Vegetation Photosynthetic Activity Research Articles

Investigating the temporal lag and accumulation effect of climatic factors on vegetation photosynthetic activity over subtropical China

Monitoring vegetation photosynthesis in China’s subtropical regions using remote sensing is challenging because of the complex ecosystems and climate variability. Previous studies often pay less attention on the influence of multiple climatic factors on the temporal effects (lag and accumulation) of vegetation photosynthesis, thereby underestimating their impact. This study utilizes a dataset comprising Solar-induced chlorophyll fluorescence (SIF) data (GOSIF product), MODIS Land Cover product (MCD12C1), and various climatic variables. Analytical methods including Theil-Sen Median trend analysis, the Mann-Kendall test, partial correlation analysis, and the optimal parameter-based geographical detector (OPGD) model were employed to explore the temporal dynamics of subtropical vegetation SIF responses to climatic factors and to identify their climate drivers in subtropical China. The study findings indicate that (1) vegetation photosynthesis, as indicated by SIF, exhibited an increasing trend in the majority of Chinese subtropical regions, which constitute over 80 % of the study area, with particularly pronounced enhancements in the southern and central western parts of the Chinese subtropics. (2) Soil moisture primarily exhibits lag effects on SIF, particularly in evergreen needleleaf forests, deciduous broadleaf forests, and mixed forests, whereas temperature does not exhibit significant temporal effects. Solar radiation and vapor pressure deficits impact SIF through both lag and accumulation effects. Under the lag and accumulation effects, the proportion of significant correlations between climatic factors and vegetation SIF increases by 36.71 % ∼ 43.8 %, excluding temperature. (3) Temperature is the dominant factor affecting vegetation SIF, particularly in the evergreen needleleaf forest. Interactions between climatic factors have a significantly stronger influence on SIF than individual factors. Notably, the explanatory power of the vapor pressure deficit increases substantially when it interacts with other factors. Studying the lag and accumulation effects of climatic factors on photosynthesis aids in accurately predicting vegetation responses to climate change, thereby improving the accuracy of global carbon cycle models and guiding the development of carbon sequestration management strategies.

Downscaling canopy photochemical reflectance index to leaf level by correcting for the soil effects

The photochemical reflectance index (PRI) is a promising remote sensing signal for monitoring vegetation physiology. Variations in leaf PRI are usually attributed to either the energy-dependent xanthophyll cycle or the carotenoid-chlorophyll ratio, both indicative of leaf physiology. However, canopy PRI is subject to soil, canopy structure, and incident and viewing angles, and thus has a weaker and more complicated relationship with vegetation photosynthetic activity or leaf pigment composition. Therefore, downscaling canopy PRI to the leaf level is essential for accurate remote sensing of vegetation physiology using PRI. An earlier investigation (P.Yang, RSE, 279, 113,133, 2022) illustrates that structural and angular variations in canopy PRI primarily result from varying degrees of soil interference. In this study, a soil correction method is proposed to mitigate the soil effects on top-of-canopy (TOC) reflectance at the PRI bands. The soil effects on TOC reflectance at 531 nm and 570 nm are respectively estimated as R531soil×R675/R675soil and R570soil×R675/R675soil, where R675 is TOC red reflectance, Rλsoil soil reflectance at wavelengthλ. R675/R675soil approximates the fraction of the observed sunlit soil, as leaves are nearly black at 675 nm due to strong absorption of chlorophyll, and R675 is mainly contributed from soil. To assess the effectiveness of the soil correction method, a wheat field dataset, a corn field dataset, and a simulated dataset, were utilized. The soil-adjusted and the original canopy PRI were compared with the leaf PRI for the real and synthetic scenarios that had various soil brightness, leaf chlorophyll content and canopy structure. Both the field and numerical experiments demonstrate that, for the canopies with low vegetation coverage and substantial soil contamination, the original canopy PRI was largely different from the leaf PRI, displaying substantial structural and angular dependence. In comparison, the soil-adjusted canopy PRI was more closely aligned with the PRI observed in the sunlit leaves in all three datasets. This study shows that accounting for the soil effects with TOC red reflectance allows downscaling canopy PRI to the leaf level. The soil-adjusted canopy PRI contributes to remote sensing of the xanthophyll cycle or the carotenoid-chlorophyll ratio from canopy PRI.

Potential of solar-induced chlorophyll fluorescence (SIF) to access long-term dynamics of soil salinity using OCO-2 satellite data and machine learning method

The accumulation of soil salt becomes a worldwide widespread phenomenon, being a major threat to global production. As an environmental stress, soil salinity can reduce the vegetation photosynthetic activity. Solar-induced chlorophyll fluorescence (SIF) is an electromagnetic signal actively released by vegetation during photosynthesis. SIF not only can capture lower vegetation photosynthetic activity due to environmental stress promptly, but also is less affected by atmosphere and soil background. However, the ability of SIF observation to detect soil salinity remains unclear. Here, we use standardizedsolar-induced chlorophyll iluorescence index (SIFI) from SIF observation time series (2000 ∼ 2020) of global OCO-2 based SIF product (GOSIF) to develop soil salinity model. The results show that: SIF observation can identify salt affected soil (EC ≥ 2 ∼ 4 dS m−1) and salinity class a global scale. The SIFI calculated from SIF observation at May ∼ August (hereafter SIFI5-8) is the optimal sensitivity indices for rainfed cropland, herbaceous cover, irrigated cropland, shrubland, grassland. SIFI10-11 is the optimal sensitivity indices for forest and sparse vegetation; (2) By comparison, the ovrerall classification accuracy of predicted salinity class is above 70 %. The order of classification accuracy is rainfed cropland > irrigated cropland > bare area > forest > grassland > shrubland > herbaceous cover > sparse vegetation; (3) During at least three-quarters of the period from 2000 to 2020, the area of salt affected soil (EC ≥ 2 ∼ 4 dS m−1) was 4.9 Mkm2; (4) The annual change rate of global soil salt content is generally between −0.05 and 0.05 dS m−1 yr−1. Soil salinity in South Africa and West Asia increased greatly with an annual change rate of 0.02 ∼ 0.03 dS m−1 yr−1. These results demonstrate the ability of SIF observation to estimate soil salinity, providing a new perspective for explaining and evaluating soil salinity variation.

Evaluation of the Effects of Climate Change and Human Activities on Vegetation Photosynthetic Activity in the Yellow River Basin using Solar-induced Chlorophyll Fluorescence

Evaluation of the Effects of Climate Change and Human Activities on Vegetation Photosynthetic Activity in the Yellow River Basin using Solar-induced Chlorophyll Fluorescence

Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study.

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.

Improved Vegetation Photosynthetic Phenology Monitoring in the Northern Ecosystems Using Total Canopy Solar‐Induced Chlorophyll Fluorescence Derived From TROPOMI

AbstractSolar‐Induced chlorophyll Fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) with substantially improved spatiotemporal resolutions provides a new potential to improve satellite‐based phenology monitoring. The performance of TROPOMI SIF for tracking vegetation photosynthetic phenology, and how it compares to conventional vegetation indices (VIs)‐based approaches, however, have not been adequately assessed. Total canopy SIF, as a better proxy of Gross Primary Productivity (GPP) than original directional SIF, is a new SIF to estimate phenology while its performance has not been investigated. This study assesses the capability of TROPOMI SIF before and after canopy correction for phenology monitoring and improves our understanding of these questions. Benchmarked by tower‐based GPP, TROPOMI SIF generally performed better than VIs, especially for capturing the End Of Season (EOS) of vegetation photosynthetic activity at deciduous broadleaf forest (DBF), evergreen forest (ENF), and croplands (CRO) sites, but not for Start Of Season (SOS). This suggested that the advantage of SIF over VIs depended on phenological metrics. The total canopy SIF emission obtained through canopy correction generally performed better than the original SIF retrievals, especially in estimating the EOS of forest sites (DBF, MF, ENF), but soil correction did not further improve the accuracy of phenological monitoring. When comparing SIF‐ and VI‐based phenological metrics over northern terrestrial ecosystems, SIF showed earlier senescence date widely, while the differences in onset date were region dependent. These results indicate the necessity of canopy correction to convert directional SIF to canopy total SIF when using satellite SIF products to estimate phenological metrics.

Low frequency changes in CO2 concentration in East Asia related to Pacific decadal oscillation and Atlantic multi-decadal oscillation for mid-summer and early fall

The climatological seasonal maximum and minimum CO2 concentrations in East Asia for 1987–2020 have been recorded at April and August, respectively. We found that the CO2 concentration in East Asia during July, August, and September (JAS) is lower than normal before the late 1990s and after the early 2010s (Low_CO2 period), and higher than normal from the late 1990s to the early 2010s (High_CO2 period). The low-frequency variability of CO2 concentration in East Asia during JAS correlates with both Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO)-related sea surface temperatures (SSTs). We analyzed atmospheric and oceanic conditions during JAS between the two periods, finding that precipitation in East Asia decreased during JAS in High_CO2 period than that in Low_CO2 period, possibly due to PDO and AMO-related SST forcing, which decreases vegetation's photosynthetic activity. This may lead to a higher CO2 concentration than normal in East Asia in High_CO2 period through reduced uptake of CO2 from the atmosphere. This implies that terrestrial vegetation activity influenced by remote SST forcings should be monitored to better understand regional carbon cycles in East Asia.

Assessing impacts of canopy 3D structure on chlorophyll fluorescence radiance and radiative budget of deciduous forest stands using DART

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 (ε1D−3D and ε0D−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 ε1D−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 (ε1D−3D up to 40%), the attenuation of incident photosynthetically active radiation (ε1D−3D less than 5%), and the SIF emission yield (ε1D−3D less than 2%). The influence of forest architecture on the nadir SIF escape factor and SIF yield (ε1D−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 (ε up to 17%) and their “blocking” effect (ε ≈ 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.

Comparative Analysis on the Estimation of Diurnal Solar-Induced Chlorophyll Fluorescence Dynamics for a Subtropical Evergreen Coniferous Forest

Solar-induced chlorophyll fluorescence (SIF) is considered as a prospective indicator of vegetation photosynthetic activity and the ecosystem carbon cycle. The current coarse spatial-temporal resolutions of SIF data from satellite missions and ground measurements still cannot satisfy the corroboration of its correlation with photosynthesis and carbon flux. Practical approaches are needed to be explored for the supplementation of the SIF measurements. In our study, we clarified the diurnal variations of leaf and canopy chlorophyll fluorescence for a subtropical evergreen coniferous forest and evaluated the performance of the canopy chlorophyll concentration (CCC) approach and the backward approach from gross primary production (GPP) for estimating the diurnal variations of canopy SIF by comparing with the Soil Canopy Observation Photosynthesis Energy (SCOPE) model. The results showed that the canopy SIF had similar seasonal and diurnal variations with the incident photosynthetically active radiation (PAR) above the canopy, while the leaf steady-state fluorescence remained stable during the daytime. Neither the CCC nor the raw backward approach from GPP could capture the short temporal dynamics of canopy SIF. However, after improving the backward approach with a correction factor of normalized PAR incident on leaves, the variation of the estimated canopy SIF accounted for more than half of the diurnal variations in the canopy SIF (SIF687: R2 = 0.53, p < 0.001; SIF760: R2 = 0.72, p < 0.001) for the subtropical evergreen coniferous forest without water stress. Drought interfered with the utilization of the improved backward approach because of the decoupling of SIF and GPP due to stomatal closure. This new approach offers new insight into the estimation of diurnal canopy SIF and can help understand the photosynthesis of vegetation for future climate change studies.

Modelling sun-induced fluorescence for improved evaluation of forest carbon flux (GPP): Case study of tropical deciduous forest, India

Reliable estimation of forest carbon fluxes in space and time is a major challenge. Sun-Induced Chlorophyll Fluorescence (SIF) has been found closely linked to vegetation photosynthetic activity. But the application of SIF for direct estimation of Gross Primary Productivity (GPP) is not well investigated. In this study, we demonstrate the estimation of GPP for tropical deciduous forests located in North Western Himalayan Foothills in India using simulated SIF and fluorescence quantum yield (ФF) through a modified Light Use Efficiency (LUEf) model. SIF simulated using Soil Canopy Observation, Photochemistry, and Energy fluxes (SCOPE) model provided the estimate of Photosynthetically Active Radiation (PAR) intercepted by the canopy foliage and ФF. ФF, measured through the Pulse-Amplitude Modulated (PAM) fluorometer quantifies the emission rate of SIF per unit of the absorbed photon. The integration of these factors into the proposed LUEf model offered real-time photosynthetic efficiency and improved the estimate of GPP of the deciduous forest. The results show that GPP estimated from SIF (SIFTOC) was closely related to GPPEddy with R2 = 0.91 (p<0.05) with RMSE= 29.24 gC m−2 month−1 than GPP estimated from LUE model (R2=0. 79, p<0.05) and RMSE = 96.25 gC m−2 month−1). GPPMODIS underestimated the GPP with a non-significant correlation to GPPEddy for all the months with R2=0. 36 (p<0.05) and RMSE = 188.96 gC m−2 month−1. The results obtained are promising and the proposed model can be used for estimating carbon fluxes of forests over a larger spatial scale using satellite-derived SIF.

Challenges in the atmospheric characterization for the retrieval of spectrally resolved fluorescence and PRI region dynamics from space

In the coming years, Earth Observation missions like the FLuorescence EXplorer (FLEX) will acquire the radiance signal from the visible to the near-infrared at a very high spectral resolution, enabling exciting prospects for new insights in satellite-based photosynthetic studies. In this context, the process of de-coupling atmospheric and vegetation-related spectral signatures will become essential to guarantee a reliable estimation of the vegetation photosynthetic activity from space. Dynamic changes related to the vegetation photosynthetic status result in subtle contributions to the top of atmosphere radiance signal, e.g. due to the emission of the solar-induced chlorophyll fluorescence (~ 650–800 nm) or due to changes in surface reflectance spectra (500–600 nm) indicating variations in the vegetation photoprotection and light use efficiency. Conversely, atmospheric effects (molecular and aerosol absorption and scattering) dominate the spectral interval of interest for vegetation studies. This article presents a comprehensive overview of the atmospheric radiative effects caused by aerosols, ozone (O3), water vapor (H2O), oxygen (O2), and atmospheric pressure and temperature changes within the visible and near-infrared spectral interval, and paying special attention to the co-occurring vegetation-related spectral changes associated with the fluorescence emission and the activation of the photoprotection mechanisms. Since the largest uncertainties in the atmospheric correction process are associated with the characterization of the aerosol radiative effects, this work largely concentrates on the satellite retrieval-related implications under different aerosol absorbing and scattering scenarios on a global scale. Through a simulation exercise, it is evaluated to what extent aerosol climatology could influence the accuracy of satellite-derived surface apparent reflectance spectra impacting; therefore, any vegetation-related satellite product on a seasonal and global scale.

Montane forest productivity across a semiarid climatic gradient.

High-elevation montane forests are disproportionately important to carbon sequestration in semiarid climates where low elevations are dry and characterized by low carbon density ecosystems. However, these ecosystems are increasingly threatened by climate change with seasonal implications for photosynthesis and forest growth. As a result, we leveraged eddy covariance data from six evergreen conifer forest sites in the semiarid western United States to extrapolate the status of carbon sequestration within a framework of projected warming and drying. At colder locations, the seasonal evolution of gross primary productivity (GPP) was characterized by a single broad maximum during the summer that corresponded to snow melt-derived moisture and a transition from winter dormancy to spring activity. Conversely, winter dormancy was transient at warmer locations, and GPP was responsive to both winter and summer precipitation such that two distinct GPP maxima were separated by a period of foresummer drought. This resulted in a predictable sequence of primary limiting factors to GPP beginning with air temperature in winter and proceeding to moisture and leaf area during the summer. Due to counteracting winter (positive) and summer (negative) GPP responses to warming, leaf area index and moisture availability were the best predictors of annual GPP differences across sites. Overall, mean annual GPP was greatest at the warmest site due to persistent vegetation photosynthetic activity throughout the winter. These results indicate that the trajectory of this region's carbon sequestration will be sensitive to reduced or delayed summer precipitation, especially if coupled to snow drought and earlier soil moisture recession, but summer precipitation changes remain highly uncertain. Given the demonstrated potential for seasonally offsetting responses to warming, we project that decadal semiarid montane forest carbon sequestration will remain relatively stable in the absence of severe disturbance.

Towards a generic approach to remote non-invasive estimation of foliar carotenoid-to-chlorophyll ratio

Changes of chlorophyll (Chl) and carotenoid (Car) contents and their ratio (Car/Chl) represent a sensitive indicator of vegetation photosynthetic activity, developmental changes, and stress responses. The goal of this study was to design methods for estimating Car/Chl in plants across species, seasonal changes and ontogenetic phases requiring no species-specific parameterization. Four tree species (maple, chestnut, beech, and elm), wild vine shrub, and two crops species (maize and soybean) featuring contrasting leaf structure and photosynthetic pathways, a wide variation of pigment content and composition were studied. Two models based on leaf pigment absorption coefficients retrieved from reflectance spectra were proposed and tested. The first model uses the ratio of absorption coefficients at 500 and 700 nm and the second one—the difference between absorption coefficients at 500 and 660 nm. Both models accurately described Car/Chl changes in the range from 0.15 to 0.6 with determination coefficients R2 of 0.87 for the first model and 0.82 for the second; algorithms for Car/Chl estimation did not require parameterization for each species accurately assessing Car/Chl with normalized root mean square error below 11 % and 14 %, respectively. The findings of a close relationship between leaf absorption coefficients, retrieved from reflectance, and Car/Chl present the first step towards accurate generic quantification of pigment composition and hence the progression of developmental stages, impact of stresses, and potential photosynthetic activity.

Fluorescence Correction Vegetation Index (FCVI): A physically based reflectance index to separate physiological and non-physiological information in far-red sun-induced chlorophyll fluorescence

Sun-induced chlorophyll fluorescence (SIF) has been used to track vegetation photosynthetic activity for improving estimation of gross primary productivity (GPP) and detecting plant stress. There are both physical and physiological controls of SIF measured at the surface and retrieved from remote sensing including satellite observations. In order to accurately use SIF for monitoring of plant physiology, the effects of physically-based radiation processes related to leaf and canopy structure, notably photosynthetically active radiation (PAR) absorption and SIF scattering and re-absorption, must be characterized. In this study, we investigate both PAR absorption and SIF scattering processes and find that although it is difficult to quantify their effects individually by using just reflectance, the combined effects of the two processes can be well approximated by a reflectance index. This index, referred to as FCVI (Fluorescence Correction Vegetation Index), is defined as the difference between near-infrared (NIR) and broad-band visible (VIS, 400–700 nm) reflectance acquired under identical sun-canopy-observer geometry of the SIF measurements. The development of the index was based on the physical connection between reflectance and far-red SIF, which was revealed by using the spectral invariant theory. The utility of FCVI to correct far-red SIF for PAR absorption and scattering effects, thus improving the link to photosynthesis, was tested with data from: (i) a field experiment for a growing season; and (ii) a numerical experiment which included a number of scenarios generated by a radiative transfer model. For both the observations and simulations, the FCVI provided a promising estimate of the impact of the physically-based radiation processes on far-red SIF of moderately dense canopies (i.e., FCVI ≥ 0.18). Normalizing the TOC far-red SIF by both the incident PAR (iPAR) and the FCVI provided a good estimate of the far-red fluorescence emission efficiency of the canopies examined. This approach enhances our ability to generalize retrievals for vegetation processes as they change through natural growth phases and seasons. Taken together, far-red SIF and FCVI may enable the assessment of the light partitioning of vegetation canopies, an essential step to facilitate the use of far-red SIF for tracking physiological processes.

Significant Seasonal Variations in Isotopic Composition of Atmospheric Total Gaseous Mercury at Forest Sites in China Caused by Vegetation and Mercury Sources.

In this study, isotopic compositions of atmospheric total gaseous mercury (TGM) were measured in the Mt. Changbai (MCB) temperate deciduous forest and the Mt. Ailao (MAL) subtropical evergreen forest over a 1-year period. Higher δ202HgTGM values were observed under the forest canopy than above the forest canopy in the MCB forest. The vertical gradients in δ202HgTGM and Δ199HgTGM are positively correlated with the satellite-based normalized difference vegetation index (NDVI, representing the vegetation photosynthetic activity), suggesting that a strong vegetation activity (high NDVI) induces both mass-dependent and mass-independent fractionation of TGM isotopes. The observed δ202HgTGM and Δ199HgTGM showed seasonal variations. Mean δ202HgTGM and Δ199HgTGM in summer were 0.35-0.99‰ and 0.06-0.09‰ higher than those in other seasons in the MCB forest. In contrast, the highest seasonal δ202HgTGM in the MAL forest was observed in winter at 0.07-0.40‰ higher than the values found in other seasons. The variability of δ202HgTGM and Δ199HgTGM in MCB was attributed to vegetation activities, whereas the seasonal δ202HgTGM in the MAL forest was driven by the exposure of air masses to anthropogenic emissions. Using the data in this study and in the literature, we concluded that vegetation activity and anthropogenic Hg release are the main drivers for the spatial variations in TGM isotopic compositions in the northern hemisphere.

Urban-rural gradients reveal joint control of elevated CO2 and temperature on extended photosynthetic seasons.

Photosynthetic phenology has large effects on the land-atmosphere carbon exchange. Due to limited experimental assessments, a comprehensive understanding of the variations of photosynthetic phenology under future climate and its associated controlling factors is still missing, despite its high sensitivities to climate. Here, we develop an approach that uses cities as natural laboratories, since plants in urban areas are often exposed to higher temperatures and carbon dioxide (CO2) concentrations, which reflect expected future environmental conditions. Using more than 880 urban-rural gradients across the Northern Hemisphere (≥30° N), combined with concurrent satellite retrievals of Sun-induced chlorophyll fluorescence (SIF) and atmospheric CO2, we investigated the combined impacts of elevated CO2 and temperature on photosynthetic phenology at the large scale. The results showed that, under urban conditions of elevated CO2 and temperature, vegetation photosynthetic activity began earlier (-5.6 ± 0.7 d), peaked earlier (-4.9 ± 0.9 d) and ended later (4.6 ± 0.8 d) than in neighbouring rural areas, with a striking two- to fourfold higher climate sensitivity than greenness phenology. The earlier start and peak of season were sensitive to both the enhancements of CO2 and temperature, whereas the delayed end of season was mainly attributed to CO2 enrichments. We used these sensitivities to project phenology shifts under four Representative Concentration Pathway climate scenarios, predicting that vegetation will have prolonged photosynthetic seasons in the coming two decades. This observation-driven study indicates that realistic urban environments, together with SIF observations, provide a promising method for studying vegetation physiology under future climate change.

Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model

Solar-induced chlorophyll fluorescence (SIF), an electromagnetic signal that can potentially indicate vegetation photosynthetic activity, can be retrieved from ground-based, airborne and satellite measurements. However, due to the scattering and re-absorption effects inside the leaves and canopy, SIF measured at the canopy level is only a small part of the total SIF emission at the photosystem level. Therefore, a downscaling mechanism of SIF from the canopy level to the photosystem level is important for better understanding the relationship between SIF and the vegetation gross primary production (GPP). In this study, firstly, we analyzed the canopy scattering effects using a simple parameterization model based on the spectral invariant theory. The probability for SIF photons to escape from the canopy was found to be related to the anisotropic spectral reflectance, canopy interception of the upward solar radiation, and leaf absorption. An empirical approach based on a Random Forest (RF) regression algorithm was applied to downscale SIF constrained by the red, red-edge and far-red anisotropic reflectance. The RF was trained using simulations conducted with the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. The performance of the SIF downscaling method was evaluated with SCOPE and Discrete Anisotropic Radiative Transfer (DART) model simulations, ground measurements and airborne data. Results show that estimated SIF at the photosystem level matches well with simulated reference data, and the relationship between SIF and photosynthetically active radiation absorbed by chlorophyll is improved by SIF downscaling. This finding in combination with other evaluation criteria suggests the downscaling of canopy SIF as an efficient strategy to normalize species dependent effects of canopy structure and varying solar-view geometries. Based on our results for the SIF-APAR relationship, we expect that such normalization approaches can be helpful to improve estimates of photosynthesis using remote sensing measurements of SIF.

ANALYSIS OF SPATIAL-TEMPORAL VARIATION OF LAND SURFACE TEMPERATURE, VEGETATION AND SNOW COVER IN LAR NATIONAL PARK OF IRAN

Abstract. Changes in land surface reflectance measured by remote sensing data can be useful in climate change studies. This study attempts to analyze the spatial-temporal extent change of vegetation greenness, Land Surface Temperature (LST), and Normalized Difference Snow Index (NDSI) in late spring at the Lar National Park of Iran using Landsat data. Vegetation indices (VIs), LST, and NDSI maps were calculated for each date (1985, 1994, 2010, and 2015). All VIs have shown an increasing trend from 1985 to 2015 which depicted increase of vegetation. Spectral reflectance of all bands is declining from 1985 to 2015 except in near-infrared (NIR) bands. High reflectance in NIR bands is due to increased vegetation greenness. The reduction was seen in the visible bands that show increased vegetation photosynthetic activity. In the short-wave infrared bands (SWIR) were observed reduced trend from 1985 to 2015 which is indicate increased vegetation. Also, in the mid-wave infrared (MWIR) bands were observed a declining trend which is the result of decreasing soil fraction from 1985 to 2015. LST has increased from 23.27 °C in 1985 to 27.45 °C in 2015. Snow patches were decreased over the study period. In conclusion, VIs and surface reflectance bands are considered the main tool to display vegetation change. Also, high VIs values showed healthy and dense vegetation. The results of our study will provide valuable information in preliminary climate change studies.

Seasonal and interannual changes in vegetation activity of tropical forests in Southeast Asia

Tropical vegetation has been suggested to be vulnerable to future climate change. At the regional scale, few studies have focused on the fluctuations of vegetation activity of tropical forests in Southeast Asia, which is an important area of tropical vegetation. Here, we investigated the spatio-temporal variability of vegetation photosynthetic activity in Southeast Asia using three independent satellite-derived Normalized Difference Vegetation Index (NDVI) products (Global Inventory Modeling and Mapping Studies group using data from Advanced Very High Resolution Radiometer (GIMMS AVHRR) NDVI3g, Système Pour l’Observation de la Terre VEGETATION (SPOT-VGT), and MODerate resolution Imaging Spectroradiometer (MODIS)). We also used the recently developed Sun-Induced chlorophyll Fluorescence (SIF) from the Greenhouse gases Observing SATellite (GOSAT), a proxy of actual photosynthesis, as a complement to NDVI. In forests with pronounced wet-dry seasonal cycle (the north and south regions of Southeast Asia), we found consistent relationships between NDVI and climate factors (precipitation, temperature and solar radiation). At the seasonal scale, all of the datasets showed that the NDVI continuously decreased during the dry season, particularly in deciduous forests (DF). Similarly, SIF in the north region also displayed a decreasing trend during the dry season, although the magnitude of the decrease was relatively small compared to that of the NDVI. This result is in contrast to that reported for the Amazon forest where a significant increase in vegetation greenness during the dry season has been observed. During the wet season, different seasonal variations were observed between SIF and the NDVI in the north region. For both evergreen forests (EF) and DF, the maximum SIF was observed in the months with the maximum precipitation, while the maximum NDVI value was observed at the end of the wet season. At the interannual scale, NDVI decrease nonlinearly along drought severity in dry seasons. During wet seasons, the greenness is insensitive to droughts but more related to radiation. Our results demonstrate that forests in Southeast Asia are water-limited during the dry season, indicating a vulnerability to future increase in the intensity of El Nino events and to temperature-driven increases in evapotranspiration.

Retrieval of the photochemical reflectance index for assessing xanthophyll cycle activity: a comparison of near-surface optical sensors

Abstract. Unattended optical sensors are increasingly being deployed on eddy covariance flux towers and are often used to complement existing vegetation and micrometeorological measurements to enable assessment of biophysical states and biogeochemical processes over a range of spatial scales. Of particular interest are sensors that can measure the photochemical reflectance index (PRI), which can provide information pertaining to leaf pigments and photosynthetic activity. This interest has facilitated the production of a new range of lower-cost multispectral sensors specifically designed to measure temporal changes in the PRI signal. However, little is known about the characteristics (spectral, radiometric and temporal) of many of these PRI sensors, making it difficult to compare data obtained from these sensors across time, geographical locations and instruments. Furthermore, direct testing of the capability of these sensors to actually detect the conversion of the xanthophyll cycle, which is the original biological basis of the PRI diurnal signal, is largely absent, often resulting in an unclear interpretation of the signal, particularly given the wide range of factors now known to influence PRI. Through a series of experiments, we assess the sensitivity of one of the leading brands of PRI sensor (Skye SKR 1800) to changes in vegetation photosynthetic activity in response to changing irradiance. We compare the results with those obtained using a more expensive industry-standard visible to near-infrared hyperspectral spectrometer (PP Systems UniSpec) and determine the radiometric compatibility of measurements made by the different instruments. Results suggest that the SKR 1800 instrument is able to track rapid (seconds to minutes) and more gradual diurnal changes in photosynthetic activity associated with xanthophyll cycle pigment conversion. Measurements obtained from both the high and lower cost instrument were significantly linearly correlated but were subject to a large systematic bias, illustrating that differences in instrument configuration (e.g. spectral response functions and band positions) can have a large impact on the PRI measurement values obtained. Despite differences in absolute PRI values, significant correlations were observed between the canopy PRI derived from both the SKR 1800 and the UniSpec instruments, and the epoxidation state of the xanthophyll cycle (r2 = 0.46 p &lt; 0.05 and r2 = 0.76 p &lt; 0.01, respectively). However, the dynamic range of the SKR 1800 PRI signal was often lower than more expensive instruments and thus the lower cost multispectral instrument may be less sensitive to pigment dynamics related to photosynthetic activity. Based on our findings, we make a series of recommendations for the effective use of such sensors under field conditions and advocate that sensors should be fully characterized prior to their field deployment.