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Open AccessArticle

Optimal Soybean (Glycine max L.) Land Suitability Using GIS-Based Multicriteria Analysis and Sentinel-2 Multitemporal Images

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Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
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Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(9), 1463; https://doi.org/10.3390/rs12091463
Received: 24 March 2020 / Revised: 3 May 2020 / Accepted: 4 May 2020 / Published: 5 May 2020
Soybean is regarded as one of the most produced crops in the world, presenting a source of high-quality protein for human and animal diets. The general objective of the study was to determine the optimal soybean land suitability and conduct its mapping based on the multicriteria analysis. The multicriteria analysis was based on Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) integration, using Sentinel-2 multitemporal images for suitability validation. The study area covered Osijek-Baranja County, a 4155 km2 area located in eastern Croatia. Three criteria standardization methods (fuzzy, stepwise and linear) were evaluated for soybean land suitability calculation. The delineation of soybean land suitability classes was performed by k-means unsupervised classification. An independent accuracy assessment of calculated suitability values was performed by a novel approach with peak Normalized Difference Vegetation Index (NDVI) values, derived from four Sentinel-2 multispectral satellite images. Fuzzy standardization with the combination of soil and climate criteria produced the most accurate suitability values, having the top coefficient of determination of 0.8438. A total of 14.5% of the study area (602 km2) was determined as the most suitable class for soybean cultivation based on k-means classification results, while 64.3% resulted in some degree of suitability. View Full-Text
Keywords: AHP; standardization; NDVI; fuzzy algorithms; k-means; FAO suitability classification AHP; standardization; NDVI; fuzzy algorithms; k-means; FAO suitability classification
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Radočaj, D.; Jurišić, M.; Gašparović, M.; Plaščak, I. Optimal Soybean (Glycine max L.) Land Suitability Using GIS-Based Multicriteria Analysis and Sentinel-2 Multitemporal Images. Remote Sens. 2020, 12, 1463.

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