Relationship between light use efficiency and photochemical reflectance index in soybean leaves as affected by soil water content

Transcription

1 International Journal of Remote Sensing Vol. 27, No. 22, 20 November 2006, Relationship between light use efficiency and photochemical reflectance index in soybean leaves as affected by soil water content Y. INOUE*{ and J. PEÑUELAS{ {National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki 305, Japan {Ecophysiology Unit CSIC-CEAB-CREAF (Centre for Ecological Research and Forestry Applications), Universitat Autònoma de Barcelona, Bellaterra, Spain (Received 23 July 2005; in final form 23 July 2005 ) Photosynthetic light response curves and reflectance spectra ( nm) were measured for soybean (Glycine max L. Merr.) leaves with a range of chlorophyll concentrations at various soil water contents. Regression lines for the relationship between the photosynthetic light use efficiency (LUEp) and photochemical reflectance index (PRI) under different soil water content h almost all passed through a common point (PRI, LUEp)5(20.04, 0), so that the LUEp could be expressed simply as LUEp5kAPRI using an adjusted PRI [APRI5(r5312r570)/ (r5312r570) ]. The effect of soil moisture was strong under dry conditions and gradually decreased with increasing h. There was no effect of h above 25% (v/ v). The effect of h on the APRI LUEp relationship was expressed by a simple exponential function. These results should provide a new basis for applications in dynamic diagnosis of photosynthetic functioning of plant leaves and in the prediction of plant productivity. The change in the slope of LUE vs. APRI may provide further ways of assessing volumetric soil water content. 1. Introduction The assessment of photosynthetic functioning is important in the diagnosis and prediction of plant growth as well as in carbon exchange between ecosystems and the atmosphere. Remote and non-contact assessment of photosynthetic functioning is useful for estimating the regional and global dynamics of vegetation growth as well as for precision crop management (Inoue 2003). Several reports have shown that the photochemical reflectance index (PRI) derived from hyperspectral measurements at 531 and 570 nm (Gamon et al. 1992, Peñuelas et al. 1995) may be useful for estimating photosynthetic light use efficiency (LUEp, mmol CO 2 mmol photons 21 ) at leaf and canopy scales (e.g. Gamon et al. 1997, Peñuelas and Filella 1998, Peñuelas and Inoue 2000, Trotter et al. 2002). Ground-based (e.g. Inoue and Peñuelas 2001), airborne (e.g. CASI and AVIRIS) and space-borne (e.g. Hyperion) hyperspectral imagers would allow the application of such hyperspectral indices. However, the PRI LUEp relationship is still too broad to use PRI as a general indicator for diagnosis or as a parameter for process-based models. The relationship is not clear, especially under water-stressed conditions (Gamon et al. 1992). *Corresponding author. yinoue@niaes. affrc.go.jp International Journal of Remote Sensing ISSN print/issn online # 2006 Taylor & Francis DOI: /

2 5110 Y. Inoue and J. Peñuelas The objective of this study was to investigate the effect of water stress on the relationship between LUEp and PRI, and to propose a simple model to estimate LUEp from PRI under diverse environmental conditions. 2. Materials and methods Photosynthetic and hyperspectral measurements were made on leaves of soybean plants (Glycine max L. Merr.) grown in 20 pots (diameter 25 cm, height 30 cm) under natural light conditions (Tsukuba, Japan). The plants were grown under different nitrogen and water conditions. The effects of these two treatment factors were assessed by measuring the leaf chlorophyll index, CI (SPAD502, Minolta, Japan), and the volumetric soil water content h (TRIME-EZ, Germany). The CI has been validated to have a near-linear relationship (r50.97) with the chlorophyll content (Inada 1985). The soil was a humic volcanic ash soil (Andisol), which is the major cultivated soil for upland crops in Japan. Reflectance spectra of the soybean leaves were obtained at nm using a portable spectroradiometer (FS-FR1000, ASD, USA) under stable solar light conditions. More than 70 leaves were measured under various combinations of soil water and light conditions. The photosynthetic photon flux density (PPFD) at the leaf surface was monitored by a quantum sensor (LI-190SB, Li-Cor Inc.) to confirm the stability of the light conditions. The field of view of the radiometer was 22u and measurements were made at an angle normal to the leaf surface at a distance of about 5 cm. The spectral measurements were carried out 10 times for each leaf and averaged for analyses. The reflectance spectra were derived through calibration using the white reference board (Spectralon, Labsphere, USA). The value of the spectral reflectance at certain wavelength (##nm) if expressed as p### in this paper. Photosynthetic data were obtained by using a portable gas exchange system (LI- 6400, Li-Cor Inc., USA) just after the measurement of reflectance spectra under the same atmospheric conditions. The photosynthetic measurements were taken for each leaf under successive PPFD intensities of 2000, 1500, 1000, 500, 200, 100 and 50 mmol m 22 s 21, keeping the air conditions inside the leaf chamber at ambient humidity and 400 ppm CO 2 concentration. We assumed that light net photosynthesis curves would be more informative on leaf photosynthetic status than a single/ instantaneous measurement of net photosynthetic rate, Pn. We obtained a set of photosynthetic and reflectance spectra for soybean leaves under a wide range of PPFD ( mmol m 22 s 21 ), h ( %) and CI ( ). The light net photosynthesis curves were determined using equation (1) to derive the photosynthetic parameters: Pn~Ps½1{exp ð{wppfdþš{r ð1þ where Pn is the net photosynthetic rate, Ps is the saturated photosynthetic rate, w is the quantum use efficiency and R is the dark respiration. All data fit the model well (r ), so that values of Pn and LUEp (Pn/PPFD) under the conditions at which the reflectance spectra were measured were determined using this model. The PRI was calculated as the normalized difference of r531 and r570: (r5312r570)/(r531 + r570). Other spectral indices such as the normalized difference vegetation index (NDVI) [(r8502r650)/(r850 + r650)], water index (WI) (r900/ r970), green ratio index (GRI) (r830/r550), structural independent pigment index (SIPI) [(r8002r445)/(r800 + r680)], normalized phaeophytinization index (NPQI)

3 [(r4152r435)/(r415 + r435)], shortwave infrared water index (SWWI) (r800/r1650), and WI/NDVI were calculated as these indices were presumed to have significant relationships with the photosynthetic parameters (Inoue et al. 1993, Peñuelas and Filella 1998, Peñuelas and Inoue 1999). We examined the linear correlations among four photosynthetic parameters (Pn, Ps, w and LUEp), three environmental parameters (PPFD, CI and h), and all the reflectance indices. 3. Results and discussion Remote Sensing Letters 5111 High correlation coefficients (r.0.7) were obtained for three pairs of parameters: GRI vs. CI (r50.86), PRI vs. LUEp (r50.78, figure 1) and PPFD vs. LUEp (r520.73). Moderate correlation (r.0.55) was found for three additional pairs, SIPI vs. CI(r520.69), NDVI vs. CI(r50.63) and LUEp vs. h (r50.58), while all the other pairs had lower correlation coefficients. It is well known that the leaf and canopy chlorophyll concentration is closely related to the GRI (e.g. Inada 1985), but other pigment-related indices showed no significant relationships with the CI. The spectral indices related to leaf water contents such as WI and SWWI had little correlation with soil water content h. Thus, it was confirmed that PRI LUEp was the only highly significant relationship for inferring the photochemical status of plant leaves directly from reflectance spectra, although the correlation coefficient was still not high (r50.78), as shown in figure 1. Assessing the PRI LUEp and LUEp h relationships together, we found that the PRI LUEp relationship was strongly affected by the soil water content h. Figure 1 shows the regression lines for the sets of data points grouped by h. Average h values were: A, 7.9%; B, 12.3%; C, 19.9%; D, 25.1%; and E, 31.3%. These regression lines had different slopes for corresponding h, but almost all passed through a common point (PRI, LUEp)5(20.04, 0). Hence, for simplicity in interpretation and analysis, Figure 1. Relationship between LUEp and PRI as affected by soil water content h. The overall correlation coefficient was r Regression lines A E were determined for data grouped by soil water content.

4 5112 Y. Inoue and J. Peñuelas an index adjusted PRI (APRI) was defined as in equation (2) so that the regression lines went through the same point: APRI~ ½ðr531{r570Þ= ðr531zr570þšz0:04 ð2þ Consequently, the effect of h on the APRI LUEp relationship was represented simply by the slope k of APRI LUEp lines, i.e. LUEp5kAPRI. We found a close relationship between h and k that could be expressed by a simple exponential equation (3) (figure 2). k~2:00½1{ expð0:2hþ{1:46š ð3þ The value of k was closely related to h under dry conditions, but the effect of h on k saturated at around 30% of h. Whether similar relationships between h and k exist for various plant species warrants further investigation. Thus, the photosynthetic light use efficiency LUEp was expressed by the following equation: LUEp~kAPRI~2APRI½1{ expð0:2hþ{1:46š ð4þ Figure 3 shows a comparison of modelled and measured values of LUEp for the same dataset. The scattering of data points is much less than that in figure 1 when incorporating the effect of h. It may be worthwhile investigating the photochemical meaning of the point (PRI, LUEp)5(20.04, 0), which has been first recognized through this study. This phenomenon could be a useful key for the interpretation or utilization of the spectral response of the chloroplast, although more precise photochemical measurements under controlled environments would be required. None of the water-related spectral indices (e.g. WI, SWWI) has a similar role to h in equation (3), presumably because the APRI LUEp relationship is affected more directly by the soil water conditions than the leaf water content. Physico-chemical change might occur before apparent changes in leaf water content. In this Figure 2. The effect of soil water content h on the slope k of the regression line between LUEp and the spectral index APRI. Data points are shown with the standard deviation for each group of h. APRI5[r5312r570]/[r531 + r570]

5 Remote Sensing Letters 5113 Figure 3. Comparison between the values of LUEp measured by using an infrared gas analyser (IRGA) and estimated from the spectral index APRI and soil water content h. experiment, we undertook no destructive plant measurements such as leaf water content or potential. Therefore, it may be worthwhile examining the APRI LUEp relationship together with simultaneous measurements of water contents/potentials in both soil and leaves. The chlorophyll concentration CI had no significant effect on LUEp and APRI LUEp while the range of CI was large enough. This may be because both LUEp and APRI indicate the photosynthetic functioning of chloroplast although the CI simply indicates the density of chloroplast per unit leaf area. The radiation use efficiency (RUE, g DM MJ 21 ) is often used as a key parameter in growth models where the canopy absorbance, that is the fraction of absorbed photosynthetically active radiation (APAR), can be estimated by optical remote sensing (e.g. Inoue et al. 1998). The RUE is usually defined as the ratio of dry matter increment to the APAR for a day or longer term. Hence the RUE is robust and representative of plant productivity at the canopy scale. On the contrary, the LUEp is a more dynamic measure of photosynthetic functioning than RUE. Thus, it may be reasonable to use RUE as a measure of canopy scale indicator at a day or longer temporal resolution, and LUEp as a dynamic indicator for leaf-scale diagnosis. Scaling from leaf to canopy is an important but difficult issue especially for these narrow-band indices (Barton and North 2001), while the seasonal variability of RUE is also an important issue in modelling approaches. Therefore, LUEp may be useful for the dynamic estimation of RUE. For operational use of equation (4), information on the soil water content as well as the reflectance spectra is needed. Nevertheless, remote sensing of soil water content has been improving (see review by Inoue 2003), and the synergistic linkage between process modelling and remote sensing is useful for dynamic estimation of multiple ecosystem variables including the soil water content (e.g. Inoue and Olioso 2004).

6 5114 Remote Sensing Letters In conclusion, the present results may provide a new basis for a range of applications of PRI in dynamic diagnosis of photosynthetic functioning of plant leaves as well as in prediction of plant productivity. The change in the slope of LUE vs. APRI may provide further ways of assessing volumetric soil water content, at least in species with similar functional and structural characteristics to the soybean plant. References BARTON, C.V.M. and NORTH, P.R.J., 2001, Remote sensing of canopy light use efficiency using the photochemical reflectance index model and sensitivity analysis. Remote Sensing of Environment, 78, pp GAMON, J.A., FIELD, C.B. and PEÑUELAS, J., 1992, A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of the Environment, 41, pp GAMON, J.A., SERRANO, L. and SURFUS, J.S., 1997, The photosynthetic reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecoligia, 112, pp INADA, K., 1985, Spectral ratio of reflectance for estimating chlorophyll content of leaf. Japanese Journal of Crop Science, 154, pp INOUE, Y., 2003, Synergy of remote sensing and modeling for estimating ecophysiological processes in plant production. Plant Production Science, 6, pp INOUE, Y., MORAN, M.S. and HORIE, T., 1998, Analysis of spectral measurements in rice paddies for predicting rice growth and yield based on a simple crop simulation model. Plant Production Science, 1, pp INOUE, Y., MORINAGA, S. and SHIBAYAMA, M., 1993, Non-destructive estimation of water status of intact crop leaves based on spectral reflectance measurements. Japanese Journal of Crop Science, 62, pp INOUE, Y. and OLIOSO, A., 2004, Estimating dynamics of CO 2 flux in agro-ecosystems based on synergy of remote sensing and process modeling a methodological study. In Global Environmental Change in the Ocean and on Land, M. Shiyomi, et al., pp (Tokyo: Terrapub). INOUE, Y. and PEÑUELAS, J., 2001, An AOTF-based hyperspectral imaging system for field use in ecophysiological and agricultural applications. International Journal of Remote Sensing, 22, pp PEÑUELAS, J. and FILELLA, I., 1998, Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science, 3, pp PEÑUELAS, J., FILELLA, I. and GAMON, J.A., 1995, Assessment of plant photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist, 131, pp PEÑUELAS, J. and INOUE, Y., 1999, Reflectance indices indicative of changes in water and pigment content of peanut and wheat leaves. Photosynthetica, 36, pp PEÑUELAS, J. and INOUE, Y., 2000, Reflectance assessment of canopy CO 2 uptake. International Journal of Remote Sensing, 21, pp TROTTER, G.M., WHITEHEAD, D. and PINKNEY, E.J., 2002, The photochemical reflectance index as a measure of photosynthetic light use efficiency for plants with varying foliar nitrogen contents. International Journal of Remote Sensing, 23, pp