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Extraction of planting structure information in the Baojixia irrigation district based on planet scope satellite and UAV multispectral imagery 

LUO Zhen1,2,YANG Ni1,2,SHANG Xiao-Hui1,2,YU Xin-Cheng1,2,ZHU Jing-Yi1,2,YANG Guang1,2,HU Xiao-Tao1,2,*   

  1. 1 College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; 2 Key Laboratory of Arid Land Agricultural Soil and Water Engineering, Ministry of Education, Northwest A&F University, Yangling 712100, Shaanxi, China
  • Received:2025-05-10 Revised:2025-08-13 Accepted:2025-08-13 Published:2025-08-25
  • Supported by:
    This study was supported by National-level Innovative Training Project (202410712267) and the National Natural Science Foundation of China (U2243235)

Abstract:

High-resolution mapping of crop planting structures is crucial for ensuring food security and optimizing agricultural policies. However, in practical applications, extracting crop planting structures using multi-source remote sensing data faces challenges such as satellite–UAV collaboration issues due to spectral resolution differences, and the interference of mixed pixels in satellite imagery during area information extraction. This study proposes a method for extracting crop planting structures based on UAV multispectral data with Planet Scope satellite imagery. Taking wheat, maize, grapes, and kiwifruit in the Baojixia Irrigation District as case crops, spatial distribution and area information were extracted. First, satellite spectral bands were corrected by calculating the reflectance ratio between satellite and UAV pixels, thereby refining the threshold for crop distribution extraction. Second, UAV images were classified using machine learning algorithms to estimate the proportion of pure crop areas within mixed satellite pixels. Finally, a genetic algorithm-optimized random forest model was employed to establish a quantitative relationship between vegetation indices and area weights in mixed pixels. The results showed that in the crop distribution map (6 m resolution) generated using the multi-source remote sensing approach, the number of overlapping pixels decreased by 35.75% compared to results from satellite imagery alone, demonstrating that integrating multi-source data can effectively mitigate the issue of “same spectrum, different objects.” Among the crops, wheat and maize had the most accurate area extraction. Compared with single-temporal satellite images, the relative error rates for wheat and maize in representative regions decreased by 19.17% and 38.49%, respectively. Overall, the relative error rates for wheat, maize, grapes, and kiwifruit area estimates were -4.83%, 0.51%, 6.55%, and 8.79%, respectively. The cross-scale collaborative observation approach from UAV to satellite proposed in this study provides technical and data support for crop management strategies in irrigation districts. It offers new perspectives for extending crop spatial distribution and area extraction from the field scale to the irrigation district scale, and contributes to advancing precision agriculture technologies in irrigated regions.

Key words: planet scope satellite, unmanned aerial vehicle (UAV), multispectral data, planting structure, supervised classification, random forest optimized by genetic algorithm

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