| dc.contributor |
Forest Resources and Environmental Conservation |
|
| dc.creator |
Brooks, Evan B. |
|
| dc.creator |
Wynne, Randolph H. |
|
| dc.creator |
Thomas, Valerie A. |
|
| dc.date |
2018-10-31T16:58:39Z |
|
| dc.date |
2018-10-31T16:58:39Z |
|
| dc.date |
2018-09-20 |
|
| dc.date |
2018-10-31T15:26:33Z |
|
| dc.date.accessioned |
2023-03-01T18:51:12Z |
|
| dc.date.available |
2023-03-01T18:51:12Z |
|
| dc.identifier |
Brooks, E.B.; Wynne, R.H.; Thomas, V.A. Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data. Remote Sens. 2018, 10, 1502. |
|
| dc.identifier |
http://hdl.handle.net/10919/85602 |
|
| dc.identifier |
https://doi.org/10.3390/rs10101502 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/281485 |
|
| dc.description |
The continued development of algorithms using multitemporal Landsat data creates opportunities to develop and adapt imputation algorithms to improve the quality of that data as part of preprocessing. One example is de-striping Enhanced Thematic Mapper Plus (ETM+, Landsat 7) images acquired after the Scan Line Corrector failure in 2003. In this study, we apply window regression, an algorithm that was originally designed to impute low-quality Moderate Resolution Imaging Spectroradiometer (MODIS) data, to Landsat Analysis Ready Data from 2014–2016. We mask Operational Land Imager (OLI; Landsat 8) image stacks from five study areas with corresponding ETM+ missing data layers, using these modified OLI stacks as inputs. We explored the algorithm’s parameter space, particularly window size in the spatial and temporal dimensions. Window regression yielded the best accuracy (and moderately long computation time) with a large spatial radius (a 7 × 7 pixel window) and a moderate temporal radius (here, five layers). In this case, root mean square error for deviations from the observed reflectance ranged from 3.7–7.6% over all study areas, depending on the band. Second-order response surface analysis suggested that a 15 × 15 pixel window, in conjunction with a 9-layer temporal window, may produce the best accuracy. Compared to the neighborhood similar pixel interpolator gap-filling algorithm, window regression yielded slightly better accuracy on average. Because it relies on no ancillary data, window regression may be used to conveniently preprocess stacks for other data-intensive algorithms. |
|
| dc.description |
Published version |
|
| dc.format |
application/pdf |
|
| dc.format |
application/pdf |
|
| dc.language |
en |
|
| dc.publisher |
MDPI |
|
| dc.relation |
http://hdl.handle.net/10919/50852 |
|
| dc.rights |
Creative Commons Attribution 4.0 International |
|
| dc.rights |
http://creativecommons.org/licenses/by/4.0/ |
|
| dc.subject |
Landsat |
|
| dc.subject |
gap-filling |
|
| dc.subject |
imputation |
|
| dc.subject |
Landsat 7 |
|
| dc.subject |
Optimization |
|
| dc.title |
Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data |
|
| dc.title |
Remote Sensing |
|
| dc.type |
Article - Refereed |
|
| dc.type |
Text |
|