2019
DOI: 10.3390/resources8020070
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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 … Show more

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Cited by 61 publications
(37 citation statements)
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“…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].…”
Section: Description Of Protocolmentioning
confidence: 99%
“…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].…”
Section: Description Of Protocolmentioning
confidence: 99%
“…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.…”
Section: Filling Spatial and Temporal Data Gapsmentioning
confidence: 86%
“…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.…”
Section: Introductionmentioning
confidence: 99%