Assessments on the impact of highresolutionsensor pixel sizes for common agricultural policy and smart farming services in European regions Abstract   Highresolution (5xe2x80x9350xc2xa0m) remote sensing satellite sensors provide a reliable, free and open data infrastructure for public and private agriculture and land use services. The further market penetration of these services critically depends on the fraction of agricultural fields and area that the services can cover. EUxe2x80x99s Common Agricultural Policy (CAP) and smart farming services require a minimum of spectrally pure measurements per agricultural field. The impact of pixel size on the coverage of agriculture is studied in this paper considering present free and open optical sensors (Sentinel2 and LANDSAT). It further studies the implications of the selection of spatial resolution of planned extensions of these sensors, i.e. the next generation of Sentinel2, as well as Copernicusxe2x80x99s hyperspectral CHIME and thermal LSTM future candidate missions.  The paper analyzes the 2018 vector boundaries and crop types of 3.6 million agricultural fields in the German States of Bavaria and Lower Saxony and the Netherlands. The fields were rasterized using Sentinel2 flight geometry and a pixel spacing of 5, 10, 20, 30 and 50xc2xa0m. The study specifically considered: (1) fields with no pure pixel inside where no CAP services can be provided and (2) fields with less than 50 pure pixels inside, which is estimated to be the critical number for sitespecific smart farming. The percentage of agricultural fields and agricultural area was determined for the main crop types. It shows, that with 10xc2xa0m pixel spacing 2xe2x80x934% and 20xc2xa0m pixel spacing 12xe2x80x9322% of the agricultural fields in the study area do not contain a single pure spectral sample (Sentinel2 case). This fraction decreases to 1xe2x80x933% at 5xc2xa0m spacing and increases to 25xe2x80x9340% for 30xc2xa0m (LANDSAT and CHIME) and 50xe2x80x9370% for 50xc2xa0m (LSTM) spacing. The percentage of fields with less than 50 pure pixels is 20xe2x80x9350% at 10xc2xa0m and 70xe2x80x9385% at 20xc2xa0m spacing (Sentinel2). This fraction decreases to 5xe2x80x9312% for 5xc2xa0m spacing and reaches the level of 92xe2x80x9397% for 30xc2xa0m (LANDSAT) and 99% for 50xc2xa0m spacing (LSTM). Our analysis shows, that with a pixel spacing of 5xc2xa0m the Sentinel2based sitespecific smart farming services could increase their potential customer base from 50% to 90% of the agricultural fields and could potentially cover 99% of the regionsxe2x80x99 agricultural area. A 20xc2xa0m pixel spacing would increase the agriculture area from 23% to 56% in the Central and Western European study regions on which the Copernicus hyperspectral candidate mission CHIME is capable to measure pure and full spectra for highly advanced future sitespecific management services. LSTM would also profit from a spatial resolution of 30xc2xa0m, which would raise coverage of the agricultural area in Central Europe with pure thermal measurements from 3% at 50xc2xa0m to 23% at 30xc2xa0m.

A spectral imaging system for precision agriculture: From its inception till a precommercial prototype New trends in the development of tools and strategies to optimize the productivity of agricultural fields have led to exciting opportunities based on a combination of two key technologies: Unmanned Aerial Vehicles (UAVs) and spectral sensors. Both of them come from different domains, the former playing a significant role mainly in military applications, and the latter in the space sector. UAVs and earth observation satellites represent the two extremes of monitoring systems in which airborne and High Altitude Pseudo Satellites (HAPS) are located in the middle range. From these four options, UAVs represent a good compromise between cost, performance and ease of operation in their numerous civil and environmental applications. However, they are also very challenging mainly due to the integration of specific payload which requires engineering experience in optics, electronics, physics and aerodynamics. In this paper, a hyperspectral UAV platform is presented, detailing every decision adopted since its inception for its use in precision agriculture, solving the problems encountered in its construction and developing an adhoc application to control the flight trajectory and manage data acquisition optimizing its battery limitations. The result is a robust autonomous system tested in a real scenario to monitor the health status of a vineyard in just a few minutes, based on different Vegetation Indices (VIs), reducing the time and efforts employed by traditional farmers and opening new avenues in the management of agricultural production and environmental respect.