The accurate and timely retrieval of agricultural water use, crop health and related plant biophysical parameters, represent key elements in delivering an effective crop management and monitoring strategy. In arid and semi-arid environments, where the availability of water is generally limited, determining the dynamics of these variables is especially important. With the rapid developments in unmanned aerial vehicles (UAVs), the capacity to develop customized retrievals of crop information now exists. While there remain challenges in the routine application of autonomous airborne systems, the state of current technology together with sensor developments provide an opportunity to further explore the operational potential. UAVs offer the capacity to bridge the spatio-temporal divide that exists between satellite and ground based sensing, offering new insights into process dynamics and behavior and providing the data necessary to produce a truly multi-scale framework for improved agricultural characterization. In this paper we will focus on the retrieval of vegetation parameters from UAV platforms, using high-resolution satellite data from RapidEye and Landsat sensors to evaluate retrievals. A focus on traditional parameters such as the Normalised Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) will be supplemented with estimates of thermal infrared based land surface temperature. We will also discuss how UAVs can support the development of farm-level monitoring, particularly in the determining crop water-use and crop health.
|Title of host publication
|21st International Congress on Modelling and Simulation: Partnering with Industry and the Community for Innovation and Impact through Modelling, MODSIM 2015 - Held jointly with the 23rd National Conference of the Australian Society for Operations Research and the DSTO led Defence Operations Research Symposium, DORS 2015
|Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ)
|Number of pages
|Published - Jan 1 2015
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST).