Spencer R. Van Leeuwen

On the decomposition of foliar hyperspectral signatures for the high-fidelity discrimination and monitoring of crops

Hyperspectral technologies are being increasingly employed in precision agriculture. By separating the surface and subsurface components of foliar hyperspectral signatures using polarization optics, it is possible to enhance the remote discrimination of different plant species and optimize the assessment of different factors associated with the crops’ health status such as chlorophyll levels and water content. These initiatives, in turn, can lead to higher crop yield and lower environmental impact through a more effective use of freshwater supplies and fertilizers (reducing the risk of nitrogen leaching). It is important to consider, however, that the main varieties of crops, represented by C3 (e.g., soy) and C4 (e.g., maize) plants, have markedly distinct morphological characteristics. Accordingly, the influence of these characteristics on their interactions with impinging light may affect the selection of optimal probe wavelengths for specific applications making use of combined hyperspectral and polarization measurements. In this work, we compare the sensitivity of the surface and subsurface reflectance responses of C3 and C4 plants to different spectral and geometrical light incidence conditions. In our comparisons, we also consider intra- species variability with respect to specimen characterization data. This investigation is supported by measured biophysical data and predictive light transport simulations. The results of our comparisons indicate that the surface and subsurface reflectance responses of C3 and C4 plants depict well-defined patterns of sensitivity to varying illumination conditions. We believe that these patterns should be considered in the design of new high-fidelity crop discrimination and monitoring procedures.

Identifying the optical phenomena responsible for the blue appearance of veins

Blue in nature is often associated with beauty. It can be observed all around us, from captivating blue eyes to iridescent blue butterfly wings. While colours in nature are often the result of pigmentation, the majority of natural blue is produced by structural coloration. The colour of the sky, for example, is primarily caused by Rayleigh scattering. In this paper, we examine a single occurrence of blue in nature, specifically the blue appearance of veins near the surface of human skin. The most comprehensive investigation of this coloration to date showed that it arises from a combination of the scattering properties of skin and the absorptance of venous blood. However, that work only considered broad optical properties of these mediums and did not identify the source of the colour. In this paper, we employ in silico experiments, performed using first-principles light interaction models for skin and blood, to investigate the net effect of skin and vein optical properties on their aggregate reflectance across the visible range. We show that the contribution of skin to the distinct appearance of veins primarily results from Rayleigh scattering occurring within the papillary dermis, a sublayer of the skin. The results of this paper, in addition to addressing an old open scientific question, may have practical implications for performing non-invasive measurements of the physiological properties of skin and blood.

Detecting and monitoring water stress states in maize crops using spectral ratios obtained in the photosynthetic domain

Abstract. The reliable detection and monitoring of changes in the water status of crops composed of plants like maize, a highly adaptable C4 species in large demand for both food and biofuel production, are longstanding remote sensing goals. Existing procedures employed to achieve these goals rely predominantly on the spectral signatures of plant leaves in the infrared domain where the light absorption within the foliar tissues is dominated by water. It has been suggested that such procedures could be implemented using subsurface reflectance to transmittance ratios obtained in the visible (photosynthetic) domain with the assistance of polarization devices. However, the experiments leading to this proposition were performed on detached maize leaves, which were not influenced by the whole (living) plant’s adaptation mechanisms to water stress. In this work, we employ predictive simulations of light–leaf interactions in the photosynthetic domain to demonstrate that the living specimens’ physiological responses to dehydration stress should be taken into account in this context. Our findings also indicate that a reflectance to transmittance ratio obtained in the photosynthetic domain at a lower angle of incidence without the use of polarization devices may represent a cost-effective alternative for the assessment of water stress states in maize crops.

In silico analysis of decomposed reflectances of C3 and C4 plants aiming at the effective assessment of crop needs

Abstract. By separating the surface and subsurface components of foliar hyperspectral signatures using polarization optics, it is possible to enhance the remote discrimination of different plant species and optimize the assessment of different factors associated with their health status. These initiatives, in turn, can lead to higher crop yield and lower environmental impact. It is important to consider, however, that the main varieties of crops, represented by C3 (e.g., soy) and C4 (e.g., maize) plants, have markedly distinct morphological characteristics. Accordingly, the influence of these characteristics on their interactions with impinging light may affect the selection of optimal probe wavelengths for specific applications making use of combined hyperspectral and polarization measurements. In this paper, we compare the sensitivity of the total (including surface and subsurface components) and subsurface reflectance responses of C3 and C4 plants to different spectral and geometrical light incidence conditions. This investigation is supported by measured biophysical data and predictive light transport simulations. The results of our comparisons indicate that the total and subsurface reflectance responses of C3 and C4 plants depict well-defined patterns of sensitivity for varying illumination conditions. We believe that these patterns should be considered in the design of high-fidelity crop discrimination and monitoring procedures.

High-fidelity iridal light transport simulations at interactive rates: High-fidelity iridal light transport simulations at interactive rates

Predictive light transport models based on first‐principles simulation approaches have been proposed for complex organic materials. The driving force behind these efforts has been the high‐fidelity reproduction of material appearance attributes without one having to rely on the manipulation of ad hoc parameters. These models, however, are usually considered excessively time consuming for rendering and visualization applications requiring interactive rates. In this paper, we propose a strategy to address this open problem with respect to one of the most challenging of these organic materials, namely the human iris. More specifically, starting with the configuration of a predictive iridal light transport model on a parallel‐computing platform, we analyze the sensitivity of iridal appearance attributes to key model running parameters in order to achieve an optimal balance between fidelity and performance. We believe that the proposed strategy represents a step toward the real‐time and predictive synthesis of high‐fidelity iridal images for rendering and visualization applications, and it can be extended to other organic materials.

On the detection and monitoring of reduced water content in plants using spectral responses in the visible domain

The water status of cultivated plants can have a significant impact not only on food production, but also on the appropriate usage of increasingly scarce freshwater supplies. Accordingly, the cost-effective detection and monitoring of changes in their water content are longstanding remote sensing goals. Existing procedures employed to achieve these goals are largely based on the spectral responses of plant leaves in the infrared domain where the light absorption within the foliar tissues is dominated by water. Recently, it has been suggested that such procedures could be implemented using spectral responses, more specifically spectral subsurface reflectance to transmittance ratios, obtained in the visible domain. The basis for this proposition resides on the premise that a reduced water content (RWC) can result in histological changes whose effects on the foliar optical properties may not be limited to the infrared domain. However, the experiments leading to this proposition were performed on detached leaves, which were not influenced by the whole plant’s adaptation mechanisms to water stress. In this work, we investigate whether the spectral responses of living plant leaves in the visible domain can lead to reliable RWC estimations. We employ measured biophysical data and predictive light transport simulations in order to extend qualitatively and quantitatively the scope of previous studies in this area. Our findings indicate that the living specimens’ physiological responses to water stress should be taken into account in the design of new procedures for the cost-effective RWC estimation using visible subsurface reflectance to transmittance ratios.