Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches

Anis Gasmi,Cécile Gomez,Abdelghani Chehbouni,Driss Dhiba,Hamza Elfil

doi:10.3390/rs14051103

Abstract

Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.e., TM, OLI, ASTER, and S2-MSI). Then, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. The resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the Multilayer Perceptron with backpropagation learning algorithm (MLP-BP) method. The results suggest that for clay content prediction: (i) the spectral bands’ images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. Soil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture.

Highlights

Licensee MDPI, Basel, Switzerland.Soil property prediction and mapping are necessary to determine a soil’s production capabilities and, as a consequence, are excellent resources for many agricultural and environmental issues [1]
This research aims (i) to assess the potential of multi-sensor bare soil images obtained by the fusion of various multispectral satellite images (i.e., Landsat-TM, Landsat-OLI, ASTER, and Sentinel-2A MSI) to predict clay content using spectral indices vs. spectral bands approaches and (ii) to compare their performances with those obtained from single-sensor images
The results of this paper suggest that the models based on satellite multi-sensor data fusion were the most precise for prediction and mapping of soil clay content

Summary

Introduction

Soil property prediction and mapping are necessary to determine a soil’s production capabilities and, as a consequence, are excellent resources for many agricultural and environmental issues [1]. In this context, digital soil mapping (DSM) [1,2] is widely used as a tool for soil information mapping around the world. Remote sensing satellite images directly estimate some fundamental soil surface properties, such as soil texture (sand, clay, and silt content), carbon content, and soil salinity, e.g., [5,6,7,8]

Objectives

Methods

Results

Discussion

Conclusion