Abstract

Soil total nitrogen (TN) is a vital nutrient element that affects the growth and rubber production of rubber trees. Especially in the coastal environment, soil nutrients will show significant differences. Using hyperspectral technology to detect soil nitrogen ion content in the offshore environment can provide technical support for nutrient management. Preprocessing hyperspectral data is a crucial step in accurate spectral model estimation. At the same time, it is considered that the traditional first-order and second-order derivatives are easily unbalanced between the signal-to-noise ratio, resulting in the loss of adequate information. Therefore, this work focuses on the feasibility of fractional order derivative (FOD) combined with partial least squares regression (PLSR) to estimate its TN content. By collecting soil samples from rubber plantations, the TN content of the soil samples was determined, and the spectral reflectance was measured. The FOD of the original spectrum was preprocessed with an interval of 0.2, and 11 spectral curves were obtained. Then, successive projections algorithm (SPA) was used to extract spectral features, and partial least squares regression (PLSR) models of soil TN content were established. The research results show that compared with the traditional integer derivative, FOD has a tremendous advantage in balancing spectral information and noise and can provide more abundant characteristic variables, which helps establish a more robust estimation model. In the range of orders 0–2, the model established by the 1.8-order is the best. Under that circumstance, the determination coefficients of validation (R2v) is 0.649, and the ratio of the performance to deviation (RPD) is 1.72. Combined with FOD, it is feasible and practical to establish an accurate and rapid estimation model of soil TN content, which can provide an important reference for large-scale detection of soil TN content in rubber plantations.

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