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Understanding the interactions between atmosphere and vegetation in changing climatic conditions is important so that we can predict the carbon sequestration potential of ecosystems. Helpful tools here are the terrestrial biosphere models (TBMs), since they include detailed ecophysiological process descriptions, e.g. the manifold interactions between the carbon and nitrogen cycles. However, the modelling of the nitrogen cycle poses challenges and having observational constraints on nitrogen cycle is crucial. Current remote sensing products offer estimates of leaf chlorophyll (Cab), that is related to the nitrogen cycle. In this study we want to assess how useful Cab observations are at site scale to constrain a TBM. In this work we are studying a temperate mixed forest, Borden, located in Canada. We use a TBM QUantifying Interactions between terrestrial Nutrient Cycles, QUINCY, to model this site. From the site we have long-term (20 years) flux tower and LAI (from PAR measurements) observations together with leaf level observations of leaf chlorophyll (Cab), leaf nitrogen, and photochemical parameters of maximum carboxylation rate (Vcmax) and maximum potential electron transport rate (Jmax). The QUINCY model was predicting too late leaf senescence, which we tuned using the site level data. The amount of leaf nitrogen was originally quite successfully simulated by QUINCY, but the amount of simulated Cab was too low. Matching the simulated Cab values with the observations did not have a pronounced effect on the GPP. Additionally, the development of LAI and Cab were originally fully coupled in QUINCY, whereas the observations showed a delayed development of Cab compared to LAI. When we implemented this decoupling between LAI and Cab, an improvement of simulated GPP compared to the observations was found. Also then the simulated Vcmax and Jmax showed better correspondence to the observations. Assessment of the long-term behaviour of the model at the site showed that the model was able to capture the drought-induced drawdown of carbon fluxes taking place in 2007. The observations showed an increase in the component fluxes of carbon during the time period, but this was not replicated by the model. The start of season (SOS) and end of season (EOS) were estimated from both the simulated and observed GPP and LAI using a simple threshold method. The model was more successful in capturing the changes in the growing season metrics estimated by LAI than by GPP. The model was predicting too late onset of GPP in many years, but captured largely the interannual variation of SOS in observed GPP. This study paves the way for work using remotely sensed leaf chlorophyll in evaluation and improvement of the QUINCY model.

Foliar traits such as specific leaf area (SLA), leaf nitrogen (N) and phosphorus (P) concentrations play an important role in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations.Here, we intercompare such global upscaled foliar trait maps at 0.5° spatial resolution (six maps for SLA, five for N, three for P), categorize the upscaling approaches used to generate them, and evaluate the maps with trait estimates from a global database of vegetation plots (sPlotOpen). We disentangled the contributions from different plant functional types (PFTs) to the upscaled maps and calculated a top-of-canopy-weighted mean (TWM) and the community-weighted mean (CWM) of sPlotOpen trait estimates.We found that the global foliar trait maps of SLA and N differ drastically and fall into two groups that are almost uncorrelated (for P only maps from one group were available). The primary factor explaining the differences between these groups is the use of PFT information combined with land cover products in one group while the other group relied only on environmental predictors. The impact of using TWM or CWM on spatial patterns is considerably smaller than that of including PFT and land cover information. The maps that used PFT and land cover information exhibit considerable similarities in spatial patterns that are strongly driven by land cover. The maps not using PFTs show a lower level of similarity and tend to be strongly driven by individual environmental variables.Overall, the maps using PFT and land cover information better reproduce the between-PFT trait differences and trait distributions of the plot-level sPlotOpen data, while the two groups performed similarly in capturing within-PFT trait variation. Upscaled maps of both groups were moderately correlated to grid-cell-level sPlotOpen data (R = 0.2-0.6), but only when accounting for the differences in processing in the upscaling approaches by applying similar scaling to the sPlotOpen data.Our findings indicate the importance of accounting for within-grid-cell trait variation, which has important implications for applications using existing maps and future upscaling efforts.

The leaf chlorophyll content (LCC) is an important plant physiological trait and is critical for accurate modeling of vegetation photosynthesis over time and space. To date, there is still a lack of a global long time-series dataset of LCC. In this study, we developed an algorithm to retrieve global LCC from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data from 2000 to 2020. An essential requirement for generating LCC time series is to capture its seasonal dynamics. This issue was addressed by using a matrix system with two pairs of vegetation indices to minimize the impacts of leaf area index and canopy nonphotosynthetic material on LCC estimation in different seasons. The matrix system algorithm was applied to Landsat data and MODIS data, respectively. The validation based on Landsat data and ground measurements reveals that the algorithm has the ability to catch the seasonal variations of LCC in different plant functional types, and the MODIS-derived LCC shows good agreement with Landsat-upscaled LCC ( R2=0.77 and RMSE = 6.9μg /cm2). The global eight-day LCC data at 500-m resolution in 2000–2020 were generated using the matrix system from MODIS and presented distinct temporal and spatial variations, which provides a new opportunity for analyzing vegetation physiological dynamics in climate change studies.

The Institute for Sustainable Food

The maximum rate of Rubisco carboxylation (Vcmax) determines leaf photosynthetic capacity and is a key parameter for estimating the terrestrial carbon cycle, but its spatial information is lacking, hindering global ecological research. Here, we convert leaf chlorophyll content (LCC) retrieved from satellite data to Vcmax, based on plants' optimal distribution of nitrogen between light harvesting and carboxylation pathways. We also derive Vcmax from satellite (GOME-2) observations of sun-induced chlorophyll fluorescence (SIF) as a proxy of leaf photosynthesis using a data assimilation technique. These two independent global Vcmax products agree well (r2=0.79,RMSE=15.46µmol m−2 s−1, P<0.001) and compare well with 3672 ground-based measurements (r2=0.69,RMSE=13.8µmol m−2 s−1 and P<0.001 for SIF; r2=0.55,RMSE=18.28µmol m−2 s−1 and P<0.001 for LCC). The LCC-derived Vcmax product is also used to constrain the retrieval of Vcmax from TROPical Ozone Mission (TROPOMI) SIF data to produce an optimized Vcmax product using both SIF and LCC information. The global distributions of these products are compatible with Vcmax computed from an ecological optimality theory using meteorological variables, but importantly reveal additional information on the influence of land cover, irrigation, soil pH, and leaf nitrogen on leaf photosynthetic capacity. These satellite-based approaches and spatial Vcmax products are primed to play a major role in global ecosystem research. The three remote sensing Vcmax products based on SIF, LCC, and SIF+LCC are available at https://doi.org/10.5281/zenodo.6466968 (Chen et al., 2022), and the code for implementing the ecological optimality theory is available at https://github.com/SmithEcophysLab/optimal_vcmax_R and https://doi.org/10.5281/zenodo.5899564 (last access: 31 August 2022) (Smith et al., 2022).

Plants invest a considerable amount of leaf nitrogen in the photosynthetic enzyme ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO), forming a strong coupling of nitrogen and photosynthetic capacity. Variability in the nitrogen-photosynthesis relationship indicates different nitrogen use strategies of plants (i.e., the fraction nitrogen allocated to RuBisCO; fLNR), however, the reason for this remains unclear as widely different nitrogen use strategies are adopted in photosynthesis models. Here, we use a comprehensive database of in situ observations, a remote sensing product of leaf chlorophyll and ancillary climate and soil data, to examine the global distribution in fLNR using a random forest model. We find global fLNR is 18.2 ± 6.2%, with its variation largely driven by negative dependence on leaf mass per area and positive dependence on leaf phosphorus. Some climate and soil factors (i.e., light, atmospheric dryness, soil pH, and sand) have considerable positive influences on fLNR regionally. This study provides insight into the nitrogen-photosynthesis relationship of plants globally and an improved understanding of the global distribution of photosynthetic potential., The fraction of leaf nitrogen allocated to RuBisCO indicates differing nitrogen use strategies of plants and varies considerably. Here the authors show that this variation is largely driven by leaf thickness and phosphorus content with light intensity, atmospheric dryness and soil pH also having considerable influence.

Spatial information on crop nutrient status is central for monitoring vegetation health, plant productivity and managing nutrient optimization programs in agricultural systems. This study maps the spatial variability of leaf chlorophyll content within fields with differing quantities of nitrogen fertilizer application, using multispectral Landsat-8 OLI data (30 m). Leaf chlorophyll content and leaf area index measurements were collected at 15 wheat (Triticum aestivum) sites and 13 corn (Zea mays) sites approximately every 10 days during the growing season between May and September 2013 near Stratford, Ontario. Of the 28 sites, 9 sites were within controlled areas of zero nitrogen fertilizer application. Hyperspectral leaf reflectance measurements were also sampled using an Analytical Spectral Devices FieldSpecPro spectroradiometer (400–2500 nm). A two-step inversion process was developed to estimate leaf chlorophyll content from Landsat-8 satellite data at the sub-field scale, using linked canopy and leaf radiative transfer models. Firstly, at the leaf-level, leaf chlorophyll content was modelled using the PROSPECT model, using both hyperspectral and simulated mulitspectral Landsat-8 bands from the same leaf sample. Hyperspectral and multispectral validation results were both strong (R2 = 0.79, RMSE = 13.62 μg/cm2 and R2 = 0.81, RMSE = 9.45 μg/cm2, respectively). Secondly, leaf chlorophyll content was estimated from Landsat-8 satellite imagery for 7 dates within the growing season, using PROSPECT linked to the 4-Scale canopy model. The Landsat-8 derived estimates of leaf chlorophyll content demonstrated a strong relationship with measured leaf chlorophyll values (R2 = 0.64, RMSE = 16.18 μg/cm2), and compared favourably to correlations between leaf chlorophyll and the best performing tested spectral vegetation index (Green Normalised Difference Vegetation Index, GNDVI; R2 = 0.59). This research provides an operational basis for modelling within-field variations in leaf chlorophyll content as an indicator of plant nitrogen stress, using a physically-based modelling approach, and opens up the possibility of exploiting a wealth of multispectral satellite data and UAV-mounted multispectral imaging systems.

Estimation of leaf photosynthetic capacity from the photochemical reflectance index and leaf pigmentsShuren Chou1#, Bin Chen2*#, Jing Chen3,4*, Miaomiao Wang2,5, Shaoqiang Wang2,5,6, Holly Croft7, Qin Shi81Space Security Center, Space Engineering University, Beijing 101416, China;2Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;3School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China4Department of Geography and Planning, University of Toronto, Toronto, Canada5College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China6College of Geography and Information Engineering, China University of Geosciences, Wuhan, China7Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield S10 2TN, U.K.8Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China;Abstract: Leaf chlorophyll content has recently been found to be a better proxy than leaf nitrogen content for leaf photosynthetic capacity in a mixed deciduous broadleaf forest. A key concept underlying the relationship between leaf photosynthetic capacity and leaf chlorophyll content was the coordinate regulation of photosynthetic components (i.e. light harvesting, photochemical, and biochemical components). In order to test this hypothesis, we measured seasonal variations in leaf nitrogen content (Nleaf), leaf photosynthetic pigments (i.e. chlorophyll (ChlLeaf), carotenoids (CarLeaf) and xanthophyll (XanLeaf)) and leaf photosynthetic capacity (i.e. the maximum rate at which ribulose bisphosphate (RuBP) is carboxylated (Vcmax25) and regenerated (Jmax25) at 25 oC) at a paddy rice site during the growing season in 2016. We investigated the effectiveness of (Nleaf), leaf photosynthetic pigments, leaf-level photochemical reflectance index at sunny noon (PRILeaf_noon) and their possible combinations for estimating leaf photosynthetic capacities (i.e. Vcmax25 and Jmax25) at a paddy rice site. ChlLeaf was highly correlated to Vcmax25 and Jmax25 (R2 = 0.89 and 0.87, respectively), which were better than Nleaf (R2 = 0.80 and 0.85, respectively). The products of PRILeaf_noon with leaf pigments (i.e. ChlLeaf, CarLeaf and XanLeaf) were also found to be highly correlated with Vcmax25 (R2 = 0.95 to 0.96). Also, the product of leaf chlorophyll a and CarLeaf was a good proxy for Vcmax25 (R2 = 0.93). In sum, this study supported the previously findings that leaf chlorophyll content was better correlated with Vcmax25 than leaf nitrogen content. Also, combining PRILeaf_noon with leaf pigments (i.e. ChlLeaf, CarLeaf and XanLeaf) offered an additional way to estimate leaf photosynthetic capacity (i.e. Vcmax25). These findings supported the hypothesis of coordinate regulation of photosynthetic components and they would be helpful to estimation of leaf photosynthetic capacity using remote sensing data.Keywords: seasonal variations; leaf nitrogen content; photosynthetic pigments; leaf maximum carboxylation rate

The rapid warming of polar regions is having a demonstrable impact on ecosystem composition and there is a pressing need to understand the carbon cycle implications of these changes. A promising approach for investigating photosynthesis at ecosystem and regional scales involves the remote sensing of Solar Induced Fluorescence (SIF). However, ground-validation of SIF and its association with carbon assimilation and other ecophysiological parameters is largely missing from the polar regions. We will present results of measurements of ground-level SIF and hyperspectral reflectance that were coupled with CO2 exchange measurements in three contrasting polar regions: shrub and bog ecosystems in northern Sweden, wet coastal tundra in Alaska and moss turf in Antarctica. We show good agreement between SIF and photosynthesis across scales, from leaf-level to surface fluxes, but with variable relationships between ecosystem types. Our results show strong potential for using SIF to help understand the impact of change in these regions.

Author(s): Luo, X; Chen, JM; Liu, J; Black, TA; Croft, H; Staebler, R; He, L; Arain, MA; Chen, B; Mo, G; Gonsamo, A; McCaughey, H | Abstract: Evapotranspiration (ET) is commonly estimated using the Penman-Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon-water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two-big-leaf scheme (TBL) and a two-leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2n=n0.71, root-mean-square errorn=n0.05nmm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light-saturated and light-unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf-level processes to the canopy scale in BL and TBL.

Leaf area index (LAI) is a critical biophysical indicator that describes foliage abundance in ecosystems. An accurate and continuous estimation of LAI is therefore desirable to quantify ecosystem status and function (e.g. carbon and water exchange between the land surface and the atmosphere). However, deriving accurate LAI measurements at regular temporal intervals remains challenging, requiring either destructive sampling or manual collection of canopy gap fraction measurements at discrete time intervals. In this study, we present four methods to obtain continuous LAI data, simply derived from above and below canopy measurements of photosynthetically active radiation (PAR) at the Borden Forest Research Station from 1999 to 2018. We compared LAI derived using the four PAR-based methods to independent measurements of LAI from optical methods and the MODIS satellite LAI product. LAI derived from all four PAR-based methods captured the seasonal changes in observed and remotely sensed LAI and showed a close linear correspondence with one another (R2 of 0.55 to 0.76 compared to MODIS LAI, and R2 of 0.78 to 0.84 compared to LAI-2000 measurements). A PAR-based method using Miller's Integral theorem showed the strongest linear relationship with LAI-2000 measurements (R2=0.84, p The four PAR-based LAI methods outlined in this study provide an LAI dataset of unprecedented temporal resolution. These methods will allow precise determination of phenological events, improve leaf to canopy scaling in process-based models, and provide valuable insight into dynamic vegetation responses to global climate change.

Maximum light use efficiency ( ${\text{LUE}}_{\rm{max}}$ ) is an important parameter in biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable crop ${\text{LUE}}_{\rm{max}}$ from satellite remote sensing data. ${\text{LUE}}_{\rm{max}}$ at the plot level was retrieved first by assimilating field measured green leaf area index and biomass into a crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. ${\text{LUE}}_{\rm{max}}$ was then mapped using the best regression model from a VI. The influence factors on ${\text{LUE}}_{\rm{max}}$ variability were also assessed. Contrary to a fixed ${\text{LUE}}_{\rm{max}}$ , our results suggest that ${\text{LUE}}_{\rm{max}}$ is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level ${\text{LUE}}_{\rm{max}}$ and VIs, particularly the two-band enhanced vegetation index for winter wheat ( Triticum aestivum ) and the green chlorophyll index for maize ( Zea mays ) at the milk stage, provided a potential to derive ${\text{LUE}}_{\rm{max}}$ from remote sensing observations. To evaluate the quality of ${\text{LUE}}_{\rm{max}}$ derived from remote sensing data, biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant ${\text{LUE}}_{\rm{max}}$ and the derived variable ${\text{LUE}}_{\rm{max}}$ . Significant improvements in biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable ${\text{LUE}}_{\rm{max}}$ . This study offers a new way to derive ${\text{LUE}}_{\rm{max}}$ for a specific PEM and to improve the accuracy of biomass estimation using remote sensing.

The leaf maximum carboxylation rate (Vcmax) is one of the crucial parameters in determining the photosynthetic capacity of plants. Providing accurate estimates of leaf Vcmax25 that cover large geographic areas and that incorporate plant seasonality is central to correctly predicting carbon fluxes within the terrestrial global carbon cycle. Chlorophyll, as the main photon-harvesting component in leaves, is closely linked to photosynthesis. However, how the nature of the relationship between the leaf maximum carboxylation rate scaled to 25 °C (Vcmax25) and leaf chlorophyll content varies according to plant type is uncertain. In this study, we investigate whether a universal and stable relationship exists between leaf Vcmax25 and leaf chlorophyll content across different C3 plant types from a plant physiological perspective and verify it using field experiments. Measurements of leaf chlorophyll content (Chl) and CO2 response curves were made on 283 crop, shrub, tree and vegetable leaves in China and the Borden Forest Research Station in southern Ontario, Canada. A strong relationship was found between the leaf Vcmax25 and chlorophyll content across different C3 plant types (R2 = 0.65, P

Leaf chlorophyll content plays a vital role in plant photosynthesis. The PROSPECT model has been widely used for retrieving leaf chlorophyll content from remote sensing data over various plant species. However, despite wide variations in leaf surface reflectance across different plant species and environmental conditions, leaf surface reflectance is assumed to be the same for different leaves in the PROSPECT model. This work extends the PROSPECT model by taking into account the variation of leaf surface reflection. In the modified model named PROSPECT-Rsurf, an additional surface layer with a variable refractive index is bounded on the N elementary layers. Leaf surface reflectance (Rs) is characterized by the difference between the refractive indices of leaf surface and interior layers. The specific absorption coefficients of the leaf total chlorophyll and carotenoids were recalibrated using a cross-calibration method and the refractive indices of leaf surface and interior layers were obtained during model inversion. Chlorophyll content (Cab) retrieval and spectral reconstruction in the visible spectral region (VIS, 400−750 nm) were greatly improved using PROSPECT-Rsurf, especially for leaves covered by heavy wax or hard cuticles that lead to high surface reflectance. The root mean square error (RMSE) of chlorophyll estimates decreased from 11.1 µg/cm2 to 8.9 µg/cm2 and the Pearson’s correlation coefficient (r) increased from 0.81 to 0.88 (p < 0.01) for broadleaf samples in validation, compared to PROSPECT-5. For needle leaves, r increased from 0.71 to 0.89 (p < 0.01), but systematic overestimation of Cab was found due to the edge effects of needles. After incorporating the edge effects in PROSPECT-Rsurf, the overestimation of Cab was alleviated and its estimation was improved for needle leaves. This study explores the influence of leaf surface reflectance on Cab estimation at the leaf level. By coupling PROSPECT-Rsurf with canopy models, the influence of leaf surface reflectance on canopy reflectance and therefore canopy chlorophyll content retrieval can be investigated across different spatial and temporal scales.

A multiple sensor payload for a multi-rotor based UAV platform was developed and tested for measuring land surface albedo and spectral measurements at user-defined spatial, temporal, and spectral resolutions. The system includes a Matrice 600 UAV with an RGB camera and a set of four downward pointing radiation sensors including a pyranometer, quantum sensor, and VIS and NIR spectrometers, measuring surface reflected radiation. A companion ground unit consisting of a second set of identical sensors simultaneously measure downwelling radiation. The reflected and downwelling radiation measured by the four sensors are used for calculating albedo for the total shortwave broadband, visible band and any narrowband at a 1.5 nm spectral resolution within the range of 350&ndash, 1100 nm. The UAV-derived albedo was compared with those derived from Landsat 8 and Sentinel-2 satellite observations. Results show the agreement between total shortwave albedo from UAV pyranometer and Landsat 8 (R2 = 0.73) and Sentinel-2 (R2 = 0.68). Further, total shortwave albedo was estimated from spectral measurements and compared with the satellite-derived albedo. This UAV-based sensor system promises to provide high-resolution multi-sensors data acquisition. It also provides maximal flexibility for data collection at low cost with minimal atmosphere influence, minimal site disturbance, flexibility in measurement planning, and ease of access to study sites (e.g., wetlands) in contrast with traditional data collection methods.

Author(s): Luo, Xiangzhong; Croft, Holly; Chen, Jing M; He, Liming; Keenan, Trevor F | Abstract: The terrestrial biosphere plays a critical role in mitigating climate change by absorbing anthropogenic CO2 emissions through photosynthesis. The rate of photosynthesis is determined jointly by environmental variables and the intrinsic photosynthetic capacity of plants (i.e. maximum carboxylation rate; V c max 25 ). A lack of an effective means to derive spatially and temporally explicit V c max 25 has long hampered efforts towards estimating global photosynthesis accurately. Recent work suggests that leaf chlorophyll content (Chlleaf ) is strongly related to V c max 25 , since Chlleaf and V c max 25 are both correlated with photosynthetic nitrogen content. We used medium resolution satellite images to derive spatially and temporally explicit Chlleaf , which we then used to parameterize V c max 25 within a terrestrial biosphere model. Modelled photosynthesis estimates were evaluated against measured photosynthesis at 124 eddy covariance sites. The inclusion of Chlleaf in a terrestrial biosphere model improved the spatial and temporal variability of photosynthesis estimates, reducing biases at eddy covariance sites by 8% on average, with the largest improvements occurring for croplands (21% bias reduction) and deciduous forests (15% bias reduction). At the global scale, the inclusion of Chlleaf reduced terrestrial photosynthesis estimates by 9 PgC/year and improved the correlations with a reconstructed solar-induced fluorescence product and a gridded photosynthesis product upscaled from tower measurements. We found positive impacts of Chlleaf on modelled photosynthesis for deciduous forests, croplands, grasslands, savannas and wetlands, but mixed impacts for shrublands and evergreen broadleaf forests and negative impacts for evergreen needleleaf forests and mixed forests. Our results highlight the potential of Chlleaf to reduce the uncertainty of global photosynthesis but identify challenges for incorporating Chlleaf in future terrestrial biosphere models.

In this study, the Carbon and Nitrogen coupled Canadian Land Surface Scheme (CN-CLASS) was used to investigate the impact of climate variability, seasonal weather effects, disturbance, and CO 2 fertilization effects on the historical carbon (C) dynamics of an eastern Canadian boreal forest landscape (6275 ha) from 1928 to 2008. The model was parameterized with ecological, soil texture, forest inventory and historical disturbance data and driven by hourly meteorological data constructed from the historical climate records. Before performing the landscape-level simulation, model results were evaluated against site-level eddy covariance (EC) measurements. Landscape-level simulated C fluxes showed that the forest ecosystem was a small C sink in all of the years prior to cutting and insect defoliation in 1963, which resulted in the removal of 23849 Mg C from the forest landscape. As a consequence, the study area was a large C source in 1963 (net biome productivity, NBP = −537 g C m −2 yr −1 ). After that, the forest landscape was mainly a net annual C sink, with total ecosystem C stocks increasing from 14.8 to 16.0 kg C m −2 by 2008, during which total biomass increased from 3.1 to 4.2 kg C m −2 . Analysis of landscape-level, age-detrended, simulated C fluxes for the undisturbed forest landscape from 1928 to 2002 indicated that summer temperature was the dominant control on C fluxes with higher temperature causing a much faster increase in landscape-level annual R e than that of GPP (i.e. 12.3 vs. 1.3 g C m −2 yr −1 °C −1 , respectively). Scenario analysis suggested that forest disturbances had a less profound impact on landscape-level C fluxes and stocks compared to inter-annual climate variability in this landscape. Climate sensitivity analysis revealed that landscape-level simulated C fluxes and stocks were sensitive to the change of air temperature, while only dead organic matter (DOM) and soil organic matter (SOM) were sensitive to the change of precipitation. This study will help to explore the impact of future climate change scenarios and forest management on boreal forest landscapes.

Clumping index quantifies the level of foliage aggregation, relative to a random distribution, and is a key structural parameter of plant canopies and is widely used in ecological and meteorological models. In this study, the inter- and intra-annual variations in clumping index values, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF product, are investigated at six forest sites, including conifer forests, a mixed deciduous forest and an oak-savanna system. We find that the clumping index displays large seasonal variation, particularly for the deciduous sites, with the magnitude in clumping index values at each site comparable on an intra-annual basis, and the seasonality of clumping index well captured after noise removal. For broadleaved and mixed forest sites, minimum clumping index values are usually found during the season when leaf area index is at its maximum. The magnitude of MODIS clumping index is validated by ground data collected from 17 sites. Validation shows that the MODIS clumping index can explain 75% of variance in measured values (bias = 0.03 and rmse = 0.08), although with a narrower amplitude in variation. This study suggests that the MODIS BRDF product has the potential to produce good seasonal trajectories of clumping index values, but with an improved estimation of background reflectance.

In forested ecosystems, the aggregation of leaves into different spatial structures at the canopy, branch and shoot scales leads to a non-random spatial distribution of foliar elements. However, effect of foliar aggregation or clumping on the estimation of terrestrial evapotranspiration (ET) is not yet well understood. To evaluate the effect of foliar clumping, the process-based Boreal Ecosystem Productivity Simulator (BEPS) is used to simulate ET at eight flux tower sites in North American forests. BEPS separates a canopy into sunlit and shaded leaf groups in the calculation of canopy-level ET and clumping affects this separation. Three cases are simulated with BEPS, and the modeled ET values are compared with flux tower measurements: Case I serves as a baseline, in which LAI and clumping index at the sites are considered. In this case, BEPS can explain 43–75% the variance of measured ET at these sites. For Case II, the LAI is considered but clumping is ignored; resulting in an overestimation of annual ET at all sites by ∼5% at the most clumped sites. In Case III, when effective LAI is used (i.e. clumping is not considered), the site-averaged mean annual ET is underestimated by 11.5%, with the largest underestimation found at the site with most clumping (CA-DF49; 19.1%). In both Case II and Case III, the more clumped a canopy is, the larger bias is found in the ET estimation ( p These results demonstrate the need for considering foliage clumping in process-based ET modeling, with potential biases having large implications for carbon cycle modeling, water budget calculations and understanding and predicting ecosystem responses to future climatic scenarios.

This study presents the energy, water, and carbon (C) flux dynamics of a young afforested temperate white pine (Pinus strobus L.) forest in southern Ontario, Canada during the initial fourteen years (2003–2016) of establishment. Energy fluxes, namely, net radiation (Rn), latent heat (LE), and sensible heat (H) flux increased over time, due to canopy development. Annual values of ground heat flux (G) peaked in 2007 and then gradually declined in response to canopy closure. The forest became a consistent C-sink only 5 years after establishment owing in part to low respiratory fluxes from the former agricultural, sandy soils with low residual soil organic matter. Mean annual values of gross ecosystem productivity (GEP), ecosystem respiration (RE), and net ecosystem productivity (NEP) ranged from 494 to 1913, 515 to 1774 and −126 to 216 g C m−2 year−1 respectively, over the study period. Annual evapotranspiration (ET) values ranged from 328 to 429 mm year−1 over the same period. Water use efficiency (WUE) increased with stand age with a mean WUE value of 3.92 g C kg−1 H2O from 2008 to 2016. Multivariable linear regression analysis conducted using observed data suggested that the overall, C and water dynamics of the stand were primarily driven by radiation and temperature, both of which explained 77%, 48%, 28%, and 76% of the variability in GEP, RE, NEP, and ET, respectively. However, late summer droughts, which were prevalent in the region, reduced NEP. The reduction in NEP was enhanced when summer drought events were accompanied by increased heat such as those in 2005, 2012 and 2016. This study contributes to our understanding of the energy, water and C dynamics of afforested temperate conifer plantations and how these forests may respond to changing climate conditions during the crucial initial stage of their life cycle. Our findings also demonstrate the potential of pine plantation stands to sequester atmospheric CO2 in eastern North America.

Chlorophyll is the main light-harvesting pigment in leaves, facilitating photosynthesis and indicating the supply of nitrogen for photosynthetic enzymes. In this study, we explore the feasibility of integrating leaf chlorophyll content (Chl leaf ) into a Terrestrial Biosphere Model (TBM), as a proxy for the leaf maximum carboxylation rate at 25 °C ( V max 25 ), for the purpose of improving carbon and water flux estimation. Measurements of Chl leaf and V max 25 were made in a deciduous forest stand at the Borden Forest Research Station in southern Ontario, Canada, where carbon and water fluxes were measured by the eddy covariance method. The use of Chl leaf -based V max 25 in the TBM significantly reduces the bias of estimated gross primary productivity (GPP) and evapotranspiration (ET) and improves the temporal correlations between the simulated and the measured fluxes, relative to the commonly employed cases of using specified constant V max 25 , leaf area index (LAI)-based V max 25 or specific leaf area (SLA)-based V max 25 . The biggest improvements are found in spring and fall, when the mean absolute errors (MAEs) between modelled and measured GPP are reduced from between 2.2–3.2 to 1.8 g C m −2 d −1 in spring and from between 2.1–2.8 to 1.8 g C m −2 d −1 in fall. The MAEs in ET estimates are reduced from 0.7–0.8 mm d −1 to 0.6 mm d −1 in spring, but no significant improvement is noted in autumn. A two-leaf upscaling scheme is used to account for the uneven distribution of incoming solar radiation inside canopies and the associated physiological differences between leaves. We found that modelled V max 25 in sunlit leaves is 34% larger than in the shaded leaves of the same Chl leaf , which echoes previous physiological studies on light acclimation of plants. This study represents the first case of the incorporation of chlorophyll as a proxy for V max 25 in a two-leaf TBM at a forest stand and demonstrates the efficacy of using chlorophyll to constrain V max 25 and reduce the uncertainties in GPP and ET simulations.

Plant pigments in terrestrial ecosystems are crucial for sustaining life on the planet, through their pivotal role in plant photosynthesis. They are also important indicators of plant health and nutrient status. As such, a range of empirical and physically based methods exist for estimating leaf pigment content, from the leaf level to larger scale mapping efforts from airborne and satellite imagery. The suitability of these methods varies according to observational scale, application, technical expertise, and cost. Obtaining accurate foliar pigment values from optical remote sensing techniques is vital for the monitoring of vegetation–environment interactions and a range of terrestrial ecosystem processes.

The exchanges of carbon dioxide (CO2), water vapor, and energy were measured nearly continuously since 1996 over a mixed mature transition forest at the Borden Forest Research Station, in southern Ontario, Canada. Borden Forest, one of the longest running flux towers in North America, is located in the temperate–boreal ecotone. This transitional region, which includes species close to the limits of their environmental range, may be particularly susceptible to changes in forest composition as a result of climate change. Here we analyze net CO2 exchange, measured using the eddy covariance method, and concurrent meteorological variables. The forest was found to be a low-to-moderate CO2 sink, with uptake of 177 ± 28 gC m−2 yr−1 (mean ± standard error). In two of the years, however, the forest was a weak CO2 source (i.e., 1996: −36 gC m−2 yr−1 and 2001: −35 gC m−2 yr−1), demonstrating that the forest can switch between source and sink. Over the 17 years of measurement, annual net ecosystem productivity (NEP) increased by 15.7 gC m−2 yr−1 yr−1, due to a decline in ecosystem respiration of 4.2 gC m−2 yr−1 yr−1 and an increase in gross ecosystem productivity of 11.6 gC m−2 yr−1 yr−1. There were notable long-term indications of climatic warming: annual air temperature rose by 0.09 °C yr−1, while soil temperature increased by 0.08 °C yr−1. Photosynthetically active radiation and soil temperature were found to be the dominant environmental drivers of interannual variations and long-term trends in NEP; on seasonal or monthly time-scales, air temperature and precipitation also influenced CO2 uptake. NEP is positively correlated with the length of the net carbon uptake period, which varied from 111 to 164 days. The large interannual variations in CO2 flux in this dataset demonstrate the need for long time series of CO2, water vapor, and energy fluxes, together with meteorological measurements; such measurements show long-term trends, which can be used to understand and predict future changes in forest-atmosphere exchanges in response to anticipated changes in climate.

Forested ecosystems represent an important part of the global carbon cycle, with accurate estimates of gross primary productivity (GPP) crucial for understanding ecosystem response to environmental controls and improving global carbon models. This research investigated the relationships between leaf area index (LAI) and leaf chlorophyll content (ChlLeaf) with forest carbon uptake. Ground measurements of LAI and ChlLeaf were taken approximately every 9 days across the 2013 growing season from day of year (DOY) 130 to 290 at Borden Forest, Ontario. These biophysical measurements were supported by on-site eddy covariance flux measurements. Differences in the temporal development of LAI and ChlLeaf were considerable, with LAI reaching maximum values within approximately 10 days of bud burst at DOY 141. In contrast, ChlLeaf accumulation only reached maximum values at DOY 182. This divergence has important implications for GPP models which use LAI to represent the fraction of light absorbed by a canopy (fraction of absorbed photosynthetic active radiation (fAPAR)). Daily GPP values showed the strongest relationship with canopy chlorophyll content (ChlCanopy; R2 = 0.69, p

The eastern North America mature deciduous forests are increasing their carbon (C) sink, which is believed to be due to a longer growing season. In this study, we investigated the impacts of land surface phenology (LSP) and carbon uptake phenology (CUP) on the net ecosystem productivity (NEP) of the two longest-running flux tower sites in the region. Our results show that there is no trend in the start (SOS), end (EOS), and length of growing season (LOS) at both sites; nor do they explain the interannual and long-term trend in NEP. We found no evidence for a changing growing season and cannot attribute the increasing C sink to growing season length. However, there is strong trend in end (ECU) and length of net positive carbon uptake (LCU) period. ECU is delaying and LCU is getting longer, and they both explain the interannual and long-term trends in NEP. There is increasing trend of photosynthetically active radiation (PAR) at both sites in line with increasing NEP. PAR is contributing the most both to NEP and ECU. CUP is affected more by the increased photosynthetic activity partially due to increased PAR, but not by timing of spring onset and autumn senescence of leaves. There is also significant ( p x ), nitrogen oxides (NO x ), total nitrate (HNO 3 + NO 3 ) and ozone since 1992. The reductions in gaseous and particulate emissions imply the occurrence of direct aerosol mediated brightening and therefore increased PAR and enhanced C uptake by eastern North America mature forests.

Spatial and temporal variations in canopy structure and leaf biochemistry have considerable influence on fluxes of CO2, water and energy and nutrient cycling in vegetation. Two vegetation indices (VI), NDVI and Macc01, were used to model the spatio-temporal variability of broadleaf chlorophyll content and leaf area index (LAI) across a growing season. Ground data including LAI, hyperspectral leaf reflectance factors (400–2500 nm) and leaf chlorophyll content were measured across the growing season and satellite-derived canopy reflectance data was acquired for 33 dates at 1200 m spatial resolution. Key phenological information was extracted using the TIMESAT software. Results showed that LAI and chlorophyll start of season (SOS) dates were at day of year (DOY) 130 and 157 respectively, and total season duration varied by 57 days. The spatial variability of chlorophyll and LAI phenology was also analyzed at the landscape scale to investigate phenological patterns over a larger spatial extent. Whilst a degree of spatial variability existed, results showed that chlorophyll SOS lagged approximately 20–35 days behind LAI SOS, and the end of season (EOS) LAI dates were predominantly between 20 and 30 days later than chlorophyll EOS. The large temporal differences between VI-derived chlorophyll content and LAI has important implications for biogeochemical models using NDVI or LAI to represent the fraction of photosynthetically active radiation absorbed by a canopy, in neglecting to account for delays in chlorophyll production and thus photosynthetic capacity.

The Sentinel-2A (S2A) Multi-Spectral Imager (MSI) is a new remote sensor launched on 23 June 2015 that provides unprecedented Earth observation with high spatial, spectral and temporal resolutions. It has high potential for chlorophyll content estimation. Chlorophyll content plays a crucial role in plant photosynthesis affecting the terrestrial carbon cycle. In this research, a physical retrieval algorithm is proposed for leaf chlorophyll content from the S2A MSI data based on 4-Scale and PROSPECT models. Satellite and ground data were collected and processed in a mixed temperate forest near Borden, Ontario, Canada from May to October 2016. Preliminary validation shows an agreement between the inverted and ground measured leaf chlorophyll contents, with r = 0.77 and RMSE = 8.82 μg/cm2, which is an improvement over those generated by the Sentinel Application Platform (SNAP). Further research is ongoing, and the algorithm will be improved in the future.

Many ecosystem variables and processes show a relationship with stand age, including leaf area index (LAI), nutrient and water cycling, biomass production and photosynthesis. However, investigations into stand age dependency have typically focused on stand structure, and are limited by the availability of measurement sites. This study uses a measured chronosequence of 9 sites, ranging in age from 15 to 90 years, supported by a time-series of satellite-derived data to further validate temporal trends in LAI and leaf chlorophyll values. Managed Pinus banksiana stands in Northern Ontario, Canada were sampled for canopy structural parameters (LAI, stand density, crown radius, tree height) and leaf biochemistry (Chlorophyll a + b). Landsat 5 TM (30 m) data was obtained from 1989 to 2011 and chlorophyll- (Revised Transformed Chlorophyll absorption ratio index; RTCARI) and LAI- (Reduced Simple Ratio; RSR) sensitive spectral vegetation indices (VI) were calculated. Stand age showed strong relationships with tree height ( R 2 = 0.95, p R 2 = 0.68; p R 2 = 0.49; p

Summary Fine-scale information on soil surface roughness (SSR) is needed for calculating heat budgets, monitoring soil degradation and parameterizing surface runoff and sediment transfer models. Previous work has demonstrated the potential of using hyperspectral, hemispherical conical reflectance factors (HCRFs) to retrieve the SSR of different soil crusting states. However, this was achieved by using dry soil surfaces, generated in controlled laboratory conditions. The primary aim of this study was therefore to test the impact that in situ variations in surface soil moisture (SSM) content had on the ability of directional reflectance factors to characterize SSR conditions. Five soil plots (20 cm × 20 cm in area) representing different agricultural conditions were subjected to different durations of natural rainfall to produce a range of different levels of SSR. The values of SSM varied from 8.7 to 20.1% across all soil plots. Point laser data (4-mm sample spacing) were geostatistically analysed to give a spatially-distributed measure of SSR, giving sill variance values from 3.2 to 23.0. The HCRFs from each soil state were measured using a ground-based hyperspectral spectroradiometer for a range of viewing zenith angles from extreme forward-scatter (θr = −60°) to extreme back-scatter (θr = +60°) at a 10° sampling resolution in the solar principal plane. The results showed that despite a large range of SSM values, forward-scattered reflectance factors exhibited a very strong relationship with SSR (R2 = 0.84 at θr = −60°). Our findings demonstrate the operational potential of HCRFs for providing spatially-distributed SSR measurements, across spatial extents containing spatio-temporal variations in SSM content.

Retrieving leaf chlorophyll content at a range of spatio-temporal scales is central to monitoring vegetation productivity, identifying physiological stress and managing biological resources. However, estimating leaf chlorophyll over broad spatial extents using ground-based traditional methods is time and resource heavy. Satellite-derived spectral vegetation indices (VIs) are commonly used to estimate leaf chlorophyll content, however they are often developed and tested on broadleaf species. Relatively little research has assessed VIs for different leaf structures, particularly needle leaves which represent a large component of boreal forest and significant global ecosystems. This study tested the performance of 47 published VIs for estimating foliar chlorophyll content from different leaf and canopy structures (broadleaf and needle). Coniferous and deciduous sites were selected in Ontario, Canada, representing different dominant vegetation species (Picea mariana and Acer saccharum) and a variety of canopy structures. Leaf reflectance data was collected using an ASD Fieldspec Pro spectroradiometer (400–2500 nm) for over 300 leaf samples. Canopy reflectance data was acquired from the medium resolution imaging spectrometer (MERIS). At the canopy level, with both leaf types combined, the DD-index showed the strongest relationship with leaf chlorophyll (R2 = 0.78; RMSE = 3.56 μg/cm2), despite differences in leaf structure. For needleleaf trees alone the relationship with the top VI was weaker (D[red], R2 = 0.71; RMSE = 2.32 μg/cm2). A sensitivity study using simulated VIs from physically-modelled leaf (PROSPECT) and canopy (4-Scale) reflectance was performed in order to further investigate these results and assess the impacts of different background types and leaf area index on the VIs’ performance. At the leaf level, the MNDVI8 index showed a strong linearity to changing chlorophyll and negligible difference to leaf structure/type. At canopy level, the best performing VIs were relatively consistent where LAI ≥ 4, but responded strongly to differences in background at low canopy coverage (LAI = 2). This research provides comprehensive assessments for the use of spectral indices in retrieval of spatially-continuous leaf chlorophyll content at the leaf (MTCI: R2 = 0.72; p < 0.001) and canopy (DD: R2 = 0.78; p < 0.001) level for resource management over different spatial and temporal scales.

A top-down model inversion method of estimating leaf reflectance from hyperspectral remote sensing measurements has been tested with an empirical approach used to estimate chlorophyll content. Leaf reflectance is obtained by inverting a geometric-optical model, 5-Scale, validated using hyperspectral AVIRIS data. The shaded scene fractions and the M factor, which includes both the multiple scattering effect and the shaded components, are computed for inverting canopy reflectance into leaf reflectance. The inversion is based on the look-up tables (LUTs) approach. The simulated leaf reflectance values are combined in hyperspectral indices for leaf chlorophyll retrieval and compared against the measured leaf chlorophyll content in the Greater Victoria Watershed District (GVWD), British Columbia (BC). The results demonstrate that the modeled canopy reflectance and AVIRS data are in good agreement for all locations. The regressions of the modified simple ratio [(R728 - R434)/(R720 - R434)] and modified normalized difference index [(R728 - R720)/(R728 + R720 -2R434)] against chlorophyll content exhibit the best fit using second-order polynomial functions with the root-mean-square errors (RMSE) of 4.434 and 4.247, and coefficients of determination of 0.588 and 0.588, respectively. Larger RMSE are observed when the direct canopy-level retrieval, using canopy-level generated vegetation indices, is considered, suggesting the importance of the proposed canopy-to-level reflectance inversion step in chlorophyll retrieval based on hyperspectral vegetation indices. This approach allows for estimation of leaf level information in the absence of leaf spectra field measurements, and simplifies further applications of hyperspectral imagery at the regional scale.

The impacts of soil water stress on photosynthesis and stomatal conductance have not been uniformly parameterized in terrestrial ecosystem models. This study firstly quantifies diurnal variations in canopy conductance (gc,w) during full-leaf periods from eddy covariance flux data at four flux sites by inverting the Penman-Monteith equation. Then, the Ball-Woodrow-Berry (BWB) slopes were derived and compared under contrasting water stress conditions by linear regression of gc,w and gross primary productivity (GPP) derived from EC measurements. Finally, the response of the leaf maximum carboxylation rate to accumulated soil water deficit (ASWD) was explored to develop a better scheme of soil water stress. Our results show: (1) The thresholds of relatively available soil moisture under which soil water stress occurs were 0.575, 0.885, 0.495 and 0.653 for the tropical savanna site (AU-How), the Mediterranean forest site (IT-Col), the Mediterranean grassland site (US-Var) and the boreal forest site (CA-Oas), respectively, derived from the logistic functions of fitting gc,w to relatively available soil moisture; (2) similar to previous work, we found that ensemble average gc,w of dry periods were lower than those of wet periods at the studied sites, and that BWB slopes did not change significantly during droughts, indicating that BWB slopes may be conserved under prolonged drought; and (3) EC-derived GPP gradually decreased with the increase of ASWD, which can be well captured by a Vmr-ASWD scheme developed in this study. In sum, the Vmr-ASWD scheme would increase the accuracy of GPP simulations of ecosystem models. This study suggests that the change of BWB slopes under prolonged drought is not justified according to the experimental data examined while adjusting Vcmax with ASWD accounts for the change in leaf physiology due to prolonged drought and is computationally feasible and efficient.

[1] There is widespread recognition that spatially distributed information on soil surface roughness (SSR) is required for hydrological and geomorphological applications. Such information is necessary to describe variability in soil structure, which is highly heterogeneous in time and space, to parameterize hydrology and erosion models and to understand the temporal evolution of the soil surface in response to rainfall. This paper demonstrates how results from semivariogram analysis can quantify key elements of SSR for such applications. Three soil types (silt, silt loam, and silty clay) were used to show how different types of structural variance in SSR evolve during simulated rainfall events. All three soil types were progressively degraded using artificial rainfall to produce a series of roughness states. A calibrated laser profiling instrument was used to measure SSR over a 10 cm × 10 cm spatial extent, at a 2 mm resolution. These data were geostatistically analyzed in the context of aggregate breakdown and soil crusting. The results show that such processes are represented by a quantifiable decrease in sill variance, from 7.81 (control) to 0.94 (after 60 min of rainfall). Soil surface features such as soil cracks, tillage lines and erosional areas were quantified by local maxima in semivariance at a given length scale. This research demonstrates that semivariogram analysis can retrieve spatiotemporal variations in soil surface condition; in order to provide information on hydrological pathways. Consequently, geostatistically derived SSR shows strong potential for inclusion as spatial information in hydrology and erosion models to represent complex surface processes at different soil structural scales.

Improving the accuracy of estimates of forest carbon exchange is a central priority for understanding ecosystem response to increased atmospheric CO2 levels and improving carbon cycle modelling. However, the spatially continuous parameterization of photosynthetic capacity (Vcmax) at global scales and appropriate temporal intervals within terrestrial biosphere models (TBMs) remains unresolved. This research investigates the use of biochemical parameters for modelling leaf photosynthetic capacity within a deciduous forest. Particular attention is given to the impacts of seasonality on both leaf biophysical variables and physiological processes, and their interdependent relationships. Four deciduous tree species were sampled across three growing seasons (2013-2015), approximately every 10 days for leaf chlorophyll content (ChlLeaf ) and canopy structure. Leaf nitrogen (NArea ) was also measured during 2014. Leaf photosynthesis was measured during 2014-2015 using a Li-6400 gas-exchange system, with A-Ci curves to model Vcmax. Results showed that seasonality and variations between species resulted in weak relationships between Vcmax normalized to 25°C (Vcmax25) and NArea (R2 = 0.62, P

Photosynthesis in vegetation is arguably the most important and variable part of the terrestrial carbon cycle. In terrestrial biospheric models, the photosynthesis rate of plant leaves is generally simulated based on the maximum carboxylation rate at an optimum temperature (often 25°C), which is often denoted as Vcmax25. In regional and global photosynthesis modeling, Vcmax25 is usually prescribed as constants for different plant functional types (PDF) based on ground measurements in order to capture the first order spatial variability associated with PTF distribution. However, experimental data show that Vcmax25 can vary by 2–3 factors for the same PFT, and it is also not a constant in different growing seasons because of leaf physiological change with season. It is therefore highly desirable to be able to map this critical parameter to address the issues of its spatial and temporal variabilities. Both terrestrial carbon and water cycle simulations can be greatly improved if we can achieve the global mapping of this parameter.

Soil organic carbon (SOC) dynamics affect soil quality, agricultural productivity and atmospheric CO2 concentration. Despite the need for spatial assessments of SOC content over time, reliable estimates from traditional field survey methods are limited by data availability; where measurements are often made at discrete point locations, at a coarse sample spacing or over a limited spatial extent. Remote sensing (RS) is in a strong position to provide spatially distributed, reproducible, scale-appropriate and resource-efficient measurements of SOC content and fluxes at field, landscape and regional scales. This paper provides a critical review of optical RS techniques for such applications. The first part of the paper reviews the methods, instruments and techniques used for developing predictive models for monitoring spatial SOC content. Secondly, sources of spatio-temporal SOC variations are examined, including the lateral transfer of SOC by erosion, soil structural breakdown and land management practices, in the context of RS data and techniques. The key challenges of using RS to monitor SOC contents are discussed along with opportunities for improving SOC predictions within a spatial framework. Such opportunities include the use of ancillary data, scale-specific methods, improved development of spectral libraries and better integration of RS technologies into empirical and simulation SOC models. This paper aims to provide a transparent assessment and practical guide to RS techniques and products in order to further advance and better incorporate the use of RS methods within soil science.