An interactive and iterative method for crop mapping through crowdsourcing optimized field samples

Author:

Yu QiangyiORCID,Duan Yulin,Wu Qingying,Liu Yuan,Wen Caiyun,Qian Jianping,Song Qian,Li Wenjuan,Sun Jing,Wu Wenbin

Funder

Shenyang Agricultural University

National Key Research and Development Program of China

Central Public-interest Scientific Institution Basal Research Fund, Chinese Academy of Fishery Sciences

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference71 articles.

1. An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery;Anchang;Int. J. Appl. Earth Obs. Geoinf.,2016

2. Integrating field sampling, geostatistics and remote sensing to map wetland vegetation in the Pantanal, Brazil;Arieira;Biogeosciences,2011

3. Remote Sensing and Cropping Practices: A Review;Bégué;Remote Sens.,2018

4. Machine learning for data-driven discovery in solid Earth geoscience;Bergen;Science,2019

5. 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

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1. Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features;International Journal of Applied Earth Observation and Geoinformation;2023-11

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