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

Tengteng Qu, Yaoyu Li, Qixin Zhao, Yunzhen Yin, Yuzhi Wang, Fuzhong Li and Wuping Zhang ()
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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

Agriculture, 2024, vol. 14, issue 3, 1-17

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.

Keywords: inversion; vegetation index; canopy leaf moisture content; soil moisture content (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: 2024
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