Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

Abstract

Abstract: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 OPEN ACCESS Remote Sens. 2015, 7 14940 accuracies using the six metrics that were the coefficients of determination for the calibration (R 2 C) and validation (R 2 V) 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 R² C, R² 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 R² C, R² 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 method may be better suited than the other methods to cope with potential confounding factors for most varieties, ecological site and growth stage; (4) The prediction accuracy was found to be higher when more wavelengths were used, though at the cost of a lower CE. The findings are of interest to the remote sensing community for the development of improved inversion schemes for hyperspectral applications concerning other types of vegetation. The examples provided in this paper may also serve to illustrate the advantages and shortcomings...

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

“…Consistent with the results of Xia Yao et al [4], our study revealed that the most informative feature wavelengths could vary in different growth stages and experimental conditions. Thus, further works need to be conducted in selecting and explaining new wavelengths for various experiments of LNC estimation, including different sites, experimental conditions and species.…”

“…Thus, further works need to be conducted in selecting and explaining new wavelengths for various experiments of LNC estimation, including different sites, experimental conditions and species. Xia Yao et al [4] and Tian, Y.C. et al [48] revealed that LNC models exhibited better and more stable performances in later stages than at the early stages.…”

“…Thus, further works need to be conducted in selecting and explaining new wavelengths for various experiments of LNC estimation, including different sites, experimental conditions and species. Xia Yao et al [4] and Furthermore, in these two stages, the higher values of R 2 for a few bands (approximately 0.86 for two bands in the booting stage and, nearby, 0.9 for two bands in the heading stage) are gained by SVMs. With an increase of the band numbers, the values of R 2 will increase correspondingly but the effect is not very evident.…”

“…Parameter b was a scalar threshold. Instead of calculating a dot product, the kernel function (K(x, y)) was used in Equation (4). In this study, the commonly used radial basis function (RBF, Equation (5)) was adopted as a kernel function of SVMs.…”

“…Apart from the fact that BP-NNs have a lower R 2 than SVMs and RBF-NNs, ANNs (BP-NNs and RBF-NNs) yield the same results which are consistent with SVMs when they are used for rice LNC estimation on the basis of fluorescence data in two stages. 1,2 Parameters in order to optimize the accuracy in SVM regression; 3 The LIF peaks at 450 nm, 510 nm, 685 nm and 740 nm; 4 The LIF indices developed with blue, red, and far-red bands using Equation (3); 5 The number of layers in BP-ANN analysis; 6 The spread of RBF in RBF-NN analysis; 7 The number of principal component (PCs) in PLSR analysis.…”