Abstract
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.
Highlights
Soil organic carbon (SOC) plays important roles in chemical and physical processes of soil environment [1], and it is a key indicator of soil quality [2]
Most previous studies have been focused on modeling SOC contents using laboratory-based reflectance spectroscopy, some studies demonstrated the feasibility of estimating SOC contents with airborne and even spaceborne hyperspectral images at within-field and regional scales [5,7]
−1 datasets are shown in Table contents for thevalue whole
Summary
Soil organic carbon (SOC) plays important roles in chemical and physical processes of soil environment [1], and it is a key indicator of soil quality [2]. Several regression models, such as multiple linear regression, partial least square regression [9], principal component regression, support vector machine regression (SVMR) [10], artificial neural networks and random forests, have been employed to estimate SOC contents from VIS-NIR spectroscopy [11]. In these methods, sufficient training samples describing soil variations of study areas play a decisive role in the accurate estimations of soil properties, including SOC contents [12]
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