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

Knauer, Kim; Geßner, Ursula; Fensholt, Rasmus; Kuenzer, Claudia

doi:10.3390/rs8050425

Cited by 62 publications

(42 citation statements)

References 34 publications

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“…2017, 9, 132 5 of 24 data in this compositing period, the best possible BRDF is derived for the correction of the reflectances [24,39]. From the red and NIR bands of the MODIS datasets, daily NDVI was calculated and further processed with the software TIMESAT [40].…”

Section: Modis Data and Preprocessingmentioning

confidence: 99%

“…MCD43A4 is a 16-day rolling composite product with the center date of this period associated to the daily dataset [39]. On the basis of the data in this compositing period, the best possible BRDF is derived for the correction of the reflectances [24,39].…”

Section: Modis Data and Preprocessingmentioning

confidence: 99%

“…Thus, in this study, a data fusion approach was chosen to combine the strengths of the two sensor systems (MODIS and Landsat) and to generate a high temporal (8-day) and high spatial (30 m) resolution time series for a better delineation of agricultural area in Burkina Faso. For this purpose, the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework for large-and cloud-prone area processing was employed [24,25].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Agricultural Expansion in Burkina Faso over 14 Years with 30 m Resolution Time Series: The Role of Population Growth and Implications for the Environment

et al. 2017

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Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. The mapping and monitoring of this expansion is difficult, even on the basis of remote sensing imagery, since the extensive farming practices and frequent cloud coverage in the area make the delineation of cultivated land from other land cover and land use types a challenging task. However, as the rapidly increasing population could have considerable effects on the natural resources and on the regional development of the country, methods for improved mapping of LULCC (land use and land cover change) are needed. For this study, we applied the newly developed ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework to generate high temporal (8-day) and high spatial (30 m) resolution NDVI time series for all of Burkina Faso for the years 2001, 2007, and 2014. For this purpose, more than 500 Landsat scenes and 3000 MODIS scenes were processed with this automated framework. The generated ESTARFM NDVI time series enabled extraction of per-pixel phenological features that all together served as input for the delineation of agricultural areas via random forest classification at 30 m spatial resolution for entire Burkina Faso and the three years. For training and validation, a randomly sampled reference dataset was generated from Google Earth images and based on expert knowledge. The overall accuracies of 92% (2001), 91% (2007), and 91% (2014) indicate the well-functioning of the applied methodology. The results show an expansion of agricultural area of 91% between 2001 and 2014 to a total of 116,900 km 2 . While rainfed agricultural areas account for the major part of this trend, irrigated areas and plantations also increased considerably, primarily promoted by specific development projects. This expansion goes in line with the rapid population growth in most provinces of Burkina Faso where land was still available for an expansion of agricultural area. The analysis of agricultural encroachment into protected areas and their surroundings highlights the increased human pressure on these areas and the challenges of environmental protection for the future.

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Section: Modis Data and Preprocessingmentioning

confidence: 99%

Section: Modis Data and Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring Agricultural Expansion in Burkina Faso over 14 Years with 30 m Resolution Time Series: The Role of Population Growth and Implications for the Environment

et al. 2017

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“…The STARFM and ESTARFM models were employed to blend the PROBA-V 100-m and 300-m S1 data because both of them have been widely and successfully used due to their ease of implementation and reasonable algorithm complexity [34][35][36][37]. We chose ESTARFM as the main fusion model because it was developed based on STARFM but overcomes STARFM's shortcoming of making inaccurate predictions in heterogeneous landscapes such as that of our study area, which has various field sizes comparable to that of a 300-m pixel.…”

Section: Daily 100-m Reflectance Dataset Generationmentioning

confidence: 99%

Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products

Zheng

Zhang

et al. 2016

Remote Sensing

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Abstract:Monitoring crop areas and yields is crucial for food security and agriculture management across the world. In this paper, we mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation (PROBA-V) products in the North China Plain (NCP). First, the daily 100-m land surface reflectance was generated by fusing the PROBA-V 100-m and 300-m S1 products. Our results show that the blended data exhibited high correlations with the referenced data (0.71 ≤ R 2 ≤ 0.94 for the red band, 0.50 ≤ R 2 ≤ 0.95 for the near-infrared band, and 0.88 ≤ R 2 ≤ 0.97 for the shortwave infrared band). The time-series Normalized Difference Vegetation Index (NDVI) derived from the synthetic reflectance was then clustered for winter wheat identification. The overall classification accuracy was between 78% and 87%, with a kappa coefficient above 0.57, which was 10%-20% higher than the classification accuracy using the 300-m data. Finally, a light use efficiency model was employed to estimate the biomass and yield. The estimation results were closely related to the field-measured biomass and yield, with high R 2 and low root mean square errors (RMSE) (0.864 ≤ R 2 ≤ 0.871 and 168 ≤ RMSE ≤ 191 g/m 2 for biomass; and 0.631 ≤ R 2 ≤ 0.663 and 41.8 ≤ RMSE ≤ 62.8 g/m 2 for yield). This paper shows the strong potential of using PROBA-V 100-m data to enhance the spatial resolution of PROBA-V 300-m data and because the proposed framework in this study was based only on the relatively high spatio-temporal resolution PROBA-V data and achieved favorable results, it provides a novel approach for crop areas and yields estimation utilizing the relatively new data set.

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“…The first category of algorithms which is robust in theory but often difficult to implement is sensor fusion, which has strict requirements, like matching temporal and spatial resolution, making it unrealizable in many cases. Knauer et al [2] fused MODIS data with Landsat 8, to generate high temporal frequency, high spatial resolution, cloud-free time series. The algorithm uses linear regression to estimate the value of each pixel at Landsat resolution, by using two time series, one for MODIS and the other for Landsat.…”

Section: Introductionmentioning

confidence: 99%

Cloud Removal in High Resolution Multispectral Satellite Imagery: Comparing Three Approaches

Adam

Mönks

Esch

et al. 2018

The 2nd International Electronic Conference on Remote Sensing

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Clouds and cloud-shadow are a persistent problem in all optical satellite imagery. Plenty of methods have been suggested in the literature to address this problem, and reconstruct the missing part of the optical signal. In this work, three methods representative of different approaches to the cloud removal problem are compared. The first method is temporal fitting using Fourier series, which benefits from the temporal continuity of the signal. The second method uses sparse spectral unmixing to fill in the missing areas. The third method employs radiometric consistency as a tool to determine the missing part of the signal. These three methods are first presented and their theoretical background described, followed by a discussion of their implied assumptions and general performance. A set of experiments using Landsat 8 time series with diverse land cover types were conducted. The quantitative results of the three methods using simulated clouds are presented. Finally, some concluding remarks about the relative advantages of the three approaches are listed, in addition to some recommendations about their use.

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

Cited by 62 publications

References 34 publications

Monitoring Agricultural Expansion in Burkina Faso over 14 Years with 30 m Resolution Time Series: The Role of Population Growth and Implications for the Environment

Monitoring Agricultural Expansion in Burkina Faso over 14 Years with 30 m Resolution Time Series: The Role of Population Growth and Implications for the Environment

Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products

Cloud Removal in High Resolution Multispectral Satellite Imagery: Comparing Three Approaches

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