Abstract
This work investigates a Sentinel-2 based crop identification methodology for the monitoring of the Common Agricultural Policy’s (CAP) Cross Compliance (CC) and Greening obligations. In this regard, we implemented and evaluated a parcel-based supervised classification scheme to produce accurate crop type mapping in a smallholder agricultural zone in Navarra, Spain. The scheme makes use of supervised classifiers Support Vector Machines (SVMs) and Random Forest (RF) to discriminate among the various crop types, based on a large variable space of Sentinel-2 imagery and Vegetation Index (VI) time-series. The classifiers are separately applied at three different levels of crop nomenclature hierarchy, comparing their performance with respect to accuracy and execution time. SVM provides optimal performance and proves significantly superior to RF for the lowest level of the nomenclature, resulting in 0.87 Cohen’s kappa coefficient. Experiments were carried out to assess the importance of input variables, where top contributors are the Near Infrared (NIR), vegetation red-edge, and Short-Wave Infrared (SWIR) multispectral bands, and the Normalized Difference Vegetation (NDVI) and Plant Senescence Reflectance (PSRI) indices, sensed during advanced crop phenology stages. The scheme is finally applied to a Lansat-8 OLI based equivalent variable space, offering 0.70 Cohen’s kappa coefficient for the SVM classification, highlighting the superior performance of Sentinel-2 for this type of application. This is credited to Sentinel-2’s spatial, spectral, and temporal characteristics.
Highlights
In the decades, the climate change and the estimated increase in the global population will have significant impact on the food sector [1]
The analysis showed that the quadratic kernel Support Vector Machines (SVMs) was the most accurate classifier, while
producer’s accuracy (PA) and user’s accuracy (UA) results presented in Table 4 refer to the quadratic kernel SVM and Random Forest (RF) classifications at the crop type nomenclature level
Summary
The climate change and the estimated increase in the global population will have significant impact on the food sector [1] In this context, increased agricultural productivity under environmentally friendly practices is an increasingly interesting topic and a top priority for the European Union, manifesting predominantly in the form of the Common Agricultural Policy (CAP) [2]. Paying agencies are required to inspect at least 5% of declarations, via means of field visits and photointerpretation of Very High Resolution (VHR) and High Resolution (HR) satellite imagery (i.e., SPOT, Worldview, IKONOS) [4,5] These methods are time-consuming, complex, and reliant on the skills of the inspector
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