Using RapidEye and MODIS Data Fusion to Monitor Vegetation Dynamics in Semi-Arid Rangelands in South Africa

Tewes, Andreas; Thonfeld, Frank; Schmidt, Michaël; Oomen, Roelof; Zhu, Xiaolin; Dubovyk, Olena; Menz, Gunter; Schellberg, J.

doi:10.3390/rs70606510

Cited by 42 publications

(26 citation statements)

References 31 publications

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“…Notably, the predictions were generally best for the SWIR band and worst for the RED band, except for the predictions for 7 May at Site 1 and 25 May at Site 2. This result is consistent with numerous studies that the ESTARFM have shown better performance at the longer wavelength [49]. For the predicted images on 7 and 25 May, the accuracy was lower for the NIR than for the RED band, which was probably a result of the changes in land-cover from seasonal farming activities in the cropped areas at the end of the growing season, and the NIR band was more sensitive to these changes than the RED and SWIR bands.…”

Section: The Estarfm Prediction Resultssupporting

confidence: 91%

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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Section: The Estarfm Prediction Resultssupporting

confidence: 91%

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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“…Gaps in the image archive can pose a problem for the procedure, particularly when the 16-day gap between Landsat observations coincides with a period of dense cloud cover. Regularly spaced observations would allow for a phenology-based approach that also has the potential for crop-type differentiation [43], but might require an image-fusion step to be applicable at field-scale-for example Landsat with MODIS data [72]; [73] or Rapideye and MODIS [74]. The authors of [75] demonstrated the use of multispectral Landsat information combined with temporal MODIS information for crop mapping.…”

Section: Future Applicationsmentioning

confidence: 99%

A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics

et al. 2016

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Abstract:Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, in particular between cropping and mining in the case of Queensland, Australia. Such spatial datasets are useful for supporting decisions on natural resource management, planning and policy. For the major broadacre cropping regions of Queensland, Australia, the complete Landsat Time Series (LTS) archive from 1987 to 2015 was used in a multi-temporal mapping approach, where spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by training data sampled across space and time for the classes Crop and No-Crop. Temporal information within summer and winter growing seasons were summarised for each year, and combined with various vegetation indices and band ratios computed from a pixel-based mid-season spectral synthetic image. All available temporal information was spatially aggregated to the scale of image segments in the mid-season synthetic image for each growing season and used to train a number of different predictive models for a Crop and No-Crop classification. Validation revealed that the predictive accuracy varied by growing season and region and a random forest classifier performed best, with κ = 0.88 to 0.91 for the summer growing season and κ = 0.91 to 0.97 for the winter growing season, and are thus suitable for mapping current and historic cropping activity.

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“…To overcome this classic discrepancy in remote sensing, fusing data from different sensors has gained increasing popularity in recent years for combining high temporal resolution sensors with medium to high spatial resolution sensors. Especially the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) [2] and its enhanced version ESTARFM [3] have been frequently applied as described in the literature (e.g., [4][5][6][7][8]). Recent comparison studies highlighted the respective advantages of the two algorithms compared to other simple fusion approaches, but the overall accuracies are found to be highest for ESTARFM [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…In their conclusion, the value of such data fusion for land management and forest conversion was highlighted [12]. Tewes et al [5] fused 250 m MODIS and 5 m RapidEye data using ESTARFM to monitor vegetation dynamics in semi-arid rangelands of South Africa (study area: 1434 km 2 ). Their results indicate the suitability of ESTARFM to characterize phenological development of different vegetation types in such heterogeneous areas.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 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 km 2 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.

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Using RapidEye and MODIS Data Fusion to Monitor Vegetation Dynamics in Semi-Arid Rangelands in South Africa

Cited by 42 publications

References 31 publications

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

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

A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics

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

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