## 2 Materials and Methods  
### Dataset Composition  
The dataset was comprised of the prices collected through a survey among multispectral image (from satellite, plane, and UAV) providers. The surveys were performed in 2017 and 2020 for UAV companies, while for plane and satellite image providers, prices were collected in 2020. Satellite price collection involved nine satellite image providers for a total of 62 prices for 17 different constellations of satellites. This dataset involved constellations of satellites whose spectral resolution was reasonable for NDVI calculation (from 600 to 900 nm), spatial resolution suitable for PA (set at 30 \(\times\) 30 m pixel size or less), and revisit time shorter than 20 days, to allow field monitoring even in cloudy environments. The spatial resolution value refers to panchromatic resolution. The panchromatic resolution is obtained from the pan sharpening process by using a wider spectral resolution band with a better spatial resolution to increase the spatial resolution of a band with a narrower spectral resolution. All information about sensor properties was retrieved from the manufacturers' websites (Table 1) and the European Space Agency (ESA) satellite mission database ([[https://directory.eoportal.org/web/eoportal/satellite-missions](https://directory.eoportal.org/web/eoportal/satellite-missions)]([https://directory.eoportal.org/web/eoportal/satellite-missions](https://directory.eoportal.org/web/eoportal/satellite-missions)), accessed on 30 April 2020). Usually, companies provide two types of products: archive data and new acquisition. Indeed, some constellations of satellites (especially high spatial resolution satellites) acquire images on-demand, according to the received inquiries. On-demand products rely on new acquisition, while images already acquired are archived. The current study considered the minimum price difference between new acquisition and archived images not older than 90 days (fresh archive), since spatial field variability monitoring may rely on relatively outdated surveys, which are less expensive. Satellite constellations and their main features are reported in Table 1. All prices collected refer to a minimum order area ranging from 2500 ha to 10,000 ha. Minimum order area represents the minimum surface commercialized by the image providers. This area is usually far from the final user needs, which would use only a part of the total surface. This point was considered in the minimum price calculation, multiplying price per hectare by the minimum order area. For open-source satellites, such as Sentinel-2 or Landsat-8, the tile dimension was considered as \"minimum order area\". The amount of data per hectare (KB ha\({}^{-1}\)) was extracted starting from radiometric resolution (bits pixel\({}^{-1}\)), spatial resolution (m\({}^{2}\) pixel\({}^{-1}\)) and the number of spectral bands, according to the following equation:  
\[\text{KB ha}^{-1}=\frac{\frac{10000}{\text{m}^{2}\text{ pixel}^{-1}}*\frac{\text{bits pixel}^{-1}}{8}*number\ of\ bands}{1000} \tag{1}\]  
Airborne image prices were collected via a survey of four Italian companies, which provided six prices for different acquisition conditions. UAV data were collected in 2017 and 2020 to highlight the evolution of this technology within the Italian market. The UAV dataset was comprised of 44 prices for 2017 and 22 prices for 2020, divided into arable and specialized crops, based on the company's pricelist classes. Specialized crops were defined according to the USDA definition [42], which includes fruits, vegetables, tree nuts, dried fruits, horticulture, nursery crops and floriculture. On the other hand, arable crops include wheat, corn, soybeans and the majority of commodity crops. This differentiation was necessary since the average income obtained with specialized crops is higher than the average income obtained with arable crops. This study considered only services provided by professionals in PA in Italy.