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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"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 August 2016)

Special Issue Editors

Guest Editor
Prof. Dr. Clement Atzberger

University of Natural Resources and Life Sciences (BOKU), A-1190 Vienna, Austria
Website | E-Mail
Phone: +43 (1) 47654 5101
Interests: advanced remote sensing techniques for vegetation monitoring and dynamics; drought early warning systems; remote sensing for agriculture, forestry and natural resource management; imaging spectroscopy; time series analysis; radiative transfer modeling
Guest Editor
Mag. Magda Chelfaoui

Researcher, Remote Sensing & Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria
Website | E-Mail
Interests: drought monitoring; time series analysis

Special Issue Information

Dear Colleagues,

The Earth's surface is changing at a high pace, but with varying intensities across the globe. Changing land use and land cover lead to a loss in biodiversity, reduce the available land for agricultural production, and impact many ecosystem functions, processes, and services. For monitoring these changes, satellite-based Earth Observation is a key technology. Only remote sensing permits one to monitor large areas at a sufficiently high spatial resolution, with a high revisit frequency, at reasonable costs.

The time series of globally available AVHRR, SPOT-VGT, MERIS and MODIS data sets at hecta- to kilometric spatial resolutions now cover more than 30 years of data. The data provide excellent insights regarding past and ongoing changes of the land surface. To further strengthen the usefulness of these medium resolution time series, Europe is contributing two new satellite missions that will increase data availability and ensure data continuity: Proba-V and Sentinel-3.

Proba-V was launched in 2013 and delivers spectral data from visible to SWIR bands at spatial resolutions between 100 m and 1 km, with very high revisit frequency (1-5 days depending on resolution and latitude). Sentinel-3 will be launched later this year (2015) and will host an innovative instrument package delivering data by various (passive and active) instruments. The OLCI instrument on Sentinel-3 will provide, for example, data from 21 spectral bands at a 300 m grounds sampling distance. Both missions ensure very short revisit times, so as to enable detection of rapidly occurring changes in the land surface, and were built to replace SPOT-VGT, which came to the end of its lifetime in 2014.

As both satellite missions are not yet well known by our community, we wish to compile knowledge about the two satellite missions, as well as example applications, with a focus on land surfaces. We therefore invite you to submit manuscripts about your recent research, as well as review papers, with respect to the following topics (not limited):

  • Mission statuses for Proba-V and Sentinel-3
  • Data processing & European core services
  • European developments for (big) data handling and processing
  • Research projects (national, European, and international)
  • International collaborations and (larger) programs
  • Cal/Val & Intercalibration activities
  • Applications focusing on land (plus a few examples concerning the ocean, inland water, the cryosphere, and the atmosphere)
  • Comparative studies examining different satellite systems
  • Synergies between various satellite systems

Clement Atzberger
Magda Chelfaoui
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (12 papers)

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Research

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Open AccessArticle A Method for Estimating the Aerodynamic Roughness Length with NDVI and BRDF Signatures Using Multi-Temporal Proba-V Data
Remote Sens. 2017, 9(1), 6; doi:10.3390/rs9010006
Received: 25 August 2016 / Revised: 16 December 2016 / Accepted: 19 December 2016 / Published: 24 December 2016
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Abstract
Aerodynamic roughness length is an important parameter for surface fluxes estimates. This paper developed an innovative method for estimation of aerodynamic roughness length (z0m) 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 (z0m) 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 z0m 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 z0m from time series. Results show that the relationship between HDVI and z0m is more pronounced than that between NDVI and z0m 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 z0m, whereas NDVI cannot. Full article
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Open AccessArticle Dynamic Water Surface Detection Algorithm Applied on PROBA-V Multispectral Data
Remote Sens. 2016, 8(12), 1010; doi:10.3390/rs8121010
Received: 10 August 2016 / Revised: 17 November 2016 / Accepted: 1 December 2016 / Published: 10 December 2016
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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
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Open AccessArticle Evaluating the Potential of PROBA-V Satellite Image Time Series for Improving LC Classification in Semi-Arid African Landscapes
Remote Sens. 2016, 8(12), 987; doi:10.3390/rs8120987
Received: 26 April 2016 / Revised: 2 November 2016 / Accepted: 21 November 2016 / Published: 30 November 2016
PDF Full-text (3544 KB) | HTML Full-text | XML Full-text
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
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Open AccessArticle Crop Mapping Using PROBA-V Time Series Data at the Yucheng and Hongxing Farm in China
Remote Sens. 2016, 8(11), 915; doi:10.3390/rs8110915
Received: 9 August 2016 / Revised: 11 October 2016 / Accepted: 26 October 2016 / Published: 3 November 2016
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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
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Open AccessArticle Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products
Remote Sens. 2016, 8(10), 824; doi:10.3390/rs8100824
Received: 20 July 2016 / Revised: 26 September 2016 / Accepted: 28 September 2016 / Published: 7 October 2016
Cited by 1 | PDF Full-text (20045 KB) | HTML Full-text | XML Full-text
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
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Figure 1

Open AccessArticle Assessment of Automated Snow Cover Detection at High Solar Zenith Angles with PROBA-V
Remote Sens. 2016, 8(9), 699; doi:10.3390/rs8090699
Received: 8 June 2016 / Revised: 11 August 2016 / Accepted: 17 August 2016 / Published: 24 August 2016
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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
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Open AccessArticle Crop Area Mapping Using 100-m Proba-V Time Series
Remote Sens. 2016, 8(7), 585; doi:10.3390/rs8070585
Received: 25 April 2016 / Revised: 30 June 2016 / Accepted: 5 July 2016 / Published: 11 July 2016
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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
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Open AccessArticle In-Orbit Radiometric Calibration and Stability Monitoring of the PROBA-V Instrument
Remote Sens. 2016, 8(7), 546; doi:10.3390/rs8070546
Received: 30 April 2016 / Revised: 14 June 2016 / Accepted: 20 June 2016 / Published: 29 June 2016
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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
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Open AccessArticle Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m
Remote Sens. 2016, 8(3), 232; doi:10.3390/rs8030232
Received: 6 January 2016 / Revised: 25 February 2016 / Accepted: 29 February 2016 / Published: 11 March 2016
Cited by 11 | PDF Full-text (16197 KB) | HTML Full-text | XML Full-text
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
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Open AccessArticle Cloud Cover Assessment for Operational Crop Monitoring Systems in Tropical Areas
Remote Sens. 2016, 8(3), 219; doi:10.3390/rs8030219
Received: 5 November 2015 / Revised: 24 February 2016 / Accepted: 1 March 2016 / Published: 8 March 2016
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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
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Open AccessArticle Single- and Multi-Date Crop Identification Using PROBA-V 100 and 300 m S1 Products on Zlatia Test Site, Bulgaria
Remote Sens. 2015, 7(10), 13843-13862; doi:10.3390/rs71013843
Received: 26 June 2015 / Revised: 6 October 2015 / Accepted: 14 October 2015 / Published: 22 October 2015
Cited by 7 | PDF Full-text (1670 KB) | HTML Full-text | XML Full-text
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 km2 test 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
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Other

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Open AccessTechnical Note PROBA-V Mission Exploitation Platform
Remote Sens. 2016, 8(7), 564; doi:10.3390/rs8070564
Received: 25 April 2016 / Revised: 22 June 2016 / Accepted: 22 June 2016 / Published: 2 July 2016
Cited by 1 | PDF Full-text (3213 KB) | HTML Full-text | XML Full-text
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
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