From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2

Bingwen Qiu, Duoduo Lin, Chongcheng Chen, Peng Yang, Zhenghong Tang, Zhenong Jin, Zhiyan Ye, Xiaolin Zhu, Mingjie Duan, Hongyu Huang, Zhiyuan Zhao, Weiming Xu, Zuoqi Chen

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Detailed and updated maps of actively cropped fields on a national scale are vital for global food security. Unfortunately, this information is not provided in existing land cover datasets, especially lacking in smallholder farmer systems. Mapping national-scale cropped fields remains challenging due to the spectral confusion with abandoned vegetated land, and their high heterogeneity over large areas. This study proposed a large-area mapping framework for automatically identifying actively cropped fields by fusing Vegetation-Soil-Pigment indices and Synthetic-aperture radar (SAR) time-series images (VSPS). Three temporal indicators were proposed and highlighted cropped fields by consistently higher values due to cropping activities. The proposed VSPS algorithm was exploited for national-scale mapping in China without regional adjustments using Sentinel-2 and Sentinel-1 images. Agriculture in China illustrated great heterogeneity and has experienced tremendous changes such as non-grain orientation and cropland abandonment. Yet, little is known about the locations and extents of cropped fields cultivated with field crops on a national scale. Here, we produced the first national-scale 20 m updated map of cropped and fallow/abandoned land in China and found that 77 % of national cropland (151.23 million hectares) was actively cropped in 2020. We found that fallow/abandoned cropland in mountainous and hilly regions were far more than we expected, which was significantly underestimated by the commonly applied VImax-based approach based on the MODIS images. The VSPS method illustrates robust generalization capabilities, which obtained an overall accuracy of 94 % based on 4,934 widely spread reference sites. The proposed mapping framework is capable of detecting cropped fields with a full consideration of a high diversity of cropping systems and complexity of fallow/abandoned cropland. The processing codes on Google Earth Engine were provided and hoped to stimulate operational agricultural mapping on cropped fields with finer resolution from the national to the global scale.

Original languageEnglish (US)
Article number103006
JournalInternational Journal of Applied Earth Observation and Geoinformation
StatePublished - Sep 2022

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (grant no. 42171325 , 41771468 ), the Science Bureau of Fujian Province (2020N5002), the Ministry of Natural Resources of China (KY-010000-04-2000-002) and the Fujian provincial department of ecology and environment ( 2022R023 ). Thanks to our research group members and collaborators for collecting the ground reference data. We are very grateful to the editors and anonymous reviewers for offering insightful suggestions which significantly improve the manuscript.

Publisher Copyright:
© 2022 The Authors


  • Comparative temporal variation
  • Cropland abandonment
  • Cropped field
  • Sentinel-1
  • Sentinel-2
  • Smallholder agriculture


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