ESTIMATION OF SOIL ORGANIC CARBON CONTENT BASED ON DEEP LEARNING AND QUANTILE REGRESSION

Wudi Zhao; Zhilu Wu; Zhendong Yin

doi:10.1109/IGARSS47720.2021.9553418

ESTIMATION OF SOIL ORGANIC CARBON CONTENT BASED ON DEEP LEARNING AND QUANTILE REGRESSION

Wudi Zhao
, Zhilu Wu
, Zhendong Yin

School of Electronics and Information Engineering, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

Since the content of soil organic carbon (SOC) is significantly correlated with the soil reflection spectrum, hyperspectral remote sensing technology provides an effective new choice for the estimation of soil properties. At present, deep learning method relies on its strong feature extraction ability and has been successfully applied to the field of data analysis. This paper attempts to apply the deep learning method to the estimation of SOC content and proposes a method combining the convolutional neural networks (CNN) and quantile regression (QR). This method constructs a three-layer CNN and adjusts the network structure with the idea of QR. The experimental results are presented for two soil datasets, LUCAS (Land Use/Land Cover Area Frame Survey) and AfSIS (Africa Soil Information Service), and compared with several advanced deep learning and traditional machine learning regression models. The experimental results show that this method performs well in estimation.

Original language	English
Title of host publication	IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3717-3720
Number of pages	4
ISBN (Electronic)	9781665403696
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9553418
State	Published - 2021
Externally published	Yes
Event	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2021-July

Conference

Conference	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/Territory	Belgium
City	Brussels
Period	12/07/21 → 16/07/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

Keywords

Deep learning
Hyspectral remote sensing
Quantile regression
Soil orgnic carbon content

Access to Document

10.1109/IGARSS47720.2021.9553418

Cite this

Zhao, W., Wu, Z., & Yin, Z. (2021). ESTIMATION OF SOIL ORGANIC CARBON CONTENT BASED ON DEEP LEARNING AND QUANTILE REGRESSION. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 3717-3720). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS47720.2021.9553418

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title = "ESTIMATION OF SOIL ORGANIC CARBON CONTENT BASED ON DEEP LEARNING AND QUANTILE REGRESSION",

abstract = "Since the content of soil organic carbon (SOC) is significantly correlated with the soil reflection spectrum, hyperspectral remote sensing technology provides an effective new choice for the estimation of soil properties. At present, deep learning method relies on its strong feature extraction ability and has been successfully applied to the field of data analysis. This paper attempts to apply the deep learning method to the estimation of SOC content and proposes a method combining the convolutional neural networks (CNN) and quantile regression (QR). This method constructs a three-layer CNN and adjusts the network structure with the idea of QR. The experimental results are presented for two soil datasets, LUCAS (Land Use/Land Cover Area Frame Survey) and AfSIS (Africa Soil Information Service), and compared with several advanced deep learning and traditional machine learning regression models. The experimental results show that this method performs well in estimation.",

keywords = "Deep learning, Hyspectral remote sensing, Quantile regression, Soil orgnic carbon content",

author = "Wudi Zhao and Zhilu Wu and Zhendong Yin",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",

year = "2021",

doi = "10.1109/IGARSS47720.2021.9553418",

language = "英语",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",

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}

Zhao, W, Wu, Z & Yin, Z 2021, ESTIMATION OF SOIL ORGANIC CARBON CONTENT BASED ON DEEP LEARNING AND QUANTILE REGRESSION. in IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., pp. 3717-3720, 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels, Belgium, 12/07/21. https://doi.org/10.1109/IGARSS47720.2021.9553418

ESTIMATION OF SOIL ORGANIC CARBON CONTENT BASED ON DEEP LEARNING AND QUANTILE REGRESSION. / Zhao, Wudi; Wu, Zhilu ; Yin, Zhendong.
IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 3717-3720 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - ESTIMATION OF SOIL ORGANIC CARBON CONTENT BASED ON DEEP LEARNING AND QUANTILE REGRESSION

AU - Zhao, Wudi

AU - Wu, Zhilu

AU - Yin, Zhendong

PY - 2021

Y1 - 2021

N2 - Since the content of soil organic carbon (SOC) is significantly correlated with the soil reflection spectrum, hyperspectral remote sensing technology provides an effective new choice for the estimation of soil properties. At present, deep learning method relies on its strong feature extraction ability and has been successfully applied to the field of data analysis. This paper attempts to apply the deep learning method to the estimation of SOC content and proposes a method combining the convolutional neural networks (CNN) and quantile regression (QR). This method constructs a three-layer CNN and adjusts the network structure with the idea of QR. The experimental results are presented for two soil datasets, LUCAS (Land Use/Land Cover Area Frame Survey) and AfSIS (Africa Soil Information Service), and compared with several advanced deep learning and traditional machine learning regression models. The experimental results show that this method performs well in estimation.

AB - Since the content of soil organic carbon (SOC) is significantly correlated with the soil reflection spectrum, hyperspectral remote sensing technology provides an effective new choice for the estimation of soil properties. At present, deep learning method relies on its strong feature extraction ability and has been successfully applied to the field of data analysis. This paper attempts to apply the deep learning method to the estimation of SOC content and proposes a method combining the convolutional neural networks (CNN) and quantile regression (QR). This method constructs a three-layer CNN and adjusts the network structure with the idea of QR. The experimental results are presented for two soil datasets, LUCAS (Land Use/Land Cover Area Frame Survey) and AfSIS (Africa Soil Information Service), and compared with several advanced deep learning and traditional machine learning regression models. The experimental results show that this method performs well in estimation.

KW - Deep learning

KW - Hyspectral remote sensing

KW - Quantile regression

KW - Soil orgnic carbon content

UR - https://www.scopus.com/pages/publications/85126039249

U2 - 10.1109/IGARSS47720.2021.9553418

DO - 10.1109/IGARSS47720.2021.9553418

M3 - 会议稿件

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BT - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Zhao W, Wu Z , Yin Z. ESTIMATION OF SOIL ORGANIC CARBON CONTENT BASED ON DEEP LEARNING AND QUANTILE REGRESSION. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 3717-3720. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS47720.2021.9553418

ESTIMATION OF SOIL ORGANIC CARBON CONTENT BASED ON DEEP LEARNING AND QUANTILE REGRESSION

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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