Assessing in-season crop classification performance using satellite data: a test case in Northern Italy. Issue 1 (1st January 2016)

• Record Type: Journal Article
• Authors: Azar, Ramin; Villa, Paolo; Stroppiana, Daniela; Crema, Alberto; Boschetti, Mirco; Brivio, Pietro Alessandro
• Is Part Of: European journal of remote sensing. Volume 49:Issue 1(2016)
• Page Start: 361
• Page End: 380
• Publication Date: 2016-01-01
• Subjects: Early mapping -- crop type -- multi-temporal data -- supervised classification -- Landsat 8 OLI; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.5721/EuJRS20164920
• Languages: English
• ISSNs: 2279-7254

Abstract

Abstract: This study investigated the feasibility of delivering a crop type map early during the growing season. Landsat 8 OLI multi-temporal data acquired in 2013 season were used to classify seven crop types in Northern Italy. The accuracy achieved with four supervised algorithms, fed with multi-temporal spectral indices (EVI, NDFI, RGRI), was assessed as a function of the crop map delivery time during the season. Overall accuracy (Kappa) exceeds 85% (0.83) starting from mid-July, five months before the end of the season, when maximum accuracy is reached (OA=92%, Kappa=0.91). Among crop types, rice is the most accurately classified, followed by forages, maize and arboriculture, while soybean or double crops can be confused with other classes.