亏缺灌溉条件下超高密度橄榄园作物水分状况及产量的无人机多光谱和热指标估算

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
J. M. Ramírez-Cuesta, M. A. Martínez-Gimeno, E. Badal, M. Tasa, L. Bonet, J. G. Pérez-Pérez
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引用次数: 0

摘要

高效的水资源管理对于地中海气候下的可持续农业至关重要,特别是在缺水构成重大挑战的超高密度橄榄果园。本研究评估了基于无人机的热成像和多光谱成像在不同调节亏缺灌溉(RDI)策略下监测作物水分状况和预测产量的潜力。在商业SHD橄榄果园(Olea europaea L., cv.)进行了两个季节(2018-2019)的研究。在西班牙Villena的“Arbequina”试验中,该试验涉及四种灌溉处理,从完全灌溉(FI)到逐步限制rdi。无人机飞行捕获关键物候阶段的热红外和多光谱图像,计算作物水分胁迫指数(CWSI)和归一化植被指数(NDVI),并通过基于植物的茎水势测量进行验证(Ψstem)。结果表明,包括冠层温度和CWSI在内的热参数能够有效识别水分胁迫水平,但其灵敏度受环境条件和传感器限制的影响。NDVI被证明是营养生长和产量的可靠指标,其值与灌溉水平和果实负荷密切相关。结合冠层和土壤反射率(NDVIcrop+地面)的方法提供了最准确的作物性能评估。这些发现突出了基于无人机的遥感技术在优化SHD橄榄园灌溉管理方面的价值,特别是在亏缺灌溉制度下。然而,建议进一步提高传感器的精度和指数归一化,以提高其在农业实践中的适用性和精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAV-based multispectral and thermal indexes for estimating crop water status and yield on super-high-density olive orchards under deficit irrigation conditions

Efficient water management is critical for sustainable agriculture in Mediterranean climates, particularly in super-high-density (SHD) olive orchards where water scarcity poses significant challenges. This study assessed the potential of UAV-based thermal and multispectral imagery to monitor crop water status and predict yield under different regulated deficit irrigation (RDI) strategies. Conducted over two seasons (2018–2019) in a commercial SHD olive orchard (Olea europaea L., cv. ‘Arbequina’) in Villena, Spain, the experiment involved four irrigation treatments ranging from full irrigation (FI) to progressively restricted RDIs. UAV flights captured thermal infrared and multispectral imagery at key phenological stages, to calculate Crop Water Stress Index (CWSI) and Normalized Difference Vegetation Index (NDVI), which were validated against plant-based measurements of stem water potential (Ψstem). The results demonstrated that thermal parameters, including canopy temperature and CWSI, effectively identified water stress levels, although their sensitivity was influenced by environmental conditions and sensor limitations. NDVI proved to be a reliable indicator of vegetative growth and yield, with values closely linked to irrigation levels and fruit load. The approach incorporating both canopy and soil reflectance (NDVIcrop+ground) provided the most accurate assessment of crop performance. These findings highlight the value of UAV-based remote sensing technologies for optimizing irrigation management in SHD olive orchards, particularly under deficit irrigation regimes. However, further advancements in sensor accuracy and index normalization are recommended to enhance their applicability and precision in agricultural practices.

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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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