Estimation of Soil Nutrient Content Using Hyperspectral Data

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 (Rcv2 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 (Rcv2 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 (Rcv2 of 0.82, RMSECV of 3.39 g kg(-1) and RPIQ = 3.57). Additionally, the highly accurate LASSO-GABP-estimated soil TK (R-2 = 0.79) reveals the feasibility of the LASSO-GABP method to retrieve soil TK content at the regional scale.

Automated summary

This study introduced a new method to improve the estimation of soil nutrient content based on hyperspectral remote-sensing techniques, successfully mapping soil TK content at a regional scale. The GBDT-GABP and LASSO-GABP methods were identified as the most accurate estimation methods for soil TN/TP and TK, respectively.