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.
Highlights
Soil moisture represents a vital component of the ecosystem sustaining life-supporting activities at micro and mega scales [1,2]
The spatial interpolation methods covered in this study focuses on four methods namely Inverse Distance Weighting (IDW), spline, kriging and linear spatial interpolation methods
The final model variogram for Ordinary Kriging (OK) and Kriging with External Drift (KED) was chosen on the basis of the lowest Root Mean Square Error (RMSE) from validation
Summary
Soil moisture represents a vital component of the ecosystem sustaining life-supporting activities at micro and mega scales [1,2]. It is highly variable with spatial and temporal scales and depends upon the topographical, soil, land cover and climatic conditions [3,4]. Resources 2019, 8, 70 part of the hydrological cycle and is essential for human and plant growth [5,6,7] Measurement of this parameter is imperative to agricultural aspects due to its importance for early monitoring of drought warnings. Reference [13] stressed the importance of soil moisture information for meteorologists, since this parameter is related to weather changes, thereby providing a more accurate weather forecast. Information on this parameter is important in biodiversity and ecosystems management [14]
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