| Title | A new method for generating a clear-sky Landsat composite for cropland from cloud-contaminated Landsat-7 and Landsat-8 images |
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| Author | Li, J; Wang, S |
| Source | International Journal of Digital Earth vol. 11, issue 5, 2017 p. 1-13, https://doi.org/10.1080/17538947.2017.1381189 |
| Image |  |
| Year | 2017 |
| Alt Series | Earth Sciences Sector, Contribution Series 20150389 |
| Publisher | Taylor & Francis |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf; html |
| Program | Remote Sensing Science |
| Released | 2017 09 25 |
| Abstract | A new method was developed in this study for producing a clear-sky Landsat composite for cropland from cloud-contaminated Landsat images acquired in a short time period. It used Thiel-Sen regression to
normalize all Landsat scenes to a MODIS image to make all Landsat images radiometrically consistent and comparable. Pixel selection criteria combining the modified maximum vegetation index and the modified minimum visible reflectance selection
methods were designed to enhance the pixel selection of land/water over cloud/shadow in the image compositing. The advantages of the method include (1) avoiding complicated atmospheric corrections but with reliable surface reflectance results, (2)
being insensitive to errors induced by image co-registration uncertainties between Landsat and MODIS images, (3) avoiding the lack of samples for the regression analysis using the full Landsat scenes (rather than overlay regions), and (4) enhancing
cloud/shadow detection. The composite image has MODIS-like surface reflectance, thus making MODIS algorithms applicable for retrieving biophysical parameters. The method was automatically implemented on a set of 13 cloud-contaminated (>39%) Landsat-7
(Scan-Line Corrector-Off) and Landsat-8 scenes acquired during peak growing season in a crop region of Manitoba, Canada. The result was a 95.8% cloud-free image. The method can also substantially increase the usage of cloud-contaminated Landsat data.
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| Summary | (Plain Language Summary, not published)
A field work was conducted in the summer of 2013 to collect ground truths for land cover mapping in the Spiritwood valley, which stretches from southwest
Manitoba, Canada to north Dakota, USA. The dominant land cover is agriculture, intermingled with grassland and forests. The acquisition timing of Landsat images are key to map this region because of inter-annual changes in crop types due to crop
rotation or different phenological stages in a short time window. However a cloud-free Landsat image are not available within the summer of 2013. The lack of a clear-sky Landsat image motivated the authors to develop a Landsat compositing method for
producing a clear-sky Landsat image from multiple cloud contaminated (>39%) Landsat-7 (SLC-OFF) and Landsat-8 scenes acquired in the peak growing season in 2013 over this region. The method produced a 95.8% percent cloud free image, which meets our
needs for the land cover mapping. The method provides an efficient way for generating high-quality clear-sky Landsat images, which will increase the usability of contaminated Landsat data. |
| GEOSCAN ID | 297504 |
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