Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity. Issue 14 (17th July 2016)

• Record Type: Journal Article
• Authors: Waldner, François; De Abelleyra, Diego; Verón, Santiago R.; Zhang, Miao; Wu, Bingfang; Plotnikov, Dmitry; Bartalev, Sergey; Lavreniuk, Mykola; Skakun, Sergii; Kussul, Nataliia; Le Maire, Guerric; Dupuy, Stéphane; Jarvis, Ian; Defourny, Pierre
• Is Part Of: International journal of remote sensing. Volume 37:Issue 14(2016)
• Page Start: 3196
• Page End: 3231
• Publication Date: 2016-07-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1194545
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: 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.