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Drone-Based Multispectral Remote Sensing Inversion for Typical Crop Soil Moisture under Dry Farming Conditions

Author

Listed:
  • Tengteng Qu

    (School of Software, Shanxi Agricultural University, Taigu, Jinzhong 030801, China)

  • Yaoyu Li

    (School of Agricultural Engineering, Shanxi Agricultural University, Taigu, Jinzhong 030801, China)

  • Qixin Zhao

    (School of Software, Shanxi Agricultural University, Taigu, Jinzhong 030801, China)

  • Yunzhen Yin

    (School of Software, Shanxi Agricultural University, Taigu, Jinzhong 030801, China)

  • Yuzhi Wang

    (School of Software, Shanxi Agricultural University, Taigu, Jinzhong 030801, China)

  • Fuzhong Li

    (School of Software, Shanxi Agricultural University, Taigu, Jinzhong 030801, China)

  • Wuping Zhang

    (School of Software, Shanxi Agricultural University, Taigu, Jinzhong 030801, China)

Abstract

Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture using data from drone-based multispectral remote sensing. Maize, millet, sorghum, and potatoes were selected for this study, with multispectral data, canopy leaf, and soil moisture content at various depths collected every 3 to 6 days. Vegetation indices highly correlated with crop canopy leaf moisture content ( p < 0.01) and were identified using Pearson correlation analysis, leading to the development of linear and nonlinear regression models for predicting moisture content in canopy leaves and soil. The results show a significant linear correlation between the predicted and actual canopy leaf moisture levels for the four crops, according to the chosen vegetation indices. The use of canopy leaf moisture content to predict surface soil moisture (0–20 cm) demonstrated enhanced accuracy. The models designed for the top 20 cm of soil moisture successfully estimated deep soil moisture levels (up to 200 cm) for all four crops. The 20 cm range soil moisture model showed improvements over the 10 cm range model, with increases in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R 2 ), and Nash–Sutcliffe Efficiency Coefficient (NSE) by 0.4, 0.8, 0.73, and 0.34, respectively, in the corn area; 0.28, 0.69, 0.48, and 0.25 in the millet area; 0.4, 0.48, 0.22, and 0.52 in the sorghum area; and 1.14, 0.81, 0.73, and 0.56 in the potato area, all with an average Relative Error (RE) of less than 10% across the crops. Using drone-based multispectral technology, this study forecasts leaf water content via vegetation index analysis, facilitating swift and effective soil moisture inversion. This research introduces a novel method for monitoring and managing agricultural water resources, providing a scientific basis for precision farming and moisture variation monitoring in dryland areas.

Suggested Citation

  • Tengteng Qu & Yaoyu Li & Qixin Zhao & Yunzhen Yin & Yuzhi Wang & Fuzhong Li & Wuping Zhang, 2024. "Drone-Based Multispectral Remote Sensing Inversion for Typical Crop Soil Moisture under Dry Farming Conditions," Agriculture, MDPI, vol. 14(3), pages 1-17, March.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:3:p:484-:d:1358372
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    References listed on IDEAS

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    1. Vincent Humphrey & Alexis Berg & Philippe Ciais & Pierre Gentine & Martin Jung & Markus Reichstein & Sonia I. Seneviratne & Christian Frankenberg, 2021. "Soil moisture–atmosphere feedback dominates land carbon uptake variability," Nature, Nature, vol. 592(7852), pages 65-69, April.
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