Nitrogen availability and summer drought, but not N:P imbalance, drive carbon use efficiency of a Mediterranean tree-grass ecosystem

All ecosystems contain both sources and sinks for atmospheric carbon (C). A change in their balance of net and gross ecosystem carbon uptake, ecosystem-scale carbon use efficiency (CUEECO), is a change in their ability to buffer climate change. However, anthropogenic nitrogen (N) deposition is increasing N availability, potentially shifting terrestrial ecosystem stoichiometry towards phosphorus (P) limitation. Depending on how gross primary production (GPP, plants alone) and ecosystem respiration (RECO, plants and heterotrophs) are limited by N, P or associated changes in other biogeochemical cycles, CUEECO may change. Seasonally, CUEECO also varies as the multiple processes that control GPP and respiration and their limitations shift in time. We worked in a Mediterranean tree-grass ecosystem (locally called 'dehesa') characterized by mild, wet winters and summer droughts. We examined CUEECO from eddy covariance fluxes over 6 years under control, +N and + NP fertilized treatments on three timescales: annual, seasonal (determined by vegetation phenological phases) and 14-day aggregations. Finer aggregation allowed consideration of responses to specific patterns in vegetation activity and meteorological conditions. We predicted that CUEECO should be increased by wetter conditions, and successively by N and NP fertilization. Milder and wetter years with proportionally longer growing seasons increased CUEECO, as did N fertilization, regardless of whether P was added. Using a generalized additive model, whole ecosystem phenological status and water deficit indicators, which both varied with treatment, were the main determinants of 14-day differences in CUEECO. The direction of water effects depended on the timescale considered and occurred alongside treatment-dependent water depletion. Overall, future regional trends of longer dry summers may push these systems towards lower CUEECO.

Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves

Leaf nitrogen concentration (LNC) is a critical indicator of crop nutrient status. In this study, the feasibility of using visible and near-infrared spectroscopy combined with deep learning to estimate LNC in cotton leaves was explored. The samples were collected from cotton's whole growth cycle, and the spectra were from different measurement environments. The random frog (RF), weighted partial least squares regression (WPLS), and saliency map were used for characteristic wavelength selection. Qualitative models (partial least squares discriminant analysis (PLS-DA), support vector machine for classification (SVC), convolutional neural network classification (CNNC) and quantitative models (partial least squares regression (PLSR), support vector machine for regression (SVR), convolutional neural network regression (CNNR)) were established based on the full spectra and characteristic wavelengths. Satisfactory results were obtained by models based on CNN. The classification accuracy of leaves in three different LNC ranges was up to 83.34%, and the root mean square error of prediction (RMSEP) of quantitative prediction models of cotton leaves was as low as 3.36. In addition, the identification of cotton leaves based on the predicted LNC also achieved good results. These results indicated that the nitrogen content of cotton leaves could be effectively detected by deep learning and visible and near-infrared spectroscopy, which has great potential for real-world application.

Seasonal peak photosynthesis is hindered by late canopy development in northern ecosystems

The seasonal dynamics of the vegetation canopy strongly regulate the surface energy balance and terrestrial carbon fluxes, providing feedbacks to climate change. Whether the seasonal timing of maximum canopy structure was optimized to achieve a maximum photosynthetic carbon uptake is still not clear due to the complex interactions between abiotic and biotic factors. We used two solar-induced chlorophyll fluorescence datasets as proxies for photosynthesis and the normalized difference vegetation index and leaf area index products derived from the moderate resolution imaging spectroradiometer as proxies for canopy structure, to characterize the connection between their seasonal peak timings from 2000 to 2018. We found that the seasonal peak was earlier for photosynthesis than for canopy structure in >87.5% of the northern vegetated area, probably leading to a suboptimal maximum seasonal photosynthesis. This mismatch in peak timing significantly increased during the study period, mainly due to the increasing atmospheric CO₂, and its spatial variation was mainly explained by climatic variables (43.7%) and nutrient limitations (29.6%). State-of-the-art ecosystem models overestimated this mismatch in peak timing by simulating a delayed seasonal peak of canopy development. These results highlight the importance of incorporating the mechanisms of vegetation canopy dynamics to accurately predict the maximum potential terrestrial uptake of carbon under global environmental change.

Modified photochemical reflectance index to estimate leaf maximum rate of carboxylation based on spectral analysis

The maximum rate of carboxylation in leaves (V_cmax) is an important parameter used to determine the photosynthetic rate. An accurate estimation of V_cmax is important for evaluating the photosynthetic capacity of vegetation productivity and the carbon budget of forest ecosystems. In this study, we measured the light use efficiency (LUE) and V_cmax of leaves and their corresponding spectral characteristics. Spectral analysis was used to find a modified photochemical reflectance index (PRI) of leaves to estimate LUE and thereby estimate leaf V_cmax. The results showed that the precision with which the modified ratio PRI index estimated LUE was significantly increased. The R² of the estimation model was 0.69, and the root mean square error (RMSE) was 0.0024. The R² of the model for estimating leaf V_cmax based on the LUE was 0.72, and the RMSE was 8.439 μmol/m²/s. The V_cmax values of conifer trees (Scots pine, Korean pine, and larch) were higher than those of broadleaf trees (white birch, Manchurian walnut, Siberian elm, Manchurian ash, Mongolian oak, Korean aspen, and Amur linden). This method avoids problem that conventional PRI is not sensitive to high leaf LUE values and provides important parameter data for the quantitative estimation of the gross primary productivity of forest ecosystems.

Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice

Spectra of reflectance (Sr) and fluorescence (Sf) are significant for crop monitoring and ecological environment research, and can be used to indicate the leaf nitrogen content (LNC) of crops indirectly. The aim of this work is to use the Sr-Sf features obtained with hyperspectral and laser-induced fluorescence LiDAR (HSL, LIFL) systems to construct novel combined spectral indices (NCIH-F) for multi-year rice LNC estimation. The NCIH-F is in a form of FWs* Φ + GSIs* Φ , where Φ is the Sr-Sf features, and FWs and GSIs are the feature weights and global sensitive indices for each characteristic band. In this study, the characteristic bands were chosen in different ways. Firstly, the Sr-Sf characteristics which can be the intensity or derivative variables of spectra in 685 and 740 nm, have been assigned as the Φ value in NCIH-F formula. Simultaneously, the photochemical reflectance index (PRI) formed with 531 and 570 nm was modified based on a variant spectral index, called PRIfraction, with the Sf intensity in 740 nm, and then compared its potential with NCIH-F on LNC estimation. During the above analysis, both NCIH-F and PRIfraction values were utilized to model rice LNC based on the artificial neural networks (ANNs) method. Subsequently, four prior bands were selected, respectively, with high FW and GSI values as the ANNs inputs for rice LNC estimation. Results show that FW- and GSI-based NCIH-F are closely related to rice LNC, and the performance of previous spectral indices used for LNC estimation can be greatly improved by multiplying their FWs and GSIs. Thus, it can be included that the FW- and GSI-based NCIH-F constitutes an efficient and reliable constructed form combining HSL (Sr) and LIFL (Sf) data together for rice LNC estimation.

Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance

As a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and abaxial leaf surfaces from three common leafy green vegetables: Pakchoi var. Shanghai Qing (Brassica chinensis L. var. Shanghai Qing), Chinese white cabbage (Brassica campestris L. ssp. Chinensis Makino var. communis Tsen et Lee), and Romaine lettuce (Lactuca sativa var longifoliaf. Lam) were measured to estimate the leaf chlorophyll content. Modeling based on spectral indices and the partial least squares regression (PLS) was tested using the reflectance data from the two surfaces (adaxial and abaxial) of leaves in the datasets of each individual vegetable and the three vegetables combined. The PLS regression model showed the highest accuracy in estimating leaf chlorophyll content of pakchoi var. Shanghai Qing (R2 = 0.809, RMSE = 62.44 mg m-2), Chinese white cabbage (R2 = 0.891, RMSE = 45.18 mg m-2) and Romaine lettuce (R2 = 0.834, RMSE = 38.58 mg m-2) individually as well as of the three vegetables combined (R2 = 0.811, RMSE = 55.59 mg m-2). The good predictability of the PLS regression model is considered to be due to the contribution of more spectral bands applied in it than that in the spectral indices. In addition, both the uninformative variable elimination PLS (UVE-PLS) technique and the best performed spectral index: MDATT, showed that the red-edge region (680-750 nm) was effective in estimating the chlorophyll content of vegetables with reflectance from two leaf surfaces. The combination of the PLS regression model and the red-edge region are insensitive to the difference between the adaxial and abaxial leaf structure and can be used for estimating the chlorophyll content of leafy green vegetables accurately.

Biotic responses buffer warming-induced soil organic carbon loss in Arctic tundra

Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming-induced biotic changes may influence biologically related parameters and the consequent projections in ESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over 5 years from a soil warming experiment at the Eight Mile Lake, Alaska, into the Terrestrial ECOsystem (TECO) model with a probabilistic inversion approach. The TECO model used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment-corrected) turnover rates of SOC in both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. The TECO model predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87 g/m² , respectively, without or with changes in those parameters. Thus, warming-induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes in ESMs to improve the model performance in predicting C dynamics in permafrost regions.

Landscape variation in canopy nitrogen and carbon assimilation in a temperate mixed forest

Canopy nitrogen (N) is a key factor regulating carbon cycling in forest ecosystems through linkages among foliar N and photosynthesis, decomposition, and N cycling. This analysis examined landscape variation in canopy nitrogen and carbon assimilation in a temperate mixed forest surrounding Harvard Forest in central Massachusetts, USA by integration of canopy nitrogen mapping with ecosystem modeling, and spatial data from soils, stand characteristics and disturbance history. Canopy %N was mapped using high spectral resolution remote sensing from NASA's AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) instrument and linked to an ecosystem model, PnET-II, to estimate gross primary productivity (GPP). Predicted GPP was validated with estimates derived from eddy covariance towers. Estimated canopy %N ranged from 0.5 to 2.9% with a mean of 1.75% across the study region. Predicted GPP ranged from 797 to 1622 g C m^-2 year^-1 with a mean of 1324 g C m^-2 year^-1. The prediction that spatial patterns in forest growth are associated with spatial patterns in estimated canopy %N was supported by a strong, positive relationship between field-measured canopy %N and aboveground net primary production. Estimated canopy %N and GPP were related to forest composition, land-use history, and soil drainage. At the landscape scale, PnET-II GPP was compared with predicted GPP from the BigFoot project and from NASA's MODIS (Moderate Resolution Imaging Spectroradiometer) data products. Estimated canopy %N explained much of the difference between MODIS GPP and PnET-II GPP, suggesting that global MODIS GPP estimates may be improved if broad-scale estimates of foliar N were available.

The effect of nutrients shortage on plant's efficiency to capture solar radiations under semi-arid environments

Radiation use efficiency (RUE) is considered critical for calculation of crop yield. The crop productivity can be improved by increasing the interception of solar radiation and maintaining higher RUE for plants. Irrigation water and nitrogen (N) supply are the main limiting factors for RUE in maize (Zea mays L.) across the semi-arid environments. Field experiments were conducted during two consecutive growing seasons (2009-2010) to optimize RUE in relation to N application timings and rates with varying irrigation water management practices. In experiment 1, three N application timings were made, while in experiment 2, three possible water management practices were used. In both experiments, five N rates (100, 150, 200, 250, and 300 kg N ha^-1) were applied to evaluate the effects of irrigation water and N on cumulative photosynthetic active radiation (PARi), dry matter RUE (RUE_DM), and grain yield RUE (RUE_GY). The results demonstrated that cumulative PARi and RUEs were not constant during the plant growth under varying the nutrients. The water and N significantly influenced cumulative PARi and RUEs during the both growing seasons. In experiment 1, the maximum cumulative PARi was observed by application of 250 kg N ha^-1 in three splits (1/3 N at V2, 1/3 N at V16, and 1/3 N at R1 stage), and the highest RUE_DM was achieved by the application of 300 kg N ha^-1. However, the highest RUE_GY was observed by application of 250 kg N ha^-1. In experiment 2, the maximum cumulative PARi was attained at normal irrigation regime with 250 kg N ha^-1, while the highest RUE_DM and RUE_GY were recorded at normal irrigation regime with the application of 300 kg N ha^-1. The regression analysis showed significant and positive correlation of RUE_GY with grain yield. Therefore, optimum water and N doses are important for attaining higher RUE, which may enhance maize grain yield semi-arid environment; this may be considered in formulating good agricultural practices for the environmental conditions resembling to those of this study.

Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits

The aim of this study was to systematically analyze the potential and limitations of using plant functional trait observations from global databases versus in situ data to improve our understanding of vegetation impacts on ecosystem functional properties (EFPs). Using ecosystem photosynthetic capacity as an example, we first provide an objective approach to derive robust EFP estimates from gross primary productivity (GPP) obtained from eddy covariance flux measurements. Second, we investigate the impact of synchronizing EFPs and plant functional traits in time and space to evaluate their relationships, and the extent to which we can benefit from global plant trait databases to explain the variability of ecosystem photosynthetic capacity. Finally, we identify a set of plant functional traits controlling ecosystem photosynthetic capacity at selected sites. Suitable estimates of the ecosystem photosynthetic capacity can be derived from light response curve of GPP responding to radiation (photosynthetically active radiation or absorbed photosynthetically active radiation). Although the effect of climate is minimized in these calculations, the estimates indicate substantial interannual variation of the photosynthetic capacity, even after removing site-years with confounding factors like disturbance such as fire events. The relationships between foliar nitrogen concentration and ecosystem photosynthetic capacity are tighter when both of the measurements are synchronized in space and time. When using multiple plant traits simultaneously as predictors for ecosystem photosynthetic capacity variation, the combination of leaf carbon to nitrogen ratio with leaf phosphorus content explains the variance of ecosystem photosynthetic capacity best (adjusted R2 = 0.55). Overall, this study provides an objective approach to identify links between leaf level traits and canopy level processes and highlights the relevance of the dynamic nature of ecosystems. Synchronizing measurements of eddy covariance fluxes and plant traits in time and space is shown to be highly relevant to better understand the importance of intra- and interspecific trait variation on ecosystem functioning.

Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (M(area)), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ15N) as well as pixel-wise uncertainties in their estimates based on error propagation in the analytical methods. Both M(area) and %N PLSR models had a R2 > 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with R2 > 0.65 and carbon and cellulose with R2 > 0.45. Although R2 of %C and cellulose were lower than M(area) and %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ15N was the lowest (R2 = 0.48, RMSE = 0.95 per thousand), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first-of-its-kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub-boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non-discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.

Engineering a sensitive visual-tracking reporter system for real-time monitoring phosphorus deficiency in tobacco

Plant phosphorus (P) diagnosis is widely used for monitoring P status and guiding P fertilizer application in field conditions. The common methods for predicting plant response to P are time- and labour-consuming chemical measurements of the extractable soil P and plant P concentrations. In this study, we successfully generated a visual reporter system in tobacco (Nicotiana tabacum L.) to monitor plant P status by expressing of a Purple gene (Pr) isolated from cauliflower (Brassica oleracea var botrytis) driven by the promoter (Pro) of OsPT6, a P-starvation-induced rice gene. The leaves of OsPT6pro::Pr (PT6pro::Pr) transgenic tobacco continuously turned into dark purple with the increase of duration and severity of P deficiency, and recovered rapidly to basal green colour upon resupply of P. The expression of several anthocyanin biosynthesis involving genes was strongly activated in the transgenic tobacco in comparison to wild type under P-deficient condition. Such additive purple colour was not detected by deficiencies of other major- and micronutrients or stresses of salt, drought and cold. There was an extremely high correlation between P concentration and anthocyanin accumulation in the transgenic tobacco leaves. Using a hyperspectral sensing technology, P concentration in the leaves of transgenic plants could be predicted by the reflectance spectra at 554 nm wavelength with approximately 0.16 as the threshold value of the P deficiency. Taken together, the colour-based visual reporter system could be specifically and readily used for monitoring the plant P status by naked eyes and accurately assessed by spectral reflectance.

Physiological mechanisms in plant growth models: do we need a supra-cellular systems biology approach?

In the first part of this paper, we review the extent to which various types of plant growth models incorporate ecophysiological mechanisms. Many growth models have a central role for the process of photosynthesis; and often implicitly assume C-gain to be the rate-limiting step for biomass accumulation. We subsequently explore the extent to which this assumption actually holds and under what condition constraints on growth due to a limited sink strength are likely to occur. By using generalized dose-response curves for growth with respect to light and CO₂, models can be tested against a benchmark for their overall performance. In the final part, a call for a systems approach at the supra-cellular level is made. This will enable a better understanding of feedbacks and trade-offs acting on plant growth and its component processes. Mechanistic growth models form an indispensable element of such an approach and will, in the end, provide the link with the (sub-)cellular approaches that are yet developing. Improved insight will be gained if model output for the various physiological processes and morphological variables ('virtual profiling') is compared with measured correlation networks among these processes and variables. Two examples of these correlation networks are presented.

Key canopy traits drive forest productivity

Quantifying the mechanistic links between carbon fluxes and forest canopy attributes will advance understanding of leaf-to-ecosystem scaling and its potential application to assessing terrestrial ecosystem metabolism. Important advances have been made, but prior studies that related carbon fluxes to multiple canopy traits are scarce. Herein, presenting data for 128 cold temperate and boreal forests across a regional gradient of 600 km and 5.4°C (from 2.4°C to 7.8°C) in mean annual temperature, I show that stand-scale productivity is a function of the capacity to harvest light (represented by leaf area index, LAI), and to biochemically fix carbon (represented by canopy nitrogen concentration, %N). In combination, LAI and canopy %N explain greater than 75 per cent of variation in above-ground net primary productivity among forests, expressed per year or per day of growing season. After accounting for growing season length and climate effects, less than 10 per cent of the variance remained unexplained. These results mirror similar relations of leaf-scale and canopy-scale (eddy covariance) maximum photosynthetic rates to LAI and %N. Collectively, these findings indicate that canopy structure and chemistry translate from instantaneous physiology to annual carbon fluxes. Given the increasing capacity to remotely sense canopy LAI, %N and phenology, these results support the idea that physiologically based scaling relations can be useful tools for global modelling.

Sources of variability in canopy reflectance and the convergent properties of plants

How plants interact with sunlight is central to the existence of life and provides a window to the functioning of ecosystems. Although the basic properties of leaf spectra have been known for decades, interpreting canopy-level spectra is more challenging because leaf-level effects are complicated by a host of stem- and canopy-level traits. Progress has been made through empirical analyses and models, although both methods have been hampered by a series of persistent challenges. Here, I review current understanding of plant spectral properties with respect to sources of uncertainty at leaf to canopy scales. I also discuss the role of evolutionary convergence in plant functioning and the difficulty of identifying individual properties among a suite of interrelated traits. A pattern that emerges suggests a synergy among the scattering effects of leaf-, stem- and canopy-level traits that becomes most apparent in the near-infrared (NIR) region. This explains the widespread and well-known importance of the NIR region in vegetation remote sensing, but presents an interesting paradox that has yet to be fully explored: that we can often gain more insight about the functioning of plants by examining wavelengths that are not used in photosynthesis than by examining those that are.