Towards a set of agrosystem - specific cropland mapping methods to address the global cropland diversity

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...

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Otros Autores: Waldner, François, Abelleyra, Diego de, Verón, Santiago Ramón, Zhang, Miao, Wu, Bingfang, Plotnikov, Dimitry, Bartalev, Sergey, Lavreniuk, Mykola, Skakun, Sergii, Kussul, Natalia, Le Maire, Guerric, Dupuy, Stéphane, Jarvis, Ian, Defourny, Pierre
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Lenguaje:Inglés
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Acceso en línea:http://ri.agro.uba.ar/files/intranet/articulo/2016waldner.pdf
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245 1 0 |a Towards a set of agrosystem - specific cropland mapping methods to address the global cropland diversity 
520 |a 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 per cent to 95 per cent, 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 per cent to 30 per cent]. 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 per cent of the calibration data, e.g. 2 per cent 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. 
650 |2 Agrovoc  |9 26 
653 0 |a AGROSYSTEM 
653 0 |a CROPLAND 
653 0 |a DIVERSITY 
653 0 |a MAPPING 
700 1 |a Waldner, François  |9 69332 
700 1 |a Abelleyra, Diego de  |9 11479 
700 1 |9 11455  |a Verón, Santiago Ramón 
700 1 |a Zhang, Miao  |9 69333 
700 1 |a Wu, Bingfang  |9 69334 
700 1 |a Plotnikov, Dimitry  |9 69335 
700 1 |a Bartalev, Sergey  |9 69336 
700 1 |a Lavreniuk, Mykola  |9 69337 
700 1 |a Skakun, Sergii  |9 69338 
700 1 |a Kussul, Natalia  |9 69339 
700 1 |a Le Maire, Guerric  |9 69340 
700 1 |a Dupuy, Stéphane  |9 69341 
700 1 |a Jarvis, Ian  |9 69342 
700 1 |a Defourny, Pierre  |9 69343 
773 |t International Journal of Remote Sensing  |g Vol.37, no.14 (2016), p.3196-3231, tbls., grafs., fot., mapas 
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