Monitoring Leaf Nitrogen Accumulation With Optimized Spectral Index in Winter Wheat Under Different Irrigation Regimes

Abstract

Rapid and non-destructive estimation of leaf nitrogen accumulation (LNA) is essential to field nitrogen management. Currently, many vegetation indices have been used for indicating nitrogen status. Few studies systematically analyzed the performance of vegetation indices of winter wheat in estimating LNA under different irrigation regimes. This study aimed to develop a new spectral index for LNA estimation. In this study, 2 years of field experiments with different irrigation regimes were conducted from 2015 to 2017. The original reflectance (OR) and three transformed spectra [e.g., the first derivative reflectance (FDR), logarithm of the reciprocal of the spectra (Log(1/R)), and continuum removal (CR)] were used to calculate two- and three-band spectral indices. Correlation analyses and univariate linear and non-linear regression between transformed-based spectral indices and LNA were performed. The performance of the optimal spectral index was evaluated with classical vegetation index. The results showed that FDR was the most stable transformation method, which can effectively enhance the relationships to LNA and improve prediction performance. With a linear relationship with LNA, FDR-based three-band spectral index 1 (FDR-TBI1) (451, 706, 688) generated the best performance with coefficient of determination (R2) of 0.73 and 0.79, the root mean square error (RMSE) of 1.267 and 1.266 g/m2, and the ratio of performance to interquartile distance (RPIQ) of 2.84 and 2.71 in calibration and validation datasets, respectively. The optimized spectral index [FDR-TBI1 (451, 706, 688)] is more effective and might be recommended as an indicator for estimating winter wheat LNA under different irrigation regimes.

Comparison of Different Dimensional Spectral Indices for Estimating Nitrogen Content of Potato Plants over Multiple Growth Periods

“…More specifically, the wavelengths at which single-band reflectance is sensitive to PNC over multiple potato growth periods are all located in the red-edge (670-740 nm) or green-edge (502-554 nm) and most of the sensitive wavelengths of the three-band spectral indices also include these spectral regions. In contrast, the sensitive wavelengths of the six two-band spectral indices all contain the 490-494 nm band, which is consistent with the results of Hansen and Schjoerring, Pettersson and Eckersten, and Sun et al [12,49,50] This is because different treatments (density and N and K fertilization) lead to different potato PNC, which is more evident in the green-and red-edge due to the strong absorption and reflection of chlorophyll. Thus, these spectral regions provide a good indication of the PNC.…”

“…However, increasing the number of variables and the modeling complexity increases the computational cost and application threshold of the model [13]. Agricultural workers who lack professional mathematical and statistical knowledge and remote sensing skills prefer to use a single spectral index to estimate the N status of crops over multiple growth periods, an approach that is also more conducive to the integration and development of N-monitoring devices [12,32]. Therefore, this study investigates the use of single-, two-, and three-dimensional spectral indices for estimating, via a linear regression method, potato PNC over multiple growth periods.…”

“…Vegetation indices (VIs) are indicators obtained by the mathematical transformation of two or more specific crop canopy reflectance bands that are simple in form and efficient in the calculation. VIs have thus become an essential tool for monitoring the N nutrient status of crops [12][13][14][15][16][17]. The results of existing studies have indicated that, although it is feasible to monitor the N nutrient status of crops based on VIs, some limitations remain, mainly in the inconsistency of N monitoring models constructed based on VIs from different periods due to the influence of seasons, growth periods, cultivars, and growth environment, preventing the generalization of the models [18][19][20].…”

Advances and Developments in Monitoring and Inversion of the Biochemical Information of Crop Nutrients Based on Hyperspectral Technology

“…To improve the prediction accuracy of inversion models of crop nutrient biochemical information, many scholars have proposed many new spectral indices based on the optimization of published spectral indices to reduce background signals or noise, to resolve overlapping spectral features and to enhance the relationship between spectral data and crop nutrient biochemical information [106]. Liang et al [107] designed two new spectral indices based on the first-order derivatives of reflectance spectra FD-NDNI and FD-SRNI for estimating the nitrogen content of wheat, and the comparative analysis showed that the accuracy of the models constructed based on FD-NDNI and FD-SRNI was better than that of the commonly used indices such as MNDVI (705) and NDNI.…”