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Spatiotemporal prediction of grassland net primary productivity using deep learning models integrating temporal decomposition and spatial clustering: a case study in source region of the Yellow River, China

Crossref DOI link: https://doi.org/10.1007/s12145-025-01834-9

Published Online: 2025-03-17

Published Print: 2025-04

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Liu, Fujiang

Wu, Tianqi

Lin, Weihua

Guo, Yan
Funding

Funding for this research was provided by:

International Research Center of Big Data for Sustainable Development Goals (CBAS2022GSP05, CBAS2022GSP05, CBAS2022GSP05, CBAS2022GSP05)

Open Fund of State Key Laboratory of Remote Sensing Science (6142A01210404, 6142A01210404, 6142A01210404, 6142A01210404)

Hubei Key Laboratory of Intelligent Geo-Information Processing (KLIGIP-2022-B03, KLIGIP-2022-B03, KLIGIP-2022-B03, KLIGIP-2022-B03)

Metallogenic patterns and mineralization predictions for the Daping gold deposit in Yuanyang County, Yunnan Province (2022026821, 2022026821, 2022026821, 2022026821)
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Text and Data Mining valid from 2025-03-17

Version of Record valid from 2025-03-17
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Article History

Received: 13 November 2024

Accepted: 2 March 2025

First Online: 17 March 2025

Declarations

:

: The authors declare no competing interests.

: Data and code can be requested from the corresponding author.

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