Photophysiology and Spectroscopy of Sun and Shade Leaves of Phragmites australis and the E ﬀ ect on Patches of Di ﬀ erent Densities

: Remote sensing of vegetation has largely been revolving around the measurement of passive or active electromagnetic radiation of the top of the canopy. Nevertheless, plants hold a vertical structure and di ﬀ erent processes and intensities take place within a plant organism depending on the environmental conditions. One of the main inputs for photosynthesis is photosynthetic active radiation (PAR) and a few studies have taken into account the e ﬀ ect of the qualitative and quantitative changes of the available PAR within the plants canopies. Mostly large plants (trees, shrubs) are a ﬀ ected by this phenomena, while signs of it could be observed in dense monocultures, too. Lake Balaton is a large lake with 12 km 2 dense reed stands, some of which have been su ﬀ ering from reed die-back; consequently, the reed density and stress condition exhibit a vertical PAR variability within the canopy due to the structure and condition of the plants but also a horizontal variability attributed to the reedbed’s heterogeneous density. In this study we investigate the expression of photosynthetic and spectroscopic parameters in di ﬀ erent PAR conditions. We concentrate on chlorophyll ﬂuorescence as this is an early-stage indicator of stress manifestation in plants. We ﬁrst investigate how these parameters di ﬀ er across leaf samples which are exposed to a higher degree of PAR variability due to their vertical position in the reed culm (sun and shade leaves). In the second part, we concentrate on how the same parameters exhibit in reed patches of di ﬀ erent densities. We then look into hyperspectral regions through graphs of coe ﬃ cient of determination and associate the former with the physiological parameters. We report on the large variability found from measurements taken at di ﬀ erent parts of the canopy and the association with spectral regions in the visible and near-infrared domain. We ﬁnd that at low irradiance plants increase their acclimation to low light conditions. Plant density at Phragmites stands a ﬀ ects the vertical light attenuation and consequently the photophysiological response of basal leaves. Moreover, the hyperspectral response from the sun and shade leaves has been found to di ﬀ er; charts of the coe ﬃ cient of determination indicate that the spectral region around the red-edge inﬂection point for each case of sun and shade leaves correlate strongly with ETR max and α . When analysing the data cumulatively, independent of their vertical position within the stand, we found correlations of R 2 = 0.65 (band combination 696 and 651) and R 2 = 0.61 (band combination 636 and 642) for the ETR max and α , respectively.


Introduction
Light is the primary source of energy for plants, affecting their growth, development and structure in both terrestrial and aquatic settings, and creating strong selection pressure through its variability [1][2][3]. relate to vegetation traits other than photosynthesis; for instance, chlorophyll content has been a feature suggested to be approximated by the ratio of reflectances at the red-edge wavelengths at 750 nm and 710 nm (i.e., R750/R710) by Zarco-Tejada et al. [23], R750/R550 and R750/R700 by Gitelson and Merzlyak [24] and the empirical indices modified Normalized Difference Vegetation Index (mNDVI) [25], the Plant Senescence Reflectance Index (PSRI) [26] and the Structure Insensitive Pigment Index (SIPI) [27], just to name a few. Other traits have also been related, such as xanthophyll with the Photochemical Reflectance Index (PRI) [28]. Another variable frequently attempted to relate to spectroscopic information is the poor performance of the plant in terms of the photo-efficiency, which is generically named as "stress". In any case, it is not a mainstream procedure that the physiology/photosynthetic process of the plant is directly associated to the spectral information; despite the fact that both chlorophyll fluorescence and remote sensing are quite established techniques, only lately this field is gaining attention (e.g., [22,[29][30][31][32]). This has mainly taken place in the context of passive remote sensing targeting the analysis of sun induced fluorescence features (e.g., [33]), which are a precursor sign of plant stress and reduced photosynthetic activity. Narrow-band PRI has been proposed to assess PS II Light-Use Efficiency (LUE) [34] and suggested as a good proxy for photosynthetic efficiency at different spatial and temporal scales [35]; however, PRI may vary and depends on environmental conditions such as nutrients, water status or temperature [36]. Other studies have reported strong correlation of popular empirical indices, such as the the Normalized Difference Vegetation Index (NDVI), with canopy photosynthesis (e.g., [37,38]). In a recent study Dechant et al. [39] suggest that nitrogen content is the dominant mechanism through which photosynthesis is related to leaf reflectance spectra; they also argue that their method is applicable to many tree species and both sun and shade leaves. Finally, in the context of foliar and plant density, another important application of earth observation is mapping the density variability with remote sensing images. The explanation of this variability might be related to spectral indices. This spatio-temporal exhibition is especially suitable for the study of emergent macrophytes.
This papers studies the expression of the photosynthetic and spectroscopic parameters in different PAR conditions; more specifically, we focus on two aspects. First, we measure certain physiological and hyperspectral parameters from Phragmites leaf samples, which are exposed to a different degree of available PAR due to their vertical position in the reed culm (sun and shade leaves) and we investigate if and how the parameters differ and associate with each other. In the second part, we look into the exhibition of these parameters on leaves grown in reed patches of different densities.

Study Site
Lake Balaton is a large (596 km 2 ) and relatively shallow (average water depth 3.5 m) freshwater lake located in Central Europe. Extended and continuous populations of helophytic perennial plants at the littoral zone of the lake form reed stands scattered along 112 km of the 240 km of the total shoreline [40,41]. The reed stands cover cumulatively an area of approximately 16 km 2 with the majority of it (73%-12 km 2 ) encountered at the northern part of the shore. The steep windward northern shore's sediment consists of dolomite limestone, while the shallow southern shore is dominated by sandy sediment. On the northern shore of the lake the average and maximum depth of water that reed stand can be found is 1.5 m and 2.4 m respectively, while on the southern shore the average depth on the waterward side of reed stands is 1 m [40,41] Lake Balaton has received significant scientific attention due to the reed die-back phenomenon observed (e.g., [40][41][42][43]), the macrophytic traits and species mapping (e.g., [44][45][46][47]) and the dynamic trophic gradient of the water (e.g., [48,49]). The field data for this study were collected at the Kerekedi bay, an oligo-mesotrophic bay at the easternmost basin of Lake Balaton situated at the geographic coordinates 46 •

Data Survey
Chlorophyll fluorescence and spectroradiometric measurements were collected along a perpendicular-to-the-shore transect. Two types of measurements were performed on Phragmites plants on-site. Firstly, the largest fully emerged green apical and the largest intact green basal leaves, of approximately 4 m high plants in a dense (>80 plants m −2 ) and non-degrading stand, were selected. These leaves are referred to thereafter as the sun (youngest, mature, apical) and shade (oldest, mature, basal, green) leaves, respectively. Second, photophysiological parameters of oldest, mature, basal, still green leaves of Phragmites were determined in reed patches of varying density. The plant density was estimated from a photograph captured at ground level (water or soil) from an upward looking camera. Figure 1. Map of the study area in Lake Balaton (inset) and field sampling points' location in the Kerekedi Bay (main).

Data Survey
Chlorophyll fluorescence and spectroradiometric measurements were collected along a perpendicular-to-the-shore transect. Two types of measurements were performed on Phragmites plants on-site. Firstly, the largest fully emerged green apical and the largest intact green basal leaves, of approximately 4 m high plants in a dense (>80 plants m −2 ) and non-degrading stand, were selected. These leaves are referred to thereafter as the sun (youngest, mature, apical) and shade (oldest, mature, basal, green) leaves, respectively. Second, photophysiological parameters of oldest, mature, basal, still green leaves of Phragmites were determined in reed patches of varying density. The plant density was estimated from a photograph captured at ground level (water or soil) from an upward looking camera.

Photophysiological Measurements
The light response curves (i.e., the electron transport rate (ETR) of photosystem II (PSII) as a function of PAR intensity) of leaves were measured with a pulse amplitude modulated chlorophyll fluorometer (PAM-2500, Heinz Walz GmbH, Effeltrich, Germany). A 20 min dark adaptation period was applied in the beginning of the routine and the same actinic light was used for both sun and shade leaves. First, the fluorescence values were detected for a dark adapted leaf with a pulse of a saturated light (630 nm, intensity 3000 µmol m −2 s −1 ). Thereafter, the leaves were exposed to 11 actinic lights (at 5,9,67,104,144,201,274,366,477, 622 and 788 µmol m −2 s −1 intensity, see Figure 2) at 630 nm wavelength with a duration of 15 s; subsequently, the fluorescence values were measured after each illumination step with a new pulse of saturated light (3000 µmol m −2 s −1 ). During this process the minimal (F 0 ) and maximal (F m ) fluorescence yield of a dark-adapted leaf, as well as the fluorescence yield (F), maximal fluorescence yield (F m ') during saturation pulse and minimal fluorescence yield (F 0 ') of a pre-illuminated sample were determined. The equipment calculated other parameters using these fluorescence yields (Table 1).

Spectroradiometric Measurements
The reflectance values of sun and shade Phragmites leaves were recorded using a hand-held ASD portable FieldSpec 2 spectroradiometer (Analytical Spectral Devices Inc., Boulder, Colorado, USA). The instrument records radiation intensity in 750 consecutive narrow-bands between 325 nm and 1075 nm with a spectral resolution of less than 3 nm at 700 nm. Leaf reflectance values were acquired with the ASD leaf clip attached to the device with an optical fibre. The source of light was integrated in the leaf clip and the black reference panel on the opposing side was used to calibrate the instrument in order to convert the recorded radiance values to reflectance. For each leaf sample 10 measurements were recorded from the same point at 544 ms integration time and then averaged. Three positions of the leaf were identified (i.e., next to the stem, middle of the leaf and upper third of the leaf) which did not yield any statistical differences in reflectance values (data not shown here), therefore, the measurements from the upper third of the leaf blade were used.

Data Analysis
The mean values of the photophysiological properties of sun and shade leaves were compared on the basis of the Student's t-test and the Mann-Whitney Rank Sum test. The measurements were taken from the same position as the hyperspectral measurements aforementioned. Parameters were correlated with Pearson and Spearman Rank Order correlations. The light response data were fitted with an exponentially saturating curve [50] with the method of the least squares applied on exponential rise; subsequently, the maximum ETR (ETR max ), theoretical saturation light intensity (I k ) and the maximum quantum yield for whole chain electron transport at low radiation (α) were retrieved [18]. The analysis was performed in the statistical software R version 2.15.3 [51]. Sigma Plot v 12.5 (Systat Software Inc., San Jose, California, USA) was used for the curve fitting and the production of the graphics.
The hyperspectral data were first exported using the software ViewSpec Pro 6.0 (Analytical Spectral Devices Inc., Boulder, Colorado, USA) and the marginal wavelengths at 325-400 nm and 1000-1075 nm from all spectra were removed as they contained a high level of noise. Subsequently, the spectra were divided in shade and sun samples according to the collection protocol and the curve of the mean and standard deviation of the reflectance value for each group was plotted. Moreover, the first derivative of the mean value was explored and the wavelength of the inflection point (λ p ) was used as the measure of the position of the red-edge reflectance critical value. Thereafter, the exhaustive combination of spectral indices for all spectral bands, as in similar studies (e.g., [22,52,53]), was calculated based on the mathematical formula: where NDSI is the Normalised Difference Spectral Index (NDSI) and Ri and Rj are the reflectance values for bands i and j, respectively. Three physiological parameters were correlated with the NDSI, namely α, ETRmax and I k . For each parameter and each NDSI combination, we calculated the coefficient of determination (R 2 ) and the corresponding significance level (p) assuming a linear relationship for the n pair of measurements of hyperspectrum and physiological parameter recorded in each experimental Remote Sens. 2020, 12, 200 7 of 18 setup. The results were visualized using raster maps of the coefficient of determination. The R 2 values corresponding to spectral pairs yielding p > 0.05 (which is deemed statistically insignificant) were masked out to highlight only the statistically significant regions, based on which the results were interpreted. The spectral regions which yields the highest R 2 between the NDSIi,j and the parameter under study can then be identified. Moreover, several popular empirical indices were extracted as presented in Table 2. For the hyperspectral calculations, the R programming language was used as well [51].

Sun and Shade Leaves Comparison
The light intensity within a dense Phragmites stand decreased exponentially (data not shown), resulting in significant illumination difference at the apical and basal parts of the plants, accounting for 1251 ± 245 µmol m −2 s −1 and 268 ± 88 µmol m −2 s −1 respectively (Mann-Whitney Rank Sum Test, T = 6442, p ≤ 0,001). Prolonged exposure of leaves to different light intensities resulted in substantially different photophysiological parameters of the sun and shade leaves of Phragmites in dense reed Remote Sens. 2020, 12, 200 8 of 18 patches (Figures 2-4). As an effect of the lower irradiance, the I k , ETR max , Y(II) and qP of shade leaves decreased, while α and qN significantly increased (Figures 3 and 4).
respectively, and were deemed insignificant and disregarded.
The R 2 graph resulting from the measurements during the two consecutive days of fieldwork cumulatively is presented in Figure 7. Values of R 2 corresponding to spectral pairs yielding p > 0.05 were deemed statistically insignificant and have been masked out of the graph for ease of interpretation. The graphs for parameters Ik and ETRmax were very similar (data not shown here), hence the graph of Ik has been disregarded for further analysis and is not presented. Figure 8 presents the statistical results from the extraction of a few empirical vegetation indices, which were selected on the basis of differing statistical distribution between the shade and the sun leaves. Figure 3. The maximum quantum yield for whole chain electron transport (α), the maximum electron transport capacity (ETRmax) and the theoretical light saturation intensity (Ik) for the sun and shade leaves of Phragmites australis in dense patches. Boxes encompass the 25% and 75% quartiles of all the data, the central solid and dashed lines represents the median and the average, bars extend to the 95% confidence limits, and dots represent outliers. For all bars n = 15. Differences were tested with the Mann-Whitney Rank Sum Test; *** indicates p < 0.001.   These changes in photophysiological properties also appear on the reflectance of leaves affecting primarily the red-edge and the near-infrared (NIR) spectral regions. Figure 5 presents the leaf reflectance spectra between 400 nm and 1000 nm measured for sun and shade leaves of Phragmites. The reflectance in the NIR (700-1000 nm) spectrum for sun leaves was 6.5% higher compared to the shade leaves. The red edge inflection point shifted to a shorter wavelength from λ p = 713 nm for the sun leaves to λ p = 701 nm for the shade leaves. Figure 4. The quantum yield of PSII (Y(II)), photochemical (qP) and non-photochemical (qN) quenching for the sun and shade leaves of Phragmites australis in dense patches. Boxes encompass the 25% and 75% quartiles of all the data, the central solid and dashed lines represents the median and the average, bars extend to the 95% confidence limits, and dots represent outliers. For all bars n = 15. Differences were tested with the Mann-Whitney Rank Sum Test; ** indicates p < 0.01, while *** indicates p < 0.001.  The Principal Component Analysis (PCA) of the spectroradiometric data further revealed the difference between the reflectance of sun and shade leaves of Phragmites ( Figure 6). The major diversity of the data is reflected in the NIR responses of the leaves, while the RGB component had no significant contribution in the explanation of the first two orthogonal principal components. The third and fourth principal components explained only the 2.6 and 1.5 of the variation inherited in the data, respectively, and were deemed insignificant and disregarded.
The R 2 graph resulting from the measurements during the two consecutive days of fieldwork cumulatively is presented in Figure 7. Values of R 2 corresponding to spectral pairs yielding p > 0.05 were deemed statistically insignificant and have been masked out of the graph for ease of interpretation. The graphs for parameters I k and ETR max were very similar (data not shown here), hence the graph of I k has been disregarded for further analysis and is not presented. Figure 8 presents the statistical results from the extraction of a few empirical vegetation indices, which were selected on the basis of differing statistical distribution between the shade and the sun leaves.

Density Effect
In the second part of the experiment, the effect of the density of Phragmites stands on the photophysiological parameters and the reflectance of the oldest basal green leaves were studied. The majority of the photophysiological parameters (seven of nine) correlated with the density of plants (Table 3). Mostly negative correlations were determined (with increase in density the parameters were decreasing), although the different indices of the non-photochemical quenching positively correlated with the plant density. On the other hand, the majority of vegetation indices (13 of 21) showed no significant correlation (Table 3). Table 3. Spearman rank order correlation between the density of the Phragmites australis patch and empirical vegetation indices and photophysiological data. n = 24, ns-Not significant, * p < 0.05, ** p < 0.01. In bold values the parameters for which the correlation is significant (i.e., 0 < p < 0.05).

Density Effect
In the second part of the experiment, the effect of the density of Phragmites stands on the photophysiological parameters and the reflectance of the oldest basal green leaves were studied. The majority of the photophysiological parameters (seven of nine) correlated with the density of plants (Table 3). Mostly negative correlations were determined (with increase in density the parameters were decreasing), although the different indices of the non-photochemical quenching positively correlated with the plant density. On the other hand, the majority of vegetation indices (13 of 21) showed no significant correlation (Table 3). Table 3. Spearman rank order correlation between the density of the Phragmites australis patch and empirical vegetation indices and photophysiological data. n = 24, ns-Not significant, * p < 0.05, ** p < 0.01. In bold values the parameters for which the correlation is significant (i.e., 0 < p < 0.05).

Discussion
Due to its high adaptability, high growth rate and predominantly clonal vegetative reproduction, Phragmites monopolized littoral zones of most freshwater lakes and became the dominant species [40,[67][68][69]. As seed dispersal in Phragmites stands is impossible in water covered areas, the spatial distribution of their shoots (ramets) is mainly determined by vegetative (clonal) spread using rhizomes. Clonal Phragmites is physiologically integrated within the genets (clones) facilitating cooperation [70][71][72] and, thus, substantially changing our perception of competition within a reed stand. In the end, the interconnected clone is the basic physiological unit that interacts with its environment [70][71][72]. The density of Phragmites plants within the stand is affected by two counteracting processes: the plants tend to remain as dense as the rhizome architecture and the habitat's resource-supplying capacity allows, while the density of plants is limited, among others, by optical properties within the stand [69,73]. Respectively, the density of Phragmites affects the optical environment of the stands: the denser the stand, the less light reaches the basal parts of the plants.
In this study performed in a temperate zone freshwater lake, Phragmites exhibited major differences between apical (sun) and basal (shade) leaves. Our results (Figures 2-4) show that reed in dense stands could vertically alleviate irradiance, so that while the apical part of plants exposed to sun could develop sun adapted leaves, the fully shaded conditions at the basal part result in shade-tolerant adaptations of Phragmites. All the determined changes showed adaptations to specific light intensities of the apical and basal part of Phragmites: electron transport rates (proxy of photosynthetic activity), photochemical quenching and theoretical light saturation intensities of the sun leaves were 84%, 63% and 113% higher than in shade leaves showing that leaves were adapted to utilise higher light intensities. On the other hand, the shade-tolerant leaves were able to react better to smallest changes in light environment (21% higher quantum yield for whole chain electron transport) and maintain alternative metabolic pathways (17% higher nonphotochemical quenching). Moreover, the results indicate that the shade leaves inherit more variability in their spectral profile (standard deviation in Figure 5 and the PCs in Figure 6), and are thus potentially able to absorb larger spectral variety as compared to sun leaves. These adaptations were consistent and were translated to foliar reflectance data. Similar changes in the vertical profile of vegetation have been observed in other studies; for instance, D'Odorico et al. [74], when studying the leaf photosynthetic traits for top and bottom canopy leaves for winter barley (Hordeum vulgare L.) and rape seed (Brassica napus L.), found that traits such as the maximum photosynthetic capacity, dark respiration, leaf nitrogen and chlorophyll contents and leaf mass per area presented consistently higher values for top leaves throughout the growing season and for both crop types.
A closer look at the reflectance curves of sun and shade leaves ( Figure 5) presents that the red-edge shift observed, from λ p = 713 nm for the sun leaves to λ p = 701 nm for the shade leaves, is affecting the vegetation indices, and especially the ones that use this part of the spectrum, such as the VOG2 and the mNDVI which demonstrate the highest correlation amongst the empirical vegetation indices (Table 2). A similar slight change is observed in the the NIR reflectance (700-1000 nm) spectrum for which sun leaves demonstrated 6.5% higher reflectance compared to the shade leaves; however, these both changes are comparatively small and as the standard deviation indicates in Figure 5, they might be attributed to variability within the data. Similar studies have presented contrasting results; in an older study, Schultz [9] reports that leaf reflectance was independent of the sun-shade environment of the samples within the canopy, regardless of the leaf age and the acquisition time during the season; however his samples were grapevine (Vitis vinifera L.) leaves grown under commercial practice, which normally are not grown in as a dense environment as Phragmites. An earlier study, however, from Gausman [10] suggest that there are considerable differences in the visible reflectance of the upper surface of the Valencia orange tree leaves.
While the reflectance curves ( Figure 5) present the spectral differences of the two experimental sample groups, Figure 7 presents the association between the full combination of derived spectral NDSI indices and the physiological parameters ETR max and α. An observation arising from the results is that the red-edge region is the strongest associated with ETR max for the sun leaves and the cumulative measurements; this is not so apparent in the shade leaves graph, perhaps because the physiological variability (as indicated by the standard deviation) of the shade leaves is smaller ( Figure 3) and consequently the correlation weaker. Just above 700 nm there is a spectral area in the sun leaves for ETR max with a correlation of R 2 > 0.60 (maximum R 2 = 0.65 at wavelength combination 708/558); this area coincides with the inflection point of the sun leaves (i.e., λ p = 713 nm) ( Figure 5) which indicates the importance of the inflection point in regard to ETR max . In the cumulative graph of ETR max the same highest correlation is encountered in the same spectral area in a slightly narrower but wider window; the narrow-band pair with the highest correlation is at 696/651 with R 2 = 0.65. In the graph of ETR max for the shade leaves there is no correlation with R 2 > 0.5. In regard to α, the opposite from the ETR max is observed; the statistical distribution for the shade leaves has a larger standard deviation and an outlier is apparent (Figure 3). The graph for the sun leaves and α do not present statistically significant results, however, the graph for the shade leaves presents two areas of interest; first, there is a narrow isolated spectral combination at 708/525 with R 2 = 0.68 which is the highest correlation coefficient we have found in this study; this fact seconds the importance of the inflection point for the shade leaves (λ p = 701 nm) which falls within this area and is 7 nm apart from the spectral combination with the maximum R 2 . Secondly, there is a wider spectral region constructed at the very short visible and the very long near-infrared wavelengths of the available spectrum, which provides R 2 > 0.5. The cumulative graph for α indicates a narrow area at 636/642 with a correlation of R 2 = 0.61 which is also evidenced in the shade leaves graph.
It is worth noting that, in order to collect the chlorophyll fluorescence measurements, a 20 min adaptation period is required for each sample; this fact in combination with the requirement to record the data around solar noon (which was between 10:00 am and 13:00 pm local time) restricts the number of measurements that can actually be recorded within this time period from a single PAM device, and consequently results to a low repeat count in the experimental set up for a single day; this was the reason that we used cumulatively the data collected during two consecutive days as presented above. Moreover, we attempted to analyse the data separately for these two days; the differences in this case were large (results not shown here) and we believe that the main reason is the small sample size for a single individual day. Second, environmental (e.g., temperature) and vegetative (e.g., density, heterogeneity) conditions change even between consecutive days and we only report the similarities in the clear sky conditions, which is just one of the many variables that can affect the physiological and spectral behaviour of plants. Figure 8 presents the results from selected spectral indices indicating which ones are sensitive and insensitive to shade and sun leaves; this can provide information in estimating the ratio of sun and shade leaves from the canopy. It is apparent that the spectral indices NDVI 705 , mNDVI, EVI, mSR 705 , VOG2 provide distinguishing results for sun and shade leaves, while the spectral indices NDVI, ARVI, SR, PRI, SIPI do not demonstrate any important differences for sun and shade leaves in their respective statistical distribution. It is worth noting the considerably different results from NDVI and NDVI 705 ; the former results to almost identical distributions for sun and shade leaves while the latter provides distinguishing results; this is an indication that narrow-band indices highly depend on the wavelength used and are not to be assumed identical with the broadband indices and generalized. Moreover, PRI, which is a spectral index assumed to be related to photochemistry, did not yield any significant differences for the two leaf sets.
Our study relied on spectroscopic data and the spectral results indicate narrow-bands of correlation with the photophysiological parameters. In the context of wetlands, several studies have been utilizing such data, as for instance, Pengra et al. [75] used EO-1 Hyperion hyperspectral sensor to map the extent and location of invasive monodominant Phragmites in Green Bay, WI, USA. with 81.4% accuracy. These type of satellite hyperspectral data could be used, based on the findings of our study, to investigate the density and physiological status of Phragmites based on the proposed narrow-band NDSI spectral indices. Nevertheless, care should be taken when translating our findings, which are based on foliar data, to satellite or airborne imagery which observes foliar data; in this case, a radiative transfer model could be used to bridge the gap from leaf to canopy level.
Nevertheless, wetland characteristics are mapped with remote sensing based mainly on multispectral data from a variety of platforms, such as unmanned aerial vehicles (UAVs) (e.g., [76][77][78][79]), or satellite passive sensors, such as the Landsat Thematic Mapper (TM) and Indian Remote Sensing Satellite (IRS) [80], Satellite pour l'Observation de la Terre (SPOT) [81], WorldView-2 [82] and Sentinel-2 [83]). Figure 7 indicates that there are only a few broadband spectral regions correlating with the parameters under study, and specifically the reflectance of the shade leaves with α at the shorter wavelengths. However, as more of these satellites integrate narrow-band spectral channels in their capabilities (e.g., Sentinel-2, WorldView 2/3, RapidEye) it would be possible to utilize the findings of our results in regard to ETR and α, since we suggest that the red-edge is the most important spectral region, and this is the available spectral region that the narrow-band channels onboard satellites currently integrate.

Conclusions
This study examined the intertwined effect of plant density and sun/shade leaves' photophysiology in regard to chlorophyll fluorescence and hyperspectral response. Although Phragmites is considered a sun adapted species, in our study we showed that basal parts of the plant exhibited a shade-tolerant nature in photophysiological response to foliage shading. Low irradiance increased the acclimation of plants to low light conditions. At normal conditions and at high plant densities only the signals from sun leaves could reach from the canopy level, although even the slightest move of canopy (breeze, wind) could expose the basal, shade leaves affecting the acquired high resolution (not satellite) data. At relatively lower densities within the stand the vertical light attenuation was still large, and this modified the photophysiology of basal foliage, so Phragmites exhibit signals from both sun and shade leaves, thus showing higher variability at the data level of canopy reflectance. At low densities, the vertical light attenuation is low, making all the leaves exhibit dominantly sun-adopted properties. The hyperspectral response from the sun and shade leaves has been found to differ and the charts of the coefficient of determination indicate that there are specific narrow-band spectral regions associated to the leaf vertical position and the photophysiological parameter under study. The spectral bands around the red-edge inflection point for each case of sun and shade leaves has been found to correlate strongly with ETR max and α. When analyzing the data cumulatively, independent of their vertical position within the stand, we found correlations of R 2 = 0.65 (696/651) and R 2 = 0.61 (636/642) for the ETR max and α, respectively. The outcomes of this study could help to predict density of reed stands or correct previously seen anomalies.
Funding: This research was funded by GIONET, supported by the European Commission, Marie Curie Programme, Initial Training Networks, grant agreement number PITN-GA-2010-264509. This study was funded by grant from the Hungarian National Research, Development and Innovation Office NKFIH KH-129505.