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Remote Sens. 2016, 8(5), 425; doi:10.3390/rs8050425

An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes

1
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany
2
Department of Remote Sensing, University of Wuerzburg, 97074 Wuerzburg, Germany
3
Department of Geosciences and Natural Resource Management, University of Copenhagen, 1350 Copenhagen, Denmark
*
Author to whom correspondence should be addressed.
Academic Editors: Eileen H. Helmer, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 23 March 2016 / Revised: 10 May 2016 / Accepted: 12 May 2016 / Published: 19 May 2016
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Abstract

Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series of high temporal and spatial resolution for such areas. In order to overcome this problem, data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) have been established and frequently used in recent years to generate high-resolution time series. In order to make it applicable to larger scales and to increase the input data availability especially in cloud-prone areas, an ESTARFM framework was developed in this study introducing several enhancements. An automatic filling of cloud gaps was included in the framework to make best use of available, even partly cloud-covered Landsat images. Furthermore, the ESTARFM algorithm was enhanced to automatically account for regional differences in the heterogeneity of the study area. The generation of time series was automated and the processing speed was accelerated significantly by parallelization. To test the performance of the developed ESTARFM framework, MODIS and Landsat-8 data were fused for generating an 8-day NDVI time series for a study area of approximately 98,000 km2 in West Africa. The results show that the ESTARFM framework can accurately produce high temporal resolution time series (average MAE (mean absolute error) of 0.02 for the dry season and 0.05 for the vegetative season) while keeping the spatial detail in such a heterogeneous, cloud-prone region. The developments introduced within the ESTARFM framework establish the basis for large-scale research on various geoscientific questions related to land degradation, changes in land surface phenology or agriculture. View Full-Text
Keywords: vegetation dynamics; ESTARFM; MODIS; Landsat; phenology; West Africa; cloud gap filling; time series analysis vegetation dynamics; ESTARFM; MODIS; Landsat; phenology; West Africa; cloud gap filling; time series analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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