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19 October 2012 Mapping salinity stress in sugarcane fields with hyperspecteral satellite imagery
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Soil salinity is a huge problem negatively affecting physiological and metabolic processes in plant life, ultimately diminishing growth and yield. An area with more than 70,000 ha sugarcane farming and its by-products are the major agricultural activities in the Khuzestan province, in the southwest of Iran. Therefore, mapping and identification of soil salinity is the most important issue to improve management of large scale crop production in this area. Besides labour intensive fieldwork, remote sensing is the most suitable technique to assess soil salinity for large areas. This study was carried out to investigate the capability of Hyperion spaceborne hyperspecteral data for mapping the salinity stress in the sugarcane fields and determine the best method to classify soil salinity into 3 classes (low, moderate and high salinity). For this purpose the capability of different classification methods like support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) in conjunction with different band combinations (all bands, principle component analysis (PCA), Vegetation Indices) as an input data was performed. Results indicated that best method for classification is SVM classifier when we use all bands or PCA(1-5) as an input data for classification with an overall accuracy and kappa coefficient of 78.7% and 0.68 respectively. Therefore, salinity stress can be classified in agricultural fields using Hyperion satellite imagery with good accuracy and salinity map can be very useful for management of agricultural activity and increase the crop production.
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S. Hamzeh, A.A. Naseri, S.K. Alavi Panah, B. Mojaradi, H.M. Bartholomeus, and M. Herold "Mapping salinity stress in sugarcane fields with hyperspecteral satellite imagery", Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 85312B (19 October 2012);

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