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Crop-type mapping from a sequence of Sentinel 1 images

  • Published:2018-04-12 Issue:19 Volume:39 Page:6383-6404
  • ISSN:0143-1161
  • Container-title:International Journal of Remote Sensing
  • language:en
  • Short-container-title:International Journal of Remote Sensing

Author:

Kenduiywo Benson Kipkemboi1,Bargiel Damian2,Soergel Uwe3

Affiliation:

1. Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

2. Institute of Geodesy, Technische Universität Darmstadt, Darmstadt, Germany

3. Institute for Photogrammetry, University of Stuttgart, Stuttgart, Germany

Funder

German Federal Ministry of Economic Affairs and Energy

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Cited by 33 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. Deep learning based crop-type mapping using SAR and optical data fusion;International Journal of Applied Earth Observation and Geoinformation;2024-05

2. A generalized model for mapping sunflower areas using Sentinel-1 SAR data;Remote Sensing of Environment;2024-05

3. WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping;Earth System Science Data;2023-12-06

4. A review of globally available data sources for modelling the Water-Energy-Food Nexus;Earth-Science Reviews;2023-08

5. Early Crop Identification Using a Prior Probability-Adjusted Machine Learning Algorithms;2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics);2023-07-25
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