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Soil Nitrogen Content Influence on Canopy Reflectance Spectra

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Transactions of the ASABE. 54(6): 2343-2349. (doi: 10.13031/2013.40644) @2011
Authors:   Y. Shao, Y. Bao, Y. He
Keywords:   Keyword Independent component analysis (ICA), Least squares support vector machines (LS-SVM), Rice, Soil nitrogen

Making nitrogen (N) recommendations without knowing the N supply capability of a soil can lead to inefficient use of N and potential pollution of the groundwater. Conventional soil test techniques are destructive and time-consuming. Remote sensing of canopy reflectance has the potential capability of non-destructive and rapid estimation of crop total N. In addition, this technique could be used for evaluation on soil N availability. This study was conducted on an experimental field at Zhejiang University with rice in the tillering and booting stages because these stages require maximum N for proper growth. The soil total N content of variable N application treatments was measured at the two stages. The rice canopy reflectance spectra were measured by visible and near-infrared spectroscopy (Vis/NIRS, 350 to 1075 nm). The partial least squares (PLS) method was used to build a calibration model between rice canopy reflectance and soil total N. The model was optimized with four latent variables (LVs), with coefficient of prediction (rp), root mean square error of prediction (RMSEP), and bias of 0.81, 8.44, and 2.03 for the tillering stage and 0.91, 7.01, and -1.50 for the booting stage, respectively. Moreover, independent component analysis (ICA) was used to select several sensitive wavelengths (SWs) based on loading weights. The optimal least squares support vector machines (LS-SVM) model was achieved with SWs (560 nm, 720-730 nm, and 655-680 nm) selected by ICA. This model had better performance for soil N estimation in both the tillering and booting stages, with correlation coefficient (r), RMSEP, and bias of 0.83, 7.80, and 2.15, respectively. The results show that ICA was effective with respect to the selection of SWs. In addition, the use of Vis/NIRS canopy reflectance spectra can effectively estimate the soil total N content.

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