A Comparison of Spatial Interpolation Methods for Mapping Soil pH by Depths Sara Zandi, Akbar Ghobakhlou, Philip Sallis Geoinformatics Research Centre Auckland University of Technology New Zealand szandi@aut.ac.nz akbar@aut.ac.nz psallis@aut.ac.nz Abstract— Adjusting soil acidity or alkalinity improves soil nutrition without adding extra fertilizers. Soil nutrients needed by plants in the largest amount are referred to as macronutrients. In addition to macronutrients, plants also need trace nutrients and both macro and trace nutrient availability is controlled by soil pH. Understanding of spatial variability of soil properties is important in site-specific management. The special variability of soil property is often measured using various interpolation methods resulting in map generation. Selecting a proper spatial interpolation method is crucial in surface analysis, since different methods of interpolation can lead to different surface results. Ordinary Kriging (OK), Inverse Distance Weighting (IDW), and Radial Basis Functions (RBF) are three well-known spatial interpolation techniques commonly used for characterising the spatial variability and interpolating between sampled points and generating the prediction maps. The variety of available interpolation methods has led to questions about which is most appropriate in different contexts and has stimulated several comparative studies. In this work, three common interpolation methods are used to study the spatial distributions of soil pH in a vineyard. Interpolation techniques were used to estimate the pH measurement in unsampled points and create a continuous dataset that could be represented over a map of the entire study area. The performance of conventional statistics showed that soil pH had a moderate variation in this study area. Keywords— Spatial variability, Geostatistics, Ordinary Kriging, Inverse Distance Weighting, RBF. I. INTRODUCTION Site-specific management has received considerable attention due to the three main potential benefits of increasing input efficiency, improving the economic margins of crop production and reducing environmental risks. Uniform management of crops grown under spatially variable conditions can result in less than optimum yields due to nutrient deficiencies as well as excessive fertilizer application that may potentially reduce environmental quality [21]. Improvement of soil productivity, quality and capacity of soil also known as soil restorative is the basis of a sustainable agricultural system. Soil pH has an influential affect on plant nutrient availability by controlling the chemical forms of the nutrient. Knowledge about spatial variation of soil properties is considered a key variable when implementing "good farming rules" towards sustainable rural development [12]. The sustainable soil properties for efficient crop production depend heavily on the structural properties and the concentration of the soil solution. Spatial variability of soil properties is somewhat inherent in nature because of variations in soil parent materials and microclimate [28]. Knowledge of soil spatial variability and the relationships among soil properties is important for evaluating agricultural land management practices [10]. Geostatistics can be used for studying and predicting the spatial structure of georeferenced variables and generating soil properties map [13]. Soil variability in the field is generally defined with classic statistical methods and is assumed to have a random vari-ability [5]. According to Webster [26] soil characteristics generally show spatial dependence. Samples close to each other have similar properties than those far away from each other. Classical statistics are not capable of analyzing the spatial dependency of the variables since the data assumed to be measured independently [24]. Managing spatial variability which is popularly known as precision farming is essential for serving dual purpose of enhancing productivity and reducing ecological degradation [20]. Ordinary Kriging (OK), Inverse Distance Weighting (IDW),