LEAF REFLECTANCE TO ASSESS PHOTOSYNTHETIC

In document Screening genetic variation for photosynthetic capacity and efficiency in wheat (Page 30-33)

CHAPTER 1 GENERAL INTRODUCTION

1.6 LEAF REFLECTANCE TO ASSESS PHOTOSYNTHETIC

Reflection is ‘the redirection of a beam of radiation when it encounters a boundary’. The beam can be reflected coherently as happens with a mirror or can be scattered by unequal surfaces. The beam is electromagnetic radiation, which because of the time spent travelling in magnetic and electric fields can be seen as electromagnetic waves. A wavelength

measured in meters is the distance between adjacent wave crests from the electromagnetic wave, and frequency measured in cycles is the number of waves that go across a certain

31 point in one second. Electromagnetic waves from all frequencies form the electromagnetic spectrum (Jones and Vaughan, 2010). Some regions of the spectrum are: Far (vacuum) ultraviolet (UV) (10-180 nm), Near UV (180-350 nm), visible (VIS) (350-770 nm) (Ingle and Crouch, 1988). Photosynthetically Active Radiation is defined from 400 to 700 nm (McCree, 1971).

Reflectance from the first part of the electromagnetic spectrum has been related to xanthophylls, chlorophylls, and water in plants (Figure 1.4.a), and the red edge in the derivative of reflectance is commonly related to photosynthesis (Figure 1.4.b) (Peñuelas and Filella, 1998).

The IR region is commonly divided in to three bands: near infrared (770-1300), short wave infrared 1 (SWIR1) region (1300-1900 nm), and short wave infrared 2 (SWIR2) region (1900-2500 nm). Research in this part of the spectrum has increased because hyperspectral cameras and radiometers can more easily measure the full spectrum, 350-2500 nm and secondly because the information has been useful. IR spectra measured in leaves have been correlated with photosynthetic parameters (Vcmax and J) (Serbin et al., 2012), and have been used to predict carbon, nitrogen and phosphorus in leaf extracts (Gillon et al., 1999). Other uses are in imaging, for example vision at night (Figure 1.5).

Figure 1.4 Reflectance (a) and the first derivative of reflectance (b) spectra for typical healthy leaves. The main wavelengths used in physiological reflectance indices are indicated: 430 and 445 nm for carotenoids; 531 and 570 nm for xanthophylls; 550–680 nm and `red-edge' position for chlorophyll; 700–800 nm for brown pigments; 800 and 900 nm as structural reference wavelengths; 970 nm for water; and 800–900 nm and 680 nm for green biomass’ (Peñuelas and Filella, 1998).

General Introduction. Chapter 1

32

Figure 1.5 The same photo taken during the night using (a) a normal camera (b) using a SWIR camera. http://www.sensorsinc.com/gallery/images.

1.6.1 Measuring reflectance from a canopy

Reflection from vegetation has been measured with radiometers and images from satellites for global vegetation programs which began in 1972 with the multi-spectral satellite LANDSAT 1. Reflectance has been used to estimate terrestrial photosynthesis and light use efficiency from vegetation because it is the source of primary production on the planet (Grace et al., 2007). Numerous vegetation indices (VI) using the visible and infrared region of the spectrum have been proposed to measure chlorophyll in vegetation (Zarco-Tejada et

al., 2001). The most successful are the photochemical reflectance index (PRI) and the

normalized difference vegetation index (NDVI). PRI is correlated with the xanthophyll cycle which protects plants from photodamage, and uses reflectance from the visible region at 531 and 570 nm (Gamon et al., 1992). NDVI is used to track active photosynthesis in the biomass of a plant canopy using reflectance in the visible and infrared region of the

electromagnetic spectrum (Tucker, 1979).

More recently, measurements of the full spectrum from 350-1000 nm or 350-2500 nm depending on the instrument have been used to estimate leaf chemical properties and leaf dry mass per area (LMA). For instance, high spectral resolution remote sensing from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) has successfully predicted leaf chemical properties and leaf mass per area (LMA) from a tropical forest using the partial least square regression (PLSR) (Asner and Martin, 2008; Asner et al., 2009; Asner et al., 2011a; Asner et al., 2011b). Leaf nitrogen, chlorophyll a and b, carotenoids, LMA and assimilation of CO2 have also been predicted from spectral reflectance at canopy level.

Correlations of predictions varied from R2=0.49 for A

max to R2=0.9 for LMA (Doughty et

33 1.6.2 Measuring reflectance from a leaf

One advantage of measuring leaf reflectance is that the spectra are not too contaminated by reflectance from the soil and the atmosphere. Both of these factors can complicate the usefulness of canopy reflectance spectra. Leaf measurements are important because they have allowed scaling up to canopy level and provide a link to biochemical measurements in the laboratory.

Reflectance measurements of leaves have been reported since 1929. In 1961, a colorimeter with a reflectance attachment was used to measure the percentage of 625 nm light that was reflected. This value showed a high correlation with chlorophyll content in soybean and Valencia orange leaves, thus providing a useful indicator of chlorophyll content in leaves (Benedict and Swidler, 1961). A chlorophyll-meter based on transmittance of 670 and 750 nm, correlated strongly (0.998) with chlorophyll content. Consequently, this method was developed for estimating the deepness of green colour and the chlorophyll content per unit area of the leaves (Inada, 1963). Nowadays, there are several portable leaf chlorophyll meters available in the market, such as the Minolta SPAD chlorophyll meter. SPAD measures the chlorophyll content via light transmittance through absorbance of red light at 650 nm and infrared light 940 nm, it is hand-held battery portable and there is a model that permit to save the information and download in the computer (Mullan and Mullan, 2012). Following from the success of remote sensing at the canopy level, hyperspectral reflectance has been developed for predicting physiological and biochemical leaf parameters at leaf level. Successful predictions of photosynthetic parameters have been obtained for tropical trees, aspen, cotton and soybean (Doughty et al., 2011; Serbin et al., 2012; Ainsworth et al., 2014), and nitrogen content and LMA in wheat (Ecarnot et al., 2013). These examples show the potential of using hyperspectral reflectance (350-2500 nm) to screen wheat for

photosynthetic parameters.

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