%0 Journal Article
%T Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications
%A Alexander Gikov
%A Eugenia Roumenina
%A Georgi Jelev
%A Herman Eerens
%A Lachezar Filchev
%A Petar Dimitrov
%A Qinghan Dong
%J Remote Sensing | An Open Access Journal from MDPI
%D 2019
%R https://doi.org/10.3390/rs11111370
%X This paper presents the results of a sub-pixel classification of crop types in Bulgaria from PROBA-V 100 m normalized difference vegetation index (NDVI) time series. Two sub-pixel classification methods, artificial neural network (ANN) and support vector regression (SVR) were used where the output was a set of area fraction images (AFIs) at 100 m resolution with pixels containing estimated area fractions of each class. High-resolution maps of two test sites derived from Sentinel-2 classifications were used to obtain training data for the sub-pixel classifications. The estimated area fractions have a good correspondence with the true area fractions when aggregated to regions of 10 กม 10 km 2, especially when the SVR method was used. For the five dominant classes in the test sites the R 2 obtained after the aggregation was 86% (winter cereals), 81% (sunflower), 92% (broad-leaved forest), 89% (maize), and 67% (grasslands) when the SVR method was used. View Full-Tex
%U https://www.mdpi.com/2072-4292/11/11/1370