Estimation of Soil Nutrient Content Using Hyperspectral Data

Yiping Peng,Zhenhua Liu,Lu Wang,Li Zhao,Chenjie Lin,Yueming Hu,Luo Liu

doi:10.3390/agriculture11111129

Abstract

Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is of great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow for the quick monitoring of soil nutrients. However, at present, obtaining accurate estimates proves to be difficult due to the weak spectral features of soil nutrients and the low accuracy of soil nutrient estimation models. This study proposed a new method to improve soil nutrient estimation. Firstly, for obtaining characteristic variables, we employed partial least squares regression (PLSR) fit degree to select an optimal screening algorithm from three algorithms (Pearson correlation coefficient, PCC; least absolute shrinkage and selection operator, LASSO; and gradient boosting decision tree, GBDT). Secondly, linear (multi-linear regression, MLR; ridge regression, RR) and nonlinear (support vector machine, SVM; and back propagation neural network with genetic algorithm optimization, GABP) algorithms with 10-fold cross-validation were implemented to determine the most accurate model for estimating soil total nitrogen (TN), total phosphorus (TP), and total potassium (TK) contents. Finally, the new method was used to map the soil TK content at a regional scale using the soil component spectral variables retrieved by the fully constrained least squares (FCLS) method based on an image from the HuanJing-1A Hyperspectral Imager (HJ-1A HSI) of the Conghua District of Guangzhou, China. The results identified the GBDT-GABP was observed as the most accurate estimation method of soil TN ( of 0.69, the root mean square error of cross-validation (RMSECV) of 0.35 g kg−1 and ratio of performance to interquartile range (RPIQ) of 2.03) and TP ( of 0.73, RMSECV of 0.30 g kg−1 and RPIQ = 2.10), and the LASSO-GABP proved to be optimal for soil TK estimations ( of 0.82, RMSECV of 3.39 g kg−1 and RPIQ = 3.57). Additionally, the highly accurate LASSO-GABP-estimated soil TK (R2 = 0.79) reveals the feasibility of the LASSO-GABP method to retrieve soil TK content at the regional scale.

Highlights

The rapid and efficient monitoring of soil nutrients has become an important prerequisite for agricultural production management and ensuring the healthy development of crops
(PCC, LASSO, and GBDT) were implemented on 6272 spectral data of the R, first derivative (FD), second derivative (SD), and (Figure 2) and soil nutrient contents in the 75 sample points collected across the province
reciprocal logarithmic (RL) (Figure 2) and soil nutrient contents in the 75 sample points collected across the provFigure 6 illustrates the correlation coefficients of the spectral variables

Summary

Introduction

The rapid and efficient monitoring of soil nutrients has become an important prerequisite for agricultural production management and ensuring the healthy development of crops. Current soil nutrient estimations are often obtained using field sampling and laboratory analysis, which is time-consuming and costly. The monitoring of soil nutrients via hyperspectral remote-sensing techniques is rapid and efficient, and numerous related studies have been performed within the past 30 years [1,2,3,4]. Current research on the retrieval of soil nutrients via hyperspectral remote-sensing technology typically focuses on two factors: the determination of characteristic variables and the construction of the estimation model. Liu et al (2007) obtained the 620–810 nm characteristic variables of soil organic matter by correlation and multiple regression analyses [5]

Methods

Results

Discussion

Conclusion