Abstract
We consider the use of remote sensing for large-scale monitoring of agricultural land use, focusing on classification of tillage and vegetation cover for individual field parcels across large spatial areas. From the perspective of remote sensing and modelling, field parcels are challenging as objects of interest due to highly varying shape and size but relatively uniform pixel content and texture. To model such areas we need representations that can be reliably estimated already for small parcels and that are invariant to the size of the parcel. We propose representing the parcels using density estimates of remote imaging pixels and provide a computational pipeline that combines the representation with arbitrary supervised learning algorithms, while allowing easy integration of multiple imaging sources. We demonstrate the method in the task of the automatic monitoring of autumn tillage method and vegetation cover of Finnish crop fields, based on the integrated analysis of intensity of Synthetic Aperture Radar (SAR) polarity bands of the Sentinel-1 satellite and spectral indices calculated from Sentinel-2 multispectral image data. We use a collection of 127,757 field parcels monitored in April 2018 and annotated to six tillage method and vegetation cover classes, reaching 70% classification accuracy for test parcels when using both SAR and multispectral data. Besides this task, the method could also directly be applied for other agricultural monitoring tasks, such as crop yield prediction.
Highlights
Remote sensing offers a cost-efficient approach for large-scale agricultural land use monitoring for administrative and research purposes, especially when combined with machine learning (ML) methods for estimating land use characteristics for individual crop field parcels [1,2,3] or other small spatial regions
Remote sensing tasks related to agricultural land use frequently involve delineated areas of crop fields, for example, field parcels, as bounded objects of interest that have distributed pixel content with varying degrees of texture
We provided a practical computational pipeline for large-scale agricultural monitoring tasks, combining robust distributional representations computed for individual parcels with standard classifiers
Summary
Remote sensing offers a cost-efficient approach for large-scale agricultural land use monitoring for administrative and research purposes, especially when combined with machine learning (ML) methods for estimating land use characteristics for individual crop field parcels [1,2,3] or other small spatial regions. These methods require a representation for each parcel derived from its pixels, either an explicitly engineered collection of features or an internal representation learnt in a data-driven fashion as in popular deep learning methods such as Convolutional Neural Networks (CNN) [4,5,6,7].
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