GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques

Abstract

Soil moisture represents a vital component of the ecosystem, sustaining life-supporting activities at micro and mega scales. It is a highly required parameter that may vary significantly both spatially and temporally. Due to this fact, its estimation is challenging and often hard to obtain especially over large, heterogeneous surfaces. This study aimed at comparing the performance of four widely used interpolation methods in estimating soil moisture using GPS-aided information and remote sensing. The Distance Weighting (IDW), Spline, Ordinary Kriging models and Kriging with External Drift (KED) interpolation techniques were employed to estimate soil moisture using 82 soil moisture field-measured values. Of those measurements, data from 54 soil moisture locations were used for calibration and the remaining data for validation purposes. The study area selected was Varanasi City, India covering an area of 1535 km2. The soil moisture distribution results demonstrate the lowest RMSE (root mean square error, 8.69%) for KED, in comparison to the other approaches. For KED, the soil organic carbon information was incorporated as a secondary variable. The study results contribute towards efforts to overcome the issue of scarcity of soil moisture information at local and regional scales. It also provides an understandable method to generate and produce reliable spatial continuous datasets of this parameter, demonstrating the added value of geospatial analysis techniques for this purpose.

Health risk assessment of nitrate in groundwater resources of Iranshahr using Monte Carlo simulation and geographic information system (GIS)

“…The weight will change linearly according to sample data distance [27]. Kriging is similar stochastic approximation to IDW which use linear combination of weight to estimate the value among sample data [28]. The assumption of this method is the distance among sample data showing the important geographical correlation on the interpolation result [29].…”

New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

“…To fill all missing data pixels in the study area, universal kriging interpolation technique is used. Universal kriging showed good performances compared to other commonly used interpolation techniques, almost as good as kriging with an external drift [44]. The advantage is that universal kriging does not require additional variables within the interpolation process.…”

The Evaluation of the Accuracy of Interpolation Methods in Crafting Maps of Physical and Hydro-Physical Soil Properties

“…Interpolation in the framework of understanding of GIS technology is a workflow that converts discrete spatial point information into continuous spatial information based on the assumption of spatial autocorrelation. While the usage of interpolation methods to express the shape of topography (digital terrain model) belongs among the key processes in water erosion modeling [32][33][34], its usage in other areas of soil science is less abundant e.g., [35][36][37]. Discrete point information can be a tachymetrically (geodetically) pointed field with a specified altitude, an array of line points representing contour fields, a network of meteorological stations with regular air temperature measurements or, as in our case, an irregular point field of soil probes representing selected soil properties.…”