基于微尺度无人机高光谱技术的冬小麦雹害制图

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Jacopo Furlanetto, Nicola Dal Ferro, Daniele Caceffo, Francesco Morari
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引用次数: 0

摘要

冰雹对农业构成直接威胁,经常造成产量损失,并使农民的农业活动恶化。传统的冰雹灾害估计方法,由保险现场检查员进行,由于其复杂性、部分主观性和缺乏对空间变异性的考虑而受到质疑。因此,遥感集成在估算过程中可以提供有价值的帮助。本研究以冬小麦(Triticum aestivum L.)及其近红外(NIR)光谱区域对病害的响应为研究重点,重点研究了棕色色素作为产量病害估算和定位的替代指标。试验分两个种植季节(2020-2021和2021-2022)在两个地点进行,使用专门设计的原型机,在最小面积为400 m2的地块上,与未受损条件相比,使用低、中、高损害梯度模拟关键开花期和乳白色阶段的冰雹损害。在损伤模拟后,利用无人机(UAV)飞行测量了高光谱可见光-近红外反射率,并测量了叶绿素和叶面积指数(LAI)。采用联合收割机记录每次处理的最终产量。在选择具有代表性的受损和未受损植被光谱来绘制受损区域后,使用光谱混合分析(SMA)观察并评估了近红外区域(780-950 nm)的吸光度增加。每次处理中受损端元像素的丰度与最终产量呈良好关系(R2 = 0.73),确定了受损最严重的区域。采用新设计的多光谱指数(TAI)进一步分析吸光度特征,并与选定的指数进行了测试,结果表明该指数与最终收率的关系最高(R2 = 0.64)。这两种方法都有效地突出了不同日期和发育阶段的吸光度特征,为冬小麦雹害制图提供了有效的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mapping hailstorm damage on winter wheat (Triticum aestivum L.) using a microscale UAV hyperspectral approach

Mapping hailstorm damage on winter wheat (Triticum aestivum L.) using a microscale UAV hyperspectral approach

Hailstorms pose a direct threat to agriculture, often causing yield losses and worsening farmers’ agricultural activity. Traditional methods of hail damage estimation, conducted by insurance field inspectors, have been questioned due to their complexity, partial subjectivity, and lack of accounting for spatial variability. Therefore, remote sensing integration in the estimation process could provide a valuable aid. The focus of this study was on winter wheat (Triticum aestivum L.) and its response to damage in the near-infrared (NIR) spectral region, with a particular emphasis on the study of brown pigments as a proxy for yield damage estimation and mapping. An experiment was conducted during two cropping seasons (2020–2021 and 2021–2022) at two sites, simulating hail damage at critical flowering and milky stages using a specifically designed prototype machinery with low, medium, and high damage gradients compared to undamaged conditions in plots with a minimum of 400 m2 area. After the damage simulation, hyperspectral visible-NIR reflectance was measured with Unmanned Aerial Vehicle (UAV) flights, and measurements of chlorophyll and of leaf area index (LAI) were contextually taken. Final yield per treatment was recorded using a combine. An increase in absorbance in the NIR region (780–950 nm) was observed and evaluated using a spectral mixture analysis (SMA) after selecting representative damaged and undamaged vegetation spectra to map the damage. The abundance of damaged endmember pixels per treatment resulted in a good relationship with the final yield (R2 = 0.73), identifying the most damaged areas. The absorbance feature was further analysed with a newly designed multispectral index (TAI), which was tested against a selection of indices and resulted in the highest relationship with the final yield (R2 = 0.64). Both approaches were effective in highlighting the absorbance feature over different dates and development stages, defining an effective mean for hailstorm damage mapping in winter wheat.

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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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