Soil Moisture Mapping in an Arid Area Using a Land Unit Area (LUA) Sampling Approach and Geostatistical Interpolation Techniques

Abstract

Soil moisture (SM) plays a key role in many environmental processes and has a high spatial and temporal variability. Collecting sample SM data through field surveys (e.g., for validation of remote sensing-derived products) can be very expensive and time consuming if a study area is large, and producing accurate SM maps from the sample point data is a difficult task as well. In this study, geospatial processing techniques are used to combine several geo-environmental layers relevant to SM (soil, geology, rainfall, land cover, etc.) into a land unit area (LUA) map, which delineates regions with relatively homogeneous geological/geomorphological, land use/land cover, and climate characteristics. This LUA map is used to guide the collection of sample SM data in the field, and the field data is finally spatially interpolated to create a wall-to-wall map of SM in the study area (Garmsar, Iran). The main goal of this research is to create a SM map in an arid area, using a land unit area (LUA) approach to obtain the most appropriate sample locations for collecting SM field data. Several environmental GIS layers, which have an impact on SM, were combined to generate a LUA map, and then field surveying was done in each class of the LUA map. A SM map was produced based on LUA, remote sensing data indexes, and spatial interpolation of the field survey sample data. The several interpolation methods (inverse distance weighting, kriging, and co-kriging) were evaluated for generating SM maps from the sample data. The produced maps were compared to each other and validated using ground truth data. The results show that the LUA approach is a reasonable method to create the homogenous field to introduce a representative sample for field soil surveying. The geostatistical SM map achieved adequate accuracy; however, trend analysis and distribution of the soil sample point locations within the LUA types should be further investigated to achieve even better results. Co-kriging produced the most accurate SM map of the study area.

Uncertainty in Upscaling In Situ Soil Moisture Observations to Multiscale Pixel Estimations with Kriging at the Field Level

“…Kriging is based on the theory and structural properties of a semivariogram, and it makes optimal and unbiased estimations for regionalized variables at unsampled locations [28,39]. A semivariogram is the basic of geostatistics, and can quantify the spatial heterogeneity of the regionalized variable, soil moisture Z(x) [40,41].…”

“…where µ denotes Lagrange's multiplier, γ nn denotes the semivariogram values computed using the observation points, and γ n denotes the semivariogram values computed using observation points and the points to be estimated [23,39]. For BK, each weight coefficient ω i is obtained from Equations (4) and (5):…”

A top–down soil moisture and sap flux sampling design of a rain–snow transition mountain watershed

“…An alternative and commonly used approach for sampling design is to use overlays of environmental data layers (elevation, land use, soil and vegetation maps, and etc.) to define the sampling strategy (Schröter, Paasche, Dietrich, & Wollschlager, ; Van Arkel & Kaleita, Gharechelou, Tateishi, Sharma, & Johnson, ). Using hydrologic model for soil moisture and sap flux sampling design has several advantages.…”

A Novel Simplified Algorithm for Bare Surface Soil Moisture Retrieval Using L-Band Radiometer

“…In addition, it plays a crucial role in water resources management, vegetation growth, flood monitoring, and climate prediction [1][2][3]. Remote sensing is one of the most effective tools used in soil moisture monitoring due to its wide range of observations and the high frequency of repeated measurements.…”