Inversion of Soil Moisture Content in Silage Corn Root Zones Based on UAV Remote Sensing

Qihong Da, Jixuan Yan (), Guang Li, Zichen Guo, Haolin Li, Wenning Wang, Jie Li, Weiwei Ma, Xuchun Li and Kejing Cheng
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Qihong Da: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
Jixuan Yan: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
Guang Li: State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China
Zichen Guo: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
Haolin Li: College of Environmental Science and Engineering, Beijing University of Technology, Beijing 100124, China
Wenning Wang: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
Jie Li: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
Weiwei Ma: State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China
Xuchun Li: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
Kejing Cheng: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China

Agriculture, 2025, vol. 15, issue 3, 1-19

Abstract: Accurately monitoring soil moisture content (SMC) in the field is crucial for achieving precision irrigation management. Currently, the development of UAV platforms provides a cost-effective method for large-scale SMC monitoring. This study investigates silage corn by employing UAV remote sensing technology to obtain multispectral imagery during the seedling, jointing, and tasseling stages. Field experimental data were integrated, and supervised classification was used to remove soil background and image shadows. Canopy reflectance was extracted using masking techniques, while Pearson correlation analysis was conducted to assess the linear relationship strength between spectral indices and SMC. Subsequently, convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and partial least squares regression (PLSR) models were constructed to evaluate the applicability of these models in monitoring SMC before and after removing the soil background and image shadows. The results indicated that: (1) After removing the soil background and image shadows, the inversion accuracy of SMC for CNN, BPNN, and PLSR models improved at all growth stages. (2) Among the different inversion models, the accuracy from high to low was CNN, PLSR, BPNN. (3) From the perspective of different growth stages, the inversion accuracy from high to low was seedling stage, tasseling stage, jointing stage. The findings provide theoretical and technical support for UAV multispectral remote sensing inversion of SMC in silage corn root zones and offer validation for large-scale soil moisture monitoring using remote sensing.

Keywords: inversion of SMC; supervised classification; best variable combination; best result algorithm; best growth stage (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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