Comparison of Crop Area Estimation for the Winter Season Using the Google Earth Engine (GEE) Platform and Traditional Method of Supervisor Classification
Asmaa, Sh. Amr *
RS and GIS Unit, Soils, Water and Environment Research Institute, ARC, Giza, Egypt.
A. S. Sheta
Department of Soil Science, Faculty of Agriculture, Ain Shams University, Cairo, Egypt.
M. S. Elwahed
Department of Soil Science, Faculty of Agriculture, Ain Shams University, Cairo, Egypt.
M. Ismail
RS and GIS Unit, Soils, Water and Environment Research Institute, ARC, Giza, Egypt.
A. F. Abou-Hadid
Department of Horticulture, Faculty of Agriculture, Ain Shams University, Cairo, Egypt.
*Author to whom correspondence should be addressed.
Abstract
There is a significant development in the methods and techniques for classifying satellite images during these years. One of the most recently launched methods is using the Google Earth Engine platform for its contribution in providing a huge amount of satellite data with different dates and multiple satellite images sources, with a method of work that is easy to deal with, but it needed to learn the computer language such as Java and Python, which is the method of work depends on writing a script that gives application commands to classify the different satellite image. Therefore, this research aims to apply classification of satellite images using the Google Earth Engine platform for three models Classification and Regression Trees (CART), Support Vector Machine (SVM), and Random Forest (RF), then compare the results calculated from using images classification (Supervised classification: Maximum likelihood) in terms of the degree of accuracy of the classification and matching the results of the classified satellite images in addition. to boundary clarify of the classification methods. This study showed that, using the Google Earth Engine (GEE) platform is considered one of the important methods that facilitated researchers interested in using remote sensing techniques and geographic information systems, due to the availability of this platform of free satellite sources with multiple spatial resolutions, in addition to the presence of a powerful system that collects, processes, and classifies satellite images using different classification models and indexes. But we need to apply these modern techniques at a number of levels, such as at Markas, Governorates, International, and regional levels, to judge their accuracy and suitability for large areas.
Keywords: GEE platform, CART, SVM, RF, supervised classification, MLKH, Qewsina
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