Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

Said Nawar,Henning Buddenbaum,Joachim Hill

doi:10.3390/rs70201181

Abstract

Modeling and mapping of soil properties has been identified as key for effective land degradation management and mitigation. The ability to model and map soil properties at sufficient accuracy for a large agriculture area is demonstrated using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Soil samples were collected in the El-Tina Plain, Sinai, Egypt, concurrently with the acquisition of ASTER imagery, and measured for soil electrical conductivity (ECe), clay content and soil organic matter (OM). An ASTER image covering the study area was preprocessed, and two predictive models, multivariate adaptive regression splines (MARS) and the partial least squares regression (PLSR), were constructed based on the ASTER spectra. For all three soil properties, the results of MARS models were better than those of the respective PLSR models, with cross-validation estimated R2 of 0.85 and 0.80 for ECe, 0.94 and 0.90 for clay content and 0.79 and 0.73 for OM. Independent validation of ECe, clay content and OM maps with 32 soil samples showed the better performance of the MARS models, with R2 = 0.81, 0.89 and 0.73, respectively, compared to R2 = 0.78, 0.87 and 0.71 for the PLSR models. The results indicated that MARS is a more suitable and superior modeling technique than PLSR for the estimation and mapping of soil salinity (ECe), clay content and OM. The method developed in this paper was found to be reliable and accurate for digital soil mapping in arid and semi-arid environments.

Highlights

Modeling and mapping soils for their physical and chemical properties typically involves extensive field work and laboratory analysis, which are expensive and time consuming [1]
The aim of this study was to extend the capabilities of the models elaborated in [58] to map soil salinity, clay content and organic matter and to test the performance of ASTER data in mapping these soil properties using a combination of field sampling and spectral reflectance derived from ASTER
The specific objectives of the study were to: (I) evaluate the potential of ASTER data for the modeling of soil properties; (II) develop predictive models based on ASTER spectra for assessing soil salinity, clay content and organic matter using multivariate adaptive regression splines (MARS) and to compare its performance to the results acquired with the partial least squares regression (PLSR) method; and (III) map the spatial distribution of soil properties based on ASTER

Summary

Introduction

Modeling and mapping soils for their physical and chemical properties typically involves extensive field work and laboratory analysis, which are expensive and time consuming [1]. Digital soil mapping (DSM) relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties at different spatial and temporal scales [2], to provide up-to-date soil information [3,4]. Remotely-sensed imagery in combination with field measurements has been used intensively for the last two decades in modeling and mapping of soil properties in a cost-effective manner at various scales [7,8]. Satellite sensors, such as the Landsat Thematic Mapper (TM) Enhanced Thematic Mapper

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Conclusion