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Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data

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  • Zhang, Chen
  • Di, Liping
  • Lin, Li
  • Li, Hui
  • Guo, Liying
  • Yang, Zhengwei
  • Yu, Eugene G.
  • Di, Yahui
  • Yang, Anna

Abstract

Mapping crop types from satellite images is a promising application in agricultural systems. However, it is a challenge to automate in-season crop type mapping over a large area because of the insufficiency of ground truth and issues of scalability, reusability, and accessibility of the classification model. This study introduces a framework for automatic crop type mapping using spatiotemporal crop information and Sentinel-2 data based on Google Earth Engine (GEE). The main advantage of the framework is using the trusted pixels extracted from the historical Cropland Data Layer (CDL) to replace ground truth and label training samples in satellite images.

Suggested Citation

  • Zhang, Chen & Di, Liping & Lin, Li & Li, Hui & Guo, Liying & Yang, Zhengwei & Yu, Eugene G. & Di, Yahui & Yang, Anna, 2022. "Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data," Agricultural Systems, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:agisys:v:201:y:2022:i:c:s0308521x22000981
    DOI: 10.1016/j.agsy.2022.103462
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    2. Jing Tang & Xiaoyong Zhang & Zhengchao Chen & Yongqing Bai, 2022. "Crop Identification and Analysis in Typical Cultivated Areas of Inner Mongolia with Single-Phase Sentinel-2 Images," Sustainability, MDPI, vol. 14(19), pages 1-16, October.

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