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Open AccessArticle

Exploring the Role of the Spatial Characteristics of Visible and Near-Infrared Reflectance in Predicting Soil Organic Carbon Density

1
College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
2
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
3
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of National Administration of Surveying, Mapping and GeoInformation & ShenzhenKey Laboratory of Spatial Smart Sensing and Services & College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
4
Geographical and Sustainability Sciences, The University of Iowa, Iowa City, IA 52246, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(10), 308; https://doi.org/10.3390/ijgi6100308
Received: 26 August 2017 / Revised: 26 September 2017 / Accepted: 16 October 2017 / Published: 18 October 2017
Soil organic carbon stock plays a key role in the global carbon cycle and the precision agriculture. Visible and near-infrared reflectance spectroscopy (VNIRS) can directly reflect the internal physical construction and chemical substances of soil. The partial least squares regression (PLSR) is a classical and highly commonly used model in constructing soil spectral models and predicting soil properties. Nevertheless, using PLSR alone may not consider soil as characterized by strong spatial heterogeneity and dependence. However, considering the spatial characteristics of soil can offer valuable spatial information to guarantee the prediction accuracy of soil spectral models. Thus, this study aims to construct a rapid and accurate soil spectral model in predicting soil organic carbon density (SOCD) with the aid of the spatial autocorrelation of soil spectral reflectance. A total of 231 topsoil samples (0–30 cm) were collected from the Jianghan Plain, Wuhan, China. The spectral reflectance (350–2500 nm) was used as auxiliary variable. A geographically-weighted regression (GWR) model was used to evaluate the potential improvement of SOCD prediction when the spatial information of the spectral features was considered. Results showed that: (1) The principal components extracted from PLSR have a strong relationship with the regression coefficients at the average sampling distance (300 m) based on the Moran’s I values. (2) The eigenvectors of the principal components exhibited strong relationships with the absorption spectral features, and the regression coefficients of GWR varied with the geographical locations. (3) GWR displayed a higher accuracy than that of PLSR in predicting the SOCD by VNIRS. This study aimed to help people realize the importance of the spatial characteristics of soil properties and their spectra. This work also introduced guidelines for the application of GWR in predicting soil properties by VNIRS. View Full-Text
Keywords: visible and near-infrared reflectance; soil organic carbon density; geographically weighted regression model; partial least squares regression model; spatial characteristics visible and near-infrared reflectance; soil organic carbon density; geographically weighted regression model; partial least squares regression model; spatial characteristics
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Guo, L.; Chen, Y.; Shi, T.; Zhao, C.; Liu, Y.; Wang, S.; Zhang, H. Exploring the Role of the Spatial Characteristics of Visible and Near-Infrared Reflectance in Predicting Soil Organic Carbon Density. ISPRS Int. J. Geo-Inf. 2017, 6, 308.

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