## 2 Materials and Methods
### Price Composition, Modelization and Profitability Analysis  
Comparison among the different platforms and sensors was carried out by dividing satellite sensors into groups using K-means unsupervised classification algorithm [47] based on sensors features (Table 1). This classification algorithm is frequently used for price discretization [48; 49]. The elbow method was used to identify the optimal number of groups. These three groups obtained by K-mean classification can be summarized as Very High Resolution (VHR; spatial resolution <1 m), High Resolution (HR; spatial resolution from 1 to 5 m) and Medium Resolution satellites (MR; spatial resolution >10 m). A principal component analysis (PCA) was performed using the satellite features dataset to investigate  
the main features affecting satellite price per hectare. A regression model was fitted to estimate the price of satellite images. To evaluate the download costs of images provided by different satellite constellations, the amount of data per hectare was multiplied by the average cost of mobile broadband connection in the European Union-28 (0.93 EUR GB\({}^{-1}\) in 2019) [50]. Although nowadays broadband connection costs are relatively low and could be ignored, download cost was considered in this analysis in order to estimate the cost of the amount of data per hectare, which otherwise would not be considered. Although images are geometrically and radiometrically corrected, atmospheric correction at the Bottom of the Atmosphere (BOA) is not always performed. In the price analysis of the satellite images, a correction cost quota proportional to the spatial resolution was included. The correction cost was estimated according to the labor cost of the geospatial analysis, which is strongly influenced by the spatial resolution. Accordingly, correction cost was 150 EUR for VHR, 100 EUR for HR, and 50 EUR for MR satellites. The correction cost was applied once per image and was not related to the overall area elaborated. These three quotas were estimated considering the average labor cost in the Euro-area, which is 49.2 EUR*h\({}^{-1}\) for service employees [51]. An additional quota was added to the final price to take into account the analysis costs from professional users to define the management zones, produce prescription maps, and produce agronomic advice. Although management zone delineation and prescription map definition are complex tasks and require knowledgeable professionals, automatic and unsupervised algorithms can nowadays provide meaningful and effective services. According to this latter consideration, processing costs were established as follows: 75 EUR for VHR, 50 EUR for HR and 25 EUR for MR satellites, thus considering the major processing time for higher-resolution images. This processing cost was applied once per image and was not related to the overall area elaborated. The prices of airborne platforms were composed of the price per hectare obtained from quotes, added to a fixed cost for take-off. The take-off quota was retrieved from the average cost provided by the companies (2000 EUR per flight). The UAV companies involved in the survey did not declare a minimum flight surface and a take-off cost. UAV and plane companies provided prices of NDVI images already elaborated (geo-referenced, mosaicized and radiometrically corrected). For this reason, estimation of the elaboration costs was not performed. As for VHR satellite data, the processing cost (management zone definition and agronomic advice) for UAV and plane images was established as 75 EUR. The resolution of UAV and plane images was, indeed, comparable to that of VHR satellites. Table 2 summarizes the final prices of each platform.