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Remote Sens. 2017, 9(1), 29;

Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression

Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
Department of Geography, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, China
College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Suzhou Institute of Wuhan University, Suzhou 215123, China
Author to whom correspondence should be addressed.
Academic Editors: José A.M. Demattê, Lenio Soares Galvao and Prasad S. Thenkabail
Received: 12 July 2016 / Revised: 14 December 2016 / Accepted: 28 December 2016 / Published: 3 January 2017
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
Full-Text   |   PDF [6562 KB, uploaded 3 January 2017]   |  


Visible and near infrared (VIS-NIR) spectroscopy has been applied to estimate soil organic carbon (SOC) content with many modeling strategies and techniques, in which a crucial and challenging problem is to obtain accurate estimations using a limited number of samples with reference values (labeled samples). To solve such a challenging problem, this study, with Honghu City (Hubei Province, China) as a study area, aimed to apply semi-supervised regression (SSR) to estimate SOC contents from VIS-NIR spectroscopy. A total of 252 soil samples were collected in four field campaigns for laboratory-based SOC content determinations and spectral measurements. Semi-supervised regression with co-training based on least squares support vector machine regression (Co-LSSVMR) was applied for spectral estimations of SOC contents, and it was further compared with LSSVMR. Results showed that Co-LSSVMR could improve the estimations of SOC contents by exploiting samples without reference values (unlabeled samples) when the number of labeled samples was not excessively small and produce better estimations than LSSVMR. Therefore, SSR could reduce the number of labeled samples required in calibration given an accuracy threshold, and it holds advantages in SOC estimations from VIS-NIR spectroscopy with a limited number of labeled samples. Considering the increasing popularity of airborne platforms and sensors, SSR might be a promising modeling technique for SOC estimations from remotely sensed hyperspectral images. View Full-Text
Keywords: visible and near-infrared reflectance; soil organic carbon content; semi-supervised regression; co-training visible and near-infrared reflectance; soil organic carbon content; semi-supervised regression; co-training

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, H.; Shi, T.; Chen, Y.; Wang, J.; Fei, T.; Wu, G. Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression. Remote Sens. 2017, 9, 29.

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