Leaf optical system modeled as a stochastic process

Hyperspectral leaf reflectance simulation considering internal structure

The mainstream radiation transport models represented by the PROSPECT model regard the internal substances of the leaf as uniformly distributed. The influence of the uneven distribution of substances inside the leaf on the spectra of the leaf was not considered. The PIOSL-3 model proposed in this study assumes that the leaf is composed of three layers of optical properties. The particle swarm optimization algorithm (PSO) was used to determine the distribution proportion of biochemical parameters including chlorophyll, water and dry matter in each layer of leaves. In this paper, the LOPEX and ANGERS datasets were used to verify the spectral simulation effect of the PIOSL-3 model, and it can be seen from the optimization results of the parameters that the structural parameters in the leaves of different plant types are higher in the upper layer, and the distribution of chlorophyll also shows similar characteristics, while water and dry matter mainly exist in the lower layer of the leaves, and some plants also show different characteristics. In terms of simulated spectra, the PIOSL-3 model reduced RMSE mean values by 1.78, 0.39, 6.12, and 0.9, and SAM mean values by 0.07, 0.0094, 0.2267, and 0.03 compared to the PROSPECT model on the LOPEX and ANGERS datasets, respectively. The hypothesis of layered simulation is feasible, and the proposal of PIOSL-3 model provides a new idea for modeling the leaf radiation transmission process.

Aquaphotomic, E-Nose and Electrolyte Leakage to Monitor Quality Changes during the Storage of Ready-to-Eat Rocket

The consumption of ready-to-eat (RTE) leafy vegetables has increased rapidly due to changes in consumer diet. RTE products are perceived as fresh, high-quality, and health-promoting. The monitoring of the RTE quality is crucial in relation to safety issues. This study aimed to evaluate the maintenance of RTE rocket salad freshness packed under modified atmospheres. A portable E-nose, the electrolyte leakage test (which measures the index of leaf damage-I_LD), and NIR spectroscopy and Aquaphotomics were employed. Two trials were carried out, using the following gas mixtures: (A) atmospheric air (21% O₂, 78% N₂); (B) 30% O₂, 70% N₂; (C) 10% CO₂, 5% O₂, 85% N₂. Samples were stored at 4 °C and analyzed at 0, 1, 4, 7, 11, and 13 days. ANOVA, PCA, PLS were applied for data processing. E-nose and I_LD results identified the B atmosphere as the best for maintaining product freshness. NIR spectroscopy was able to group the samples according to the storage time. Aquaphotomics proved to be able to detect changes in the water structure during storage. These preliminary data showed a good agreement NIR/I_LD suggesting the use of NIR for non-destructive monitoring of the damage to the plant membranes of RTE rocket salad.

Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion

Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning validation dataset and calculating chlorophyll maps from empirical remotely sensed hyperspectral data and comparing them to TCARI OSAVI , an index that has strong negative correlation with chlorophyll concentration. With the validation dataset, coefficients of determination ( R 2 ) of 0.97 were obtained for chlorophyll a and 0.95 for chlorophyll b. The chlorophyll maps correlate with the TCARI OSAVI map. The correlation coefficient (R) is −0.87 for chlorophyll a and −0.68 for chlorophyll b in selected plots. These results indicate that the approach is highly promising approach for estimating vegetation chlorophyll content.

A three-dimensional canopy photosynthesis model in rice with a complete description of the canopy architecture, leaf physiology, and mechanical properties

In current rice breeding programs, morphological parameters such as plant height, leaf length and width, leaf angle, panicle architecture, and tiller number during the grain filling stage are used as major selection targets. However, so far, there is no robust approach to quantitatively define the optimal combinations of parameters that can lead to increased canopy radiation use efficiency (RUE). Here we report the development of a three-dimensional canopy photosynthesis model (3dCAP), which effectively combines three-dimensional canopy architecture, canopy vertical nitrogen distribution, a ray-tracing algorithm, and a leaf photosynthesis model. Concurrently, we developed an efficient workflow for the parameterization of 3dCAP. 3dCAP predicted daily canopy RUE for different nitrogen treatments of a given rice cultivar under different weather conditions. Using 3dCAP, we explored the influence of three canopy architectural parameters-tiller number, tiller angle and leaf angle-on canopy RUE. Under different weather conditions and different nitrogen treatments, canopy architecture optimized by manipulating these parameters can increase daily net canopy photosynthetic CO2 uptake by 10-52%. Generally, a smaller tiller angle was predicted for most elite rice canopy architectures, especially under scattered light conditions. Results further show that similar canopy RUE can be obtained by multiple different parameter combinations; these combinations share two common features of high light absorption by leaves in the canopy and a high level of coordination between the nitrogen concentration and the light absorbed by each leaf within the canopy. Overall, this new model has potential to be used in rice ideotype design for improved canopy RUE.

Using Imaging Spectrometry to Study Changes in Crop Area in California’s Central Valley during Drought

In California, predicted climate warming increases the likelihood of extreme droughts. As irrigated agriculture accounts for 80% of the state’s managed water supply, the response of the agricultural sector will play a large role in future drought impacts. This study examined one drought adaptation strategy, changes in planting decisions, using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery from June 2013, 2014, and 2015 from the Central Valley of California. We used the random forest classifier to classify crops into categories of similar water use. Classification accuracy was assessed using the random forest out-of-bag accuracy, and an independently validated accuracy at both the pixel and field levels. These results were then compared to simulated Landsat Operational Land Imager (OLI) and simulated Sentinel-2B results. The classification was further analyzed for method portability and band importance. The resultant crop maps were used to analyze changes in crop area as one measure of agricultural adaptation in times of drought. The results showed overall field-level accuracies of 94.4% with AVIRIS, as opposed to 90.4% with Landsat OLI and 91.7% with Sentinel, indicating that hyperspectral imagery has the potential to identify crops by water-use group at a single time step at higher accuracies than multispectral sensors. Crop maps produced using the random forest classifier indicated that the total crop area decreased as the drought persisted from 2013 to 2015. Changes in area by crop type revealed that decisions regarding which crop to grow and which to fallow in times of drought were not driven by the average water requirements of crop groups, but rather showed possible linkages to crop value and/or crop permanence.

Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model

BACKGROUND

Leaf biochemical composition corresponds to traits related to the plant state and its functioning. This study puts the emphasis on the main leaf absorbers: chlorophyll a and b ([Formula: see text]), carotenoids ([Formula: see text]), water ([Formula: see text]) and dry mater ([Formula: see text]) contents. Two main approaches were used to estimate [[Formula: see text] [Formula: see text], [Formula: see text], [Formula: see text]] in a non-destructive way using spectral measurements. The first one consists in building empirical relationships from experimental datasets using either the raw reflectances or their combination into vegetation indices (VI). The second one relies on the inversion of physically based models of leaf optical properties. Although the first approach is commonly used, the calibration of the empirical relationships is generally conducted over a limited dataset. Consequently, poor predictions may be observed when applying them on cases that are not represented in the training dataset, i.e. when dealing with different species, genotypes or under contrasted environmental conditions. The retrieval performances of the selected VIs were thus compared to the ones of four PROSPECT model versions based on reflectance data acquired at two phenological stages, over six wheat genotypes grown under three different nitrogen fertilizations and two sowing density modalities. Leaf reflectance was measured in the lab with a spectrophotometer equipped with an integrating sphere, the leaf being placed in front of a white Teflon background to increase the sensitivity to leaf biochemical composition. Destructive measurements of [[Formula: see text] [Formula: see text], [Formula: see text], [Formula: see text]] were performed concurrently.

RESULTS

The destructive measurements demonstrated that the carotenoid, [Formula: see text], and chlorophyll, [Formula: see text], contents were strongly correlated (r2 = 0.91). The sum of [Formula: see text] and [Formula: see text], i.e. the total chlorophyllian pigment content, [Formula: see text], was therefore used in this study. When inverting the PROSPECT model, accounting for the brown pigment content, [Formula: see text], was necessary when leaves started to senesce. The values of [Formula: see text] and [Formula: see text] were well estimated (r2 = 0.81 and r2 = 0.88 respectively) while the dry matter content, [Formula: see text], was poorly estimated (r2 = 0.00). Retrieval of [Formula: see text] from PROSPECT versions was only slightly biased, while substantial overestimation of [Formula: see text] was observed. The ranking between estimated values of [Formula: see text] and [Formula: see text] from the several PROSPECT versions and that derived using the VIs were similar to the ranking observed over the destructively measured values of [Formula: see text] and [Formula: see text].

CONCLUSIONS

PROSPECT model inversion and empirical VI approach provide similar retrieval performances and are useful methods to estimate leaf biochemical composition from spectral measurements. However, the PROSPECT model inversion gives potential access to additional traits on surface reflectivity and leaf internal structure. This study suggests that non-destructive estimation of leaf chlorophyll and water contents is a relevant method to provide leaf traits with relatively high throughput.

Optimal crop canopy architecture to maximise canopy photosynthetic CO2 uptake under elevated CO2 - a theoretical study using a mechanistic model of canopy photosynthesis

Canopy architecture has been a major target in crop breeding for improved yields. Whether crop architectures in current elite crop cultivars can be modified for increased canopy CO2 uptake rate (Ac) under elevated atmospheric CO2 concentrations (Ca) is currently unknown. To study this question, we developed a new model of canopy photosynthesis, which includes three components: (i) a canopy architectural model; (ii) a forward ray tracing algorithm; and (iii) a steady-state biochemical model of C3 photosynthesis. With this model, we demonstrated that the Ac estimated from 'average' canopy light conditions is ~25% higher than that from light conditions at individual points in the canopy. We also evaluated theoretically the influence of canopy architectural on Ac under current and future Ca in rice. Simulation results suggest that to gain an optimal Ac for the examined rice cultivar, the stem height, leaf width and leaf angles can be manipulated to enhance canopy photosynthesis. This model provides a framework for designing ideal crop architectures to gain optimal Ac under future changing climate conditions. A close linkage between canopy photosynthesis modelling and canopy photosynthesis measurements is required to fully realise the potential of such modelling approaches in guiding crop improvements.

Spectral properties of plant leaves pertaining to urban landscape design of broad-spectrum solar ultraviolet radiation reduction

Human exposure to harmful ultraviolet (UV) radiation has important public health implications. Actual human exposure to solar UV radiation depends on ambient UV irradiance, and the latter is influenced by ground reflection. In urban areas with higher reflectivity, UV exposure occurs routinely. To discover the solar UV radiation regulation mechanism of vegetation, the spectral reflectance and transmittance of plant leaves were measured with a spectrophotometer. Typically, higher plants have low leaf reflectance (around 5%) and essentially zero transmittance throughout the UV region regardless of plant species and seasonal change. Accordingly, incident UV radiation decreases to 5% by being reflected and is reduced to zero by passing through a leaf. Therefore, stratified structures of vegetation are working as another terminator of UV rays, protecting whole terrestrial ecosystems, while vegetation at waterfronts contributes to protect aquatic ecosystems. It is possible to protect the human population from harmful UV radiation by urban landscape design of tree shade and the botanical environment. Even thin but uniformly distributed canopy is effective in attenuating UV radiation. To intercept diffuse radiation, UV screening by vertical structures such as hedges should be considered. Reflectivity of vegetation is around 2%, as foliage surfaces reduce incident UV radiation via reflection, while also eliminating it by transmittance. Accordingly, vegetation reduces incident UV radiation to around 2% by reflection. Vegetation influence on ambient UV radiation is broad-spectrum throughout the UV region. Only trees provide cool UV protective shade. Urban landscapes aimed at abating urban heat islands integrated with a reduction of human UV over-exposure would contribute to mitigation of climate change.

Nondestructive measurement of chlorophyll pigment content in plant leaves from three-color reflectance and transmittance

We propose a nondestructive or optical method of measuring the chlorophyll content in a leaf after constructing a mathematical model of reflectance and transmittance of plant leaves as a function of their chlorophyll pigment content. The model is based on the Kubelka-Munk theory and involves the modeling of the multiple reflection of light in a leaf that is assumed to be composed of a stack of four layers. It also includes the assumption that the scattering coefficient and the absorption coefficient of the Kubelka-Munk theory can be expressed as a linear function of the pigment content of a plant leaf. In the proposed method, the chlorophyll content is calculated from reflectances and transmittances at three bands whose center wavelengths are 880,720, and 700 nm. Experiments were performed to confirm the applicability of the model and the method. Reflectance and transmittance calculated with the model showed good agreement with measured values. Furthermore, several unmeasurable constants necessary in the calculation were determined by a least-squares fit. We also confirmed that these results were consistent with several well-known facts in the botanical field. The method proposed here showed a small estimation error of 6.6 microg/cm (2) over the 0-80 microg/cm(2) chlorophyll content range for all kinds of plant tested.

Calculating the reflectance map

It appears that the development of machine vision may benefit from a detailed understanding of the imaging process. The reflectance map, showing scene radiance as a function of surface gradient, has proved to be helpful in this endeavor. The reflectance map depends both on the nature of the surface layers of the objects being imaged and the distribution of light sources. Recently, a unified approach to the specification of surface reflectance in terms of both incident and reflected beam geometry has been proposed. The reflecting properties of a surface are specified in terms of the bidirectional reflectance-distribution function (BRDF). Here we derive the reflectance map in terms of the BRDF and the distribution of source radiance. A number of special cases of practical importance are developed in detail. The significance of this approach to the understanding of image formation is briefly indicated.