Abstract

Defining the most profitable remote sensing platforms is a difficult decision-making process, as it requires agronomic and economic considerations. In this paper, the price and profitability of three levels of remote sensing platforms were evaluated to define a decision-making process. Prices of satellite, plane and UAV-acquired vegetation indices were collected in Italy during 2020 and compared to the economic benefits resulting from variable rate nitrogen application, according to a bibliographic meta-analysis performed on grains. The quality comparison of these three technologies was performed considering the error propagation in the NDVI formula. The errors of the single bands were used to assess the optical properties of the sensors. Results showed that medium-resolution satellite data with good optical properties could be profitably used for variable rate nitrogen applications starting from 2.5 hectares, in case of medium resolution with good optical properties. High-resolution satellites with lower optical quality were profitable starting from 13.2 hectares, while very high-resolution satellites with good optical properties could be profitably used starting from 76.8 hectares. Plane-acquired images, which have good optical properties, were profitable starting from 66.4 hectares. Additionally, a reference model for satellite image price is proposed.

Highlights

  • Site-specific management is one of the main drivers of precision agriculture (PA).Spatial and temporal variability can be deployed to support management decisions and improve the profitability and sustainability of agricultural production, taking advantage of information and communications technologies [1]

  • The dataset was comprised of the prices collected through a survey among multispectral image providers

  • 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, spatial resolution suitable for PA, and revisit time shorter than 20 days, to allow field monitoring even in cloudy environments

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Summary

Introduction

Spatial and temporal variability can be deployed to support management decisions and improve the profitability and sustainability of agricultural production, taking advantage of information and communications technologies [1]. Homogeneous management zones are usually identified based on farmers’ knowledge [3], it is common practice to take advantage of proximal or remote sensors [4], taking into consideration standardized and consistent data. Proximal data provided by tractor-embedded sensors can be used to detect indirect suitable information to manage field variability (e.g., speed, engine power supplied, fuel consumption). Proximal sensors can collect high-resolution data (high number of samples per hectare). They require field surveys, which may be time-consuming and tricky to perform (e.g., after the emergence of arable crops)

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