Time series from hyperion to track productivity in pivot agriculture in saudi arabia
Type
Conference PaperKAUST Department
Biological and Environmental Sciences and Engineering (BESE) DivisionEnvironmental Science and Engineering Program
Water Desalination and Reuse Research Center (WDRC)
Date
2017-12-13Online Publication Date
2017-12-13Print Publication Date
2017-07Permanent link to this record
http://hdl.handle.net/10754/626592
Metadata
Show full item recordAbstract
The hyperspectral satellite sensing capacity is expected to increase substantially in the near future with the planned deployment of hyperspectral systems by both space agencies and commercial companies. These enhanced observational resources will offer new and improved ways to monitor the dynamics and characteristics of terrestrial ecosystems. This study investigates the utility of time series of hyperspectral imagery, acquired by Hyperion onboard EO-1, for quantifying variations in canopy chlorophyll ($Chl_{c}$), plant productivity, and yield over an intensive farming area in the desert of Saudi Arabia. $Chl_{c}$ is estimated on the basis of predictive multi-variate empirical models established via a machine learning approach using a training dataset of in-situ measured target variables and explanatory hyperspectral indices. Resulting time series of $Chl_{c}$ are translated into Gross Primary Productivity (GPP) and Yield based on semi-empirical relationships, and evaluated against ground-based observations. Results indicate significant benefit in utilizing the full suite of hyperspectral indices over multi-spectral indices constructible from Landsat-8 and Sentinel-2.Citation
Houborg R, McCabe MF, Angel Y, Middleton EM (2017) Time series from hyperion to track productivity in pivot agriculture in saudi arabia. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Available: http://dx.doi.org/10.1109/IGARSS.2017.8127641.Additional Links
http://ieeexplore.ieee.org/document/8127641/ae974a485f413a2113503eed53cd6c53
10.1109/IGARSS.2017.8127641