Uncertainty propagation and intercomparison of multi-sensor measurements of vegetation stress in sub-optimal conditions

IF 7.6 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2026-09-26 DOI:10.1007/s11119-026-10455-1

Mike Werfeli,Michal Antala,Abdallah Yussuf Ali Abdelmajeed,Álvaro Sánchez-Virosta,Zoe Halem,Alexander Merrington,Yousra El-Mejjaouy,Bojana Petrovic,Andreas Hueni,El Houssaine Bouras,Shawn Carlisle Kefauver,Sahameh Shafiee,Anshu Rastogi,Laura Mihai

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Abstract

Abstract Purpose This study evaluates the metrological consistency and uncertainty propagation of spectral reflectance and vegetation indices derived from multiple remote sensing sensors under sub-optimal and variable illumination conditions. While multi-sensor data fusion is increasingly common in precision farming, the extent to which sensor differences arise from biological variation versus measurement uncertainty remains poorly quantified. Methods and results Field measurements were conducted on spring wheat ( Triticum aestivum L.) using three ground-based field spectroradiometers and two uncrewed aerial vehicles (UAV)-mounted multispectral cameras. Following a standardised Guide to the Expression of Uncertainty in Measurement (GUM) framework, uncertainties from sensor noise, spatial plot heterogeneity, and transient cloud cover were propagated using Monte Carlo simulations. The results indicate that total reflectance uncertainty peaked at approximately 11% in the red-edge and near-infrared regions, primarily driven by plot-level heterogeneity and fluctuating irradiance. Among the evaluated indices, the Optimized Soil-Adjusted Vegetation Index (OSAVI) demonstrated the highest stability across platforms, whereas the Enhanced Vegetation Index (EVI) proved highly sensitive to environmental noise, leading to metrological breakdown in sensor interoperability. The findings demonstrate that under unstable atmospheric conditions, the window for reliable multi-sensor data integration is limited to synchronous acquisitions within minutes. Conclusion This research provides a rigorous statistical foundation for identifying the limits of sensor agreement, ensuring that management decisions in precision agriculture are based on true crop signals rather than measurement artifacts.

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次优条件下植被应力多传感器测量的不确定性传播与相互比较

摘要目的研究了在次优光照和可变光照条件下，多个遥感数据反演的光谱反射率和植被指数的计量一致性和不确定性传播。虽然多传感器数据融合在精准农业中越来越普遍，但由于生物变异和测量不确定性引起的传感器差异的程度仍然难以量化。方法与结果利用3台地基野外光谱仪和2台无人机多光谱相机对春小麦（Triticum aestivum L.）进行田间测量。根据测量不确定性表达标准指南（GUM）框架，利用蒙特卡罗模拟传播来自传感器噪声、空间地块异质性和瞬态云覆盖的不确定性。结果表明，在红边和近红外区域，总反射率不确定度达到11%左右，这主要是由样点水平的非均质性和辐照度波动造成的。其中，优化土壤调整植被指数（OSAVI）在不同平台上的稳定性最高，而增强植被指数（EVI）对环境噪声高度敏感，导致传感器互操作性的计量崩溃。研究结果表明，在不稳定的大气条件下，可靠的多传感器数据集成窗口仅限于几分钟内的同步采集。本研究为识别传感器一致性的限制提供了严格的统计基础，确保精准农业的管理决策是基于真实的作物信号，而不是测量的伪产物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.