Prediction of soil moisture content using unmanned aerial vehicle technology
DOI:
https://doi.org/10.5564/mjas.v18i43.3681Keywords:
drone, wheat, yield, vegetation index, growth stageAbstract
The application of remote sensing technology is commonly used to predict the soil moisture content of cropland and has become an effective method for smart agriculture. Therefore, this study aims to (i) quantify seasonal variations in soil moisture content at different soil depths during wheat growth stages, (ii) examine the relationships between soil moisture, wheat yield, and UAV-derived vegetation indices, and (iii) evaluate the effectiveness of UAV multispectral data for predicting soil moisture content under semi-arid conditions.
We hypothesize that vegetation indices derived from UAV multispectral imagery are significantly correlated with soil moisture content at different soil depths and can be used as indirect predictors of soil moisture. Soil moisture was measured using the gravimetric method, with samples taken at 0–20 cm and 20–40 cm depths from 60 plots in the field at all wheat growth stages. The soil reaction range of the study area varies from 7.3 to 8.0, the soil volume was 1.14 to 1.35 g/cm3, soil organic carbon was 0.82-2.18%, and total soil nitrogen was 0.9-0.23%. From the results, the air temperature was low; it rained in July of the year between the stem elongation heading stages of wheat. According to the soil sampling, the soil moisture content of the third ten days of July (the early heading stage of wheat) had the highest values (11.7–30.1%), while the soil moisture content had the lowest values (13.53–21.21%) in the sensitive period of wheat from seedling growth to the heading stage. When correlation analysis is performed between soil moisture and wheat yield, the soil moisture in the depth of 0–20 cm has a weakly negative correlation (r = -0.13 (-0.38)), and the soil moisture in the depth of 20–40 cm has a weak to moderate correlation (r = -0.13 (-0.43)). The regression analysis of soil moisture content and vegetation indices calculated by spectral data shows a positive correlation (with an R2 value of 0.44 at 0–20 cm, p<0.0001; an R2 value of 0.2 at 20–40 cm, p<0.01). Using the UAV, the determination coefficient is R2 = 0.32 at 20–40 cm depth and R2 = 0.29, p<0.01.
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Copyright (c) 2025 Shinebayar Turbat, Erdenechimeg Zorigt, Myagmarjav Indra, Batbileg Bayaraa, Enkhjargal Baljii, Khishigjargal Mookhor, Erdenechandmani Jargalsaikhan, Ariuntsetseg Dugar
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