1. Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications.
- Author
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Dimitrov, Petar, Dong, Qinghan, Eerens, Herman, Gikov, Alexander, Filchev, Lachezar, Roumenina, Eugenia, and Jelev, Georgi
- Subjects
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PIXELS , *NORMALIZED difference vegetation index , *ARTIFICIAL neural networks , *OPTICAL resolution , *VEGETATION mapping , *CORN - Abstract
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 × 10 km2, especially when the SVR method was used. For the five dominant classes in the test sites the R2 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. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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