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20256 KiB

Open AccessArticle

A Method for Estimating the Aerodynamic Roughness Length with NDVI and BRDF Signatures Using Multi-Temporal Proba-V Data

by Mingzhao Yu, Bingfang Wu, Nana Yan, Qiang Xing and Weiwei Zhu

Remote Sens. 2017, 9(1), 6; https://doi.org/10.3390/rs9010006 - 24 Dec 2016

Cited by 20 | Viewed by 6048

Abstract

Aerodynamic roughness length is an important parameter for surface fluxes estimates. This paper developed an innovative method for estimation of aerodynamic roughness length (z_0m) over farmland with a new vegetation index, the Hot-darkspot Vegetation Index (HDVI). To obtain this new index, [...] Read more.

Aerodynamic roughness length is an important parameter for surface fluxes estimates. This paper developed an innovative method for estimation of aerodynamic roughness length (z_0m) over farmland with a new vegetation index, the Hot-darkspot Vegetation Index (HDVI). To obtain this new index, the normalized-difference hot-darkspot index (NDHD) is introduced using a semi-empirical, kernel-driven bidirectional reflectance model with multi-temporal Proba-V 300-m top-of-canopy (TOC) reflectance products. A linear relationship between HDVI and z_0m was found during the crop growth period. Wind profiles data from two field automatic weather station (AWS) were used to calibrate the model: one site is in Guantao County in Hai Basin, in which double-cropping systems and crop rotations with summer maize and winter wheat are implemented; the other is in the middle reach of the Heihe River Basin from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, with the main crop of spring maize. The iterative algorithm based on Monin–Obukhov similarity theory is employed to calculate the field z_0m from time series. Results show that the relationship between HDVI and z_0m is more pronounced than that between NDVI and z_0m for spring maize at Yingke site, with an R² value that improved from 0.636 to 0.772. At Guantao site, HDVI also exhibits better performance than NDVI, with R² increasing from 0.630 to 0.793 for summer maize and from 0.764 to 0.790 for winter wheat. HDVI can capture the impacts of crop residue on z_0m, whereas NDVI cannot. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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9736 KiB

Open AccessArticle

Dynamic Water Surface Detection Algorithm Applied on PROBA-V Multispectral Data

by Luc Bertels, Bruno Smets and Davy Wolfs

Remote Sens. 2016, 8(12), 1010; https://doi.org/10.3390/rs8121010 - 10 Dec 2016

Cited by 11 | Viewed by 5991

Abstract

Water body detection worldwide using spaceborne remote sensing is a challenging task. A global scale multi-temporal and multi-spectral image analysis method for water body detection was developed. The PROBA-V microsatellite has been fully operational since December 2013 and delivers daily near-global synthesis with [...] Read more.

Water body detection worldwide using spaceborne remote sensing is a challenging task. A global scale multi-temporal and multi-spectral image analysis method for water body detection was developed. The PROBA-V microsatellite has been fully operational since December 2013 and delivers daily near-global synthesis with a spatial resolution of 1 km and 333 m. The Red, Near-InfRared (NIR) and Short Wave InfRared (SWIR) bands of the atmospherically corrected 10-day synthesis images are first Hue, Saturation and Value (HSV) color transformed and subsequently used in a decision tree classification for water body detection. To minimize commission errors four additional data layers are used: the Normalized Difference Vegetation Index (NDVI), Water Body Potential Mask (WBPM), Permanent Glacier Mask (PGM) and Volcanic Soil Mask (VSM). Threshold values on the hue and value bands, expressed by a parabolic function, are used to detect the water bodies. Beside the water bodies layer, a quality layer, based on the water bodies occurrences, is available in the output product. The performance of the Water Bodies Detection Algorithm (WBDA) was assessed using Landsat 8 scenes over 15 regions selected worldwide. A mean Commission Error (CE) of 1.5% was obtained while a mean Omission Error (OE) of 15.4% was obtained for minimum Water Surface Ratio (WSR) = 0.5 and drops to 9.8% for minimum WSR = 0.6. Here, WSR is defined as the fraction of the PROBA-V pixel covered by water as derived from high spatial resolution images, e.g., Landsat 8. Both the CE = 1.5% and OE = 9.8% (WSR = 0.6) fall within the user requirements of 15%. The WBDA is fully operational in the Copernicus Global Land Service and products are freely available. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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3544 KiB

Open AccessArticle

Evaluating the Potential of PROBA-V Satellite Image Time Series for Improving LC Classification in Semi-Arid African Landscapes

by Johannes Eberenz, Jan Verbesselt, Martin Herold, Nandin-Erdene Tsendbazar, Giovanni Sabatino and Giancarlo Rivolta

Remote Sens. 2016, 8(12), 987; https://doi.org/10.3390/rs8120987 - 30 Nov 2016

Cited by 11 | Viewed by 5912

Abstract

Satellite based land cover classification for Africa’s semi-arid ecosystems is hampered commonly by heterogeneous landscapes with mixed vegetation and small scale land use. Higher spatial resolution remote sensing time series data can improve classification results under these difficult conditions. While most large scale [...] Read more.

Satellite based land cover classification for Africa’s semi-arid ecosystems is hampered commonly by heterogeneous landscapes with mixed vegetation and small scale land use. Higher spatial resolution remote sensing time series data can improve classification results under these difficult conditions. While most large scale land cover mapping attempts rely on moderate resolution data, PROBA-V provides five-daily time series at 100 m spatial resolution. This improves spatial detail and resilience against high cloud cover, but increases the data load. Cloud-based processing platforms can leverage large scale land cover monitoring based on such finer time series. We demonstrate this with PROBA-V 100 m time series data from 2014–2015, using temporal metrics and cloud filtering in combination with in-situ training data and machine learning, implemented on the ESA (European Space Agency) Cloud Toolbox infrastructure. We apply our approach to two use cases for a large study area over West Africa: land- and forest cover classification. Our land cover classification reaches a 7% to 21% higher overall accuracy when compared to four global land cover maps (i.e., Globcover-2009, Cover-CCI-2010, MODIS-2010, and Globeland30). Our forest cover classification shows 89% correspondence with the Tropical Ecosystem Environment Observation System (TREES)-3 forest cover data which is based on spatially finer Landsat data. This paper illustrates a proof of concept for cloud-based “big-data” driven land cover monitoring. Furthermore, we show that a wide range of temporal metrics can be extracted from detailed PROBA-V 100 m time series data to continuously optimize land cover monitoring. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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6102 KiB

Open AccessArticle

Crop Mapping Using PROBA-V Time Series Data at the Yucheng and Hongxing Farm in China

by Xin Zhang, Miao Zhang, Yang Zheng and Bingfang Wu

Remote Sens. 2016, 8(11), 915; https://doi.org/10.3390/rs8110915 - 03 Nov 2016

Cited by 28 | Viewed by 8147

Abstract

PROBA-V is a new global vegetation monitoring satellite launched in the second quarter of 2013 that provides data with a 100 m to 1 km spatial resolution and a daily to 10-day temporal resolution in the visible and near infrared (VNIR) bands. A [...] Read more.

PROBA-V is a new global vegetation monitoring satellite launched in the second quarter of 2013 that provides data with a 100 m to 1 km spatial resolution and a daily to 10-day temporal resolution in the visible and near infrared (VNIR) bands. A major mission of the PROBA-V satellite is global agriculture monitoring, in which the accuracy of crop mapping plays a key role. In countries such as China, crop fields are typically small, in assorted shapes and with various management approaches, which deem traditional methods of crop identification ineffective, and accuracy is highly dependent on image resolution and acquisition time. The five-day temporal and 100 m spatial resolution PROBA-V data make it possible to automatically identify crops using time series phenological information. This paper takes advantage of the improved spatial and temporal resolution of the PROBA-V data, to map crops at the Yucheng site in Shandong Province and the Hongxing farm in Heilongjiang province of China. First, the Swets filter algorithm was employed to eliminate noisy pixels and fill in data gaps on time series data during the growing season. Then, the crops are classified based on the Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering, the maximum likelihood method (MLC) and similarity analysis. The mapping results were validated using field-collected crop type polygons and high resolution crop maps based on GaoFen-1 satellite (GF-1) data in 16 m resolution. Our study showed that, for the Yucheng site, the cropping system is simple, mainly dominated by winter wheat–maize rotation. The overall accuracy of crop identification was 73.39% which was slightly better than the result derived from MODIS data. For the Hongxing farm, the cropping system is more complex (i.e., more than three types of crops were planted). The overall accuracy of the crop mapping by PROBA-V was 73.29% which was significantly higher than the MODIS product (46.81%). This study demonstrates that time series PROBA-V data can serve as a useful source for reliable crop identification and area estimation. The high revisiting frequency and global coverage of the PROBA-V data show good potential for future global crop mapping and agricultural monitoring. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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20045 KiB

Open AccessArticle

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

by Yang Zheng, Miao Zhang, Xin Zhang, Hongwei Zeng and Bingfang Wu

Remote Sens. 2016, 8(10), 824; https://doi.org/10.3390/rs8100824 - 07 Oct 2016

Cited by 31 | Viewed by 6574

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). [...] Read more.

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 ≤ R² ≤ 0.94 for the red band, 0.50 ≤ R² ≤ 0.95 for the near-infrared band, and 0.88 ≤ R² ≤ 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 R² and low root mean square errors (RMSE) (0.864 ≤ R² ≤ 0.871 and 168 ≤ RMSE ≤ 191 g/m² for biomass; and 0.631 ≤ R² ≤ 0.663 and 41.8 ≤ RMSE ≤ 62.8 g/m² 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. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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11270 KiB

Open AccessArticle

Assessment of Automated Snow Cover Detection at High Solar Zenith Angles with PROBA-V

by Florent Hawotte, Julien Radoux, Guillaume Chomé and Pierre Defourny

Remote Sens. 2016, 8(9), 699; https://doi.org/10.3390/rs8090699 - 24 Aug 2016

Cited by 4 | Viewed by 4588

Abstract

Changes in the snow cover extent are both a cause and a consequence of climate change. Optical remote sensing with heliosynchronous satellites currently provides snow cover data at high spatial resolution with daily revisiting time. However, high latitude image acquisition is limited because [...] Read more.

Changes in the snow cover extent are both a cause and a consequence of climate change. Optical remote sensing with heliosynchronous satellites currently provides snow cover data at high spatial resolution with daily revisiting time. However, high latitude image acquisition is limited because reflective sensors of many satellites are switched off at high solar zenith angles (SZA) due to lower signal quality. In this study, the relevance and reliability of high SZA acquisition are objectively quantified in the purpose of high latitude snow cover detection, thanks to the PROBA-V (Project for On-Board Autonomy-Vegetation) satellite. A snow cover extent classification based on Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI) has been performed for the northern hemisphere on latitudes between 55°N and 75°N during the 2015–2016 winter season. A stratified probabilistic sampling was used to estimate the classification accuracy. The latter has been evaluated among eight SZA intervals to determine the maximum usable angle. The global overall snow classification accuracy with PROBA-V, 82% ± 4%, was significantly larger than the MODIS (Moderate-resolution Imaging Spectroradiometer) snow cover extent product (75% ± 4%). User and producer accuracy of snow are above standards and overall accuracy is stable until 88.5° SZA. These results demonstrate that optical remote sensing data can still be used with large SZA. Considering the relevance of snow cover mapping for ecology and climatology, the data acquisition at high solar zenith angles should be continued by PROBA-V. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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3194 KiB

Open AccessArticle

Crop Area Mapping Using 100-m Proba-V Time Series

by Yetkin Özüm Durgun, Anne Gobin, Ruben Van De Kerchove and Bernard Tychon

Remote Sens. 2016, 8(7), 585; https://doi.org/10.3390/rs8070585 - 11 Jul 2016

Cited by 26 | Viewed by 6564

Abstract

A method was developed for crop area mapping inspired by spectral matching techniques (SMTs) and based on phenological characteristics of different crop types applied using 100-m Proba-V NDVI data for the season 2014–2015. Ten-daily maximum value NDVI composites were created and smoothed in [...] Read more.

A method was developed for crop area mapping inspired by spectral matching techniques (SMTs) and based on phenological characteristics of different crop types applied using 100-m Proba-V NDVI data for the season 2014–2015. Ten-daily maximum value NDVI composites were created and smoothed in SPIRITS (spirits.jrc.ec.europa.eu). The study sites were globally spread agricultural areas located in Flanders (Belgium), Sria (Russia), Kyiv (Ukraine) and Sao Paulo (Brazil). For each pure pixel within the field, the NDVI profile of the crop type for its growing season was matched with the reference NDVI profile based on the training set extracted from the study site where the crop type originated. Three temporal windows were tested within the growing season: green-up to senescence, green-up to dormancy and minimum NDVI at the beginning of the growing season to minimum NDVI at the end of the growing season. Post classification rules were applied to the results to aggregate the crop type at the plot level. The overall accuracy (%) ranged between 65 and 86, and the kappa coefficient changed from 0.43–0.84 according to the site and the temporal window. In order of importance, the crop phenological development period, parcel size, shorter time window, number of ground-truth parcels and crop calendar similarity were the main reasons behind the differences between the results. The methodology described in this study demonstrated that 100-m Proba-V has the potential to be used in crop area mapping across different regions in the world. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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5116 KiB

Open AccessArticle

In-Orbit Radiometric Calibration and Stability Monitoring of the PROBA-V Instrument

by Sindy Sterckx, Stefan Adriaensen, Wouter Dierckx and Marc Bouvet

Remote Sens. 2016, 8(7), 546; https://doi.org/10.3390/rs8070546 - 29 Jun 2016

Cited by 10 | Viewed by 5165

Abstract

Since its launch in May 2013, the in-orbit radiometric performance of PROBA-V has been continuously monitored. Due to the absence of on-board calibration devices, in-flight performance monitoring and calibration relies fully on vicarious calibration methods. In this paper, the multiple vicarious calibration techniques [...] Read more.

Since its launch in May 2013, the in-orbit radiometric performance of PROBA-V has been continuously monitored. Due to the absence of on-board calibration devices, in-flight performance monitoring and calibration relies fully on vicarious calibration methods. In this paper, the multiple vicarious calibration techniques used to verify radiometric accuracy and to perform calibration parameter updates are discussed. Details are given of the radiometric calibration activities during both the commissioning and operational phase. The stability of the instrument in terms of overall radiometry and dark current is analyzed. Results of an independent comparison against MERIS and SPOT VEGETATION-2 are presented. Finally, an outlook is provided of the on-going activities aimed at improving both data consistency over time and within-scene uniformity. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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16197 KiB

Open AccessArticle

Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m

by Marie-Julie Lambert, François Waldner and Pierre Defourny

Remote Sens. 2016, 8(3), 232; https://doi.org/10.3390/rs8030232 - 11 Mar 2016

Cited by 51 | Viewed by 7906

Abstract

Early warning systems for food security require accurate and up-to-date information on the location of major crops in order to prevent hazards. A recent systematic analysis of existing cropland maps identified priority areas for cropland mapping and highlighted a major need for the [...] Read more.

Early warning systems for food security require accurate and up-to-date information on the location of major crops in order to prevent hazards. A recent systematic analysis of existing cropland maps identified priority areas for cropland mapping and highlighted a major need for the Sahelian and Sudanian agrosystems. This paper proposes a knowledge-based approach to map cropland in the Sahelian and Sudanian agrosystems that benefits from the 100-m spatial resolution of the recent PROBA-V sensor. The methodology uses five temporal features characterizing crop development throughout the vegetative season to optimize cropland discrimination. A feature importance analysis validates the efficiency of using a diversity of temporal features. The fully-automated method offers the first cropland map at 100-m using the PROBA-V sensor with an overall accuracy of 84% and an F-score for the cropland class of 74%. The improvements observed compared to existing cropland products are related to the hectometric resolution, to the methodology and to the quality of the labeling layer from which reliable training samples were automatically extracted. Classification errors are mainly explained by data availability and landscape fragmentation. Further improvements are expected with the upcoming enhanced cloud screening of the PROBA-V sensor. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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3570 KiB

Open AccessArticle

Cloud Cover Assessment for Operational Crop Monitoring Systems in Tropical Areas

by Isaque Daniel Rocha Eberhardt, Bruno Schultz, Rodrigo Rizzi, Ieda Del’Arco Sanches, Antonio Roberto Formaggio, Clement Atzberger, Marcio Pupin Mello, Markus Immitzer, Kleber Trabaquini, William Foschiera and Alfredo José Barreto Luiz

Remote Sens. 2016, 8(3), 219; https://doi.org/10.3390/rs8030219 - 08 Mar 2016

Cited by 41 | Viewed by 8820

Abstract

The potential of optical remote sensing data to identify, map and monitor croplands is well recognized. However, clouds strongly limit the usefulness of optical imagery for these applications. This paper aims at assessing cloud cover conditions over four states in the tropical and [...] Read more.

The potential of optical remote sensing data to identify, map and monitor croplands is well recognized. However, clouds strongly limit the usefulness of optical imagery for these applications. This paper aims at assessing cloud cover conditions over four states in the tropical and sub-tropical Center-South region of Brazil to guide the development of an appropriate agricultural monitoring system based on Landsat-like imagery. Cloudiness was assessed during overlapping four months periods to match the typical length of crop cycles in the study area. The percentage of clear sky occurrence was computed from the 1 km resolution MODIS Cloud Mask product (MOD35) considering 14 years of data between July 2000 and June 2014. Results showed high seasonality of cloud occurrence within the crop year with strong variations across the study area. The maximum seasonality was observed for the two states in the northern part of the study area (i.e., the ones closer to the Equator line), which also presented the lowest averaged values (15%) of clear sky occurrence during the main (summer) cropping period (November to February). In these locations, optical data faces severe constraints for mapping summer crops. On the other hand, relatively favorable conditions were found in the southern part of the study region. In the South, clear sky values of around 45% were found and no significant clear sky seasonality was observed. Results underpin the challenges to implement an operational crop monitoring system based solely on optical remote sensing imagery in tropical and sub-tropical regions, in particular if short-cycle crops have to be monitored during the cloudy summer months. To cope with cloudiness issues, we recommend the use of new systems with higher repetition rates such as Sentinel-2. For local studies, Unmanned Aircraft Vehicles (UAVs) might be used to augment the observing capability. Multi-sensor approaches combining optical and microwave data can be another option. In cases where wall-to-wall maps are not mandatory, statistical sampling approaches might also be a suitable alternative for obtaining useful crop area information. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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1670 KiB

Open AccessArticle

Single- and Multi-Date Crop Identification Using PROBA-V 100 and 300 m S1 Products on Zlatia Test Site, Bulgaria

by Eugenia Roumenina, Clement Atzberger, Vassil Vassilev, Petar Dimitrov, Ilina Kamenova, Martin Banov, Lachezar Filchev and Georgi Jelev

Remote Sens. 2015, 7(10), 13843-13862; https://doi.org/10.3390/rs71013843 - 22 Oct 2015

Cited by 23 | Viewed by 7366

Abstract

The monitoring of crops is of vital importance for food and environmental security in a global and European context. The main goal of this study was to assess the crop mapping performance provided by the 100 m spatial resolution of PROBA-V compared to [...] Read more.

The monitoring of crops is of vital importance for food and environmental security in a global and European context. The main goal of this study was to assess the crop mapping performance provided by the 100 m spatial resolution of PROBA-V compared to coarser resolution data (e.g., PROBA-V at 300 m) for a 2250 km²test site in Bulgaria. The focus was on winter and summer crop mapping with three to five classes. For classification, single- and multi-date spectral data were used as well as NDVI time series. Our results demonstrate that crop identification using 100 m PROBA-V data performed significantly better in all experiments compared to the PROBA-V 300 m data. PROBA-V multispectral imagery, acquired in spring (March) was the most appropriate for winter crop identification, while satellite data acquired in summer (July) was superior for summer crop identification. The classification accuracy from PROBA-V 100 m compared to PROBA-V 300 m was improved by 5.8% to 14.8% depending on crop type. Stacked multi-date satellite images with three to four images gave overall classification accuracies of 74%–77% (PROBA-V 100 m data) and 66%–70% (PROBA-V 300 m data) with four classes (wheat, rapeseed, maize, and sunflower). This demonstrates that three to four image acquisitions, well distributed over the growing season, capture most of the spectral and temporal variability in our test site. Regarding the PROBA-V NDVI time series, useful results were only obtained if crops were grouped into two broader crop type classes (summer and winter crops). Mapping accuracies decreased significantly when mapping more classes. Again, a positive impact of the increased spatial resolution was noted. Together, the findings demonstrate the positive effect of the 100 m resolution PROBA-V data compared to the 300 m for crop mapping. This has important implications for future data provision and strengthens the arguments for a second generation of this mission originally designed solely as a “gap-filler mission”. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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3213 KiB

Open AccessTechnical Note

PROBA-V Mission Exploitation Platform

by Erwin Goor, Jeroen Dries, Dirk Daems, Martine Paepen, Fabrizio Niro, Philippe Goryl, Philippe Mougnaud and Andrea Della Vecchia

Remote Sens. 2016, 8(7), 564; https://doi.org/10.3390/rs8070564 - 02 Jul 2016

Cited by 12 | Viewed by 6183

Abstract

As an extension of the PROBA-Vegetation (PROBA-V) user segment, the European Space Agency (ESA), de Vlaamse Instelling voor Technologisch Onderzoek (VITO), and partners TRASYS and Spacebel developed an operational Mission Exploitation Platform (MEP) to drastically improve the exploitation of the PROBA-V Earth Observation [...] Read more.

As an extension of the PROBA-Vegetation (PROBA-V) user segment, the European Space Agency (ESA), de Vlaamse Instelling voor Technologisch Onderzoek (VITO), and partners TRASYS and Spacebel developed an operational Mission Exploitation Platform (MEP) to drastically improve the exploitation of the PROBA-V Earth Observation (EO) data archive, the archive from the historical SPOT-VEGETATION mission, and derived products by researchers, service providers, and thematic users. The analysis of the time series of data (petabyte range) is addressed, as well as the large scale on-demand processing of the complete archive, including near real-time data. The platform consists of a private cloud environment, a Hadoop-based processing environment and a data manager. Several applications are released to the users, e.g., a full resolution viewing service, a time series viewer, pre-defined on-demand processing chains, and virtual machines with powerful tools and access to the data. After an initial release in January 2016 a research platform was deployed gradually, allowing users to design, debug, and test applications on the platform. From the PROBA-V MEP, access to, e.g., Sentinel-2 and Sentinel-3 data will be addressed as well. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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