ARPHA Preprints, doi: 10.3897/arphapreprints.e115388

D5.4 Mapping of vegetation indices and metrics, and their utility in FSA mapping at CS scale

Guido Riembauer^‡, Markus Metz^‡, Guy Ziv^§, Jodi Gunning^§, James Bullock^|, Paul Evans^¶, Tomáš Václavík^#, Fanny Langerwisch^#, Marek Bednář^#, Sanja Brdar^¤, Predrag Lugonja^¤

‡ mundialis GmbH & Co KG, Cologne, Germany§ University of Leeds, Leeds, United Kingdom| UK Centre for Ecology & Hydrology, Wallingford, United Kingdom¶ UK Centre for Ecology and Hydrology, Wallingford, United Kingdom# Palacký University Olomouc, Olomouc, Czech Republic¤ BioSense Institute, Novi Sad, Serbia

Corresponding author: Guido Riembauer ( riembauer@mundialis.de )

This is an open access preprint distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation: Riembauer G, Metz M, Ziv G, Gunning J, Bullock J, Evans P, Václavík T, Langerwisch F, Bednář M, Brdar S, Lugonja P (2023) D5.4 Mapping of vegetation indices and metrics, and their utility in FSA mapping at CS scale. ARPHA Preprints. https://doi.org/10.3897/arphapreprints.e115388

Abstract

This deliverable provides an overview of all work conducted in the context of Activity 5.3.1 (Developing remote sensing indicators) with respect to Farming System Archetype (FSA) Mapping (Task 5.3). This work is based on the FSA definition and mapping in ‘D2.2 - Conceptual Framework’ and ‘D3.5 - Farming System Archetypes for each CS’ and investigates the potential of remote sensing methods to inform different dimensions of FSAs. Findings from this analysis will contribute to the BESTMAP roadmap (Task 5.4). Specifically, methodologies for crop type mapping, crop yield estimation, and field boundary mapping are investigated in different case study regions and their relevance for FSAs are shown.

Keywords

farming, remote sensing, crop, ecosystem services, agriculture