Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints
Author:
Affiliation:
1. TETIS, Université de Montpellier, CIRAD/CNRS/INRAE, Montpellier, France
2. Atos France, Technical Services, Bezons, France
3. CESBIO, Université de Toulouse, CNES/CNRS/ INRAE/IRD/UPS, Toulouse, France
Funder
French Space Study Center
National Research Institute for Agriculture
Food and the Environment
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9973430/10254255.pdf?arnumber=10254255
Reference61 articles.
1. Agricultural Monitoring in Northeastern Ontario, Canada, Using Multi-Temporal Polarimetric RADARSAT-2 Data
2. A New Multiple Phenological Spectral Feature for Mapping Winter Wheat
3. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications
4. Early- and in-season crop type mapping without current-year ground truth: Generating labels from historical information via a topology-based approach
5. Estimating canola phenology using synthetic aperture radar
Cited by 2 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
1. Crop Classification using Sentinel-1 and Sentinel-2: A Machine Learning Method;2024 Second International Conference on Data Science and Information System (ICDSIS);2024-05-17
2. Determining Effective Temporal Windows for Rapeseed Detection Using Sentinel-1 Time Series and Machine Learning Algorithms;Remote Sensing;2024-01-31
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