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 Sensing

Shuo Li¹  Wenjun Ji¹  Songchao Chen¹  Jie Peng¹  Yin Zhou¹  Zhou Shi¹  Eyal Ben-Dor²  Josef Kellndorfer² 

[1] Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; E-Mails:Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China;

关键词: nitrogen fertilizer;    soil testing and formulated fertilization;    proximal soil sensing;    spectral library;    LWR;    SVMDA;   

DOI  :  10.3390/rs70607029

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.