## 1 Introduction  
Soil-available potassium is one of the most important nutrients for crop growth, and its availability is related to the soil organic matter content. Therefore, it is of great significance to guide fertilization and promote the development of precision agriculture by the rapid and accurate acquisition of soil-available potassium nutrient information. However, traditional methods used to detect soil nutrient information are all based on chemical analysis and have high requirements for detection personnel, low detection efficiency, high cost, a likelihood of causing environmental pollution, and other problems, and can no longer meet the development requirements of modern precision agriculture. In recent years, near-infrared analysis technology has received increasing attention for the quantitative determination of soil nutrients due to its advantages of easy operation and no pollution [1; 2; 3; 4; 5; 6].  
Visible near-infrared ray (VIS-NIR) spectroscopy of soil nutrient elements mainly focuses on organic matter, nitrogen, and water, and only a few studies have focused on the quantitative prediction of soil-available potassium. Some experts have researched the application of VIS-NIR spectroscopy for the determination of soil-available potassium. [PERSON] et al. utilized infrared spectroscopy and the Least-squares support vector machines(LS-SVM ) model to predict the nitrogen, phosphorus, and potassium concentrations in soil in 2011 [7]. [PERSON] and [PERSON] utilized the standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky-Golay (SG) methods for pretreatment in the VIS-NIR spectral range, which is only from 325 to 1075 nm. The results showed that the coefficient of determination (\(R^{2}\)) was 0.82 and 0.72 for available phosphorus and available potassium in soil, respectively [8]. [PERSON] et al. used the recursive partial least squares(RPLS) method to detect the soil-available phosphorus and available potassium, and the \(R^{2}\) and the ratio of performance of deviation (RPD) were 0.61 and 0.76 and 1.6 and 2.05, respectively [9]. The soil-available potassium content exhibits an unbalanced distribution; therefore, as the diversity of the soil samples increases, the difficulty in predicting soil properties accurately increases. [PERSON] et al. separated the available potassium content in soil into high and low levels, which were individually calibrated by the partial least squares (PLS) method, and showed that the difference in the soil-available potassium content might influence the performance of the VIS-NIR regression [10]. In these studies, these quantitative prediction methods were meaningful for the analysis of these spectral algorithms [7; 8; 9; 10; 11]. Despite VIS-NIR spectroscopy having been extensively used for the prediction of soil nutrients, the regression model for soil-available potassium exhibited the worst performance. Consequently, the main purpose of this study is to gain an understanding of the pretreatment and regression algorithm required for VIS-NIR spectroscopy to determine the impact of soil-available potassium; thus, this study not only compares various pretreatment and regression methods but also finds the advantage of those methods.  
This research mainly studied yellow loam in southern Anhui Province. Through field soil sampling, indoor physical and chemical analysis, spectral acquisition, pretreatment methods, and so on, a series of working, valid boosting regression algorithms were calibrated for soil-available potassium content with a near-infrared spectrum. Meanwhile, the performance of these models was evaluated via the RPD and ratio of performance to interquartile distance (RPIQ) to analyze the reliability and stability of soil-available potassium predictions. This manuscript also provides a reference for the remote sensing monitoring of soil information.