Crop Identification in Topographically Varied Complex Areas in the Loess Plateau Based on Multifeature Integration of Sentinel-2 and ALOS DEM

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Abstract
Crop identification with remote sensing images has currently emerged as prominent research topic with a focus on flat regions dominated by crop cultivation. However, farmland is also distributed in vast complex terrain areas where the study on crop identification is necessary and of importance. This study aims to propose innovative method to address the challenges by investigating what complex terrain posed on the feature extraction and classification algorithms. Shouyang Loess Hill and Jinzhong Basin were chosen as the study area. Three indices, topographic indices, vegetation indices and texture indices were extracted from ALOS DEM and time-series Sentinel-2 spectral bands, respectively. Various feature combinations of spectral bands and three indices were ingested into the algorithm of Random Forest (RF), and then five models were built to investigate the impact of various feature combinations on the accuracy of crop identification in both hilly and flat areas. Results indicate that the combination of multiple features can significantly improve the accuracy of crop identification in complex terrain area while the differences in flat region were less pronounced. This study revealed that the combination of diverse features with topographic features is an effective method to improve the accuracy of crop identification in complex terrain area. The high accuracy crop maps can provide valuable insights for optimizing agricultural practices and resource management in complex terrain area. The future study should extend this method to other more complex terrain area with efficient optimization of topographic features.
Funding Information
  • Science and Technology Department of Gansu Province (202304041101051)
  • Central Government’s Guiding Local Science and Technology Development Foundation (YDZJSX2025D014)
  • Ministry of Science and Technology, Government of Nepal (95177)

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