Abstract
A comprehensive strategy combining remote sensing and field data can be helpful for more effective agriculture management. Satellite data are suitable for monitoring large areas over time, while LiDAR provides specific and accurate data on height and relief. Both types of data can be used for calibration and validation purposes, avoiding field visits and saving useful resources. In this paper, we propose a process for objective and automated identification of agricultural parcel features based on processing and combining Sentinel-2 data (to sense different types of irrigation patterns) and LiDAR data (to detect landscape elements). The proposed process was validated in several use cases in Spain, yielding high accuracy rates in the identification of irrigated areas and landscape elements. An important application example of the work reported in this paper is the European Union (EU) Common Agriculture Policy (CAP) funds assignment service, which would significantly benefit from a more objective and automated process for the identification of irrigated areas and landscape elements, thereby enabling the possibility for the EU to save significant amounts of money yearly.
Highlights
IntroductionThe sheer amount of open and free satellite data (e.g., made available through the Copernicus program), combined with other types of available data (e.g., LiDAR), has the potential to enhance decision-making processes in a wide variety of Earth observation domains
The sheer amount of open and free satellite data, combined with other types of available data (e.g., LiDAR), has the potential to enhance decision-making processes in a wide variety of Earth observation domains
Data submitted by such stakeholders could be incorrect or inaccurate, which results in an unfair funds assignment and expenditure on audits
Summary
The sheer amount of open and free satellite data (e.g., made available through the Copernicus program), combined with other types of available data (e.g., LiDAR), has the potential to enhance decision-making processes in a wide variety of Earth observation domains An example of such a domain is agriculture, where the ability to objectively and automatically identify different types of agricultural features (e.g., irrigation patterns and landscape elements) can lead to more effective agriculture management. In this context, one important decision-making problem in Europe is the Common Agricultural Policy (CAP) funds assignment to farmers and land owners. A possible way to do the assignments more objectively is to share new, and often underused, datasets and cross-check the references in order to obtain a clearer, more objective and better picture of agricultural parcels
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