Using Constrained K-Means Clustering for Soil Texture Mapping with Limited Soil Samples

Soil texture is one of the most important physical properties of soil and plays a crucial role in determining its suitability for crop cultivation. Currently, supervised classification machine learning methods are most commonly used in digital soil mapping. However, these methods may not yield optimal predictive performance due to the limited number of soil samples. Therefore, we propose using Constrained K-Means Clustering to combine a small number of labeled samples with a large amount of unlabeled data, thereby achieving improved prediction in soil texture mapping. In this study, we focused on a typical hilly region in northern Jurong City, Jiangsu Province, China, and used Constrained K-Means Clustering as our mapping model. GF-2 remote sensing imagery and the ALOS digital elevation model (DEM), along with their derived variables, were employed as environmental variables. In Constrained K-Means Clustering, the choice of distance method is a key parameter. Here, we used four different distance methods (euclidean, maximum, manhattan, and canberra) and compared the results with those of the random forest (RF) and multilayer perceptron (MLP) models. Notably, the euclidean distance method within Constrained K-Means Clustering achieved the highest overall accuracy (OA), Kappa coefficient, and Macro F1 Score, with values of 0.77, 0.68, and 0.75, respectively. These methods were higher than those obtained by the RF and MLP models by 0.12, 0.18, and 0.12, and 0.18, 0.26, and 0.18, respectively. This indicates that Constrained K-Means Clustering demonstrates strong predictive performance in soil texture mapping. Moreover, land use (LU), multi-resolution of ridge top flatness index (MRRTF), topographic position index (TPI), and plan curvature (PlC) emerged as the key environmental variables for predicting soil texture. Overall, Constrained K-Means Clustering proves to be an effective digital soil mapping approach, offering a novel perspective for soil texture mapping with limited samples.