Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression, Egypt

ISPRS International Journal of Geo-Information

Filippo Amato²  Josef Havel²  Abd-Alla Gad¹  Ahmed Mohamed El-Zeiny¹ 

[1] National Authority for Remote Sensing and Space Sciences (NARSS), P.O. Box 1564, Alf Maskan, 11843 Cairo, Egypt; E-Mails:;Department of Chemistry, Faculty of Science, Masaryk University, Kampus Bohunice, Kamenice 5/A14, 625 00 Brno, Czech Republic; E-Mail:

关键词: remote sensing;    soil classification;    desertification;    land use/cover;    soil taxonomy;    eigenvalues analysis;    principal components analysis;    artificial neural networks;   

DOI  :  10.3390/ijgi4020677

来源: mdpi

【 摘 要 】

Earth observation and monitoring of soil quality, long term changes of soil characteristics and deterioration processes such as degradation or desertification are among the most important objectives of remote sensing. The georeferenciation of such information contributes to the development and progress of the Digital Earth project in the framework of the information globalization process. Earth observation and soil quality monitoring via remote sensing are mostly based on the use of satellite spectral data. Advanced techniques are available to predict the soil or land use/cover categories from satellite imagery data. Artificial Neural Networks (ANNs) are among the most widely used tools for modeling and prediction purposes in various fields of science. The assessment of satellite image quality and suitability for analysing the soil conditions (e.g., soil classification, land use/cover estimation, etc.) is fundamental. In this paper, methodology for data screening and subsequent application of ANNs in remote sensing is presented. The first stage is achieved via: (i) elimination of outliers, (ii) data pre-processing and (iii) the determination of the number of distinguishable soil “classes” via Eigenvalues Analysis (EA) and Principal Components Analysis (PCA). The next stage of ANNs use consists of: (i) building the training database, (ii) optimization of ANN architecture and database cleaning, and (iii) training and verification of the network. Application of the proposed methodology is shown.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.