Publications by authors named "Momchil Yordanov"
Article Synopsis
- An advanced 10-meter resolution crop map for 2022 has been created for the EU and Ukraine, which includes 19 crop types and improves upon data from 2018.
- The mapping used a combination of Earth Observation data and in-situ data, implementing a Random Forest machine learning approach to create two classification layers: a primary map and a gap-filling map for areas affected by clouds.
- The final maps show 79.3% accuracy for major land cover classes and 70.6% accuracy for all crop types, and the model effectively produced a reliable map for Ukraine, even in data-scarce regions.
Massive and high-quality in situ data are essential for Earth-observation-based agricultural monitoring. However, field surveying requires considerable organizational effort and money. Using computer vision to recognize crop types on geo-tagged photos could be a game changer allowing for the provision of timely and accurate crop-specific information.
View Article and Find Full Text PDFAccurately characterizing land surface changes with Earth Observation requires geo-located ground truth. In the European Union (EU), a tri-annual surveyed sample of land cover and land use has been collected since 2006 under the Land Use/Cover Area frame Survey (LUCAS). A total of 1351293 observations at 651780 unique locations for 106 variables along with 5.
View Article and Find Full Text PDFA novel methodology is proposed to robustly map oil seed rape (OSR) flowering phenology from time series generated from the Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) sensors. The time series are averaged at parcel level, initially for a set of 229 reference parcels for which multiple phenological observations on OSR flowering have been collected from April 21 to May 19, 2018. The set of OSR parcels is extended to a regional sample of 32,355 OSR parcels derived from a regional S2 classification.
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