Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2015-11, Vol.7 (11), p.14939-14966
• Main Authors: Yao, Xia, Huang, Yu, Shang, Guiyan, Zhou, Chen, Cheng, Tao, Tian, Yongchao, Cao, Weixing, Zhu, Yan
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Calibration; Canopies; comparative analysis; Cultivars; Derivatives; Field tests; leaf nitrogen concentration; Leaves; Mathematical models; monitoring accuracy; Nitrogen; number of wavelengths; Performance assessment; Prediction models; Reflectance; Remote sensing; six algorithms; Triticum aestivum; Vegetation; Wavelengths; Wheat; Winter wheat
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs71114939

The rapid and non-destructive monitoring of the canopy leaf nitrogen concentration (LNC) in crops is important for precise nitrogen (N) management. Nowadays, there is an urgent need to identify next-generation bio-physical variable retrieval algorithms that can be incorporated into an operational processing chain for hyperspectral satellite missions. We assessed six retrieval algorithms for estimating LNC from canopy reflectance of winter wheat in eight field experiments. These experiments represented variations in the N application rates, planting densities, ecological sites and cultivars and yielded a total of 821 samples from various places in Jiangsu, China over nine consecutive years. Based on the reflectance spectra and their first derivatives, six methods using different numbers of wavelengths were applied to construct predictive models for estimating wheat LNC, including continuum removal (CR), vegetation indices (VIs), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural networks (ANNs), and support vector machines (SVMs). To assess the performance of these six methods, we provided a systematic evaluation of the estimation accuracies using the six metrics that were the coefficients of determination for the calibration (R2C) and validation (R2V) sets, the root mean square errors of prediction (RMSEP) for the calibration and validation sets, the ratio of prediction to deviation (RPD), the computational efficiency (CE) and the complexity level (CL). The following results were obtained: (1) For the VIs method, SAVI(R1200, R705) produced a more accurate estimation of the LNC than other indices, with R super(2)C, R super(2)V, RMSEP, RPD and CE values of 0.844, 0.795, 0.384, 2.005 and 0.10 min, respectively; (2) For the SMLR, PLSR, ANNs and SVMs methods, the SVMs using the first derivative canopy spectra (SVM-FDS) offered the best accuracy in terms of R super(2)C, R super(2)V, RMSEP, RPD, and CE, at 0.96, 0.78, 0.37, 2.02, and 21.17, respectively; (3) The PLSR-FDS, ANN-OS and SVM-FDS methods yield similar accuracies if the CE and CL are not considered, however, ANNs and SVMs performed better on calibration set than the validation set which indicate that we should take more caution with the two methods for over-fitting. Except PLS method, the performance for most methods did not enhance when the spectrum were operated by the first derivative. Moreover, the evaluation of the robustness demonstrates that SVM