Utilizing of principal component analysis and geographic information system approach for assessing soil quality index under different land uses: case study

Abstract

One of the most popular indicators for delivering accurate information about soil quality is the soil quality index (SQI) that commonly calculated using principal component analysis (PCA). The purpose of this work is to evaluate a SQI using a PCA using additive and weights approaches presuming that different soil uses have varied effects on different soil attributes. Principal component analysis has been used to select a minimal dataset (MDS) from soils with various uses (old cultivated, new cultivated and barren soils) to construct SQI from nine soil indicators. The weights for each indicator were assigned by PCA to the SQI integrated by weights (SQI-W) from the MDS. While, the additive SQI (SQI-A) based on the MDS determined by PCA was finally generated. The results demonstrated that the SQI computed from the PCA using the weights and additive integration PCA could clearly distinguish between the three land uses. As a result, the two techniques were sensitive and capable to distinguish between the soils of the three land uses.