Global crop calendars of maize and wheat in the framework of the WorldCereal project
| Autor: | Belén Franch, Juanma Cintas, Inbal Becker-Reshef, María José Sanchez-Torres, Javier Roger, Sergii Skakun, José Antonio Sobrino, Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams, Benjamin Koetz, Zoltan Szantoi, Alyssa Whitcraft |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | GIScience & Remote Sensing, Vol 59, Iss 1, Pp 885-913 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1548-1603 1943-7226 15481603 |
| DOI: | 10.1080/15481603.2022.2079273 |
| Popis: | Crop calendars provide valuable information on the timing of important stages of crop development such as the planting or Start of Season (SOS) and harvesting dates or End of Season (EOS). This information is critical for many crop monitoring applications such as crop-type mapping, crop condition monitoring, and crop yield estimation and forecasting. Spatially detailed information on the crop calendars provides an important asset in this respect, as it allows the algorithms to account for specific local circumstances while also maximizing their robustness and global applicability. Existing global crop calendar products, as produced by the Group on Earth Observations’ Global Agricultural Monitoring (GEOGLAM) Crop Monitor, the United States Department of Agriculture Foreign Agricultural Service (USDA-FAS), the Food and Agriculture Organization (FAO), and the European Commission Joint Research Center’s Anomaly hot Spots of Agricultural Production (ASAP), generally provide this information only at national or subnational level. In this work, we present gridded SOS and EOS maps for wheat and maize that represent the crop calendars’ spatial variability at 0.5° spatial resolution. These maps are generated in the framework of WorldCereal, which is a European Space Agency (ESA) funded project whose cropland and crop-type wheat and maize algorithms at global scale and at 10 m spatial resolution require this information. The proposed maps are built leveraging the above noted global products (Crop Monitor, USDA-FAS, FAO, ASAP) whose datasets are combined into a baseline map and sampled to train a Random Forest algorithm based on climatic and geographic data. Their evaluation against test data from the baseline maps set aside for validation purposes show a good performance with SOS (EOS) R2 of 0.87 (0.92) and a root mean square error (RMSE) of 27 (26) days for wheat, showing the lowest errors (RMSE |
| Databáze: | Directory of Open Access Journals |
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