An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Landsat-8 Operational Land Imager (OLI)
3.2. Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4, Collection 6
3.3. Method for the Generation of High Temporal Resolution Time Series
3.3.1. Modified Selection of Similar Pixels
3.3.2. Integration of Cloud-Affected Landsat Scenes
3.3.3. Quality Information
3.3.4. Automation and Improvement of Performance
3.3.5. Application of the Enhance Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) Framework in the Study Area
3.4. Accuracy Assessment
4. Results
4.1. Spatio-Temporal Patterns of the ESTARFM Time Series
4.2. Accuracy Assessment of the ESTARFM Predictions
4.3. Input Data Availability and Influence on ESTARFM Predictions
5. Discussion
5.1. Uncertainties in the Prediction with ESTARFM
5.2. Added Value of the ESTARFM Framework for Cloud-Prone Areas
5.3. Added Value of the ESTARFM Framework for Large-Scale Analyses
6. Conclusions and Outlook
Acknowledgments
Input | Original ESTARFM | ESTARFM Framework (9 Cores) |
---|---|---|
4 bands | 24 h 49 min | 4 h 38 min |
NDVI | 15 h 32 min | 1 h 45 min |
Row | DOY 191 | DOY 271 | DOY 287 | DOY 303 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | R2 | MAE | RMSE | R2 | MAE | RMSE | R2 | MAE | RMSE | R2 | |
52 | 0.05 | 0.07 | 0.74 | 0.10 | 0.12 | 0.41 | 0.07 | 0.10 | 0.53 | 0.03 | 0.05 | 0.76 |
53 | 0.06 | 0.07 | 0.58 | 0.07 | 0.09 | 0.30 | 0.06 | 0.08 | 0.33 | 0.05 | 0.06 | 0.56 |
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Knauer, K.; Gessner, U.; Fensholt, R.; Kuenzer, C. An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes. Remote Sens. 2016, 8, 425. https://doi.org/10.3390/rs8050425
Knauer K, Gessner U, Fensholt R, Kuenzer C. An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes. Remote Sensing. 2016; 8(5):425. https://doi.org/10.3390/rs8050425
Chicago/Turabian StyleKnauer, Kim, Ursula Gessner, Rasmus Fensholt, and Claudia Kuenzer. 2016. "An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes" Remote Sensing 8, no. 5: 425. https://doi.org/10.3390/rs8050425