Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its applicability for analysing plant traits, i.e. assessing Cercospora beticola disease severity or leaf chlorophyll content. To test the accuracy of the obtained data, these were compared with reflectance and transmittance measurements of selected leaves acquired by the point spectroradiometer ASD FieldSpec, equipped with the FluoWat device. The working principle of the HyperART system relies on the upward redirection of transmitted and reflected light (range of 400 to 2500 nm) of a plant sample towards two line scanners. By using both the reflectance and transmittance image, an image of leaf absorption can be calculated. The comparison with the dynamically high-resolution ASD FieldSpec data showed good correlation, underlying the accuracy of the HyperART system. Our experiments showed that variation in both leaf chlorophyll content of four different crop species, due to different fertilization regimes during growth, and fungal symptoms on sugar beet leaves could be accurately estimated and monitored. The use of leaf reflectance and transmittance, as well as their sum (by which the non-absorbed radiation is calculated) obtained by the HyperART system gave considerably improved results in classification of Cercospora leaf spot disease and determination of chlorophyll content. The HyperART system offers the possibility for non-invasive and accurate mapping of leaf transmittance and absorption, significantly expanding the applicability of reflectance, based on mapping spectroscopy, in plant sciences. Therefore, the HyperART system may be readily employed for non-invasive determination of the spatio-temporal dynamics of various plant properties.

An analysis of the sensitivity of synthetic aperture radar (SAR) backscatter (σ^o) to crop and soil conditions was conducted using 57 RADARSAT-2 C-band quad-polarized SAR images acquired from April to September 2009 for large fields of wheat, barley, oat, corn, onion, and alfalfa in Barrax, Spain. Preliminary results showed that the cross-polarized σ_HV^o was particularly useful for monitoring both crop and soil conditions and was the least sensitive to differences in beam incidence angle. The greatest separability of barley, corn, and onion occurred in spring after the barley had been harvested or in the narrow time window associated with grain crop heading when corn and onion were still immature. The time series of σ^o offered reliable information about crop growth stage, such as jointing and heading in grain crops and leaf growth and reproduction in corn and onion. There was a positive correlation between σ^o and the Normalized Difference Vegetation Index for onion and corn but not for all crops, and the impact of view direction and incidence angle on the time series was minimal compared to the signal response to crop and soil conditions. Related to planning for future C-band SAR missions, we found that quad-polarization with image acquisition frequency from 3-6 days was best suited for distinguishing crop types and for monitoring crop phenology, single- or dual-polarization with an acquisition frequency of 3-6 days was sufficient for mapping crop green biomass, and single- or dual-polarization with daily image acquisition was necessary to capture rapid changes in soil moisture condition.

Most studies assessing chlorophyll fluorescence (ChlF) have examined leaf responses to environmental stress conditions using active techniques. Alternatively, passive techniques are able to measure ChlF at both leaf and canopy scales. However, the measurement principles of both techniques are different, and only a few datasets concerning the relationships between them are reported in the literature. In this study, we investigated the potential for interchanging ChlF measurements using active techniques with passive measurements at different temporal and spatial scales. The ultimate objective was to determine the limits within which active and passive techniques are comparable. The results presented in this study showed that active and passive measurements were highly correlated over the growing season across nitrogen treatments at both canopy and leaf-average scale. At the single-leaf scale, the seasonal relation between techniques was weaker, but still significant. The variability within single-leaf measurements was largely related to leaf heterogeneity associated with variations in CO₂ assimilation and stomatal conductance, and less so to variations in leaf chlorophyll content, leaf size or measurement inputs (e.g. light reflected and emitted by the leaf and illumination conditions and leaf spectrum). This uncertainty was exacerbated when single-leaf analysis was limited to a particular day rather than the entire season. We concluded that daily measurements of active and passive ChlF at the single-leaf scale are not comparable. However, canopy and leaf-average active measurements can be used to better understand the daily and seasonal behaviour of passive ChlF measurements. In turn, this can be used to better estimate plant photosynthetic capacity and therefore to provide improved information for crop management.

Imaging and non-imaging spectroscopy employed in the field and from aircraft is frequently used to assess biochemical, structural, and functional plant traits, as well as their dynamics in an environmental matrix. With the increasing availability of high-resolution spectroradiometers, it has become feasible to measure fine spectral features, such as those needed to estimate sun-induced chlorophyll fluorescence (F), which is a signal related to the photosynthetic process of plants. The measurement of F requires highly accurate and precise radiance measurements in combination with very sophisticated measurement protocols. Additionally, because F has a highly dynamic nature (compared with other vegetation information derived from spectral data) and low signal intensity, several environmental, physiological, and experimental aspects have to be considered during signal acquisition and are key for its reliable interpretation. The European Cooperation in Science and Technology (COST) Action ES1309 OPTIMISE has produced three articles addressing the main challenges in the field of F measurements. In this paper, which is the second of three, we review approaches that are available to measure F from the leaf to the canopy scale using ground-based and airborne platforms. We put specific emphasis on instrumental aspects, measurement setups, protocols, quality checks, and data processing strategies. Furthermore, we review existing techniques that account for atmospheric influences on F retrieval, address spatial scaling effects, and assess quality checks and the metadata and ancillary data required to reliably interpret retrieved F signals.

Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIR_v,Rad), defined as the product of observed NIR radiance and normalized difference vegetation index, can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIR_v,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIR_v,Ref), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF₇₆₀). The strong linear relationship between NIR_v,Rad and absorbed photosynthetically active radiation by green leaves (APAR_green), and that between APAR_green and GPP, explain the good NIR_v,Rad-GPP relationship. The NIR_v,Rad-GPP relationship is robust and consistent across sites. The scalability and simplicity of NIR_v,Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.