Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data-Reference-Cited by-同舟云学术

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Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data

Published:2020-07 Issue: Volume:89 Page:102111
ISSN:0303-2434
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Meng Xiangtian,Bao Yilin,Liu Jiangui,Liu Huanjun,Zhang Xinle,Zhang Yu,Wang Peng,Tang Haitao,Kong Fanchang

Funder

National Key R&D Program of China

National Natural Science Foundation of China

National Earth System Science Data Center of China

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference72 articles.

1. Improving within-genus tree species discrimination using the discrete wavelet transform applied to airborne hyperspectral data;Asim;Int. J. Remote Sens.,2011

2. Development of a neural network for soybean rust epidemics;Batchelor;Trans. ASAE,1997

3. Using imaging spectroscopy to study soil properties;Ben-Dor;Remote Sens. Environ.,2009

4. Scale-location specific soil spatial variability: a comparison of continuous wavelet transform and Hilbert-Huang transform;Biswas;Catena,2018

5. Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation;Blackburn;Int. J. Remote Sens.,2007

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