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Remote Sens. 2015, 7(6), 7029-7043; doi:10.3390/rs70607029

Potential of VIS-NIR-SWIR Spectroscopy from the Chinese Soil Spectral Library for Assessment of Nitrogen Fertilization Rates in the Paddy-Rice Region, China

1
Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
2
Cyrus Tang Center for Sensor Materials and Application, Zhejiang University, Hangzhou, 310058, China
*
Author to whom correspondence should be addressed.
Academic Editors: Eyal Ben-Dor, Josef Kellndorfer and Prasad S. Thenkabail
Received: 14 March 2015 / Accepted: 15 May 2015 / Published: 29 May 2015
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
View Full-Text   |   Download PDF [2903 KB, uploaded 29 May 2015]   |  

Abstract

To meet growing food demand with limited land and reduced environmental impact, soil testing and formulated fertilization methods have been widely adopted around the world. However, conventional technology for investigating nitrogen fertilization rates (NFR) is time consuming and expensive. Here, we evaluated the use of visible near-infrared shortwave-infrared (VIS-NIR-SWIR: 400–2500 nm) spectroscopy for the assessment of NFR to provide necessary information for fast, cost-effective and precise fertilization rating. Over 2000 samples were collected from paddy-rice fields in 10 Chinese provinces; samples were added to the Chinese Soil Spectral Library (CSSL). Two kinds of modeling strategies for NFR, quantitative estimation of soil N prior to classification and qualitative by classification, were employed using partial least squares regression (PLSR), locally weighted regression (LWR), and support vector machine discriminant analogy (SVMDA). Overall, both LWR and SVMDA had moderate accuracies with Cohen’s kappa coefficients of 0.47 and 0.48, respectively, while PLSR had fair accuracy (0.37). We conclude that VIS-NIR-SWIR spectroscopy coupled with the CSSL appears to be a viable, rapid means for the assessment of NFR in paddy-rice soil. Based on qualitative classification of soil spectral data only, it is recommended that the SVMDA be adopted for rapid implementation. View Full-Text
Keywords: nitrogen fertilizer; soil testing and formulated fertilization; proximal soil sensing; spectral library; LWR; SVMDA nitrogen fertilizer; soil testing and formulated fertilization; proximal soil sensing; spectral library; LWR; SVMDA
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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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MDPI and ACS Style

Li, S.; Ji, W.; Chen, S.; Peng, J.; Zhou, Y.; Shi, Z. Potential of VIS-NIR-SWIR Spectroscopy from the Chinese Soil Spectral Library for Assessment of Nitrogen Fertilization Rates in the Paddy-Rice Region, China. Remote Sens. 2015, 7, 7029-7043.

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