Surface Optics


Precision Agriculture and Hyperspectral Sensors:

Monitoring Against Drought, Disease, and Nutrient Stress

Crop monitoring for nutrients, water-stress, disease, insect attack and overall plant health is a vital aspect of successful agricultural operations. Traditionally this has been carried out by visual examination of crops on the ground or sometimes from the air. However these methods are limited by the ability of the human eye to discriminate between healthy foliage and foliage suffering various kinds of stress. Often a specific condition must be well-advanced before visual symptoms become noticeable even to experienced observers.

Modern precision agriculture relies on site-specific management tactics to maximize yield and resources while reducing environmental impacts such as over-fertilization and the broad applications of pesticides. Pin-pointing areas requiring attention ? be it water, weed or pathogen treatment, or nutrient adjustments ? allows for spot application rather than whole-field treatment. The collection of key data at a sufficient level of accuracy depends on the availability of equipment that can be operated at a cost-effective level.


" Some of the benefits of hyperspectral and multispectral imaging are that these technologies are: low cost (when compared with traditional scouting methods), give consistent results, simple to use, allow for rapid assessments, non-destructive, highly accurate, and have a broad range of applications."


The development of aerial and ground-based hyperspectral and multispectral imaging equipment has been a major breakthrough in the expansion and practical application of precision agriculture techniques. This technology has made possible the assessment of crop stresses, characterization of soils and vegetative cover and yield estimation, in addition to its predictive capabilities. Some of the benefits of hyperspectral and multispectral imaging are that these technologies are: low cost (when compared with traditional scouting methods), give consistent results, simple to use, allow for rapid assessments, non-destructive, highly accurate, and have a broad range of applications.[1]

Basics of Hyperspectral Imaging

Spectral reflectance, measured by hyperspectral imaging equipment, is the amount of reflected light from a surface. Hyperspectral imaging is the process by which images are taken and numerical values (spectral radiance) assigned to each pixel, utilizing a range of wavelengths across the electromagnetic spectrum, including visible and infrared regions.

Through the use of specialized software and statistical analysis, these pixels are sorted and characterized to distinguish between groups of pixels or in the case of precision agriculture, plant characteristics and environmental conditions. Earlier remote sensing technology, in particular multispectral imaging, collects data at a few widely-spaced wavelengths.The data from each wave-length band is assembled into a three-dimensional hyperspectral ‘data cube’ for processing and analysis. Each layer of the cube represents data at a specific wavelength.

Spectral imaging data captured by the SOC710-VP highlights areas of Merlot leaves suffering from fungal infection. Viewed using Surface Optics hyperspectral processing and analysis software.

Detection of Stress-related Spectral Variations

The ability of hyperspectral imaging to provide valuable data on the condition and health of crops is predicated on the interaction and relationship between electromagnetic radiation (EMR) and foliage. EMR may be absorbed, transmitted or reflected and although the internal and external physical structure of vegetation affects this, the primary influences on EMR are the various photosynthetic pigments.[2]

In the red and blue parts of the visible spectrum, reflectance is primarily a result of absorption by the photosynthetic pigments. Water content is the primary influence on reflectance in the mid-infrared (MIR) while reflectance in the near-infrared area (NIR) is influenced by the shape and condition of air spaces in the spongy mesophyll.[3] Senescence, nutrient stress, pathogen and insect infestation have all been shown to significantly reduce reflectance in the mid-infrared spectral region.[6] It has been well recorded that a vegetation index of NIR and red wavelengths can monitor a range of plant-health issues including fungal pathogens, excess salt and nutrient deficiencies.

By measuring changes at waveband 531nm, which is affected by the production of zeaxanthin and comparing it with waveband 570 nm, which is not affected, a standard Photochemical Reflectance Index (PRI) has been developed which serves as a measure of photosynthetic light use efficiency. This index can be readily generated from hyperspectral imaging data.

One of the most powerful techniques for the measurement of overall photosynthetic efficiency and thus of plant productivity, is the fluorescence of chlorophyll a in photosystem II. The indexes produced give a good measure, however they are limited in their use by the need for the active excitation of photosynthesis by, for example, a saturating light pulse.[4] This severely restricts the possibility of using this measure for remote sensing, so research has been directed to finding new, genuinely remote indices suitable for hyperspectral imaging equipment.

Besides the photosynthetic pigments, reflectance is also influenced by the presence of zeaxanthin. This pigment is produced by plants to safely remove excess photons when light intensity exceeds the ability of photosystem II to absorb photons without becoming over-energized. Zeaxanthin accumulation can therefore be used as a quantitative indicator of non-photochemical energy dissipation and therefore of light-use efficiency.[4]

By measuring changes at waveband 531nm, which is affected by the production of zeaxanthin and comparing it with waveband 570 nm, which is not affected, a standard Photochemical Reflectance Index (PRI) has been developed which serves as a measure of photosynthetic light use efficiency. This index can be readily generated from hyperspectral imaging data.[5]

Of particular importance is a comparison with traditional spectrometric equipment to ensure the ability of hyperspectral imaging to deliver equivalent data to traditional equipment. A study by Rascher et al. (2007) utilizing the SOC-700 instrument showed that portable hyperspectral imaging equipment could be used to “quantify dynamic, biochemical changes in photosynthetic efficiency” by measuring PRI.[5]

In this study it was demonstrated that PRI could provide measurements of both the biochemical adaptions to high light intensity and the gradual de-activation of photosynthesis during drying, making PRI monitoring by remote sensing a valuable methodology for drought investigations. The Rascher study relied on detached leaves, but the same methodology and instrumentation has been shown in the controlled conditions of Biosphere 2 to give effective data on whole vegetation-canopies.[4]

Above is an example of the picture processing and Photochemical Reflectance Index of four tropical leaves during the drying process as seen in the Rascher et al. (2007) study. (A) True-color-composite picture of the imaged leaves; (B) Normalized Difference Vegetation Index (NDVI), calculated for all pixels; (C) NDVI threshold image, (D) PRI, calculated for all pixels.(E through H): A time series of PRI during the drying process. The mask from (C) was used to show the leaf PRI values only.

Drought Stress

Drought is a significant factor in predicting crop yields and the final success of a crop. Early detection of water related stresses in field crops can allow producers to identify specific areas for irrigation, saving water, energy, and time. Early detection might also allow producers to deliver water to crops before drought stress results in yield losses.

Colombo et al. (2008) tested various indices of drought stress and found that leaf reflectance in the infrared and visible spectrum was related to changes in leaf equivalent water thickness (EWT). Using hyperspectral imaging they tested various models for estimating EWT at the canopy level in Italian poplar plantations and found suitable models giving error levels of only 2.6%. They concluded that hyperspectral regression indices derived from hyperspectral imaging were strong tools for estimating water content at both leaf and landscape level.[7]

The SOC-700 hyperspectral imager was able to track the development of water stress four days before the effects of the stress were observed with the naked eye

Higher levels of stress do ultimately manifest themselves in changes in photosynthetic pigments. These changes lead to the familiar symptom of chlorosis when the reflectance of red wavelengths increases to equal that of green, producing the typical yellow colour. These changes are detected much earlier by hyperspectral imaging well before any change is visible to the human eye.[2]

As discussed above, Rascher et al. used the Photochemical Reflectance Index and a portable hyperspectral imager to assess drought stress in leaves of tropical trees and they could clearly observe the effect of dehydration over time on the individual tree leaves.[5]

That this stress-detection methodology could be applied to grain was demonstrated in trials of corn subjected to different water and nutrient regimes in field plots. Though the traits of leaf and canopy water stress were subtle, hyperspectral imaging technology could distinguish between treatments in both the controlled and field experiments.[8] Even with senescing leaves of barley due to flowering, the SOC-700 hyperspectral imager was able to track the development of water stress four days before the effects of the stress were observed with the naked eye. Under the field conditions, with variability of light and plot differences, the imaging technology could correctly characterize three out of the four treatment groups.This demonstrated the suitability of hyperspectral imaging for early detection of drought-stress and nutrient-stress in practical agricultural conditions.

Spatio-temporal dynamics of drought stress in barley, visualized with false color images. Images for drought-stressed plants stop at day 10 as plants were only observed until drought stress was visible to the naked eye. The green color indicated a high probability that the signature corresponds to a pixel belonging to the healthy archetypes, whereas a dark red color indicated a high probability of being associated with the stressed archetype.

Additionally, Rossini et al. (2013) showed that hyperspectral imaging could be used to detect drought stress at the farm level with corn. They conducted a comparison of three irrigation conditions with airborne remote sensing equipment and found that they were able to accurately map irrigation deficits even before water stress affected the canopy structure.[9]

Field crops are not the only application of hyperspectral imaging, this technology can be used to assess water stress and scheduling of irrigation in turf grasses. Jiang and Carrow (2005) examined the correlation of spectral reflectance and drought stress on turf grass (turf quality and leaf firing). They screened 12 grasses and found that the reflectance models varied by cultivar, suggesting that species differences should be taken into account when using indexes. They also conclude that hyperspectral imaging might be useful in screening grasses for drought tolerance.[10]

A comparison of relative reflectance between healthy and leaf rust infected wheat spectral signatures from 400-1000 nm showed a decreased reflectance for the infected wheat in the blue and green region of the visible spectrum and a strong decreased near-infrared reflectance plateau.

Venturia inaequalis is the pathogen responsible for apple scab in apple trees. Infection first appears as yellow or chlorotic spots on leaves progressing to darker spots and yellowing of the leaves. Economic losses are caused primarily by damage to the fruit surfaces. Using hyperspectral imaging and statistical procedures for classification, Delalieux et al (2007) concluded that stress from apple scab was able to be detected before symptoms were visible to the human eye.[13]

Under controlled conditions, Mahlein et al (2012) reported that hyperspectral imaging was suitable for, not only the detection, but also the identification and quantification of fungal diseases of sugar beets at the leaf level. This study examined three pathogens of sugar beets, Cercospora leaf spot (Cercospora beticola (Sacc.)), powdery mildew (Erysiphe betae (Vanha) Weltzien), and sugar beet rust (Uromyces betae (Persoon) Lev.). Hyperspectral imaging technology was able to distinguish between these three pathogens with as little as 10% diseased leaf area for powdery mildew and leaf spot.[14]

The use of hyperspectral imaging can be applied successfully on a larger scale. Orange rust of sugarcane (caused by Puccinia kuehnii) is a fungal disease that produces lesions which rupture allowing water to escape from the plant. From images taken at the field level, Apan et al. (2004) successfully discriminated patches of orange rust of sugarcane from non-affected areas by detecting changes in leaf pigments.[15] Similarly, Zhang et al. (2003) demonstrated the effectiveness of hyperspectral imaging for disease detection at the field level. Late blight of tomato, caused by Phytophthora infestans, is a major threat to tomato production in California. Using bands in the range of 0.7?0.9mm, these authors demonstrated that late blight could be successfully detected in tomatoes at the field level.[16]

Ecological Monitoring

Following on from the verification of the Photochemical Reflectance Index (PRI) for whole canopies discussed above, studies have addressed the deficiencies of current ecological monitoring systems to measure global carbon utilization.[4] Although current models can accurately obtain data on overall light intensity in the appropriate range (400-700 nm) as well as the fraction of light being absorbed, they cannot obtain accurate data on light-use efficiency. Without this third data-set, models of global carbon contain substantial uncertainties which restrict their usefulness. The development of PRI, in combination with hyperspectral imaging, provides a new methodology to obtain more accurate data on light-use efficiency of whole-canopies on a global scale.

This methodology offers promise for monitoring large areas of agricultural land for photosynthetic efficiency, which translates into land output and yield data of great value for food production estimations.


Nutrient Stress

Nutrient stress in plants causes various symptoms that may be measured by the use of hyperspectral or multispectral imaging. Both deficiencies in nutrients and heavy metal contamination of soils can be assessed with this technology. Schuerger et al (2002) measured zinc deficiency and toxicity in Bahia grass by using a hyperspectral imager to determine plant chlorophyll levels correlated with stress symptoms.[17] They state that the use of this technology could map a contaminated site for a much lower cost than traditional direct sampling methods. Similarly, mercury levels in mustard plants was assessed by Dunagan et al (2006) and spectral reflectance values were significantly correlated with levels of the contaminant.[18]

In addition to investigating contamination, another application of hyperspectral imaging is to determine areas in a crop field that are nutrient poor so that fertilizer inputs could be minimized and directly targeted to nutrient poor areas. Nitrogen and phosphorus are the major yield limiting nutrients in midwestern U.S. field crops (non-leguminous). Osborne et al. (2002) describes how hyperspectral imaging can be used to estimate nitrogen and phosphorous concentrations, biomass and yield under these nutrient stresses. One important finding of this study was that timing of the images was critical to making accurate estimations of yield.[19]


Analysis of Soil properties

The analysis and mapping of soil characteristics is also possible with hyperspectral and multispectral imaging. Maps of soil properties can improve precision agriculture technologies and enhance capabilities. Researchers in Israel were able to determine soil properties, even for soils under vegetation, with the use of hyperspectral sensors. Ben-Dor et al (2000) mapped soil organic matter, moisture, and soil salinity in a field scale experiment.[20] Rossel and Bratney (2008) accurately predicted soil organic carbon in a case study in Australia[21], and soil salinity was mapped in several locations in Europe by Farifteh et al. (2007)[22], both using hyperspectral imaging techniques.



The possibilities for these types of studies related to precision agriculture are virtually endless as indexes for each species, nutrient or soil property continue to be developed and improved. Studies have been conducted to estimate yield in corn by taking images during the midgrain filling stage and developing yield maps.[23] And, Okamoto and Lee (2009) demonstrated that immature fruits in orchards of oranges could be detected on individual trees.[24] Assessment of chilling, heat or insect injuries in the field would be another use of this technology in yield estimation. Currently, many other applications of hyperspectral and multispectral imaging are being tested in: post-harvest quality control, grading, and sorting of agricultural products, insect and contaminant detection, and numerous other uses in food safety. Hyperspectral imaging delivered by lower-cost, portable devices that still deliver high-quality accurate data has become a vital tool for researchers and farmers. The ability of these devices to enhance and enable day-to-day monitoring promises to create a new paradigm of agricultural efficiency.


Cool Roofing

How to Qualify Building Materials for LEED and the Heat

Island Reduction Credit

Building designers and developers that are pursuing the Heat island reduction credit for LEED certification require documentation of the Solar Reflectance Index (SRI) for their project’s roofing materials, shade giving structures, and paving materials.

Under the U.S. Green Building Council’s LEED 2009 rating system, credits SSc7.1 and SSc7.2 (combined as credit SSc5 in LEED v4) are intended to minimize effects on microclimates and human and wildlife habitats by reducing heat islands.

To satisfy the requirements of the Heat Island reduction credits, a certain percentage of the hardscape and roofing must have a high solar reflectance index.

The solar reflectance index (SRI) is a measure of the constructed surface’s ability to reflect solar heat, as shown by a small temperature rise. It is defined so that a standard black surface (reflectance 0.05, emittance 0.90) is 0 and a standard white surface (reflectance 0.80, emittance 0.90) is 100.[1]   

                                                                                    - U.S. Green Building Council


LEED requires specific SRI values for your individual product or material. There are three ways to obtain SRI data and documentation for LEED credit[2]:

1 Ask the manufacturer. Your product’s specifications may already be available in the form of a manufacturer datasheet which can be used as LEED documentation.

2 Lab Testing. In cases where the manufacturer can not provide the SRI of a material, the USGBC allows for SRI values to be obtained from a laboratory following the appropriate ASTM standards for reflectivity and emissivity testing.

SRI is calculated according to ASTM E 1980. Reflectance is measured according to ASTM E 903, ASTM E 1918, or ASTM C 1549. Emittance is measured according to ASTM E408 or ASTM C 1371.                                   - U.S. Green Building Council

By sending in a small sample of your building material, the Surface Optics Measurements Lab can perform SRI testing in accordance with ASTM and LEED requirements. Solar Absorptance (and it’s associated Solar Reflectance) and Total Emittance can be derived from Hemispherical Directional Reflectance (HDR) measurements. Surface Optics Corporation has been making HDR measurements for over 35 years.

Use our contact form to tell our lab how many material samples you want tested and get a quote today.

3 In-Place Testing. When laboratory testing isn’t an option for your project, solar reflectance and thermal emittance data can be captured on-site using a portable reflectometer and emissometer.


As shown in Table 1, the minimum SRI for cool roofing has increased in the newer LEED v4. In the earlier LEED 2009 requirements, cool roofing did not consider aged SRI as an option for qualification. Projects seeking LEED v4 have the option of qualifying using either initial SRI or by obtaining the 3-year aged SRI value. Surface Optics does not perform aged SRI testing.

The impact of hardscape such as roads, sidewalks, courtyards, and parking lots is an important element in earning the Heat Island reduction credit. Table 2 shows the requirements for hardscape and shade providing architectural devices and structures. In LEED version 4, paving materials require documentation for Solar Reflectance only, not the SRI asked for in LEED 2009.


Project teams are allowed to register for either LEED v4 or LEED 2009 until June 1, 2015, after which LEED v4 will be the required rating system. USGBC has made some changes to the Heat Island reduction credit requirements for version 4, which are:

  • Combined Sustainable Sites credits 7.1: Heat Island Effect — Non-Roof and 7.2: Heat Island Effect – Roofinto a single credit SSc5: Heat Island Reduction.

  • Increased the SRI requirements for cool roofing materials

  • Included 3-year aged SRI/ SR values as an alternative option for product qualification

  • Incorporated a weighted average SRI calculation methodology

  • Changed the metric by which hardscape paving materials are measured from SRI to Solar Reflectance (SR)

  • Increased minimum percent of parking spaces under cover from 50% to 75% to qualify



Hyperspectral Imaging in Art and Antiquities Conservation

The need to conserve art has a long history, back to the time when people first realized that the art objects they treasured and perhaps worshiped had begun to deteriorate. In the nineteenth century science and art first began to meet, with scientists of that time looking for methods to preserve valuable works of art.

Of course today many of their methods, such as varnishes, have been seen to actually contribute to, rather than prevent, the deterioration of paintings, but that early blend of art and science has continued up to today, where art conservation draws heavily on scientific methods for analysis of paintings, identification of conservation issues and the detection of additions and forgeries.


  • Pigment Identification and Mapping

  • Examination of Underdrawings

  • Analysis of 3-D Objects

  • Recognition of Organic Materials

  • Forgery Detection

Traditional scientific methods include chemical analysis of paint samples to determine the molecular nature of the pigments and varnishes used. X-ray technology is also often employed to see beneath surface layers and discover the intentions and processes of the artist.

Although these methods have been employed with success for some time, many of the techniques used either give limited information, are invasive to some degree, or both.

There is still a need for techniques that will yield valuable information without damaging the work directly or exposing it to extreme light levels that may accelerate deterioration. Hyperspectral imaging has been engaged with promising results to widen the range of tools available to art conservation specialists.

Reflectance spectroscopy has been used successfully on numerous occasions to gather information for art conservation activities. Reflectance spectrophotometers have been used in combination with digital imaging to produce valuable results. These methods allow non-destructive study, but are limited by the cumbersome equipment, and the limited wavelength bands available. Additionally their use is confined to spot-testing. Since the surfaces of painting are extremely heterogeneous, this is a severe limitation that can now be overcome with hyperspectral imaging, which is cheaper, gives a full range of spectral results and can be used to capture data from the complete artwork.[1]


Using the SOC710-VP hyperspectral camera, University of Georgia researchers analyzed trace pigments on a reproduction of a classic Greek sculpture. Read more here.

One key area in the analysis of artworks is the identification of pigments. This identification provides invaluable information on the age of a painting, improving dating and attribution. It also allows restorers to work with the correct pigments to match those used in the work being restored.

Spectroscopy has been used to identify the pigments used in an art-work for a number of years. However many pigments only begin to show differences in absorption and reflectance in the Ultra-Violet area of the spectrum, so the use of visible light has significant limitations. Multispectral imaging has been used to identify the pigments used in traditional Japanese paintings.[2] Estimates were made of the pigments used and these proved to be a good match to reference samples, but significant issues with signal noise were encountered which would be eliminated with hyperspectral imaging equipment.

Hyperspectral Imaging, sometimes called ‘reflection imaging spectroscopy’, makes the collection of data across a broad range of wavelengths practical, providing sufficient data for detailed analysis. It has been shown that even over limited wavelength ranges (650 – 1040nm) and with relatively coarse resolution (10 nm), invaluable data can be collected. The equipment is portable, so it can come to the artwork, rather than risking the transportation of fragile works to laboratories. It requires only benign light-levels and the equipment is relatively inexpensive.

However before analysis can be made, there needs to be a reliable set of reference samples of pigments, inks, resins, binders and so on, from relevant periods, that can then be used for the interpretation of analysis data-sets. In a collaborative effort between the University of Winnipeg, Winnipeg Art Gallery and the National Research Council of Canada, the Centre for Scientific and Curatorial Analysis of Painting Elements (C-SCAPE) has been established to gather and catalogue data on historic pigments and art materials.

Using these reference graphs for comparison, it is possible to identify the pigments used in a work of art without the need for invasive techniques to obtain samples for chemical analysis. This provides valuable data for authenticity and provenance investigations as well as knowledge of the pigments used by the artist.

In a study of Picasso’s Harlequin Musician, researchers from the National Gallery of Art and the U.S. Army Night Vision & Electronic Sensors Directorate compared the spectra collected with two Surface Optics hyperspectral cameras with those from a fiber optic reflectance spectrometer (FORS). Researchers found that hyperspectral data taken in the visible to shortwave infrared (400 – 1650nm) produces FORS quality spectra for the complete surface of the painting and could be used to separate, identify and map pigments.

Identification and mapping of the primary blue pigments used in the Harlequin Musician obtained from the combined VNIR and SWIR image cubes and compared with Vis-SWIR FORS measurements.[5]

Access the full article “Visible and Infrared Spectroscopy Imaging of Paintings: Pigment Mapping and Improved Infrared Reflectography” in the SPIE Digital Library.



A great deal of valuable information about the intentions of the creator of an artwork, such as changes made during the creative process, alterations and the manner in which the artist worked, can be determined from the examination of underlying drawings, sketch lines and work that was eventually painted out.

X-ray technology has been used for this purpose[6], but has limitations – for example charcoal sketch lines on dark backgrounds cannot be distinguished. Hyperspectral imaging offers the possibility of seeing more clearly through surface layers.

Light penetration is a function of wavelength. This is shown by the Beer-Lambert Law, demonstrating that penetration is inversely proportional to wavelength. This means that since ultraviolet light has a shorter wavelength it will penetrate further through paint layers to reveal what is below.[7] Wavelengths in the 1000-2000nm range give the best results.[4] Hyperspectral imaging can then capture information that reveals under-drawings, foundation materials and other information of great value to art conservationists.

While single-wavelength data can be of some value, hyperspectral imaging permits a dramatic increase in information since data from across the full waveband can be simultaneously displayed. Using image visualization software to process the data cube, the reflectance at each point and wavelength can be expressed in its principle components (PCs). These can be converted into gray-scale images. By removing the main PC, lesser values are revealed, which show, for example, sketch lines, and even enable discrimination between charcoal lines and ink lines.[8]

In a study at the Winnipeg Art Gallery, Canada, a 15th century drawing, Untitled (The Holy Trinity) by Viet Hirschvogel the Elder, was analyzed using hyperspectral imaging equipment, to reveal not only the lead-pencil outline used for the drawing sketch, but the several different inks used to create the base for the actual drawing.[9]

Pablo Picasso’s The Tragedy is known to have multiple compositions beneath the finished painting, identified with broadband infrared reflectography and X-ray imaging.[10] The National Gallery of Art and U.S. Army Night Vision & Electronic Sensors Directorate researchers took hyperspectral images using an 85-band Surface Optics SWIR imager and found the spectral bands at which optimal visualization of under drawings occurred.

False color infrared reflectograms showing three detailed sections from The Tragedy. Each image shows a selection of the spectral bands that best reveal the various underdrawings and caricatures on the panel. Left: Drawing for the man 1000, 1150, 1200 nm; Middle: Horse 1300, 1350, 1400 nm; Right: Sketches 1600, 1625, 1660 nm.[5]

Delaney et al. (2009) explained, “Prior broad-band infrared images revealed many of these features, however, they were difficult to discern because of the low signal to noise ratio of the sensing method and the jumble of images present. Therefore, the combination of reflectance spectra and narrow band false color composite images improve the ability to emphasize features of interest when compared to the earlier methodology.”



The versatility of hyperspectral imaging means that three-dimensional objects can also be analyzed. In a study of Michelangelo’s David, spectral ‘signatures’ from various parts of the work were taken.[4] These signatures were created by measuring changes at different wavelengths in the reflectance of a nitrogen laser shone on the sculpture. These could then be compared to the results from a sample of the original Carrara marble used to create the work. Some areas showed variations from the Carrara signature, indicating where repairs had been made.



Renaissance artists used a variety of materials to bind their pigments and often different materials were used with different pigments in the same painting. In a study of Cosimo Tura’s The Annunciation with Saint Francis and Saint Louis of Toulouse (circa 1475), it was possible to identify which binders had been used. Reference samples of the most common materials, animal skin glue and egg yolk, could be matched using near infrared hyperspectral imaging to specific areas of the painting and to specific pigments.[11] Researchers used a Surface Optics hyperspectral imager operating from 950 to 2500 nm with a spectral resolution of 4.4 nm.


For consultation about a particular application and information on leasing or purchasing a hyperspectral camera, complete our contact form.

The visible image of the Virgin panel next to the false-color mapping of pigment binders. The red corresponds with egg yolk binder, the blue is consistent with a glue binder, and the green maps the areas of Azurite in a glue binder.[11]


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