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

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 9; p. 1463
• Main Authors: Radočaj, Dorijan, Jurišić, Mladen, Gašparović, Mateo, Plaščak, Ivan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2020
• Subjects: Agricultural production; Agriculture; AHP; Analytic hierarchy process; Classification; climate; Croatia; crop production; Crops; Cultivation; diet; FAO suitability classification; Fisheries; Food; fuzzy algorithms; Geographic information systems; Glycine max; Irrigation; k-means; land suitability; Land use planning; Mapping; Mathematical analysis; Methods; Multiple criterion; NDVI; normalized difference vegetation index; Normalized difference vegetative index; Production increases; Protein sources; Remote sensing; Rural development; Satellite imagery; Satellites; soil; Soybeans; Standardization; Studies; Sustainability; Vegetation; Germany; Croatia
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12091463

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.