J. M. Ramírez-Cuesta, M. A. Martínez-Gimeno, E. Badal, M. Tasa, L. Bonet, J. G. Pérez-Pérez
{"title":"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","authors":"J. M. Ramírez-Cuesta, M. A. Martínez-Gimeno, E. Badal, M. Tasa, L. Bonet, J. G. Pérez-Pérez","doi":"10.1007/s11119-025-10240-6","DOIUrl":null,"url":null,"abstract":"<p>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 (<i>Olea europaea</i> 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 (Ψ<sub>stem</sub>). 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 (NDVI<sub>crop+ground</sub>) 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.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"17 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"UAV-based multispectral and thermal indexes for estimating crop water status and yield on super-high-density olive orchards under deficit irrigation conditions\",\"authors\":\"J. M. Ramírez-Cuesta, M. A. Martínez-Gimeno, E. Badal, M. Tasa, L. Bonet, J. G. 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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 (Ψ<sub>stem</sub>). 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 (NDVI<sub>crop+ground</sub>) 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. 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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.
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.