Comparisons of random forest and stochastic gradient treeboost algorithms for mapping soil electrical conductivity with multiple subsets using Landsat OLI and DEM/GIS-based data at a type oasis in Xinjiang, China-Reference-Cited by-同舟云学术

Comparisons of random forest and stochastic gradient treeboost algorithms for mapping soil electrical conductivity with multiple subsets using Landsat OLI and DEM/GIS-based data at a type oasis in Xinjiang, China

Published:2021-01-01 Issue:1 Volume:54 Page:158-181
ISSN:2279-7254
Container-title:European Journal of Remote Sensing
language:en
Short-container-title:European Journal of Remote Sensing

Author:

Wei Yang¹²,Ding Jianli¹²,Yang Shengtian¹²,Yang Xiaodong³⁴,Wang Fei¹²

Affiliation:

1. Xinjiang Common University Key Lab of Smart City and Environmental Stimulation, College of Resource and Environmental Sciences, Xinjiang University, Urumqi, China

2. Lab for Oasis Ecosystem, Ministry of Education, Urumqi, China

3. Ningbo University, Ningbo, China

4. Global Centre for Environmental Remediation (GCER), ATC Building, The University of Newcastle, Australia

Funder

National Natural Science Foundation of China

Key Laboratory Special Fund of Xinjiang Uygur Autonomous Region

China Postdoctoral Science Foundation

Scientific Research Foundation for Doctors of Xinjiang University

Publisher

Informa UK Limited

Subject

Applied Mathematics,Atmospheric Science,Computers in Earth Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2021.1888657

Reference86 articles.

1. Environmental factors of spatial distribution of soil salinity on flat irrigated terrain

2. Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region

3. Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy)

4. Spatial prediction of soil water retention in a Páramo landscape: Methodological insight into machine learning using random forest

5. Boettinger, J. L., Ramsey, R. D., Bodily, J. M., Cole, N. J., Kienast-Brown, S., Nield, S. J., Saunders, A. M. & Stum, A. K., 2008. Landsat spectral data for digital soil mapping. Digital Soil Mapping with Limited Data.

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. High-resolution mapping of soil carbon stocks in the western Amazon;Geoderma Regional;2024-03

2. Assessment of macronutrients status using digital soil mapping techniques: a case study in Maru’ak area in Lorestan Province, Iran;Environmental Monitoring and Assessment;2023-03-27

3. A Non-Hybrid Data-Driven Fuzzy Inference System for Coagulant Dosage in Drinking Water Treatment Plant: Machine-Learning for Accurate Real-Time Prediction;Water;2023-03-15

4. Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images;Geoderma;2023-02

5. Investigating the status of some macronutrients using digital mapping techniques in Maru’ak area, Lorestan Province, Iran;2023-01-31