By 2050, food consumption and agricultural water use will increase as a result
of a global population that is projected to reach 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 the research presented herein is to advance the
monitoring of croplands and crop yield through high-resolution satellite data. In
addressing this, we explore the utility of daily CubeSat imagery 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 provide new insights into precision
agriculture. The novel insights on crop health and conditions derived from CubeSat
data are combined with the predictive ability of crop models, with the aim of
improving crop yield predictions. To explore the latter, a sensitivity analysis-linked
Bayesian inference framework was developed, offering a tool for calibrating crop
models while simultaneously quantifying the uncertainty in input parameters. The
effect of integrating higher spatio-temporal resolution data in crop models was tested
by developing an approach that assimilates CubeSat imagery into a crop model for
early season yield prediction at the within-field scale. In addition to satellite data, the
utility of even higher spatial resolution products from unmanned aerial vehicles was
also examined in the last section of the thesis, where future research avenues are
outlined. Here, an assessment of crop height is presented, which is linked to field
biomass through the use of structure from motion techniques. These results offer
further insights into small-scale field variabilities from an on-demand basis, and
represent the cutting-edge of precision agricultural advances.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/675554 |
| Date | 01 1900 |
| Creators | Ziliani, Matteo Giuseppe |
| Contributors | McCabe, Matthew, Biological and Environmental Science and Engineering (BESE) Division, Hoteit, Ibrahim, Mishra, Himanshu, Sheffield, Justin, Manfreda, Salvatore |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Rights | 2023-01-01, At the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation will become available to the public after the expiration of the embargo on 2023-01-01. |
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