In July 2020, Quasar submitted, in collaboration with the Department of Forest Engineering at the Universidad Politécnica de Madrid, a project with title Evaluation of Agricultural Systems through Remote Sensing Time Series and Dynamic Models Prediction, to the Industrial Doctorate programme of the Comunidad de Madrid. We are happy to announce that our 3 year project has been accepted and we plan to start working on it as soon as January 2021.
Currently, in the context of climate change, the need to monitor crops and predict their evolution and production has become an urgent need. The current remote sensing systems allow this monitoring to be carried out synoptically in large regions, at different scales and with a high temporal frequency.
Specifically, the satellites of the European Union’s COPERNICUS Program provide information in the optical and Radar domains with a high spatial and temporal resolution that will allow a highly detailed monitoring, and which should be both operational and reliable. For this, it is necessary to develop analysis and classification models for large amounts of information and high computing capacities. In the academic environment of agriculture, there are capabilities to develop monitoring models, but automatic artificial intelligence algorithms and computing capabilities are not available yet. In contrast, in the technology industry, computing capabilities are available but not the knowledge of crops functioning and their relationship to remote sensing information. This work seeks a highly synergistic collaboration between the company Quasar Science Resources and the university, and will lead to:
(1) highly capable operational methodologies for monitoring agricultural areas,
(2) the training of a researcher in the capacities that the industry possesses, and,
(3) a transmission of university-business knowledge that allows the development of new beneficial products for society and for the company itself.
In this work, it is proposed,
(1) to identify indicators to evaluate and predict the evolution of crops and their production in the short and medium term,
(2) to evaluate these indicators using artificial intelligence algorithms, and,
(3) integrate these analyses and indicators into a scientific platform to make the methodologies developed quick and operational.
The ultimate goal of this work is to carry out the monitoring based on information from COPERNICUS; For this work, MODIS sensor data with long time series but lower spatial resolution, will be used as a support, as well as large amounts of field data and meteorological information.