5.12 Reflectance assessment of plant physiological status

Author: Filella I1,2, Bjerke J W3 and Macias-Fauria M4

Reviewer: Peñuelas J1,2


Measurement unit: unitless; Measurement scale: leaf – canopy; Equipment costs: €€€€€; Running costs: €; Installation effort: low; Maintenance effort: low; Knowledge need: medium; Measurement mode: manual and data logger

Measurements of reflectance can be used not only to assess the biomass of vegetation but also its physiological status. The light reflected by a leaf towards the observer is governed by the concentration of biochemical compounds in the leaf and by the foliar surface and internal structure. Chlorophyll mainly absorbs in the red visible region of the spectrum and partially in the blue and green regions that are also generally absorbed by other pigments such as xanthophylls and carotenoids (Jackson, 1986; Peñuelas & Filella, 1998; Carter & Knapp, 2001; Gitelson et al., 2003). Foliar absorption/reflection in the near-infrared region depends mainly on the structural discontinuities and water content of foliar cells (Peñuelas & Filella, 1998). The analysis of remotely measured reflected light can thus be used to extract information about plant-stress conditions and photosynthetic status for both natural vegetation and crops (Jackson, 1986; Peñuelas & Filella, 1998; Carter & Knapp, 2001; Ustin et al., 2009). More specifically, reflectance could provide a rapid and easy alternative for assessing pigment composition to estimate nutrient status (by assessment of chlorophyll), phenology, and general stress (by assessment of carotenoids and chlorophylls) or photosynthetic efficiency (by assessment of xanthophylls at a daily scale and the chlorophyll/carotenoid ratio at a seasonal scale). Reflectance can also be used to assess the water content of plants (by measuring in the water-absorption band). All these possibilities make reflectance a useful and increasingly used tool for the assessment of plant stress, applicable in most studies that focus on global-change impacts (e.g. drought, water logging, eutrophication).

While the use of reflectance values can provide valuable information about many aspects of plant physiological status, this is acquired indirectly, requiring a correlative dataset between destructively-obtained compound concentrations and reflectance values or indices (combinations of values at certain wavelengths). Thus, reflectance values will not normally represent a “Gold standard” in terms of accuracy (these will be the destructive methods discussed in other protocols in this chapter), but are highly practical since they can generate non-destructive, rapid, and scalable information.


5.12.1 What and how to measure?

Reflectance can be detected using narrow-bandwidth spectroradiometers that measure in the visible and near-infrared regions of the spectrum. In addition to reflectance at particular wavelengths, many high spectral resolution reflectance vegetation indices (which partly remove disturbances caused by external factors) have been proposed (Peñuelas & Filella, 1998). The entire spectrum can also be used to extract the most integrated information (Ustin et al., 2009). Reflectance measured at a close range (< 1 m) has traditionally been undertaken using narrow-bandwidth spectroradiometers that measure in the visible and near-infrared regions of the spectrum (Filella and Peñuelas, 1994). More recently, hand-held optical sensors have been developed to provide instant readings of different indices, such as PRI (Shrestha et al. 2012) or most frequently of the normalized difference vegetation index (NDVI) (Kitić et al., 2019), which is based on the leaf reflectance in the NIR and red bandwidths (also see protocol 5.2). Some sensors are built into digital cameras so that single NDVI values can be retrieved at pixel scale. Such readings can be used for upscaling, by comparing with NDVI data measured from Unmanned Aerial Vehicles (UAVs) or satellites (Bokhorst et al., 2012). (see protocol 5.2. Chlorophyll and carotenoid content).


Chlorophyll concentration: nitrogen status

Several studies have reported that indices based on reflectance in the far-red region can precisely estimate foliar chlorophyll concentration (Filella & Peñuelas, 1994; Gitelson & Merzlyak, 1997; Datt, 1999). Foliar optical properties within a relatively narrow spectral band near 700 nm are thus crucial for the detection of plant stress and the estimation of foliar chlorophyll concentration. Chlorophyll concentration can also be derived using reflectances at 675 and 550 nm. Indices of chlorophyll concentration have been developed that include several of these waveband reflectances (Filella et al., 1995; Gitelson & Merzlyak, 1997). Foliar chlorophyll concentration and nitrogen availability are closely correlated, so the assessment of chlorophyll content by reflectance can also be used to characterise the nitrogen status of natural vegetation and crops (Filella et al., 1995; Wang et al., 2016).


Stress: carotenoid/chlorophyll ratio

Ratios of reflectances in the blue region (where carotenoids and chlorophylls absorb) and the red region (where only chlorophylls absorb) are highly correlated with the carotenoid/chlorophyll ratio in various plant species, both at the foliar and canopy levels (Peñuelas et al., 1994, 1995; Blackburn, 1998; Merzliyak et al., 1999; Sims & Gamon, 2002). (see protocol 5.2. Chlorophyll and carotenoid content).


Photosynthetic efficiency

Some of the light energy absorbed by chlorophyll for photosynthesis is lost as heat or fluorescence, and changes in the photosynthetic rate cause complementary changes in fluorescence emission (see protocol 5.1 Chlorophyll fluorescence) or heat dissipation. Heat dissipation is linked to the xanthophyll de-epoxidation cycle, which has been correlated with reflectance at 531 nm. The photochemical reflectance index (PRI), based on reflectance at 531 nm, measures changes in reflectance caused by the interconversion and dissipation of xanthophylls (Gamon et al., 1992; Peñuelas et al., 1995) and has already been widely tested as a good estimator of light use efficiency (LUE) at the foliar, canopy, and ecosystem levels and various temporal scales (Zhang et al., 2016).


Water content

Water absorbs in the near-infrared region at 950–970 nm. A reflectance water index has been defined as the ratio R900:R970. This water index has been highly correlated with plant-water content in several species of trees, shrubs, crops, and grasses (Peñuelas et al., 1993, 1997; Serrano et al., 2000).


Canopy measurements

A detailed description of the methods involved in field spectroradiometric measurements can be found in Milton et al. (2009). Measurements can be made by pointing the sensor (spectroradiometer or optical fibre, depending on the model) at the canopy (either with single measurements or installing the spectroradiometer in fixed measuring structures for continuous measurements). The measured area is a function of the field of view of the instrument and the measuring height. The footprint area should be 100% of the target when measuring a point on a surface. Several readings should be made covering the entire object area to obtain a representative measurement of the object. Radiance reflected from the target must be calibrated against a levelled “white” (~ 100% reflectance) standard to validate reflectance retrieval (e.g. using a Spectralon panel; Labsphere).

Spectral reflectance at the canopy level is a combination of soil and vegetation reflectance, and the weighting for either of these factors depends on external parameters such as illumination or canopy structure. The source of illumination for field measurements is usually the sun, and the quality of the spectra measured is affected by changes in irradiance and by the position of the sun. Some recommendations to minimise these disturbances have been suggested:

  • Illumination conditions must be constant throughout the measurement (clear sky conditions and no changes in cloud cover).
  • Measurements should be made around solar midday, when the sun is at its highest position, and the sensor should be pointed vertically downward (nadir) to the measuring surface.
  • Reference measurements must be made simultaneously or at least immediately before or after the reflectance measurement.
  • The measured surface should not be shaded by the operator or measuring structures.


Foliar measurements

A leaf clip using either its own light source or natural light can be used for foliar measurements. The illumination conditions and other factors disturbing foliar information are controlled when using clips with their own light sources. Foliar properties can also be measured by pointing an optical fibre or sensor near enough to a horizontal leaf to ensure that the footprint area is the leaf, while avoiding shading the target.

The instruments and calibration panels must be calibrated and in good condition for all types of measurements.


Where to start

Blackburn (1998), Filella & Peñuelas (1994), Gamon et al. (1992), Gitelson et al. (2003), Peñuelas & Filella (1998), Ustin et al. (2009)


5.12.2 Special cases, emerging issues, and challenges

As explained above, some of the energy absorbed by chlorophyll that is not used for photosynthesis is dissipated as heat (and can be estimated by the photochemical reflectance index; PRI), and some is dissipated as fluorescence (see protocol 5.1 Chlorophyll fluorescence). In addition to the methods for measuring actively induced fluorescence described in protocol 5.1 Chlorophyll fluorescence, emitted fluorescence (sun-induced fluorescence; SIF) can be passively and remotely estimated using spectroradiometers with high spectral resolution (Meroni et al., 2009; Porcar-Castell et al., 2014). The combination of PRI and SIF has greatly improved remote estimates of LUE and GPP in a cornfield (Cheng et al., 2013).

UAVs mounted with various multispectral cameras are increasingly providing exciting opportunities for obtaining indices of canopy reflectance and SIF at desired spatiotemporal resolutions (Zarco-Tejada et al., 2013; Gago et al., 2015), which could provide very specialised information of the physiological status of vegetation at the canopy level. Remote sensing of the physiological characteristics of vegetation canopies is an emerging and rapidly evolving field: only UAV remote sensing will be briefly introduced in this protocol, since spaceborne and airplane-based airborne remote sensing can be used to inform and upscale field measurements but represent techniques that do not fall within field-based ecology protocols and are hence out of the scope of this chapter. The use of UAVs offers the possibility to scale-up measurements, enabling the study of spatial and temporal ranges and resolutions in the dynamics of plant physiology not available with hand-held methodologies alone. Nevertheless, it also adds more complexity to the interpretation of reflectance values. Disturbances in canopy measurements caused by factors such as the position of the sun, the canopy structure, and the heterogeneity of the target represent an additional challenge to data interpretation.

We strongly encourage the reader interested in inferring physiological characteristics of vegetation canopies through the use of UAVs to refer to UAV-specific methodological material. Nevertheless,  we briefly mention in here basic UAV best-practice advice based on Assmann et al. (2019), who provide a hands-on and detailed protocol of UAV vegetation monitoring using multispectral cameras. Recommendations provided under Canopy measurements also apply in here. The following general guidelines, addressed at increasing the replicability and quality of UAV-acquired reflectance data, will apply to hyperspectral cameras mounted on UAVs as well, although at present these are very specialised, expensive, and only used by highly-skilled pilots:

  1. Define the spatial and temporal scales adequate to the research question: this will determine field planning and technical aspects of the operation, such as flight elevation, speed, and duration, which might be constrained by mechanical limitations of the UAV.
  2. Flight planning: consider image overlap (it will impact on the mosaicking of the individual images, influencing the number of pixels captured near to nadir – 90° over the target surface, and on overall data size) and flight conditions (e.g. weather and sun angle will impact radiometric calibration).
  3. Ground control points: to be determined with high-accuracy global navigation systems (e.g. differential GPS). This step is essential for repeatability or for combination with other images.
  4. Radiometric calibration: commonly used commercial cameras come with images of known spectral characteristics that can be used before and after the flight to calibrate the sensor of the camera.
  5. Flight: it can be challenging due to sudden changes in weather conditions, or in mechanical failure, especially in respect to the UAVs internal compass.
  6. Data transfer: frequent back-ups are advised, ideally after every flight.
  7. Image processing: performed by specialised software, which takes into account the ground control points, the calibration information, and incident light sensor data to account for changes in irradiation during the flight.
  8. After point (7), the digital numbers obtained by the sensor have been converted to georeferenced absolute reflectance values, which can then be used in vegetation indices (e.g. NDVI, see protocol 5.2. Chlorophyll and carotenoid content).


5.12.3 References

Theory, significance, and large datasets

Carter and Knapp (2001), Jackson (1986), Peñuelas & Filella (1998), Ustin et al. (2009)


More on methods and existing protocols

Milton et al. (2009), Peñuelas & Filella (1998), Ustin et al. (2009)


All references

Assmann, J. J., Kerby, J. T., Cunliffe, A. M. & Myers-Smith, I. H. (2019). Vegetation monitoring using multispectral sensors — best practices and lessons learned from high latitudes. Journal of Unmanned Vehicle Systems 7(1), 54-75.

Blackburn, G. A. (1998). Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66(3), 273-285.

Bokhorst, S., Tømmervik, H., Callaghan, T. V., Phoenix, G. K. & Bjerke, J. W. (2012) Vegetation recovery following extreme winter warming events in the sub-Arctic estimated using NDVI from remote sensing and handheld passive proximal sensors. Environmental and Experimental Botany, 81(1), 18-25.

Carter, G. A., & Knapp, A. K. (2001). Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany, 88(4), 677-684.

Cheng, Y. B., Middleton, E. M., Zhang, Q., Huemmrich, K. F., Campbell, P. K., Cook, B. D., … Daughtry, C. S. (2013). Integrating solar induced fluorescence and the photochemical reflectance index for estimating gross primary production in a cornfield. Remote Sensing, 5(12), 6857-6879.

Datt, B. (1999). A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using Eucalyptus leaves. Journal of Plant Physiology, 154(1), 30-36.

Filella, I., & Peñuelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15(7), 1459-1470.

Filella, I., Serrano, L., Serra, J., & Peñuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science, 35(5), 1400-1405.

Gago, J., Douthe, C., Coopman, R., Gallego, P., Ribas-Carbo, M., Flexas, J., … Medrano, H. (2015). UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153, 9-19.

Gamon, J. A., Penuelas, J., & Field, C. B. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1), 35-44.

Gitelson, A. A., & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18(12), 2691-2697.

Gitelson, A. A., Gritz, Y., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271-282.

Jackson, R. D. (1986). Remote sensing of biotic and abiotic plant stress. Annual Review of Pytopathology, 24(1), 265-287.

Kitić, G., Tagarakis, A., Cselyuszka, N., Panić, M., Birgermajer, S., Sakulski, D. & Matović, J. (2019). A new low-cost portable multispectral optical device for precise plant status assessment. Computers and Electronics in Agriculture, 162, 300-308.

Meroni, M., Rossini, M., Guanter, L., Alonso, L., Rascher, U., Colombo, R., & Moreno, J. (2009). Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sensing of Environment, 113(10), 2037-2051.

Milton, E. J., Schaepman, M. E., Anderson, K., Kneubühler, M., & Fox, N. (2009). Progress in field spectroscopy. Remote Sensing of Environment, 113, S92-S109.

Peñuelas, J., & Filella, I. (1998). Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science, 3(4), 151-156.

Peñuelas, J., Filella, I., Biel, C., Serrano, L., & Save, R. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 14(10), 1887-1905.

Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., & Field, C. B. (1994). Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves. Remote Sensing of Environment, 48(2), 135-146.

Peñuelas, J., Baret, F., & Filella, I. (1995). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2), 221-230.

Peñuelas, J., Pinol, J., Ogaya, R., & Filella, I. (1997). Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18(13), 2869-2875.

Porcar-Castell, A., Tyystjärvi, E., Atherton, J., van der Tol, C., Flexas, J., Pfündel, E. E., … Berry, J. A. (2014). Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. Journal of Experimental Botany, 65(15), 4065-4095.

Serrano, L., Ustin, S. L., Roberts, D. A., Gamon, J. A., & Peñuelas, J. (2000). Deriving water content of chaparral vegetation from AVIRIS data. Remote Sensing of Environment, 74(3), 570-581.

Shrestha, S., Brueck, H., Asch, F. (2012). Chlorophyll index, photochemical reflectance index and   chlorophyll fluorescence measurements of rice leaves supplied with different N levels. Journal of Photochemistry and Photobiology B: Biology. 113, 7–13.

Sims, D. A., & Gamon, J. A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81(2), 337-354.

Ustin, S. L., Gitelson, A. A., Jacquemoud, S., Schaepman, M., Asner, G. P., Gamon, J. A., & Zarco-Tejada, P. (2009). Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sensing of Environment, 113, S67-S77.

Wang, Z., Wang, T., Darvishzadeh, R., Skidmore, A. K., Jones, S., Suarez, L., … Hearne, J. (2016). Vegetation indices for mapping canopy foliar nitrogen in a mixed temperate forest. Remote Sensing, 8(6), 491.

Zarco-Tejada, P. J., Morales, A., Testi, L., & Villalobos, F. J. (2013). Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance. Remote Sensing of Environment, 133, 102-115.

Zhang, C., Filella, I., Garbulsky, M. F., & Peñuelas, J. (2016). Affecting factors and recent improvements of the photochemical reflectance index (PRI) for remotely sensing foliar, canopy and ecosystemic radiation-use efficiencies. Remote Sensing, 8(9), 677.



Author: Filella I1,2, Bjerke J W3 and Macias-Fauria M4

Reviewer: Peñuelas J1,2



1 CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain

2 CREAF, Cerdanyola del Vallès, Spain

3 Norwegian Institute for Nature Research – NINA and FRAM – High North Research Centre for Climate and the Environment, Tromsø, Norway

4 School of Geography and the Environment, University of Oxford, Oxford, UK