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Sensors (Basel). 2017 Sep 14;17(9). pii: E2106. doi: 10.3390/s17092106.

Spatial Scale Gap Filling Using an Unmanned Aerial System: A Statistical Downscaling Method for Applications in Precision Agriculture.

Author information

1
Civil & Environmental Engineering, Utah State University, Logan, UT 84322, USA. leila.esfahani@aggiemail.usu.edu.
2
College of Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA. ebtehaj@umn.edu.
3
Utah Water Research Laboratory, Utah State University, Logan, UT 84322, USA. a.torres@aggiemail.usu.edu.
4
Civil & Environmental Engineering, Utah State University, Logan, UT 84322, USA. mac.mckee@usu.edu.

Abstract

Applications of satellite-borne observations in precision agriculture (PA) are often limited due to the coarse spatial resolution of satellite imagery. This paper uses high-resolution airborne observations to increase the spatial resolution of satellite data for related applications in PA. A new variational downscaling scheme is presented that uses coincident aerial imagery products from "AggieAir", an unmanned aerial system, to increase the spatial resolution of Landsat satellite data. This approach is primarily tested for downscaling individual band Landsat images that can be used to derive normalized difference vegetation index (NDVI) and surface soil moisture (SSM). Quantitative and qualitative results demonstrate promising capabilities of the downscaling approach enabling effective increase of the spatial resolution of Landsat imageries by orders of 2 to 4. Specifically, the downscaling scheme retrieved the missing high-resolution feature of the imageries and reduced the root mean squared error by 15, 11, and 10 percent in visual, near infrared, and thermal infrared bands, respectively. This metric is reduced by 9% in the derived NDVI and remains negligibly for the soil moisture products.

KEYWORDS:

Landsat; NDVI; UAV; downscaling; precision agriculture; soil moisture

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