Study on hyperspectral monitoring model of soil total nitrogen content based on fractional-order derivative

Abstract

Realizing the real-time nondestructive monitoring of soil total nitrogen (STN) content is an important task to promote precision agriculture development. In this study, a water regulation experiment was performed. Winter wheat and summer soybean soils were taken as the research objects. The effects of fractional-order derivatives (FOD) on soil spectral reflectance were studied, and the hyperspectral monitoring models of STN content were constructed based on the full spectrum band and the uninformative variable elimination (UVE) -extracted band. The main results were as follows: The FOD showed a gradual change in the integer-order derivative (IOD). When calculating the FOD, the more fitting points there were, the smaller the FOD spectrum, and the higher the correlation with the STN content. In the range of orders 0–1, the correlation increased with increasing order. In the range of orders 1–2, the correlation first decreased and then increased. When constructing the model, stepwise multiple linear regression (SMLR) was used, but the accuracies of the SMLR models were low. Afterward, the bands were extracted by UVE, and the models were constructed by partial least squares regression (PLSR). The number of bands extracted by UVE ranged from 11 to 534, mainly in the range of 1000–2450 nm. Under the same conditions, the accuracy of the PLSR model constructed with the UVE-extracted bands was higher than that of the model constructed with the full band spectrum. Among all PLSR models, when the number of fitting points, the order, and the number of bands were 40, 0.67, and 272, respectively, the model accuracy was the highest. The coefficient of determination of cross-validation (R2cv), root mean square error of cross-validation (RMSEcv), coefficient of determination of validation (R2v), root mean square error of validation (RMSEv), and relative analysis error of validation (RPDv) of the model were 0.7317, 0.2244 g·kg−1, 0.7937, 0.1976 g·kg−1, and 2.1904, respectively. This model could realize the hyperspectral monitoring of STN content in wheat and soybean fields. In this study, hyperspectral spectroscopy was used to monitor the STN content, which provided a theoretical basis and technical support for the observations.