Publications

Azar, R; Villa, P; Stroppiana, D; Crema, A; Boschetti, M; Brivio, PA (2016). Assessing in-season crop classification performance using satellite data: a test case in Northern Italy. EUROPEAN JOURNAL OF REMOTE SENSING, 49, 361-380.

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.

DOI:
10.5721/EuJRS20164920

ISSN:
2279-7254