Papers by Zbynek malenovSKY
<p&amp... more <p>The solar energy absorbed by the vegetation light-harvesting antenna complexes supplies the photosynthetic light reactions with a highly efficient transfer of quantum energy. The absorbed energy is efficiently transferred from one molecule to another, until being used by the reaction centres for the further carbon reactions. The energy transfer to the reaction centres is hereby highly regulated by the variable aggregation of pigments in the antenna complexes, allowing for quick and slower adjustments according to the incoming solar radiance. To control and protect the pigment antenna and the reaction centres from a potentially harmful solar radiance excess, these regulated photoprotective mechanisms are activated at different time scales at the antenna level, allowing vegetation to adapt to changing light conditions. The understanding of these energy regulative processes from optical measurements is essential in order to monitor plants' adaptation strategies to stressful environments and changing climates from remote sensing data.</p><p>Using high-spectral resolution leaf spectroscopy in a controlled laboratory set-up, we have observed detailed and significant absorbance shifts controlled by the pigment antennas themselves. Simultaneous measurements of both upward and downward spectrally-resolved leaf radiance (Lup(λ), Ldw(λ), W m<sup>-2</sup> sr<sup>-1</sup> nm<sup>-1</sup>) allowed us to observe the specific absorbance changes at leaf level, including changes in chlorophyll (Chl) a fluorescence emission (Fup(λ), Fdw(λ), W m<sup>-2</sup> sr<sup>-1</sup> nm<sup>-1</sup>). Interestingly, these changes due to shifts in energy redistribution were: 1) observed in the PAR region and even far beyond 700 nm, and 2) indicated a prominent role of both Carotenoid and Chl a molecules in the creation of alternative energy sinks, i.e. constraining the energy transfer to the reaction centres. Hereby, a significant redistribution of photosynthetic light energy was observed in the 500-800 nm range, highlighting this spectral region to be of potential interest for remote sensing. We further revealed that these energy redistributions do not necessary occur in parallel with Chl a fluorescence changes, illustrating the importance of different energy redistribution mechanisms constraining the photosynthetic light reactions. To conclude, a good quantitative understanding of all mechanisms of energy regulation in plants based on VIS-NIR wavelengths is essential 1) to be able to understand these trends using remote sensing data, 2) to better model the adaptations of vegetation to changing climate and environmental conditions, and 3) potentially better predict future trends in dynamic global vegetation models.</p>
The ground-based imaging spectroscopy data were acquired with the Headwall Photonics Micro-Hypers... more The ground-based imaging spectroscopy data were acquired with the Headwall Photonics Micro-Hyperspec VNIR scanner (Headwall Inc., USA) attached to a computer-controlled rotating/tilting platform. The sensor unit was placed approximately 2.5 m above the ground on a single pole mounted to a geodetic tripod. The Micro-Hyperspec is a push-broom scanner, which collects light passing through a lens objective with an aperture of f/2.8 (FOV of 49.8 degrees) and through a slit entrance of 25 microns. The spectral wavelengths are split by an aberration-corrected convex holographic diffraction grating and projected onto a charge-coupled device (CCD) matrix with a digital dynamic range of 12-bits and size of 1004 by 1004 pixel units. The CCD registers the captured light split into 324 (full spectral extent, FWHM of 4.12-4.67 nm) or 162 spectral bands (binning of two neighbouring spectral pixels as a single recording unit, FWHM of 4.75-5.25 nm). To ensure a high signal-to-noise ratio and to prevent oversaturation of the CCD dynamic range, the spectral binning (162 bands) combined with an integration time of 40 milliseconds (ms) was applied and oblique hyperspectral images (azimuth viewing angles of 44 degrees and 60 degrees) were collected at two test site
A forest canopy is a complex system with a highly structural multi-scale architecture. Physical b... more A forest canopy is a complex system with a highly structural multi-scale architecture. Physical based radiative transfer (RT) modelling has been shown to be an effective tool for retrieval of vegetation canopy biochemical/physical characteristics from optical remote sensing data. A high spatial resolution RT through a forest canopy requires several geometrical and structural parameters of trees and understory to be specified with an appropriate accuracy. Following attributes on forest canopy are required: i) basic tree allometric parameters (i.e., tree height, stem diameter and length, crown length and projection, simplified crown shape, etc.), ii) parameters describing distribution of green biomass (foliage) (e.g., leaf area index (LAI), leaf angle distribution (LAD) or average leaf angle (ALA), clumping of leaves and density of clumps, air gaps and defoliation, etc.), and iii) parameters describing distribution of woody biomass (branches and twigs) (e.g., number, position and angular orientation of the first order branchesbranches growing directly from stem, twig area index (TAI), twig angle distribution (TAD)). At very high spatial resolution (airborne image data), an insufficiently characterized structure of the forest canopy can result in inaccurate RT simulations. Direct destructive methods of measuring canopy structure are unfeasible at large-scales, therefore, in this paper we review the non-invasive Light Detection and Ranging (LIDAR) approaches. We also present some results on tree structure parameters acquired by a commercially available ground-based LIDAR scanner employed in scanning the matured Norway spruce trees.
The leaf area index (LAI) of three monocultures of Norway spruce (Picea abies (L.) Karst), differ... more The leaf area index (LAI) of three monocultures of Norway spruce (Picea abies (L.) Karst), different in age and structure, was measured by means of two indirect optical techniques of LAI field mapping: 1/ plant canopy analyser LAI-2000, and 2/ digital hemispherical photographs (DHP). The supportive measurements with the TRAC instrument were conducted to produce mainly the element clumping index. The aim of the study was to compare the performances of LAI-2000 and DHP and to evaluate effect of three different sampling strategies on field estimation of leaf area index. One of the suggested sampling designs introduced spatial oversampling around one-point measurement. The oversampling was expected to reveal the importance of sampling point position with respect to surrounding trees. In general, the LAI-2000 instrument produced higher estimates of effective leaf area index than DHP in all experimental stands. On the other hand, the higher "true" estimates of LAI were obtained from DHP. All three sampling strategies produced consistent estimates of effective and "true" LAI in all forest sites. The spatial oversampling of LAI measurement point did not significantly improve the LAI estimate of the canopy subplots.
Living Planet Symposium, Aug 1, 2016
In this study we investigated importance of the spaceborne instrument Sentinel-2 red edge spectra... more In this study we investigated importance of the spaceborne instrument Sentinel-2 red edge spectral bands and reconstructed red edge position (REP) for retrieval of the three eco-physiological plant parameters, leaf and canopy chlorophyll content and leaf area index (LAI), in case of maize agricultural fields and beech and spruce forest stands. Sentinel-2 spectral bands and REP of the investigated vegetation canopies were simulated in the Discrete Anisotropic Radiative Transfer (DART) model. Their potential for estimation of the plant parameters was assessed through training support vector regressions (SVR) and examining their P-vector matrices indicatingsignificance of each input. The trained SVR were then applied on Sentinel-2 simulated images and the acquired\nestimates were cross-compared with results from high spatial resolution airborne retrievals. Results showed that contribution of REP was significant for canopy chlorophyll content, but less significant for leaf chlorophyll content and insignificant for leaf area index estimations. However, the red edge spectral bands contributed strongly to the retrievals of all parameters, especially canopy and leaf chlorophyll content. Application of SVR on Sentinel-2 simulated images demonstrated, in general, an overestimation of leaf chlorophyll content and an underestimation of LAI when compared to the reciprocal airborne estimates. In the follow-up investigation, we will apply the trained SVR algorithms on real Sentinel-2 multispectral images acquired during vegetation seasons 2015 and 2016
IEEE Transactions on Geoscience and Remote Sensing, May 1, 2020
Unmanned aircraft system (UAS)-mounted spectroradiometers offer a new capability to measure spect... more Unmanned aircraft system (UAS)-mounted spectroradiometers offer a new capability to measure spectral reflectance and solar-induced chlorophyll fluorescence at detailed canopy scales. This capability offers potential for upscaling and comparison with airborne and space-borne observations [e.g., the upcoming European Space Agency (ESA) Fluorescence Explorer (FLEX) satellite mission]. In this respect, the accurate spatial characterization and georeferencing of the UAS acquisition footprints are essential to unravel the origin and spatial variability of optical signals acquired within the extent of airborne/satellite pixels. In this article, we present and validate a novel algorithm to georeference the footprint extent of a nonimaging spectroradiometer mounted on a multirotor UAS platform. We used information about the spectroradiometer position and orientation during flight and about topography of observed terrain to calculate the footprint geolocation. In a recursive process, the field of view (FOV) of the spectroradiometer projected on the ground. Multiple FOV ground projections retrieved during a spectroradiometer reading (i.e., a single integration time) were aggregated to calculate the footprint extent. The spatial accuracy of the footprint geolocation was validated by applying the georeferencing algorithm on checkpoint pixels of image acquired with a comounted digital camera. Geolocations of the checkpoint pixels, which served as a proxy for the spectroradiometer footprint, were successfully compared with surveyed ground checkpoints. Finally, the spectral and radiometric quality of UAS-acquired reflectance signatures was compared with ground-measured reflectance of four natural targets (three different types of grass and a bare soil), and a strong agreement was observed.
Remote Sensing of Environment, Nov 1, 2021
Abstract Although remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) is increasi... more Abstract Although remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) is increasingly used as a valuable source of information about vegetation photosynthetic activity, the RS SIF observations are significantly influenced by canopy-specific structural features (i.e., canopy architecture including leaf area index and presence of woody components), atmospheric conditions during their acquisition (e.g., proportion of direct and diffuse irradiance) and observational geometric configurations (e.g., sun and viewing directions). Radiative transfer (RT) models have the potential to provide a better understanding of the canopy structural effects on the SIF emission and RS signals. Here, we used the DART model to assess the daily influence, from morning to evening, of forest 3D architecture on SIF nadir radiance, emission, escape factor and nadir yield of eight 100 m × 100 m forest study plots established in a temperate deciduous forest of the Smithsonian Environmental Research Center (Edgewater, MD, USA). The 3D architecture of each plot was derived from airborne LiDAR. DART simulations of these 3D forest plots and their 1D (i.e., vertical profile of sun-adapted and shade-adapted leaves) and 0D (i.e., homogeneous layer of sun-adapted leaves above an homogeneous layer of shade-adapted leaves) abstractions were compared to assess the relative errors (e1D−3D and e0D−3D) associated with horizontal and vertical structural heterogeneity, respectively. Forest 3D structure, especially horizontal heterogeneity, had a great influence on forest nadir SIF radiance, resulting in e1D−3D up to 55% at 8:00 and 18:00 (i.e., for oblique sun directions). The key indicators of this impact, in the descending order of importance, were the SIF escape factor (e1D−3D up to 40%), the attenuation of incident photosynthetically active radiation (e1D−3D less than 5%), and the SIF emission yield (e1D−3D less than 2%). The influence of forest architecture on the nadir SIF escape factor and SIF yield (e1D−3D up to 40%) varied over time, with differences in forest stand structure, and per spectral domain, being always larger between 640 and 700 nm than between 700 and 850 nm. In addition, woody elements demonstrated a large influence on forest SIF radiance due to their “shading” effect (e up to 17%) and their “blocking” effect (e ≈ 10%), both of them higher for far-red than for red SIF. These results underline the importance of 3D forest canopy architecture, especially 2D heterogeneity, and inclusion of woody elements in RT modeling used for interpretation of the RS SIF signal, and subsequently for the estimation of gross primary production and detection of vegetation stress.
IEEE Transactions on Geoscience and Remote Sensing, May 1, 2020
A dual-field-of-view spectroradiometer system has been developed for measuring solar-induced chlo... more A dual-field-of-view spectroradiometer system has been developed for measuring solar-induced chlorophyll fluorescence (SIF), from an unmanned aircraft system (UAS). This "AirSIF" system measures spectral reflectance in the visible and near-infrared wavelengths as well as SIF in far-red O2-A and red O2-B absorption features at high spatial resolution. It has the potential to support the interpretation and validation of canopy-emitted SIF observed by airborne, and future spaceborne sensors at coarser spatial resolutions, as well as simulated by radiative transfer models. In this contribution, we describe the AirSIF data collection and processing workflows and present a SIF map product of spatially explicit and geometrically correct spectroradiometer footprints. We analyze two possible sources of error in SIF retrieval procedure: a sensor-specific spectral artifact called etaloning and the uncertainty of incoming irradiance during UAS flight due to airframe motion (pitching and rolling). Finally, we present results from two SIF acquisition approaches: a continuous mapping flight and a stop&go flight targeting predefined areas of interest. The results are analyzed for a case study of Alfalfa and grass canopies and validated against ground measurements using the same system. Index Terms-Airborne spectroscopy, solar-induced chlorophyll fluorescence (SIF), unmanned aerial vehicle (UAV). I. INTRODUCTION S OLAR-INDUCED chlorophyll fluorescence (SIF) has become a recent focus point of optical remote-sensing research. The quantitative mapping of SIF satellite data acquired with GOME-2 [1], GOSAT [2], OCO-2 [3] , and more recently the TROPOspheric Monitoring Instrument (TROPOMI) [4] revealed the potential to enhance our understanding of terrestrial vegetation gross primary production and
International journal of applied earth observation and geoinformation, Jun 1, 2018
Moss beds are one of very few terrestrial vegetation types that can be found on the Antarctic con... more Moss beds are one of very few terrestrial vegetation types that can be found on the Antarctic continent and as such mapping their extent and monitoring their health is important to environmental managers. Across Antarctica, moss beds are experiencing changes in health as their environment changes. As Antarctic moss beds are spatially fragmented with relatively small extent they require very high resolution remotely sensed imagery to monitor their distribution and dynamics. This study demonstrates that multisensor imagery collected by an Unmanned Aircraft System (UAS) provides a novel data source for assessment of moss health. In this study, we train a Random Forest Regression Model (RFM) with longterm field quadrats at a study site in the Windmill Islands, East Antarctica and apply it to UAS RGB and 6-band multispectral imagery, derived vegetation indices, 3D topographic data, and thermal imagery to predict moss health. Our results suggest that moss health, expressed as a percentage between 0 and 100% healthy, can be estimated with a root mean squared error (RMSE) between 7 and 12%. The RFM also quantifies the importance of input variables for moss health estimation showing the multispectral sensor data was important for accurate health prediction, such information being essential for planning future field investigations. The RFM was applied to the entire moss bed, providing an extrapolation of the health assessment across a larger spatial area. With further validation the resulting maps could be used for change detection of moss health across multiple sites and seasons.
arXiv (Cornell University), Mar 25, 2020
Models of radiative transfer (RT) are important tools for remote sensing of vegetation, as they f... more Models of radiative transfer (RT) are important tools for remote sensing of vegetation, as they facilitate forward simulations of remotely sensed data as well as inverse estimation of biophysical and biochemical properties from vegetation optical properties. The remote sensing estimation of foliar protein content is a key to monitoring the nitrogen cycle in terrestrial ecosystems in particular to better understand photosynthetic capacity of plants and improve nitrogen management in agriculture. However, no physically based leaf RT model currently allows for proper decomposition of leaf dry matter into nitrogenbased proteins and carbon-based constituents (CBC), estimated from optical properties of fresh or dry foliage. We developed a new version of the PROSPECT model, named PROSPECT-PRO, which separates nitrogenbased constituents (proteins) from CBC (including cellulose, lignin, hemicellulose and starch). PROSPECT-PRO was calibrated and validated on subsets of the LOPEX dataset, accounting for both fresh and dry broadleaf and grass samples. We applied an iterative model inversion optimization algorithm to identify optimal spectral subdomains for retrieval of leaf protein and CBC contents, with 2125-2174 nm optimal for proteins and 2025-2349 nm optimal for CBCs. PROSPECT-PRO inversions revealed a better performance in estimating proteins from optical properties of fresh than dry leaves (respectively, validation R 2 =0.75 and 0.62, NRMSE= 17.3% and 24%), but similar performances for the estimations of CBCs (respectively validation R 2 =0.92 and 0.95, NRMSE= 13.01% and 13.9%). We further tested the ability of PROSPECT-PRO to estimate leaf mass per area (LMA) as the sum of proteins and CBC using independent datasets acquired for numerous plant species. Results showed that PROSPECT-PRO is fully compatible and comparable with its predecessor PROSPECT-D in indirect estimation of LMA, with validation R 2 =0.90 and NRMSE=16.3% for PROSPECT-PRO and R 2 =0.91 and NRMSE=18.6% for PROSPECT-D across eight independent data sets. We can conclude from findings of this study that PROSPECT-PRO has a high potential in establishing the carbon-to-nitrogen ratio based on the retrieved CBC-to-proteins ratio (R 2 =0.89 and NRMSE=12.7% for fresh leaves and R 2 =0.58 and NRMSE=30.7% for dry leaves). That might be of interest for precision agriculture applications estimating carbon and nitrogen from observations of current and forthcoming airborne and satellite imaging spectroscopy sensors.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Feb 1, 2014
We present uncertainties associated with the measurement of coniferous needle-leaf optical proper... more We present uncertainties associated with the measurement of coniferous needle-leaf optical properties (OPs) with an integrating sphere using an optimized gap-fraction (GF) correction method, where GF refers to the air gaps appearing between the needles of a measured sample. We used an optically stable artificial material simulating needle leaves to investigate the potential effects of: 1) the sample holder carrying the needles during measurements and 2) multiple scattering in between the measured needles. Our optimization of integrating sphere port configurations using the sample holder showed an underestimation of the needle transmittance signal of at least 2% in flat needles and 4% in nonflat needles. If the needles have a nonflat cross section, multiple scattering of the photons during the GF measurement led to a GF overestimation. In addition, the multiple scattering of photons during the optical measurements caused less accurate performance of the GF-correction algorithms, which are based on the assumption of linear relationship between the nonGF-corrected signal and increasing GF, resulting in transmittance overestimation of nonflat needle samples. Overall, the final deviation achieved after optimizing the method is about 1% in reflectance and 6% in transmittance if the needles are flat, and if they are nonflat, the error increases to 4%-6% in reflectance and 10%-12% in transmittance. These results suggest that formulae for measurements and computation of coniferous needle OPs require modification that includes also the phenomenon of multiple scattering between the measured needles.
Tree Physiology, Dec 28, 2017
Understanding the net photosynthesis of plant canopies requires quantifying photosynthesis in cha... more Understanding the net photosynthesis of plant canopies requires quantifying photosynthesis in challenging environments, principally due to the variable light intensities and qualities generated by sunlight interactions with clouds and surrounding foliage. The dynamics of sunflecks and rates of change in light intensity at the beginning and end of sustained light (SL) events makes photosynthetic measurements difficult, especially when dealing with less accessible parts of plant foliage. High time resolved photosynthetic monitoring from pulse amplitude modulated (PAM) fluorometers has limited applicability due to the invasive nature of frequently applied saturating flashes. An alternative approach used here provides remote (<5 m), high time resolution (10 s), PAM equivalent but minimally invasive measurements of photosynthetic parameters. We assessed the efficacy of the Q A flash protocol from the Light-Induced Fluorescence Transient (LIFT) technique for monitoring photosynthesis in mature outer canopy leaves of potted Persea americana Mill. cv. Haas (Avocado) trees in a semi-controlled environment and outdoors. Initially we established that LIFT measurements were leaf angle independent between ±40°from perpendicular and moreover, that estimates of 685 nm reflectance (R 685) from leaves of similar chlorophyll content provide a species dependent, but reasonable proxy for incident light intensity. Photosynthetic responses during brief light events (≤10 min), and the initial stages of SL events, showed similar declines in the quantum yield of photosystem II (Φ II) with large transient increases in 'constitutive loss processes' (Φ NO) prior to dissipation of excitation by non-photochemical quenching (Φ NPQ). Our results demonstrate the capacity of LIFT to monitor photosynthesis at a distance during highly dynamic light conditions that potentially may improve models of canopy photosynthesis and estimates of plant productivity. For example, generalized additive modelling performed on the 85 dynamic light events monitored identified negative relationships between light event length and ΔΦ II and Δelectron transport rate using either Δphotosynthetically active radiation or ΔR 685 as indicators of leaf irradiance.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jun 22, 2016
In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (C... more In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning methodsupport vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645-710 nm, c) CR 705-780 nm, and d) CR 680-800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab < 10 µg cm-2 and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645-710 nm, whereas CR bands in range of 680-800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view.
in silico plants, Jul 1, 2021
This study presents a method for three-dimensional (3D) reconstruction of forest tree species tha... more This study presents a method for three-dimensional (3D) reconstruction of forest tree species that are, for instance, required for simulations of 3D canopies in radiative transfer modelling. We selected three forest species of different architecture: Norway spruce (Picea abies) and European beech (Fagus sylvatica), representatives of European production forests, and white peppermint (Eucalyptus pulchella), a common forest species of Tasmania. Each species has a specific crown structure and foliage distribution. Our algorithm for 3D model construction of a single tree is based on terrestrial laser scanning (TLS) and ancillary field measurements of leaf angle distribution, percentage of current-year and older leaves, and other parameters that could not be derived from TLS data. The algorithm comprises four main steps: (i) segmentation of a TLS tree point cloud separating wooden parts from foliage, (ii) reconstruction of wooden parts (trunks and branches) from TLS data, (iii) biologically genuine distribution of foliage within the tree crown and (iv) separation of foliage into two age categories (for spruce trees only). The reconstructed 3D models of the tree species were used to build virtual forest scenes in the Discrete Anisotropic Radiative Transfer model and to simulate canopy optical signals, specifically: angularly anisotropic top-of-canopy reflectance (for retrieval of leaf biochemical compounds from nadir canopy reflectance signatures captured in airborne imaging spectroscopy data) and solar-induced chlorophyll fluorescence signal (for experimentally unfeasible sensitivity analyses).
Remote Sensing of Environment, 2020
Leaf chlorophyll is central to the exchange of carbon, water and energy between the biosphere and... more Leaf chlorophyll is central to the exchange of carbon, water and energy between the biosphere and the atmosphere, and to the functioning of terrestrial ecosystems. This paper presents the first spatially continuous view of terrestrial leaf chlorophyll content (ChlLeaf) across a global scale. Weekly maps of ChlLeaf were produced from ENIVSAT MERIS full resolution (300 m) satellite data with a two-stage physically-based radiative transfer modelling approach. Firstly, leaf-level reflectance was derived from top-of-canopy satellite reflectance observations using 4-Scale and SAIL canopy radiative transfer models 3 for woody and non-woody vegetation, respectively. Secondly, the modelled leaf-level reflectance was used in the PROSPECT leaf-level radiative transfer model to derive ChlLeaf. The ChlLeaf retrieval algorithm was validated with measured ChlLeaf data from sample measurements at field locations, and covering six plant functional types (PFTs). Modelled results show strong relationships with field measurements, particularly for deciduous broadleaf forests (R 2 = 0.67; RMSE = 9.25 µg cm-2 ; p<0.001), croplands (R 2 = 0.41; RMSE = 13.18 µg cm-2 ; p<0.001) and evergreen needleleaf forests (R 2 = 0.47; RMSE = 10.63 µg cm-2 ; p<0.001). When the modelled results from all PFTs were considered together, the overall relationship with measured ChlLeaf remained good (R 2 = 0.47, RMSE = 10.79 µg cm-2 ; p<0.001). This result was an improvement on the relationship between measured ChlLeaf and a commonly used chlorophyll-sensitive spectral vegetation index; the MERIS Terrestrial Chlorophyll Index (MTCI; R 2 = 0.27, p<0.001). The global maps show large temporal and spatial variability in ChlLeaf, with evergreen broadleaf forests presenting the highest leaf chlorophyll values with global annual median of 54.4 µg cm-2. Distinct seasonal ChlLeaf phenologies are also visible, particularly in deciduous plant forms, associated with budburst and crop growth, and leaf senescence. It is anticipated that this global ChlLeaf product will make an important step towards the explicit consideration of leaf-level biochemistry in terrestrial water, energy and carbon cycle modelling.
The connection between solar-induced fluorescence (SIF) and vegetation gross primary productivity... more The connection between solar-induced fluorescence (SIF) and vegetation gross primary productivity is being widely investigated across spatial, temporal, and biological scales, including: a) studies at the leaf [1], [2], plant canopy [2]–[4] or satellite pixel scale [5], [6], b) temporally with studies spanning from diurnal [7] to seasonal scales [1], [3], [5], and b) biologically with studies covering various plant functional types (PFTs), e.g., crops [4], [7], deciduous [8] or evergreen forests [1], [3], in response to different sources of stress.
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Papers by Zbynek malenovSKY