Enhancing soil particle content prediction accuracy: advanced hyperspectral analysis and machine learning models-Reference-Cited by-同舟云学术

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Enhancing soil particle content prediction accuracy: advanced hyperspectral analysis and machine learning models

Published:2024-08-25 Issue: Volume: Page:
ISSN:1439-0108
Container-title:Journal of Soils and Sediments
language:en
Short-container-title:J Soils Sediments

Author:

Wang Xiao,Ding Jianli,Han Lijing,Tan Jiao,Ge Xiangyu

Funder

the Key Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region

the Technology Innovation Team (Tianshan Innovation Team), Innovative Team for Efficient Utilization of Water Resources in Arid Regions

the Excellent Doctoral Innovation Project of Xinjiang University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11368-024-03886-8.pdf

Reference100 articles.

1. Agrawal P, Ganesh T, Mohamed AW (2021) Chaotic gaining sharing knowledge-based optimization algorithm: an improved metaheuristic algorithm for feature selection. Soft Comput 25:9505–9528

2. Allbed A, Kumar L (2013) Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Adv Remote Sens 2(4):373–385

3. Allen B, Hajek B (1989) Mineral occurrence in soil environments. Min Soil Environ 1:199–278

4. An Y, Ding S, Shi S, Li J (2018) Discrete space reinforcement learning algorithm based on support vector machine classification. Pattern Recog Lett 111:30–35

5. Bao Y, Ustin S, Meng X, Zhang X, Guan H, Qi B, Liu H (2021) A regional-scale hyperspectral prediction model of soil organic carbon considering geomorphic features. Geoderma 403:115263

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