基于无人机遥感的玉米农田土壤水分诊断模型研究

doi:10.3864/j.issn.0578-1752.2025.23.013

Abstract

Abstract:

【Objective】 Using unmanned aerial vehicle (UAV) remote sensing technology, a rapid, non-destructive and accurate diagnostic model for monitoring soil moisture in maize farmland was constructed to maximize the efficiency of agricultural water use, so as to provide the theoretical basis and scientific basis for precise irrigation management of maize. 【Method】 In this study, the field maize was used as the research object, and four water treatments were set up through field plot experiments, namely: low water treatment (W1): 495 mm, conventional drip irrigation control treatment (W2): 575 mm, high water treatment (W3): 660 mm, and (W4): 740 mm. In the key growth period of maize, the canopy temperature (Tc), air temperature (Ta), soil moisture and other information of maize were measured synchronously, and the Tc of maize was extracted and optimized by K-Means method and statistical technology. Meanwhile, water-canopy air temperature difference index (WCAI, the sum of CWSI and canopy air temperature difference) and water-canopy air relative temperature difference index (WRTI, the sum of CWSI and relative canopy air temperature difference) were constructed based on crop water stress index (CWSI), Tc, Ta and canopy air temperature difference. The optimal diagnostic model was selected to determine the soil moisture threshold. 【Result】 Tc was negatively correlated with soil moisture. The constructed model WCAI could not well reflect the trend of soil moisture change, while the coefficient of determination R² between the predicted value and the measured value of soil moisture content based on WRTI model reached more than 0.744, indicating that WRTI was a better model for diagnosing soil moisture. Finally, by comparing the correlation between WRTI and water content in different soil layers at different growth stages of maize, it was found that WRTI had a better effect on diagnosing soil water content in 0-20 cm soil layer at jointing stage, with R² of 0.785 and 0.859. The diagnosis of soil water content in 0-40 cm soil layer at large bell stage, heading stage and filling stage was better, and the R² range was 0.796-0.900. Based on the correlation between WRTI and maize yield, the WRTI threshold range of each growth period was 0.218-0.301, and the soil moisture threshold range was 67.8%-80.1% according to the relationship between WRTI and soil moisture content. 【Conclusion】 Because the WCAI model parameter' canopy temperature difference' was greatly affected by the environment, compared with the change trend of WRTI and CWSI under different water treatments, WCAI had no obvious relationship with soil moisture, and WCAI was not suitable for soil moisture diagnosis. The WRTI model parameter' relative canopy temperature difference' weakens the impact of the environment, and its combination with CWSI could better reflect the change of soil moisture, improve the accuracy of soil moisture diagnosis based on remote sensing, effectively reduce the waste of water resources, and achieve water saving and high yield. The research results provided a reference for real-time monitoring of farmland soil moisture and precision irrigation by UAV remote sensing.

Key words: maize, soil moisture, UAV thermal infrared remote sensing, canopy temperature, crop water stress index, canopy air temperature difference

LIANG Xue, JIANG Yan, WEI ChangZhou, XUE Bing, LI FangFang, CUI YiRui, ZHANG XiaRan. Research on Soil Moisture Diagnosis Model of Maize Farmland Based on Remote Sensing of Unmanned Aerial Vehicles[J].Scientia Agricultura Sinica, 2025, 58(23): 4979-4992.

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生育时期 Growth period	登海618 DH618		先玉335 XY335
生育时期 Growth period	WRTI阈值 WRTI threshold	土壤相对含水量阈值 Threshold of soil relative water content (%)	WRTI阈值 WRTI threshold	土壤相对含水量阈值 Threshold of soil relative water content (%)
拔节期 Jointing stage	0.300	75.183	0.301	73.780
大喇叭口期 Big flare stage	0.254	79.376	0.300	78.467
抽穗期 Heading stage	0.218	79.058	0.250	80.052
灌浆期 Grain filling stage	0.241	70.222	0.286	67.797

Research on Soil Moisture Diagnosis Model of Maize Farmland Based on Remote Sensing of Unmanned Aerial Vehicles

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