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

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

Agricultural Systems, 2022, vol. 201, issue C

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.

Keywords: Crop mapping; Agriculture 4.0; Remote sensing; Cropland Data Layer; Sentinel-2; Google Earth Engine (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:201:y:2022:i:c:s0308521x22000981

DOI: 10.1016/j.agsy.2022.103462

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