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V.A. Tolpin; S.а. Bartalev; E.S. Elkina; A.V. Kashnitskii; А.м. Konstantinova; E.A. Loupian; V.V. Marchenkov; Dmitry Plotnikov; V.C. Patil; J.к. Sunil; Space Research Institute RAS; K.J. Somaiya Institute of Applied Agricultural Research. The VEGA-GEOGLAM information system: a tool for the development of methods and approaches to using satellite remote sensing data in problem-solving tasks of global agricultural monitoring. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2019, 16, 1 .
AMA StyleV.A. Tolpin, S.а. Bartalev, E.S. Elkina, A.V. Kashnitskii, А.м. Konstantinova, E.A. Loupian, V.V. Marchenkov, Dmitry Plotnikov, V.C. Patil, J.к. Sunil, Space Research Institute RAS, K.J. Somaiya Institute of Applied Agricultural Research. The VEGA-GEOGLAM information system: a tool for the development of methods and approaches to using satellite remote sensing data in problem-solving tasks of global agricultural monitoring. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2019; 16 (3):1.
Chicago/Turabian StyleV.A. Tolpin; S.а. Bartalev; E.S. Elkina; A.V. Kashnitskii; А.м. Konstantinova; E.A. Loupian; V.V. Marchenkov; Dmitry Plotnikov; V.C. Patil; J.к. Sunil; Space Research Institute RAS; K.J. Somaiya Institute of Applied Agricultural Research. 2019. "The VEGA-GEOGLAM information system: a tool for the development of methods and approaches to using satellite remote sensing data in problem-solving tasks of global agricultural monitoring." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 16, no. 3: 1.
E.A. Loupian; Space Research Institute RAS; K.A. Bulanov; P.V. Denisov; Yu.S. Krasheninnikova; Dmitry Plotnikov; V.A. Tolpin; Ksenia Troshko; I.A. Uvarov; Ministry Of Agriculture Of Russia; Analytical Center Of The Ministry Of Agriculture Of Russia; Analytical Center of the Ministry of Agriculture of Russia; Institute of Geography RAS. Analysis of winter crops development in the southern regions of the European part of Russia in February 2019 based on remote monitoring. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2019, 16, 1 .
AMA StyleE.A. Loupian, Space Research Institute RAS, K.A. Bulanov, P.V. Denisov, Yu.S. Krasheninnikova, Dmitry Plotnikov, V.A. Tolpin, Ksenia Troshko, I.A. Uvarov, Ministry Of Agriculture Of Russia, Analytical Center Of The Ministry Of Agriculture Of Russia, Analytical Center of the Ministry of Agriculture of Russia; Institute of Geography RAS. Analysis of winter crops development in the southern regions of the European part of Russia in February 2019 based on remote monitoring. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2019; 16 (1):1.
Chicago/Turabian StyleE.A. Loupian; Space Research Institute RAS; K.A. Bulanov; P.V. Denisov; Yu.S. Krasheninnikova; Dmitry Plotnikov; V.A. Tolpin; Ksenia Troshko; I.A. Uvarov; Ministry Of Agriculture Of Russia; Analytical Center Of The Ministry Of Agriculture Of Russia; Analytical Center of the Ministry of Agriculture of Russia; Institute of Geography RAS. 2019. "Analysis of winter crops development in the southern regions of the European part of Russia in February 2019 based on remote monitoring." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 16, no. 1: 1.
With the unprecedented availability of satellite data and the rise of global binary maps, the collection of shared reference data sets should be fostered to allow systematic product benchmarking and validation. Authoritative global reference data are generally collected by experts with regional knowledge through photo-interpretation. During the last decade, crowdsourcing has emerged as an attractive alternative for rapid and relatively cheap data collection, beckoning the increasingly relevant question: can these two data sources be combined to validate thematic maps? In this article, we compared expert and crowd data and assessed their relative agreement for cropland identification, a land cover class often reported as difficult to map. Results indicate that observations from experts and volunteers could be partially conflated provided that several consistency checks are performed. We propose that conflation, i.e., replacement and augmentation of expert observations by crowdsourced observations, should be carried out both at the sampling and data analytics levels. The latter allows to evaluate the reliability of crowdsourced observations and to decide whether they should be conflated or discarded. We demonstrate that the standard deviation of crowdsourced contributions is a simple yet robust indicator of reliability which can effectively inform conflation. Following this criterion, we found that 70% of the expert observations could be crowdsourced with little to no effect on accuracy estimates, allowing a strategic reallocation of the spared expert effort to increase the reliability of the remaining 30% at no additional cost. Finally, we provide a collection of evidence-based recommendations for future hybrid reference data collection campaigns.
François Waldner; Anne Schucknecht; Myroslava Lesiv; Javier Gallego; Linda See; Ana Pérez-Hoyos; Raphaël D'Andrimont; Thomas de Maet; Juan Carlos Laso Bayas; Steffen Fritz; Olivier Leo; Hervé Kerdiles; Mónica Díez; Kristof Van Tricht; Sven Gilliams; Andrii Shelestov; Mykola Lavreniuk; Margareth Simões; Rodrigo Ferraz; Beatriz Bellón; Agnès Bégué; Gerard Hazeu; Vaclav Stonacek; Jan Kolomaznik; Jan Misurec; Santiago R. Verón; Diego de Abelleyra; Dmitry Plotnikov; Li Mingyong; Mrinal Singha; Prashant Patil; Miao Zhang; Pierre Defourny. Conflation of expert and crowd reference data to validate global binary thematic maps. Remote Sensing of Environment 2018, 221, 235 -246.
AMA StyleFrançois Waldner, Anne Schucknecht, Myroslava Lesiv, Javier Gallego, Linda See, Ana Pérez-Hoyos, Raphaël D'Andrimont, Thomas de Maet, Juan Carlos Laso Bayas, Steffen Fritz, Olivier Leo, Hervé Kerdiles, Mónica Díez, Kristof Van Tricht, Sven Gilliams, Andrii Shelestov, Mykola Lavreniuk, Margareth Simões, Rodrigo Ferraz, Beatriz Bellón, Agnès Bégué, Gerard Hazeu, Vaclav Stonacek, Jan Kolomaznik, Jan Misurec, Santiago R. Verón, Diego de Abelleyra, Dmitry Plotnikov, Li Mingyong, Mrinal Singha, Prashant Patil, Miao Zhang, Pierre Defourny. Conflation of expert and crowd reference data to validate global binary thematic maps. Remote Sensing of Environment. 2018; 221 ():235-246.
Chicago/Turabian StyleFrançois Waldner; Anne Schucknecht; Myroslava Lesiv; Javier Gallego; Linda See; Ana Pérez-Hoyos; Raphaël D'Andrimont; Thomas de Maet; Juan Carlos Laso Bayas; Steffen Fritz; Olivier Leo; Hervé Kerdiles; Mónica Díez; Kristof Van Tricht; Sven Gilliams; Andrii Shelestov; Mykola Lavreniuk; Margareth Simões; Rodrigo Ferraz; Beatriz Bellón; Agnès Bégué; Gerard Hazeu; Vaclav Stonacek; Jan Kolomaznik; Jan Misurec; Santiago R. Verón; Diego de Abelleyra; Dmitry Plotnikov; Li Mingyong; Mrinal Singha; Prashant Patil; Miao Zhang; Pierre Defourny. 2018. "Conflation of expert and crowd reference data to validate global binary thematic maps." Remote Sensing of Environment 221, no. : 235-246.
We propose a method of segmentation of remote sensing time series data, which exploits multi-temporal information to identify objects’ boundaries. Extracting homogeneous objects with similar temporal behavior, the method analyzes large volumes of multi-temporal input data in a piecewise way and produces a consistent output segmentation layer for large territories. Segment building logic is simplified to minimize the computation time, while objects’ boundary identification accuracy remains sufficient for remote monitoring and mapping of vegetation, and specifically, agricultural crops. At the Space Research Institute of the RAS, the proposed method is currently applied for automated on-line satellite imagery analysis for recognition and mapping of (winter and spring) crops on large territories and land-use evaluation. The method successfully deals with gaps in remote sensing time series data and performs well even when input images are contaminated with speckle noise. Due to its ability to map dynamically homogeneous surface areas with partially missing data, the method provides a potential for their recovery.
Dmitry Plotnikov; P. A. Kolbudaev; S. A. Bartalev. Identification of dynamically homogeneous areas with time series segmentation of remote sensing data. Computer Optics 2018, 42, 447-456 .
AMA StyleDmitry Plotnikov, P. A. Kolbudaev, S. A. Bartalev. Identification of dynamically homogeneous areas with time series segmentation of remote sensing data. Computer Optics. 2018; 42 (3):447-456.
Chicago/Turabian StyleDmitry Plotnikov; P. A. Kolbudaev; S. A. Bartalev. 2018. "Identification of dynamically homogeneous areas with time series segmentation of remote sensing data." Computer Optics 42, no. 3: 447-456.
E.A. Gavrilyuk; A.S. Plotnikova; Dmitry Plotnikov; Center For Forest Ecology And Productivity Ras; Space Research Institute RAS. Land cover mapping of the Pechora-Ilych Nature Reserve and its vicinity based on reconstructed multitemporal Landsat satellite data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2018, 15, 1 .
AMA StyleE.A. Gavrilyuk, A.S. Plotnikova, Dmitry Plotnikov, Center For Forest Ecology And Productivity Ras, Space Research Institute RAS. Land cover mapping of the Pechora-Ilych Nature Reserve and its vicinity based on reconstructed multitemporal Landsat satellite data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2018; 15 (5):1.
Chicago/Turabian StyleE.A. Gavrilyuk; A.S. Plotnikova; Dmitry Plotnikov; Center For Forest Ecology And Productivity Ras; Space Research Institute RAS. 2018. "Land cover mapping of the Pechora-Ilych Nature Reserve and its vicinity based on reconstructed multitemporal Landsat satellite data." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 15, no. 5: 1.
E.A. Loupian; S.а. Bartalev; Yu.S. Krasheninnikova; Dmitry Plotnikov; V.A. Tolpin; I.A. Uvarov; Space Research Institute RAS. Analysis of winter crops development in the southern regions of the European part of Russia in spring of 2018 with use of remote monitoring. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2018, 15, 1 .
AMA StyleE.A. Loupian, S.а. Bartalev, Yu.S. Krasheninnikova, Dmitry Plotnikov, V.A. Tolpin, I.A. Uvarov, Space Research Institute RAS. Analysis of winter crops development in the southern regions of the European part of Russia in spring of 2018 with use of remote monitoring. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2018; 15 (2):1.
Chicago/Turabian StyleE.A. Loupian; S.а. Bartalev; Yu.S. Krasheninnikova; Dmitry Plotnikov; V.A. Tolpin; I.A. Uvarov; Space Research Institute RAS. 2018. "Analysis of winter crops development in the southern regions of the European part of Russia in spring of 2018 with use of remote monitoring." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 15, no. 2: 1.
Dmitry Plotnikov; P.A. Kolbudaev; S.A. Bartalev; E.A. Loupian; Space Research Institute RAS. Automated annual cropland mapping from reconstructed time series of Landsat data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2018, 15, 1 .
AMA StyleDmitry Plotnikov, P.A. Kolbudaev, S.A. Bartalev, E.A. Loupian, Space Research Institute RAS. Automated annual cropland mapping from reconstructed time series of Landsat data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2018; 15 (2):1.
Chicago/Turabian StyleDmitry Plotnikov; P.A. Kolbudaev; S.A. Bartalev; E.A. Loupian; Space Research Institute RAS. 2018. "Automated annual cropland mapping from reconstructed time series of Landsat data." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 15, no. 2: 1.
V.A. Egorov; S.A. Bartalev; P.A. Kolbudaev; Dmitry Plotnikov; S.A. Khvostikov; Space Research Institute RAS. Land cover map of Russia derived from Proba-V satellite data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2018, 15, 1 .
AMA StyleV.A. Egorov, S.A. Bartalev, P.A. Kolbudaev, Dmitry Plotnikov, S.A. Khvostikov, Space Research Institute RAS. Land cover map of Russia derived from Proba-V satellite data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2018; 15 (2):1.
Chicago/Turabian StyleV.A. Egorov; S.A. Bartalev; P.A. Kolbudaev; Dmitry Plotnikov; S.A. Khvostikov; Space Research Institute RAS. 2018. "Land cover map of Russia derived from Proba-V satellite data." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 15, no. 2: 1.
P.V. Denisov; Analytical Сenter Of The Ministry Of Agriculture Of Russia; Yu.S. Krasheninnikova; E.A. Loupian; A.S. Martyanov; Dmitry Plotnikov; I.I. Sereda; V.A. Tolpin; Ksenia Troshko; Space Research Institute RAS; Analytical Сenter of the Ministry of Agriculture of Russia; Institute of Geography RAS. Observation of winter crops development in Russia in autumn 2018 based on remote sensing data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2018, 15, 1 .
AMA StyleP.V. Denisov, Analytical Сenter Of The Ministry Of Agriculture Of Russia, Yu.S. Krasheninnikova, E.A. Loupian, A.S. Martyanov, Dmitry Plotnikov, I.I. Sereda, V.A. Tolpin, Ksenia Troshko, Space Research Institute RAS, Analytical Сenter of the Ministry of Agriculture of Russia; Institute of Geography RAS. Observation of winter crops development in Russia in autumn 2018 based on remote sensing data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2018; 15 (7):1.
Chicago/Turabian StyleP.V. Denisov; Analytical Сenter Of The Ministry Of Agriculture Of Russia; Yu.S. Krasheninnikova; E.A. Loupian; A.S. Martyanov; Dmitry Plotnikov; I.I. Sereda; V.A. Tolpin; Ksenia Troshko; Space Research Institute RAS; Analytical Сenter of the Ministry of Agriculture of Russia; Institute of Geography RAS. 2018. "Observation of winter crops development in Russia in autumn 2018 based on remote sensing data." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 15, no. 7: 1.
N.V. Shabanov; S.а. Bartalev; F.V. Eroshenko; Dmitry Plotnikov; North-Caucasian Federal Scientific Agricultural Center; Space Research Institute RAS. Development of capabilities for remote sensing estimate of Leaf Area Index from MODIS data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2018, 15, 1 .
AMA StyleN.V. Shabanov, S.а. Bartalev, F.V. Eroshenko, Dmitry Plotnikov, North-Caucasian Federal Scientific Agricultural Center, Space Research Institute RAS. Development of capabilities for remote sensing estimate of Leaf Area Index from MODIS data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2018; 15 (4):1.
Chicago/Turabian StyleN.V. Shabanov; S.а. Bartalev; F.V. Eroshenko; Dmitry Plotnikov; North-Caucasian Federal Scientific Agricultural Center; Space Research Institute RAS. 2018. "Development of capabilities for remote sensing estimate of Leaf Area Index from MODIS data." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 15, no. 4: 1.
Dmitry Plotnikov; S.A. Khvostikov; S.A. Bartalev; Space Research Institute RAS. Method for automated crop types mapping using remote sensing data and a plant growth simulation model. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2018, 15, 1 .
AMA StyleDmitry Plotnikov, S.A. Khvostikov, S.A. Bartalev, Space Research Institute RAS. Method for automated crop types mapping using remote sensing data and a plant growth simulation model. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2018; 15 (4):1.
Chicago/Turabian StyleDmitry Plotnikov; S.A. Khvostikov; S.A. Bartalev; Space Research Institute RAS. 2018. "Method for automated crop types mapping using remote sensing data and a plant growth simulation model." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 15, no. 4: 1.
Dmitry Plotnikov; S.A. Bartalev; E.A. Loupian; V.A. Tolpin. Accuracy assessment for winter crops mapping in spring-summer growing season with MODIS data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2017, 14, 132 -145.
AMA StyleDmitry Plotnikov, S.A. Bartalev, E.A. Loupian, V.A. Tolpin. Accuracy assessment for winter crops mapping in spring-summer growing season with MODIS data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2017; 14 (4):132-145.
Chicago/Turabian StyleDmitry Plotnikov; S.A. Bartalev; E.A. Loupian; V.A. Tolpin. 2017. "Accuracy assessment for winter crops mapping in spring-summer growing season with MODIS data." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 14, no. 4: 132-145.
E.A. Loupian; S.A. Bartalev; Yu.S. Krasheninnikova; Dmitry Plotnikov; V.A. Tolpin. Observation of early development of winter crops in spring 2017 in southern regions of Russia based on remote sensing data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2017, 14, 268 -272.
AMA StyleE.A. Loupian, S.A. Bartalev, Yu.S. Krasheninnikova, Dmitry Plotnikov, V.A. Tolpin. Observation of early development of winter crops in spring 2017 in southern regions of Russia based on remote sensing data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2017; 14 (2):268-272.
Chicago/Turabian StyleE.A. Loupian; S.A. Bartalev; Yu.S. Krasheninnikova; Dmitry Plotnikov; V.A. Tolpin. 2017. "Observation of early development of winter crops in spring 2017 in southern regions of Russia based on remote sensing data." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 14, no. 2: 268-272.
E.A. Loupian; S.A. Bartalev; Yu.S. Krasheninnikova; Dmitry Plotnikov; V.A. Tolpin. Abnormal development of spring crops in European Russia in 2017. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2017, 14, 324 -329.
AMA StyleE.A. Loupian, S.A. Bartalev, Yu.S. Krasheninnikova, Dmitry Plotnikov, V.A. Tolpin. Abnormal development of spring crops in European Russia in 2017. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2017; 14 (3):324-329.
Chicago/Turabian StyleE.A. Loupian; S.A. Bartalev; Yu.S. Krasheninnikova; Dmitry Plotnikov; V.A. Tolpin. 2017. "Abnormal development of spring crops in European Russia in 2017." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 14, no. 3: 324-329.
S.A. Bartalev; E.S. Elkina; E.A. Loupian; Dmitry Plotnikov; V.A. Tolpin. Remote sensing of 2017 winter crops in the Russian Federation. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2017, 14, 275 -280.
AMA StyleS.A. Bartalev, E.S. Elkina, E.A. Loupian, Dmitry Plotnikov, V.A. Tolpin. Remote sensing of 2017 winter crops in the Russian Federation. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2017; 14 (4):275-280.
Chicago/Turabian StyleS.A. Bartalev; E.S. Elkina; E.A. Loupian; Dmitry Plotnikov; V.A. Tolpin. 2017. "Remote sensing of 2017 winter crops in the Russian Federation." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 14, no. 4: 275-280.
Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring JECAM sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer MODIS mean composites red and near-infrared channels. Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data from 10% to 30%. This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.
Francois Waldner; Diego De Abelleyra; Santiago R. Verón; Miao Zhang; Bingfang Wu; Dmitry Plotnikov; Sergey Bartalev; Mykola Lavreniuk; Sergii Skakun; Nataliia Kussul; Guerric le Maire; Stéphane Dupuy; Ian Jarvis; Pierre Defourny. Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity. International Journal of Remote Sensing 2016, 37, 3196 -3231.
AMA StyleFrancois Waldner, Diego De Abelleyra, Santiago R. Verón, Miao Zhang, Bingfang Wu, Dmitry Plotnikov, Sergey Bartalev, Mykola Lavreniuk, Sergii Skakun, Nataliia Kussul, Guerric le Maire, Stéphane Dupuy, Ian Jarvis, Pierre Defourny. Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity. International Journal of Remote Sensing. 2016; 37 (14):3196-3231.
Chicago/Turabian StyleFrancois Waldner; Diego De Abelleyra; Santiago R. Verón; Miao Zhang; Bingfang Wu; Dmitry Plotnikov; Sergey Bartalev; Mykola Lavreniuk; Sergii Skakun; Nataliia Kussul; Guerric le Maire; Stéphane Dupuy; Ian Jarvis; Pierre Defourny. 2016. "Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity." International Journal of Remote Sensing 37, no. 14: 3196-3231.
Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products.
François Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data 2016, 1, 3 .
AMA StyleFrançois Waldner, Steffen Fritz, Antonio Di Gregorio, Dmitry Plotnikov, Sergey Bartalev, Nataliia Kussul, Peng Gong, Prasad Thenkabail, Gerard Hazeu, Igor Klein, Fabian Löw, Jukka Miettinen, Vinay Kumar Dadhwal, Céline Lamarche, Sophie Bontemps, Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data. 2016; 1 (1):3.
Chicago/Turabian StyleFrançois Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. 2016. "A Unified Cropland Layer at 250 m for Global Agriculture Monitoring." Data 1, no. 1: 3.
The sustainable agriculture requires a regular country-wide update of information on the status and extension of arable land in Russia. The arable land mapping method is developed based on multi-year time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The method exploits differences between the intra- and inter-annual changes in the spectral reflectance of arable land and the corresponding changes for other land cover types. It involves a set of satellite data-derived phenological metrics generated using a 6 years long time series of the perpendicular vegetation index (PVI). The approach utilizes the Locally Adaptive Global Mapping Algorithm (LAGMA), which is a supervised classification technique accounting for the spatial variability of intra-classes spectral properties. The method has been applied to produce a uniform time series of comparable annual arable land maps for Russia at 250 m spatial resolution for the years 2005–2013. Countrywide arable land area trends over the above time series were found to be consistent with official statistics (ROSSTAT).The mapping result has been evaluated using reference data providing F-score exceeding 80% for the most productive regions.
Sergey A. Bartalev; Dmitry Plotnikov; Evgeny A. Loupian. Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique. Remote Sensing Letters 2016, 7, 269 -278.
AMA StyleSergey A. Bartalev, Dmitry Plotnikov, Evgeny A. Loupian. Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique. Remote Sensing Letters. 2016; 7 (3):269-278.
Chicago/Turabian StyleSergey A. Bartalev; Dmitry Plotnikov; Evgeny A. Loupian. 2016. "Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique." Remote Sensing Letters 7, no. 3: 269-278.
F.V. Eroshenko; Sergey Bartalev; I.G. Storchak; Dmitry Plotnikov. The possibility of winter wheat yield estimation based on vegetation index of photosynthetic potential derived from remote sensing data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2016, 13, 99 -112.
AMA StyleF.V. Eroshenko, Sergey Bartalev, I.G. Storchak, Dmitry Plotnikov. The possibility of winter wheat yield estimation based on vegetation index of photosynthetic potential derived from remote sensing data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2016; 13 (4):99-112.
Chicago/Turabian StyleF.V. Eroshenko; Sergey Bartalev; I.G. Storchak; Dmitry Plotnikov. 2016. "The possibility of winter wheat yield estimation based on vegetation index of photosynthetic potential derived from remote sensing data." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 13, no. 4: 99-112.
E.A. Loupian; Space Research Institute RAS; S.A. Bartalev; I.V. Balashov; M.A. Bourtsev; V.A. Egorov; V.Yu. Efremov; V.O. Zharko; A.V. Kashnitskiy; P.A. Kolbudaev; L.S. Kramareva; A.A. Mazurov; O.Yu. Oksyukevich; Dmitry Plotnikov; A.A. Proshin; K.S. Senko; I.A. Uvarov; S.A. Khvostikov; T.S. Khovratovich; Far Eastern Center of Planeta Research Center for Space Hydrometeorology; Llc "ikiz". Vega-Primorie: complex remote forest monitoring information system. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2016, 13, 1 .
AMA StyleE.A. Loupian, Space Research Institute RAS, S.A. Bartalev, I.V. Balashov, M.A. Bourtsev, V.A. Egorov, V.Yu. Efremov, V.O. Zharko, A.V. Kashnitskiy, P.A. Kolbudaev, L.S. Kramareva, A.A. Mazurov, O.Yu. Oksyukevich, Dmitry Plotnikov, A.A. Proshin, K.S. Senko, I.A. Uvarov, S.A. Khvostikov, T.S. Khovratovich, Far Eastern Center of Planeta Research Center for Space Hydrometeorology, Llc "ikiz". Vega-Primorie: complex remote forest monitoring information system. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2016; 13 (5):1.
Chicago/Turabian StyleE.A. Loupian; Space Research Institute RAS; S.A. Bartalev; I.V. Balashov; M.A. Bourtsev; V.A. Egorov; V.Yu. Efremov; V.O. Zharko; A.V. Kashnitskiy; P.A. Kolbudaev; L.S. Kramareva; A.A. Mazurov; O.Yu. Oksyukevich; Dmitry Plotnikov; A.A. Proshin; K.S. Senko; I.A. Uvarov; S.A. Khvostikov; T.S. Khovratovich; Far Eastern Center of Planeta Research Center for Space Hydrometeorology; Llc "ikiz". 2016. "Vega-Primorie: complex remote forest monitoring information system." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 13, no. 5: 1.