Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity
François Waldner; Diego de Abelleyra; Santiago R. Verón; Miao Zhang; Bingfang Wu; Д.Е. Плотников; С.А. Барталев; Mykola Lavreniuk · 2016 · International Journal of Remote Sensing
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
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, asse
Abstract by François Waldner; Diego de Abelleyra; Santiago R. Verón; Miao Zhang; Bingfang Wu; Д.Е. Плотников; С.А. Барталев; Mykola Lavreniuk, International Journal of Remote Sensing (2016) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1080/01431161.2016.1194545