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

Knauer, Kim and Gessner, Ursula and Fensholt, Rasmus and Künzer, Claudia (2016) An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes. Remote Sensing, 8 (5), pp. 1-21. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs8050425 ISSN 2072-4292

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Official URL: http://www.mdpi.com/2072-4292/8/5/425


Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. So far, no single satellite sensor can 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 for 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.

Item URL in elib:https://elib.dlr.de/104361/
Document Type:Article
Title:An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Knauer, Kimkim.knauer (at) dlr.deUNSPECIFIED
Gessner, Ursulaursula.gessner (at) dlr.deUNSPECIFIED
Fensholt, RasmusUniversity of CopenhagenUNSPECIFIED
Künzer, ClaudiaClaudia.Kuenzer (at) dlr.deUNSPECIFIED
Date:19 May 2016
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3390/rs8050425
Page Range:pp. 1-21
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:vegetation dynamics; ESTARFM; MODIS; Landsat; phenology; West Africa; cloud gap filling; time series analysis
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Fernerkundung der Landoberfläche (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
Deposited By: Knauer, Kim
Deposited On:31 May 2016 08:21
Last Modified:14 Dec 2019 04:26

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