Precision Agriculture: Temporal and Spatial Modeling of Wheat Canopy Spectral Characteristics

Donghui Zhang, Liang Hou (), Liangjie Lv, Hao Qi, Haifang Sun, Xinshi Zhang, Si Li, Jianan Min, Yanwen Liu, Yuanyuan Tang and Yao Liao
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Donghui Zhang: Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China
Liang Hou: Institute of Agricultural Information and Economy, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, China
Liangjie Lv: Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China
Hao Qi: Institute of Agricultural Information and Economy, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, China
Haifang Sun: Institute of Agricultural Information and Economy, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, China
Xinshi Zhang: Institute of Agricultural Information and Economy, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, China
Si Li: Institute of Agricultural Information and Economy, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, China
Jianan Min: Institute of Agricultural Information and Economy, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, China
Yanwen Liu: School of Resources and Environment Science and Engineering, Hubei University of Science and Technology, Xianning 437100, China
Yuanyuan Tang: Changsha Natural Resources Comprehensive Survey Center, China Geological Survey, Changsha 410600, China
Yao Liao: Guizhou Ecological Meteorology and Agrometeorology Center, Guiyang 550002, China

Agriculture, 2025, vol. 15, issue 3, 1-30

Abstract: This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models.

Keywords: UAV remote sensing; wheat growth stages; multispectral analysis; spatio-temporal monitoring; precision agriculture (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
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