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The forest–atmosphere exchange of carbon and water is regulated by meteorological conditions as well as canopy properties such as leaf area index (LAI, m2 m−2), photosynthetic capacity (PC μmol m−2 s−1), or surface conductance in optimal conditions (Gs,opt, mmol m−2 s−1), which can vary seasonally and inter-annually. This variability is well understood for deciduous species but is poorly characterized in evergreen forests. Here, we quantify the seasonal dynamics of a temperate evergreen eucalypt forest with estimates of LAI, litterfall, carbon and water fluxes, and meteorological conditions from measurements and model simulations. We merged MODIS Enhanced Vegetation Index (EVI) values with site-based LAI measurements to establish a 17-year sequence of monthly LAI. We ran the Community Atmosphere Biosphere Land Exchange model (CABLE-POP (version r5046)) with constant and varying LAI for our site to quantify the influence of seasonal canopy dynamics on carbon and water fluxes. We observed that the peak of LAI occurred in late summer–early autumn, with a higher and earlier peak occurring in years when summer rainfall was greater. Seasonality in litterfall and allocation of net primary productivity (FNPP) to leaf growth (af, 0–1) drove this pattern, suggesting a complete renewal of the canopy before the timing of peak LAI. Litterfall peaked in spring, followed by a high af in summer, at the end of which LAI peaked, and PC and Gs,opt reached their maximum values in autumn, resulting from a combination of high LAI and efficient mature leaves. These canopy dynamics helped explain observations of maximum gross ecosystem production (FGEP) in spring and autumn and net ecosystem carbon loss in summer at our site. Inter-annual variability in LAI was positively correlated with Net Ecosystem Production (FNEP). It would be valuable to apply a similar approach to other temperate evergreen forests to identify broad patterns of seasonality in leaf growth and turnover. Because incorporating dynamic LAI was insufficient to fully capture the dynamics of FGEP, observations of seasonal variation in photosynthetic capacity, such as from solar-induced fluorescence, should be incorporated in land surface models to improve ecosystem flux estimates in evergreen forests. [ABSTRACT FROM AUTHOR]

The forest–atmosphere exchange of carbon and water is regulated by meteorological conditions as well as canopy properties such as leaf area index (LAI, m2 m−2), photosynthetic capacity (PC μmol m−2 s−1), or surface conductance in optimal conditions (Gs,opt, mmol m−2 s−1), which can vary seasonally and inter-annually. This variability is well understood for deciduous species but is poorly characterized in evergreen forests. Here, we quantify the seasonal dynamics of a temperate evergreen eucalypt forest with estimates of LAI, litterfall, carbon and water fluxes, and meteorological conditions from measurements and model simulations. We merged MODIS Enhanced Vegetation Index (EVI) values with site-based LAI measurements to establish a 17-year sequence of monthly LAI. We ran the Community Atmosphere Biosphere Land Exchange model (CABLE-POP (version r5046)) with constant and varying LAI for our site to quantify the influence of seasonal canopy dynamics on carbon and water fluxes. We observed that the peak of LAI occurred in late summer–early autumn, with a higher and earlier peak occurring in years when summer rainfall was greater. Seasonality in litterfall and allocation of net primary productivity (FNPP) to leaf growth (af, 0–1) drove this pattern, suggesting a complete renewal of the canopy before the timing of peak LAI. Litterfall peaked in spring, followed by a high af in summer, at the end of which LAI peaked, and PC and Gs,opt reached their maximum values in autumn, resulting from a combination of high LAI and efficient mature leaves. These canopy dynamics helped explain observations of maximum gross ecosystem production (FGEP) in spring and autumn and net ecosystem carbon loss in summer at our site. Inter-annual variability in LAI was positively correlated with Net Ecosystem Production (FNEP). It would be valuable to apply a similar approach to other temperate evergreen forests to identify broad patterns of seasonality in leaf growth and turnover. Because incorporating dynamic LAI was insufficient to fully capture the dynamics of FGEP, observations of seasonal variation in photosynthetic capacity, such as from solar-induced fluorescence, should be incorporated in land surface models to improve ecosystem flux estimates in evergreen forests.

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

Large-scale information on several vegetation properties ('plant traits') is critical to assess ecosystem functioning, functional diversity and their role in the Earth system. Hyperspectral remote sensing of plant canopies offers a key tool to map multiple plant traits. However, we are still lacking generalized methods to translate hyperspectral reflectance into a suite of relevant plant traits across biomes, land cover and sensor types. The absence of globally representative data sets and the gap between the available reflectance data with corresponding in-situ measurements have hampered such approaches. In recent years, the scientific community acquired multiple data sets encompassing canopy hyperspectral reflectance and plant traits from different plant types and sensors. To combine these heterogeneous data sets, we propose three multi-trait modeling approaches based on Convolutional Neural Networks (CNNs) to simultaneously infer a broad set of 20 structural and chemical traits (e.g. leaf mass per area, leaf area index, pigments, nitrogen). The performance of these multi-trait CNN models predicting these traits was compared against single-trait CNN as well as single-trait partial least squares regression (PLSR). We found that the multi-trait CNNs performances significantly increased from single-trait CNNs (nRMSE = 0.027–19.61%) and the state-of-the-art PLSR models (nRMSE = 1.94–40.07%) across a broad range of vegetation types (crops, forest, tundra, grassland, shrubland) and sensor types. Thus, providing a single model for multiple traits not only proved to be computationally more efficient, but also more accurate, since it enabled the model to incorporate traits' co-variation. Despite the data heterogeneity of the merged data set, our models performances' were comparable or exceeded those of previous studies. Overall, this study highlights the potential of weakly supervised approaches to overcome the scarcity of in-situ measurements and take a step forward in creating efficient predictive models of multiple biochemical and biophysical vegetation properties. [Display omitted] • We retrieve a set of 20 plant traits with a CNN model from canopy spectra. • The multi-trait model covers various ecosystems, vegetation types and sensors. • The multi-trait model outperforms partial least squares regression approaches. • Despite sparsity, data heterogeneity facilitated high retrieval performance. • Data sharing and collaboration advances the development of transferable models. [ABSTRACT FROM AUTHOR]

Vegetation composition and water surface area are changing in many tundra regions due to climate warming, which is twice as strong in the Arctic as compared to the global mean. Such land cover changes feed back to climate and permafrost thaw through altering the surface energy budget. We quantified the influence of vegetation type, canopy characteristics, and patchiness on the tundra shortwave radiation components. We used in situ measurements and vegetation mapping to parametrise a 3D radiative transfer model (DART) for summer conditions at the Kytalyk test site in northeast Siberia. We analysed model results assessing the most important drivers of canopy albedo, transmittance, and absorptance of photosynthetically active radiation (PAR). Tundra albedo was strongly influenced by the fractional cover of water surfaces. Albedo decreased with increasing shrub cover. However, plant area index effects on albedo were not statistically significant. Canopy transmittance and PAR absorptance (f APAR ) were almost entirely controlled by plant area index at the landscape scale. Only about one half of the total plant area index consisted of green leaves, while wood and standing dead leaves contributed equally to the other half. While spatial patterns and patch sizes of vegetation types and open water did not significantly influence the radiation budget at the landscape scale, it contributed to the large variability at the local scale. Such local variability of shortwave radiation may impact evapotranspiration and primary productivity at a range of scales. Therefore, the variation of radiation fluxes within single vegetation types potentially affects larger scale energy, water, and carbon fluxes. [ABSTRACT FROM AUTHOR]

Applying vertical green facades (VGFs) on building envelopes is an effective design strategy to reduce building energy consumption and mitigate the urban heat island effect. Although many studies have investigated the cooling and energy-saving effects of VGFs, research on the long-wave infrared radiation properties of VGFs remains scarce, limiting the understanding and application of heat-transfer models of VGFs. Therefore, in this study, theoretical analyses and field measurements were performed in the subtropical city of Guangzhou, China, to examine the long-wave infrared radiation properties of VGFs. First, an observation platform was established using six plants commonly used for VGFs in subtropical regions. The long-wave infrared radiation and leaf temperature of these plants with different coverage rates were measured to investigate the effect of coverage rate on the radiation transfer of the vegetation canopy. A Fourier spectrometer was used to analyse the spectral features of the leaves in the long-wave infrared radiation band. Based on the observation data and radiation balance of the vegetation canopy, an empirical equation for transmitting long-wave infrared radiation was established. Moreover, the monthly variations in the leaf area index and coverage rate of the VGFs were recorded throughout 2021, and an exponential function was used to determine the relationship between them. A quantitative method was used to assess the accuracy of the long-wave infrared radiation model, and the results indicated that the calculated values were in good agreement with the measured values. • Long-wave infrared radiation properties of VGFs were investigated. • Experimental data were collected to fit the ambient equivalent temperature. • The long-wave infrared radiation transfer in an uneven canopy was modelled. • The correspondence between the coverage rate and the LAI was found. [ABSTRACT FROM AUTHOR]

Urban greenery exhibits diverse cooling effect under different conditions. A field experiment was conducted in Singapore to measure the local microclimate above six vegetated surfaces and a concrete surface. Results showed that the average air temperatures above vegetated surfaces were lower than concrete surface all the daytime. Among the four moments from 3:00 am to 21:00 pm, the maximum difference of average air temperature between both surfaces occurred at 15:00 pm (from 0.56 °C to 1.72 °C), and the minimum difference occurred at 9:00 am (from 0.05 °C to 0.29 °C). A multilayer hygrothermal transfer model of greenery was developed and validated by the measured data. Sensitivity analysis based on this model shows that leaf area index has different effects on the air temperature within and above plant canopy. The top layer of plant canopy acts as heat source in the daytime and as heat sink at night. Leaf angle affects the air temperature within plant canopy significantly only when solar angle is high, and a more horizontal leaf distribution cools the soil surface better in the daytime. Plants with a higher leaf albedo and transmissivity have stronger cooling effect. The effect of minimum stomatal resistance is negligible. • Local microclimate was compared between vegetated surfaces and a concrete surface. • A multilayer hygrothermal transfer model of greenery was proposed and validated. • Canopy height has significant effect on the vertical air temperature profile. • Leaf area index has opposite effect on microclimate within and above plant canopy. [ABSTRACT FROM AUTHOR]

Grassland degradation resulting from global climate change, overgrazing, and rodent damage is expected to influence the magnitude of canopy hydrological fluxes because of reduced vegetation biomass and changed species composition. The objectives of this study were to estimate herbaceous canopy rainfall storage capacity ( S) along three different stages of sub-alpine grassland degradation (non-degraded, lightly degraded and moderately degraded) in the Qinghai-Tibetan Plateau, China, and relate changes in S to canopy properties. An artificial wetting method and the water budget balance method, using rain simulations, were used for estimating S. Grassland degradation significantly reduced S. In non-degraded, lightly degraded and moderately degraded grasslands, S estimated using the artificial wetting method were 0.612 ± 0.08 mm, 0.289 ± 0.04 mm, and 0.217 ± 0.01 mm, respectively; S estimated using the water budget balance method were 0.979 ± 0.32 mm, 0.493 ± 0.13 mm, and 0.419 ± 0.09 mm, respectively. These changes could be explained by accompanying changes in above-ground biomass and leaf area index, as well as changes in species composition. Species-specific rainfall storage capacity varied by a factor of 2.7 among the investigated species, with graminoids having the lowest values. Leaf area index was more correlated to S than was canopy coverage. Converting fresh weight of non-leaf tissues into effective leaf area of the corresponding species and then introducing a coefficient of leaf area according to the specific storage capacity of leaves improved the linear relationship between S and leaf area index. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as 'Arctic amplification'), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the 'Osprey') using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations. [ABSTRACT FROM AUTHOR]