Abstract
Satellite crop detection technologies are focused on the detection of different types of crops in fields. The information of crop-type area is more useful for food security than the earlier phenology stage is. Currently, data obtained from remote sensing (RS) are used to solve tasks related to the identification of the type of agricultural crops; additionally, modern technologies using AI methods are desired in the postprocessing stage. In this paper, we develop a methodology for the supervised classification of time series of Sentinel-2 and Sentinel-1 data, compare the accuracies based on different input datasets and find how the accuracy of classification develops during the season. In the EU, a unified Land Parcel Identification System (LPIS) is available to provide essential field borders. To increase usability, we also provide a classification of the entire field. This field classification also improves overall accuracy.
Highlights
Farooque and Farhat AbbasSatellite crop detection technology is focused on the detection of different types of crops in the field in the early stage before harvesting
When the classification was made based on 26 RVI4S1 layers from March till August and was improved by the sieve filter, the overall accuracy came to 67%
Our work shows that the use of time series can significantly improve the accuracy of the classification of individual crops compared to single-image classification
Summary
Farooque and Farhat AbbasSatellite crop detection technology is focused on the detection of different types of crops in the field in the early stage before harvesting. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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