MOT - Concurs d'idees de casos d'ús d'observació de la Terra per al sector privat

MOT – Earth Observation Module, is one of the projects developed in the first “Call for Ideas for Earth Observation Use Cases in the Private Sector,” coordinated by the Institute of Space Studies of Catalonia (IEEC) in collaboration with the Cartographic and Geological Institute of Catalonia (ICGC). This initiative is part of the NewSpace Strategy of Catalonia, promoted by the Government of Catalonia and implemented in collaboration with the IEEC, the i2CAT Foundation, and the ICGC. This project was developed by Spascat with the support of Agropixel and Codorniu.

This called “use-case” project addressed this current gap in precision agriculture by proposing a novel approach that integrates satellite spectral images with on-field observations in order to empower technicians and workers of such woody crops. The satellite data considered was ESA’s Copernicus program (Sentinel-2), and Menut, the Earth-Observation nanosatellite spearheading the NewSpace Catalonia initiative. By harnessing the information availablefrom hyperspectral imaging, and combining it with high-resolution ground-level imagery (airplane and/or on-foot photos), SPASCAT developed an AI based tool that provides comprehensive insights into the state and classificationof the cultivated plots of interest. This tool was called “Earth Observation Module” (MOT), acting as a stand-aloneadd-on to the PixelSuite platform, an agritech tool for agriculture technicians by Agropixel.

A six-month user-centered development led to the MOT’s operational deployment. MOT is now available as anadd-on for Agropixel’s PixelSuite agritech tool, and automates parcel management, satellite data processing, andvegetative index calculations. Moreover, its features include AI-driven parcel condition estimations, enhancing agricultural efficiency and reducing the workload of woody-crops agriculture technicians. Initial validations with Sentinel-2 and Menut satellites demonstrated MOT’s efficacy in improving vineyard and almond crops management through the use of integrated and processed multispectral satellite imaging resolution and automation.

The MOT use-case’s success underscores the potential of public-private collaboration, blending startup innovation with established industry needs. Menut satellite images were effectively integrated into the MOT platform, proving comparable results to the widely-used Sentinel-2 data: Menut offered additional insights, particularly in the Red-Edge band, where its superior resolution enhanced the quality of certain vegetal-related information. Although Menut currently lacks automated data processing via an API, it is expected a seamless integration in the future. Additionally, field tests demonstrated that MOT-generated maps rival those calculated manually using bespoke high-resolution imaging (like the orthophotos coming from rented airplanes), paving the way for future SaaS tools that optimize agricultural workflows.