In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy

• Published in: PloS one Vol. 9; no. 8; p. e105708
• Main Authors: Wenjun, Ji, Zhou, Shi, Jingyi, Huang, Shuo, Li
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25-08-2014; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Biology and Life Sciences; Carbon - analysis; China; Data mining; Ecology and Environmental Sciences; Feasibility studies; I.R. radiation; In situ measurement; Information processing; Information technology; Infrared spectra; Infrared spectroscopy; Laboratories; Least squares method; Near infrared radiation; Near infrared spectroscopy; Nitrogen; Nitrogen - analysis; Organic carbon; Organic matter; Organic phosphorus; Organic soils; pH effects; Phosphorus; Phosphorus - analysis; Physical Sciences; Potassium; Potassium - analysis; Remote sensing; Research and Analysis Methods; Rice; Rice fields; Soil - chemistry; Soil organic matter; Soil properties; Soil sciences; Spectroscopy; Spectroscopy, Near-Infrared; Support vector machines; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0105708

In situ measurements with visible and near-infrared spectroscopy (vis-NIR) provide an efficient way for acquiring soil information of paddy soils in the short time gap between the harvest and following rotation. The aim of this study was to evaluate its feasibility to predict a series of soil properties including organic matter (OM), organic carbon (OC), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and pH of paddy soils in Zhejiang province, China. Firstly, the linear partial least squares regression (PLSR) was performed on the in situ spectra and the predictions were compared to those with laboratory-based recorded spectra. Then, the non-linear least-square support vector machine (LS-SVM) algorithm was carried out aiming to extract more useful information from the in situ spectra and improve predictions. Results show that in terms of OC, OM, TN, AN and pH, (i) the predictions were worse using in situ spectra compared to laboratory-based spectra with PLSR algorithm (ii) the prediction accuracy using LS-SVM (R2>0.75, RPD>1.90) was obviously improved with in situ vis-NIR spectra compared to PLSR algorithm, and comparable or even better than results generated using laboratory-based spectra with PLSR; (iii) in terms of AP and AK, poor predictions were obtained with in situ spectra (R2<0.5, RPD<1.50) either using PLSR or LS-SVM. The results highlight the use of LS-SVM for in situ vis-NIR spectroscopic estimation of soil properties of paddy soils.