Next Article in Journal
Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation
Previous Article in Journal
Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space
Open AccessTechnical Note

Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method

1
Water Desalination and Reuse Center, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
2
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne 1015, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Müller and Prasad S. Thenkabail
Remote Sens. 2017, 9(1), 12; https://doi.org/10.3390/rs9010012
Received: 17 July 2016 / Revised: 10 December 2016 / Accepted: 14 December 2016 / Published: 27 December 2016
The failure of the Scan Line Corrector (SLC) on Landsat 7 imposed systematic data gaps on retrieved imagery and removed the capacity to provide spatially continuous fields. While a number of methods have been developed to fill these gaps, most of the proposed techniques are only applicable over relatively homogeneous areas. When they are applied to heterogeneous landscapes, retrieving image features and elements can become challenging. Here we present a gap-filling approach that is based on the adoption of the Direct Sampling multiple-point geostatistical method. The method employs a conditional stochastic resampling of known areas in a training image to simulate unknown locations. The approach is assessed across a range of both homogeneous and heterogeneous regions. Simulation results show that for homogeneous areas, satisfactory results can be obtained by simply adopting non-gap locations in the target image as baseline training data. For heterogeneous landscapes, bivariate simulations using an auxiliary variable acquired at a different date provides more accurate results than univariate simulations, especially as land cover complexity increases. Apart from recovering spatially continuous fields, one of the key advantages of the Direct Sampling is the relatively straightforward implementation process that relies on relatively few parameters. View Full-Text
Keywords: Landsat ETM+; gap filling; multiple-point geostatistics; Scan Line Corrector; SLC Landsat ETM+; gap filling; multiple-point geostatistics; Scan Line Corrector; SLC
Show Figures

Graphical abstract

MDPI and ACS Style

Yin, G.; Mariethoz, G.; McCabe, M.F. Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method. Remote Sens. 2017, 9, 12.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop