Accurate estimation of aerial net primary production (ANPP) using remotely acquired data is one of the main challenges in both environmental monitoring and precision agriculture. Reflectance-based techniques have been widely used for decades, but detection of fluorescence emission by chlorophyll has emerged as a promising alternative in recent years. Although passive sun-induced fluorescence (SIF) monitoring has shown interesting results, the information it provides is limited to few wavelengths (Fraunhofer and telluric lines). On the other hand, active measurements of steady-state fluorescence and its spectral distribution cover the full-emission spectrum but have not been fully explored due to obvious experimental limitations. In this work we develop a novel active fluorescence measurement procedure, based on lamps and sensors mounted on a field tractor. This technique allowed the detection of the full spectrum of fluorescence emission of a plant crop for the first time in the literature.

The main objective of this work was to analyze how the information based on reflectance and fluorescence, recorded by the new proposed methodology, tracks the differences caused by different irrigation treatments in the ANPP of three soybean varieties. We observed that reflectance-based vegetation indices showed limited sensitivity to these cumulative differences, as only EVI2, NDWI and SRWI were able to distinguish between rainfed and irrigation treatments in some few cases. Passive, irradiance-normalised SIF showed this same trend, but active fluorescence peak ratio (F_Red/F_Far-red) revealed statistically significant differences for the three cultivars studied. In addition, the latter showed a significant correlation with ANPP for two soybean varieties after correction for light re-absorption and scattering (p < 0.05, R² > 0.5), which was observed for only EVI and foliar water status VIs among passive indicators. Active fluorescence measurements at leaf level by PAM fluorometry did not show differences between treatments in the upper part of the canopy but revealed a biomass-dependent decrease in PSII yield along the vertical axis. Our study demonstrated that fluorescence emission spectrum holds highly valuable information that might allow monitoring ANPP changes upon irrigation from remote sensing applications, and therefore should be carefully studied. Lastly, it highlights the potential of SIF retrieval at both O₂-A and O₂-B lines.

使用远程获取的数据准确估算空中净初级生产 (ANPP) 是环境监测和精准农业的主要挑战之一。数十年来，基于反射的技术已被广泛使用，但近年来叶绿素荧光发射检测已成为一种有前途的替代方法。尽管被动太阳诱导荧光 (SIF) 监测显示出有趣的结果，但它提供的信息仅限于少数波长（弗劳恩霍夫线和大地线）。另一方面，稳态荧光的主动测量及其光谱分布涵盖了全发射光谱，但由于明显的实验局限性，尚未得到充分探索。在这项工作中，我们开发了一种新颖的主动荧光测量程序，基于安装在田间拖拉机上的灯和传感器。该技术在文献中首次允许检测植物作物的全光谱荧光发射。

这项工作的主要目的是分析基于反射和荧光的信息，由新提出的方法记录，如何跟踪三个大豆品种 ANPP 中不同灌溉处理引起的差异。我们观察到，基于反射率的植被指数对这些累积差异的敏感性有限，因为在少数情况下，只有 EVI2、NDWI 和 SRWI 能够区分雨养和灌溉处理。被动的、辐照度归一化的 SIF 显示出同样的趋势，但主动荧光峰比（F _Red / F _Far-red) 揭示了所研究的三个品种的统计学显着差异。此外，在对光的重吸收和散射进行校正后，后者与两个大豆品种的 ANPP 显着相关（p  < 0.05，R ² > 0.5)，在被动指标中仅观察到 EVI 和叶面水分状态 VI。通过 PAM 荧光法在叶水平上进行的主动荧光测量并未显示冠层上部处理之间的差异，但揭示了沿垂直轴的 PSII 产量的生物量依赖性降低。我们的研究表明，荧光发射光谱拥有非常有价值的信息，可以通过遥感应用监测灌溉时的 ANPP 变化，因此应该仔细研究。最后，它强调了在 O ₂ -A 和 O ₂ -B 线上进行 SIF 检索的潜力。