Planet Labs ("Planet") operate the largest fleet of active nano-satellites in orbit, offering an unprecedented monitoring capacity of daily and global RGB image capture at 3-5 m resolution. However, limitations in spectral resolution and lack of accurate radiometric sensor calibration impact the utility of this rich information source. In this study, Planet's RGB imagery was translated into a Normalized Difference Vegetation Index (NDVI): a common metric for vegetation growth and condition. Our framework employs a data mining approach to build a set of rule-based regression models that relate RGB data to atmospherically corrected Landsat-8 NDVI. The approach was evaluated over a desert agricultural landscape in Saudi Arabia where the use of near-coincident (within five days) Planet and Landsat-8 acquisitions in the training of the regression models resulted in NDVI predictabilities with an r2 of approximately 0.97 and a Mean Absolute Deviation (MAD) on the order of 0.014 (~9%). The MAD increased to 0.021 (~14%) when the Landsat NDVI training image was further away (i.e., 11-16 days) from the corrected Planet image. In these cases, the use of MODIS observations to inform on the change in NDVI occurring between overpasses was shown to significantly improve prediction accuracies. MAD levels ranged from 0.002 to 0.011 (3.9% to 9.1%) for the best performing 80% of the data. The technique is generic and extendable to any region of interest, increasing the utility of Planet's dense time-series of RGB imagery.
|Original language||English (US)|
|State||Published - Sep 19 2016|
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). We acknowledge Planet’s Ambassadors program for providing access to their
imagery archive as well as the outreach efforts of Planet’s Nuno Vilaça and Joseph Mascaro. We greatly appreciate the logistical, equipment, and scientific support offered to our team by Jack King, Alan King, and employees of
the Tawdeehiya Farm in Al Kharj, Saudi Arabia, without whom this research would not have been possible.