{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T19:11:26Z","timestamp":1775329886548,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2015,4,24]],"date-time":"2015-04-24T00:00:00Z","timestamp":1429833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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 \u201cclasses\u201d 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.<\/jats:p>","DOI":"10.3390\/ijgi4020677","type":"journal-article","created":{"date-parts":[[2015,4,24]],"date-time":"2015-04-24T10:01:59Z","timestamp":1429869719000},"page":"677-696","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression, Egypt"],"prefix":"10.3390","volume":"4","author":[{"given":"Filippo","family":"Amato","sequence":"first","affiliation":[{"name":"Department of Chemistry, Faculty of Science, Masaryk University, Kampus Bohunice,  Kamenice 5\/A14, 625 00 Brno, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Josef","family":"Havel","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Faculty of Science, Masaryk University, Kampus Bohunice,  Kamenice 5\/A14, 625 00 Brno, Czech Republic"},{"name":"Department of Physical Electronics, Faculty of Science, Masaryk University, Kotl\u00e1\u0159sk\u00e1 2,  611 37 Brno, Czech Republic"},{"name":"CEPLANT, R&D Centre for Low-Cost Plasma and Nanotechnology Surface Modifications,  Masaryk University, Kotl\u00e1\u0159sk\u00e1 2, 611 37 Brno, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abd-Alla","family":"Gad","sequence":"additional","affiliation":[{"name":"National Authority for Remote Sensing and Space Sciences (NARSS), P.O. Box 1564, Alf Maskan, 11843 Cairo, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"El-Zeiny","sequence":"additional","affiliation":[{"name":"National Authority for Remote Sensing and Space Sciences (NARSS), P.O. Box 1564, Alf Maskan, 11843 Cairo, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,4,24]]},"reference":[{"key":"ref_1","unstructured":"Kone\u010dn\u00fd, M. The Digital Earth: Spatial data infrastructures from local to global concept. Towards Digital Earth: Proceedings of the International Symposium on Digital Earth."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s00267-013-0110-0","article-title":"Evaluation and selection of indicators for land degradation and desertification monitoring: Types of degradation, causes, and implications for management","volume":"54","author":"Kairis","year":"2013","journal-title":"Environ. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/01431160701352154","article-title":"The application of artificial neural networks to the analysis of remotely sensed data","volume":"29","author":"Mas","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","first-page":"276","article-title":"Analysis of satellite images using artificial neural network","volume":"2","author":"Sharma","year":"2013","journal-title":"Int. J. Soft Comput. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.talanta.2012.01.044","article-title":"Artificial neural networks combined with experimental design: A \u201csoft\u201d approach for chemical kinetics","volume":"93","author":"Amato","year":"2012","journal-title":"Talanta"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/S0255-2701(99)00093-8","article-title":"Neural networks for modelling of chemical reaction systems with complex kinetics: Oxidation of 2-octanol with nitric acid","volume":"39","author":"Molga","year":"2000","journal-title":"Chem. Eng. Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.ejps.2004.12.005","article-title":"Prediction of kinetics of doxorubicin release from sulfopropyl dextran ion-exchange microspheres using artificial neural networks","volume":"24","author":"Li","year":"2005","journal-title":"Eur. J. Pharm. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.talanta.2013.04.031","article-title":"Development and validation of a general approach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks","volume":"115","author":"Pivetta","year":"2013","journal-title":"Talanta"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/S0021-9673(97)00918-7","article-title":"Neural networks for optimization of high-performance capillary zone electrophoresis methods: A new method using a combination of experimental design and artificial neural networks","volume":"793","author":"Havel","year":"1998","journal-title":"J. Chromatogr. A"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11435","DOI":"10.1021\/jf102014j","article-title":"Cluster analysis and artificial neural networks multivariate classification of onion varieties","volume":"58","author":"Havel","year":"2010","journal-title":"J. Agric. Food Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1017\/S0007485308005750","article-title":"Thrips (Thysanoptera) identification using artificial neural networks","volume":"98","author":"Fedor","year":"2008","journal-title":"J. Bull. Entomol. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1017\/S0007485310000295","article-title":"Polyphasic approach applying artificial neural networks, molecular analysis and postabdomen morphology to West Palaearctic Tachina spp. (Diptera, Tachinidae)","volume":"101","author":"Havel","year":"2011","journal-title":"J. Bull. Entomol. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"47","DOI":"10.2478\/v10136-012-0031-x","article-title":"Artificial neural networks in medical diagnosis","volume":"11","author":"Amato","year":"2013","journal-title":"J. Appl. Biomed."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4875","DOI":"10.1007\/s11270-012-1243-0","article-title":"Water quality monitoring using remote sensing and an artificial neural network","volume":"223","author":"Chebud","year":"2012","journal-title":"Water Air Soil Pollut."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s11707-012-0346-7","article-title":"An artificial neural network approach to estimate evapotranspiration from remote sensing and AmeriFlux data","volume":"7","author":"Chen","year":"2012","journal-title":"Front. Earth Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.rse.2013.02.015","article-title":"Deriving ocean color products using neural networks","volume":"134","author":"Ioannou","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.1016\/j.rse.2011.07.018","article-title":"Fractional snow cover mapping through artificial neural network analysis of MODIS surface reflectance","volume":"115","author":"Dobreva","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Atazadeh, I. (2011). Biomass and Remote Sensing of Biomass, InTech.","DOI":"10.5772\/939"},{"key":"ref_19","first-page":"351","article-title":"Remote sensing and artificial neural network in spatial assessment of air temperature in a semi-arid watershed","volume":"04","author":"Aher","year":"2011","journal-title":"Int. J. Earth Sci. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"579","DOI":"10.3390\/rs2020579","article-title":"Artificial neural network approach for mapping contrasting tillage practices","volume":"2","author":"Sudheer","year":"2010","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.compag.2006.08.001","article-title":"Soil texture classification with artificial neural networks operating on remote sensing data","volume":"54","author":"Zhai","year":"2006","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1002\/evan.20324","article-title":"Finding fossils in new ways: An artificial neural network approach to predicting the location of productive fossil localities","volume":"180","author":"Anemone","year":"2011","journal-title":"Evol. Anthropol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2004.03.003","article-title":"Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients","volume":"91","author":"Mertens","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_24","unstructured":"Central Laboratory for Agricultural Climate (CLAC). Available online:http:\/\/www.clac.edu.eg\/."},{"key":"ref_25","unstructured":"Hulme, M., and March, R. (1990). Global Mean Monthly Humidity Surfaces for 1930-59, 1960-89 and Projected for 2020, UNEP\/GEMS\/GRID, Climatic Research Unit, University of East Anglia."},{"key":"ref_26","unstructured":"Euroconsult (1992). Environmental Profile, Fayoum Governorate, Egypt, Al-Shorouk Press."},{"key":"ref_27","unstructured":"Abo El Enean, S.M. (1985). Pedogenesis of El-Fayoum Area. [Ph.D. Thesis, Al-Azhar University]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1021\/ac60294a012","article-title":"Computation of equilibrium constants for multicomponent systems from spectrophotometric data","volume":"42","author":"Kankare","year":"1970","journal-title":"Anal. Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/S0169-7439(01)00131-9","article-title":"Dealing with missing data: Part I","volume":"58","author":"Walczak","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/S0169-7439(01)00132-0","article-title":"Dealing with missing data: Part II","volume":"58","author":"Walczak","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_31","unstructured":"Massart, D.L., Vandeginste, B.G.M., Buydens, L.M.C., de Jong, S., Lewi, P.J., and Smeyers-Verbeke, J. (1997). Handbook of Chemometrics and Qualimetrics, Elsevier Science."},{"key":"ref_32","unstructured":"Malinowski, E.R. (2002). Factor Analysis in Chemistry, John Wiley & Sons Inc.. [3rd ed.]."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/0039-9140(85)80055-2","article-title":"Multiparametric curve fitting VII\u2014Determination of the number of complex species by factor analysis of potentiometric data","volume":"32","author":"Havel","year":"1985","journal-title":"Talanta"},{"key":"ref_34","unstructured":"Aleksander, I., and Morton, H. (1995). An Introduction to Neural Computing, International Thomson Computer Press."},{"key":"ref_35","unstructured":"Zupan, J.G.J. (1999). Neural Networks in Chemistry and Drug Design, Wiley VCH. [2nd ed.]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/1476-4598-4-29","article-title":"Artificial neural networks for diagnosis and survival prediction in colon cancer","volume":"4","author":"Ahmed","year":"2005","journal-title":"Mol. Cancer"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","article-title":"Artificial neural networks: Fundamentals, computing, design, and application","volume":"43","author":"Basheer","year":"2000","journal-title":"J. Microbiol. Methods"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.3923\/jas.2005.1394.1398","article-title":"The effects of outliers data on neural network performance","volume":"5","author":"Khamis","year":"2005","journal-title":"J. Appl. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/S0893-6080(03)00013-3","article-title":"Outlier detection in scatterometer data: Neural network approaches","volume":"16","author":"Bullen","year":"2003","journal-title":"Neural Netw."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/4\/2\/677\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:45:18Z","timestamp":1760215518000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/4\/2\/677"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,4,24]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2015,6]]}},"alternative-id":["ijgi4020677"],"URL":"https:\/\/doi.org\/10.3390\/ijgi4020677","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,4,24]]}}}