Ph.D. Defense: Matteo Ziliani

When: Thursday January 20, 2022 |

05:00 - 06:00 p.m. (UTC+3, KSA)

Join Zoom link:  kaust.zoom.us/j/94612017750

"Predicting crop yield using crop models and high-resolution remote sensing technologies"

Abstract: 

By 2050, food consumption and agricultural water use will increase as a result of a global population that is projected to reach more than 9 billion people. To address this food and water security challenge, there has been increased attention towards the concept of sustainable agriculture, which has a broad aim of securing food and water resources while preserving the environment for future generations. An element of this is the use of precision agriculture, which is designed to provide the right inputs, at the right time and in the right place. In order to optimize nutrient application, water intakes, and the profitability of agricultural areas, it is necessary to improve our understating and predictability of agricultural systems at high spatio-temporal scales.

The underlying goal of this Ph.D. study is to advance the accurate monitoring of croplands and crop yield through the use of high-resolution within-field scale satellite data. In addressing this, we explore the utility of new satellite platforms (named CubeSat) able to produce the highest spatial resolution (3 m) estimates of leaf area index and crop water use ever retrieved from space, providing an enhanced capacity to supply new insights into precision agriculture. The novel insights on crop health and conditions derived from CubeSat data are then combined with the predictive ability of crop models, with the aim of improving crop development and yield predictions during a crop growing season. This enables to derive further insights into small-scale field variabilities from an on-demand basis, and represent the cutting-edge of precision agricultural advances.

Overall, the research presented in this study, can be used to assess and predict the behavior of agricultural systems through the implementation of enhanced crop model simulations and the development of new high-spatial and temporal agricultural insights of crop health and phenology. These insights can be directed towards improving tactical management decisions and addressing food and water security concerns in a more sustainable manner.