Author:
Hamidi Masoumeh,Homayouni Saeid,Safari Abdolreza,Hasani Hadiseh
Funder
Guelph Research and Development Centre, Agriculture and Agri-Food Canada
Agriculture and Agri-Food Canada
Reference31 articles.
1. Sentinel SAR-optical fusion for crop type mapping using deep Learning and Google Earth Engine;Adrian;ISPRS J. Photogramm. Remote Sens.,2021
2. Alami Machichi, M., mansouri, l. E., Imani, Y., Bourja, O., Lahlou, O., Zennayi, Y., Bourzeix, F., Hanadé Houmma, I., & Hadria, R. (2023). Crop mapping using supervised machine learning and deep Learning: a systematic literature review. International Journal of Remote Sensing, 44(8), 2717-2753.
3. Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: Object-based and knowledge-based approach;Ban;Int. J. Remote Sens.,2010
4. Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany;Blickensdörfer;Remote Sens. Environ.,2022
5. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics;Bolton;Agric. For. Meteorol.,2013
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献