Estimation of Total Nitrogen Content in Rubber Plantation Soil Based on Hyperspectral and Fractional Order Derivative

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.

Selection of Spectral Parameters and Optimization of Estimation Models for Soil Total Nitrogen Content during Fertilization Period in Apple Orchards

“…Performing (1/R), (LogR), ( √ R), and their first derivative transformations on the original spectral reflectance R can further reduce the noise effects caused by non-target factors such as lighting conditions, soil particle size, and air moisture in spectral measurements; highlight effective information in the spectrum; improve the sensitivity of characteristic bands [28]; and explore the optimal spectral transformation form [45]. CR, also known as envelope division, can effectively enhance the spectral characteristics of the region of interest [46][47][48].…”

“…Niu et al constructed a hyperspectral estimation model for TN content in Shajiang black soil and found that the accuracy of the SVM model was slightly higher than that of the indices model, but both models could facilitate the rapid estimation of TN content in Shajiang black soil [24]. The spectral estimation technology of soil nutrients has achieved some preliminary results in the research in apple orchards [25,26], navel orange orchards [27], and rubber orchards [28]. Liu et al constructed an estimation model for soil organic matter content in apple orchards using the random forest method, and the R 2 of the modeling sample set reached 0.88 [29].…”

Hyperspectral Inversion of Soil Carbon and Nutrient Contents in the Yellow River Delta Wetland

“…In the process of modelling inversion, scholars have found that different models perform differently because of the differences in computational principles; hence, it is necessary to construct different models to compare the inversion effects to determine the best inversion model [23][24][25]. Owing to the redundancy of hyperspectral data, a mathematical transformation of spectral data or of the extraction of sensitive bands via principal component analysis and the correlation coefficient method can improve the modelling accuracy [26][27][28]. Naveen et al collected soil hyperspectral data from mangrove and salt marsh wetlands and established a partial least-squares regression model between the spectral information and soil carbon and nitrogen variables in an attempt to determine the best band for soil variable inversion [29].…”

Hyperspectral analysis of the content of the alkali-hydrolysed nitrogen in the soil of a millet field

“…Zhang et al (2022) developed a model utilising RR, PLSR, support vector machine regression (SVR), and random forest (RF) to estimate nitrogen content in winter wheat leaves quantitatively. Tang et al (2022) investigated the optimal estimation model for soil total nitrogen using fractional order derivative (FOD) and found that this enhanced the model's estimation ability. Niu et al (2023) analysed the spectral data with various pretreatment methods and concluded that the accuracy of the soil total nitrogen content prediction model was influenced by the chosen preprocessing method.…”