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

摘要/Abstract

摘要：

【目的】 采用无人机遥感技术，构建快速、无损与准确监测玉米农田土壤水分的诊断模型，最大程度地提高农业用水的效率，为玉米的精准灌溉管理提供理论基础与科学依据。【方法】 以大田玉米为研究对象，通过田间小区试验设置4个水分处理，分别为低水分处理，灌水495 mm（W1）；常规滴灌水量575 mm（对照，W2）；高水分处理，灌水 660 mm（W3）和灌水740 mm（W4）。在玉米关键生育时期，同步测定玉米冠层温度（Tc）、空气温度（Ta）、土壤水分等信息，并结合K-Means法和统计技术提取并优化玉米Tc。同时，基于作物水分胁迫指数（CWSI）、Tc、Ta、冠气温差等指标构建水分-冠气温差指数（WCAI，CWSI与冠气温差之和）和水分-冠气相对温差指数（WRTI，CWSI与相对冠气温差之和），并筛选最优诊断模型明确土壤水分阈值。【结果】 Tc与土壤水分呈负相关关系。构建的模型WCAI不能很好地反映土壤水分变化趋势，而基于WRTI模型的土壤含水量预测值与实测值决定系数 R²均达到0.744以上，表明WRTI是诊断土壤水分效果较优的模型。通过比较WRTI在玉米不同生育时期与不同土层含水量的相关性可以发现，拔节期WRTI诊断0—20 cm土层含水量效果较优，R²为0.785和0.859；而大喇叭口期、抽穗期、灌浆期诊断0—40 cm土层含水量效果较优，R²范围为0.796—0.900。基于WRTI与玉米产量的相关关系得到各生育时期WRTI阈值范围为0.218—0.301，进一步根据WRTI与土壤含水量的关系反演出土壤水分阈值范围为67.8%—80.1%。【结论】 由于WCAI模型参数“冠气温差”受环境影响大，与WRTI和CWSI在不同水分处理下的变化趋势相比，WCAI与土壤水分没有明显关系，不适用于土壤水分诊断。而WRTI模型参数“相对冠气温差”削弱了环境的影响，其与CWSI结合可以更好的反映出土壤水分的变化，提高了基于遥感诊断农田土壤水分的精度，有效降低水资源的浪费，实现节水高产。研究结果为无人机遥感实时监测农田土壤水分和实施精准灌溉提供参考。

关键词: 玉米, 土壤水分, 无人机热红外遥感, 冠层温度, 作物水分胁迫指数, 冠气温差

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

梁雪, 姜艳, 危常州, 薛冰, 李芳芳, 崔怡蕊, 张夏然. 基于无人机遥感的玉米农田土壤水分诊断模型研究[J]. 中国农业科学, 2025, 58(23): 4979-4992.

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