A Landscape-Clustering Zoning Strategy to Map Multi-Crops in Fragmented Cropland Regions Using Sentinel-2 and Sentinel-1 Imagery with Feature Selection

Guanru Fang, Chen Wang, Taifeng Dong, Ziming Wang, Cheng Cai, Jiaqi Chen, Mengyu Liu and Huanxue Zhang ()
Additional contact information
Guanru Fang: College of Geography and Environment, Shandong Normal University, Jinan 250300, China
Chen Wang: College of Geography and Environment, Shandong Normal University, Jinan 250300, China
Taifeng Dong: Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
Ziming Wang: College of Geography and Environment, Shandong Normal University, Jinan 250300, China
Cheng Cai: College of Geography and Environment, Shandong Normal University, Jinan 250300, China
Jiaqi Chen: College of Geography and Environment, Shandong Normal University, Jinan 250300, China
Mengyu Liu: College of Geography and Environment, Shandong Normal University, Jinan 250300, China
Huanxue Zhang: College of Geography and Environment, Shandong Normal University, Jinan 250300, China

Agriculture, 2025, vol. 15, issue 2, 1-25

Abstract: Crop mapping using remote sensing is a reliable and efficient approach to obtaining timely and accurate crop information. Previous studies predominantly focused on large-scale regions characterized by simple cropping structures. However, in complex agricultural regions, such as China’s Huang-Huai-Hai region, the high crop diversity and fragmented cropland in localized areas present significant challenges for accurate crop mapping. To address these challenges, this study introduces a landscape-clustering zoning strategy utilizing multi-temporal Sentinel-1 and Sentinel-2 imagery. First, crop heterogeneity zones (CHZs) are delineated using landscape metrics that capture crop diversity and cropland fragmentation. Subsequently, four types of features (spectral, phenological, textural and radar features) are combined in various configurations to create different classification schemes. These schemes are then optimized for each CHZ using a random forest classifier. The results demonstrate that the landscape-clustering zoning strategy achieves an overall accuracy of 93.52% and a kappa coefficient of 92.67%, outperforming the no-zoning method by 2.9% and 3.82%, respectively. Furthermore, the crop mapping results from this strategy closely align with agricultural statistics at the county level, with an R 2 value of 0.9006. In comparison with other traditional zoning strategies, such as topographic zoning and administrative unit zoning, the proposed strategy proves to be superior. These findings suggest that the landscape-clustering zoning strategy offers a robust reference method for crop mapping in complex agricultural landscapes.

Keywords: crop mapping; landscape heterogeneity; feature selection; crop heterogeneity zone; complex agricultural regions (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/2/186/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/2/186/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:2:p:186-:d:1568026

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().