data for modeling NPP. To address this limitation, we propose a novel approach named integrate geographic with deep spatio-temporal networks (IGDSNet). Specially, the IGDSNet uses the geodetector to explore geographic mechanism of NPP and then introduces the spatio-temporal long- and short-term memory networks (ST-LSTM) to obtain the deep spatio-temporal feature of NPP. Finally, we develop a novel decoder by combining self-attention unit (SAU) and 3D convolution (Conv3D) to produce the spatio-temporal distribution of NPP. By incorporating ST-LSTM and SAU for capturing long-term dependencies, the proposed IGDSNet is able to model both localized variations and global-scale patterns of NPP distribution. The experimental results demonstrate that the IGDSNet outperforms other deep spatio-temporal modeling methods, with improvement R2 in the range of 0.4%–10.57%. In addition, the IGDSNet is used to estimate the NPP distribution across Tibetan Plateau and create a monthly NPP dataset that the Moran's I index generally remains above 0.75. This study provides vital information for monitoring, modeling and managing the carbon cycle of Tibetan Platea.
ISSN:1753-8947
1753-8955

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