Canopy Nitrogen Content Research Articles

Remote sensing of canopy nitrogen at regional scale in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Abstract. Canopy nitrogen (N) concentration and content are linked to several vegetation processes. Therefore, canopy N concentration is a state variable in global vegetation models with coupled carbon (C) and N cycles. While there are ample C data available to constrain the models, widespread N data are lacking. Remotely sensed vegetation indices have been used to detect canopy N concentration and canopy N content at the local scale in grasslands and forests. Vegetation indices could be a valuable tool to detect canopy N concentration and canopy N content at larger scale. In this paper, we conducted a regional case-study analysis to investigate the relationship between the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) time series from European Space Agency (ESA) Envisat satellite at 1 km spatial resolution and both canopy N concentration (%N) and canopy N content (N g m−2, of ground area) from a Mediterranean forest inventory in the region of Catalonia, in the northeast of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20, 15, 10 and 5 km) and at the original spatial resolution of 1 km. The results at higher spatial resolution (1 km) yielded significant log–linear relationships between MTCI and both canopy N concentration and content: r2 = 0.32 and r2 = 0.17, respectively. We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N concentration was strongest for deciduous broadleaf and mixed plots (r2 = 0.24 and r2 = 0.44, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r2 = 0.19). At the species level, canopy N concentration was strongly related to MTCI for European beech plots (r2 = 0.69). These results present a new perspective on the application of MTCI time series for canopy N detection.

Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery

This study aims to estimate the spatial variation of sugarcane Canopy Nitrogen Concentration (CNC) using spectral data, which were measured from a spaceborne hyperspectral image. Stepwise Multiple Linear Regression (SMLR) and Support Vector Regression (SVR) were applied to calibrate and validate the CNC estimation models. The raw spectral reflectance was transformed into a First-Derivative Spectrum (FDS) and absorption features to remove the spectral noise and finally used as input variables. The results indicate that the estimation models developed by non-linear SVR based Radial Basis Function (RBF) kernel yield the higher correlation coefficient with CNC compared with the models computed by SMLR. The best model shows the coefficient of determination value of 0.78 and Root Mean Square Error (RMSE) value of 0.035% nitrogen. The narrow sensitive spectral wavelengths for quantifying nitrogen content in the combined cultivar environments existed mainly in the electromagnetic spectrum of the visible-red, longer portion of red edge, shortwave infrared regions and far-near infrared. The most important conclusion from this experiment is that spectral signals from the space hyperspectral data contain the meaningful information for quantifying sugarcane CNC across larger geographic areas. The nutrient deficient areas could be corrected by applying suitable farm management.

Carbon use efficiency of hayed alfalfa and grass pastures in a semiarid environment

AbstractManagement, environment, and agroecosystem type are key factors influencing photosynthetic carbon (C) uptake and C use efficiency (CUE), calculated as the ratio of net ecosystem production to gross ecosystem production (NEP:GEP). Current literature has mainly emphasized annual C balance in studies involving multiple years with continuous monitoring of ecosystem C fluxes, yet CUE has not been thoroughly analyzed during the growing season, particularly in paired comparisons of contrasting types of pasture under semiarid conditions. From 2009 through 2013, we used eddy covariance method to determine daily, seasonal, and annual C budgets in rainfed alfalfa (Medicago sativa L.) and grass ecosystems subjected to periodic harvest (haying) near Mandan, North Dakota, USA. We found consistently higher magnitudes of C fluxes (ecosystem respiration [ER], NEP, GEP) and hay production in alfalfa than grassland. Leaf area and canopy nitrogen content per unit land area were key driving factors for daily, seasonal, and annual differences in C fluxes between agroecosystems. Net ecosystem C balance indicated C losses occurred through haying in both ecosystems, though no changes in soil C stocks were detected in either ecosystem over the course of the study. Mean NEP:GEP ratios (±standard error) during periods of steady carbon dioxide (CO2) uptake before and after haying were 0.43 ± 0.01 and 0.26 ± 0.03 for alfalfa and grassland, respectively, implying more efficient C use in the former. Moreover, alfalfa had consistently greater CUE than grassland despite variations in sunlight, temperature, and precipitation within and between growing seasons. Ratios of ER to GEP were also repeatedly lower in alfalfa than grassland in all five growing seasons. Under drought conditions, we infer alfalfa roots accessed water in the soil profile unavailable to more shallow‐rooted grass species. Overall, hayed alfalfa was more efficient and tolerant than grassland in assimilating and using atmospheric CO2 under variable intra‐ and inter‐seasonal conditions. Outcomes from this study suggest the inclusion of alfalfa in unirrigated crop rotations can sustain high CUE, C uptake, and hay production while mitigating C losses in a semiarid environment.

Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery

Foliar nitrogen is a critical factor in leaf physiological processes, plant growth, and ecosystem functioning, which has been proposed as one of the essential biodiversity variables. Nitrogen has been quantified by a number of empirical approaches using hyperspectral data, but the retrieval of nitrogen through a physically based approach remains a challenge. A recent study by Wang et al. (2015a) has revealed that leaf protein can be successfully estimated from fresh leaf spectra using a revised leaf radiative transfer model PROPECT-5 which incorporated the effects of leaf protein and cellulose + lignin on leaf reflectance and transmittance. This provides a potential approach of estimating nitrogen using radiative transfer models given the correlation between protein and nitrogen. However, such a revised leaf model has not been tested for the estimation of leaf nitrogen at the canopy level. In this study, a canopy reflectance model INFORM, coupled with the revised PROSPECT-5 model, was used to retrieve leaf and canopy nitrogen content in a mixed temperate forest using the wavelengths of 800–2500 nm from airborne hyperspectral imagery. Ecological criteria were applied to the parameterization of the model to reduce unrealistic combinations of input parameters. Global sensitivity analysis showed that leaf protein played a small but distinct role in driving the variation of canopy reflectance in the INFORM model. More accurate estimation was obtained for canopy nitrogen content (R2 = 0.64, RMSE = 1.90, NRMSE = 0.18) than leaf nitrogen content (R2 = 0.46, RMSE = 3.79e-05, NRMSE = 0.19). Moreover, inversion techniques, particularly regularized look-up tables, further improved the estimation accuracies compared to the original tables. Our results indicate that leaf and canopy nitrogen content can be retrieved successfully at the canopy level by inversion of INFORM. Both the direct and indirect effects of nitrogen on canopy reflectance are important for nitrogen estimation. The maps of leaf and canopy nitrogen content are the first to be generated using inversion of coupled leaf-canopy models, and the spatial variation of foliar nitrogen appears to be reasonable and consistent with ecological knowledge.

BAS-ELM based UAV hyperspectral remote sensing inversion modeling of rice canopy nitrogen content

Abstract: The rapid, nondestructive, and accurate estimation of nitrogen content in rice can help to obtain the growth condition of rice in time, which is of great significance for guiding rice field management. In order to improve the accuracy of high-spectral inversion of rice canopy nitrogen content, the discrete wavelet multiscale decomposition (DWMD) was used to downscale the high-spectral information in the range of 400 nm to 1000 nm by using the UAV hyperspectral image data and the synchronously measured rice canopy nitrogen content as the data source, from which the hyperspectral characteristic variables for rice nitrogen content inversion modeling were extracted. And using the dimensionality reduction variables as the data base, three neural network inversion methods, including extreme learning machines (ELM), particle swarm optimization for extreme learning machines (PSO-ELM), and beetle antennae search algorithm for extreme learning machines (BAS-ELM), were used to establish the rice nitrogen content drone hyperspectral remote sensing inversion model, and the results showed that: (1) The hyperspectral range from 400 nm to 1000 nm was dimensionally reduced by DWMD, and finally the continuous hyperspectral reflectance information was dimensionally reduced to sixteen discrete hyperspectral features for subsequent inversion modelling of rice nitrogen content.(2) In the model adopted in this study, BAS-ELM has the highest accuracy, where the R 2 of training data, the R 2 of test data, the RMSE of training data, and the RMSE of test data is were 0.864, 0.863, 0.247, 0.254. Keywords: UAV, Nitrogen inversion, rice, Hyperspectral dimensionality reduction, ELM DOI:  10.33440/j.ijpaa.20200303.105  Citation: Yu F H, Feng S,Yao W X, Wang D K, Xing S M, Xu T Y.  BAS-ELM based UAV hyperspectral remote sensing inversion modeling of rice canopy nitrogen content.  Int J Precis Agric Aviat, 2020; 3(3): 59–64.

Inversion modeling of rice canopy nitrogen content based on MPA-GA-ELM UAV hyperspectral remote sensing

Inversion modeling of rice canopy nitrogen content based on MPA-GA-ELM UAV hyperspectral remote sensing

Investigation into the role of canopy structure traits and plant functional types in modulating the correlation between canopy nitrogen and reflectance in a temperate forest in northeast China

A precise estimate of canopy leaf nitrogen concentration (CNC, based on dry mass) is important for researching the carbon assimilation capability of forest ecosystems. Hyperspectral remote sensing technology has been applied to estimate regional CNC, which can adjust forest photosynthetic capacity and carbon uptake. However, the relationship between forest CNC and canopy spectral reflectance as well as its mechanism is still poorly understood. Using measured CNC, canopy structure and species composition data, four vegetation indices (VIs), and near-infrared reflectance (NIR) derived from EO-1 Hyperion imagery, we investigated the role of canopy structure traits and plant functional types (PFTs) in modulating the correlation between CNC and canopy reflectance in a temperate forest in northeast China. A plot-scale forest structure indicator, named broad foliar dominance index (BFDI), was introduced to provide forest canopy structure and coniferous and broadleaf species composition. Then, we revealed the response of forest canopy reflectance spectrum to BFDI and CNC. Our results showed that leaf area index had no significant effect on NIR (P>0.05) but indicated that there was a significant correlation (R2=0.76, P 0.05). On the contrary, removing the CNC effect, the partial correlation between BFDI and NIR was positively significant (R=0.69, P<0.0001). These findings proved that canopy structure and coniferous and broadleaf species composition had a greater influence on the remote sensing signal than canopy nitrogen concentration. The functional convergence of plant traits resulted in the relation of CNC and canopy structure and determined the positive correlation between CNC and NIR. We maintain that the repeatable relationship between CNC and NIR can be used in the remote sensing retrieval of CNC during various forest types. Nevertheless, the relationship cannot be considered as a feasible approach of CNC estimation for a single PFT.

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Remote sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered remote-sensing method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet (Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing remote-sensing methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications.Overall, both approaches are promising to remotely estimate LAI and canopy chlorophyll content (RMSE≤10%). In addition, VIs show a great potential to retrieve canopy nitrogen content (RMSE=10%). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs (RMSE=17%). As expected when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively. Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAV-embedded multispectral cameras, with which only a few wavebands are available.

Reading the Leaves' Palm: Leaf Traits and Herbivory along the Microclimatic Gradient of Forest Layers.

Microclimate in different positions on a host plant has strong direct effects on herbivores. But little is known about indirect effects due to changes of leaf traits. We hypothesized that herbivory increases from upper canopy to lower canopy and understory due to a combination of direct and indirect pathways. Furthermore, we hypothesized that herbivory in the understory differs between tree species in accordance with their leaf traits. We investigated herbivory by leaf chewing insects along the vertical gradient of mixed deciduous forest stands on the broad-leaved tree species Fagus sylvatica L. (European beech) with study sites located along a 140 km long transect. Additionally, we studied juvenile Acer pseudoplatanus L. (sycamore maple) and Carpinus betulus L. (hornbeam) individuals within the understory as a reference of leaf traits in the same microclimate. Lowest levels of herbivory were observed in upper canopies, where temperatures were highest. Temperature was the best predictor for insect herbivory across forest layers in our study. However, the direction was opposite to the generally known positive relationship. Herbivory also varied between the three tree species with lowest levels for F. sylvatica. Leaf carbon content was highest for F. sylvatica and probably indicates higher amounts of phenolic defense compounds. We conclude that the effect of temperature must have been indirect, whereby the expected higher herbivory was suppressed due to unfavorable leaf traits (lower nitrogen content, higher toughness and carbon content) of upper canopy leaves compared to the understory.

Measuring canopy size and nitrogen content in oilseed rape for variable plant growth regulator and nitrogen fertiliser application

Optimising oilseed rape canopy size through correct management is crucial for maximising yield. Plant growth regulators (PGRs) and nitrogen (N) fertiliser are generally applied at a flat rate, however variable applications may be useful for the optimisation of canopy size. The aim of this paper was to understand the potential for spectral reflectance indices to predict green area index (GAI) and crop N content in winter oilseed rape, with specific focus on the Fritzmeier Isaria Crop Sensor. Three large oilseed rape chessboard experiments were set up in 2015 and 2016 in the UK. The results show good correlations between the Isaria indices and both GAI and crop N content, suggesting that the Isaria may be a useful tool for variably applying PGRs and N fertiliser to oilseed rape.

Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements

Timely and accurate assessment of canopy nitrogen content (CNC) provides valuable insight into rapid and real-time nitrogen status monitoring in crops. A semi-empirical approach based on spectral index was extensively used for nitrogen content estimation. However, in many cases, due to specific vegetation types or local conditions, the applicability and robustness of established spectral indices for nitrogen retrieval were limited. The objective of this study was to investigate the optimal spectral index for winter wheat (Triticum aestivum L.) CNC estimation using Pushbroom Hyperspectral Imager (PHI) airborne hyperspectral data. Data collected from two different field experiments that were conducted during the major growth stages of winter wheat in 2002 and 2003 were used. Our results showed that a significant linear relationship existed between nitrogen and chlorophyll content at the canopy level, and it was not affected by cultivars, growing conditions and nutritional status of winter wheat. Nevertheless, it varied with growth stages. Periods around heading stage mainly worsened the relationship and CNC estimation, and CNC assessment for growth stages before and after heading could improve CNC retrieval accuracy to some extent. CNC assessment with PHI airborne hyperspectra suggested that spectral indices based on red-edge band including narrowband and broadband CIred-edge, NDVI-like and ND705 showed convincing results in CNC retrieval. NDVI-like and ND705 were sensitive to detect CNC changes less than 5g/m2, narrowband and broadband CIred-edge were sensitive to a wide range of CNC variations. Further evaluation of CNC retrieval using field measured hyperspectra indicated that NDVI-like was robust and exhibited the highest accuracy in CNC assessment, and spectral indices (CIred-edge and CIgreen) that established on narrow or broad bands showed no obvious difference in CNC assessment. Overall, our study suggested that NDVI-like was the optimal indicator for winter wheat CNC retrieval.

Revised normalized difference nitrogen index (NDNI) for estimating canopy nitrogen concentration in wetlands

Nitrogen is an important element for plant growth, photosynthesis and productivity. It has been presented to have a close correlation with wetland purification and estuarine eutrophication. The quantification of nitrogen concentration in wetland plants from hyperspectral data can provide valuable information to help develop approaches to research wetland ecosystem functioning and biophysical processes. This paper developed a revised nitrogen index (revised NDNI) on the basis of NDNI and SAVI to estimate canopy nitrogen concentration of wetland reed from ground-measured and spaceborne hyperspectral data while reducing wetland background effects. The SAVI was revised through replacing the red band by the 1510nm band and a soil self-adjustable factor to increase vegetation sensitivity and diminish background noise. The revised NDNI integrated advantages of NDNI that was sensitive to canopy nitrogen concentration and revised SAVI that was used as the denominator in revised NDNI to reduce contributions of wetland background reflectance. The results showed that the revised NDNI was more robust than NDNI for nitrogen predictions of reeds, with a relatively higher accuracy (R2=0.84, 0.88, 0.78). It was more insensitive to the variations of LAI than the compared indices, with a lower correlation coefficient (R2=0.27). These results demonstrated that the revised NDNI was both sensitive to canopy nitrogen status of reeds and resistant to the background effects. A validation was performed using Hyperion image over two experimental fields in the study area. The results of canopy nitrogen concentration estimations exhibited a good correlation with ground-based measurements (R2=0.79). The revised NDNI was proved to be more suitable for canopy nitrogen concentration prediction of aquatic vegetation (such as Phragmites australis) in the context of wetlands.

Improve the Prediction Accuracy of Apple Tree Canopy Nitrogen Content through Multiple Scattering Correction Using Spectroscopy

Method: Use Multiple Scattering Correction to eliminate the interference of scattering on spectrum in the process of field measurement so as to improve the accuracy of prediction model of tree canopy nitrogen content. Apple trees in Qixia of Yantai City were taken as the test material. The spectral reflectivity of apple tree canopy went through the First Derivative (FD) and Multiple Scattering Correction (MSC) plus first derivative, respectively. The correlation coefficients were calculated between spectral reflectivity and nitrogen content. The Support Vector Machine (SVM) method was used to establish the prediction model. The result indicates that the MSC pre-processing can improve the correlation between spectral reflectivity and nitrogen content. The SVM model with MSC + FD pre-processing was a good way to predict the nitrogen content. The calibration R2 of the model was 0.746; the validation R2 was 0.720; and its RMSE was 0.452 g·kgˉ1. MSC can commendably eliminate scattering error to improve the prediction accuracy of prediction model.

Quantification of rice canopy nitrogen balance index with digital imagery from unmanned aerial vehicle

With the rise of large-scale crop plantation in China, inexpensive but efficient remote-sensing measures for predicting the nitrogen status of crops are needed for optimal fertilizer management. In this research, a conventional digital camera with a charge coupled device was integrated into an unmanned aerial vehicle (UAV) to capture digital aerial images of paddy rice (Oryza sativa L.) at an altitude of 50 m. The fluorescence emissions of the rice leaves under light excitation were used by Multiplex® to non-destructively assess the chlorophyll and polyphenol content. The nitrogen balance index (NBI) of the rice leaves, known as the ratio of chlorophyll to polyphenols, was used to accurately determine canopy nitrogen concentrations. The dark green colour index (DGCI) available from the aerial images was used to assess the nitrogen concentrations in the field. It was found that DGCI values predicted the nitrogen concentrations and NBI with R2 (coefficient of determination) = 0.672 (p < 0.001) and R2 = 0.711 (p < 0.001), respectively. The results indicated that aerial photography from UAV had the ability to assess plant nitrogen status in the field in large-scale crop plantations.

Estimation of Canopy Nitrogen Concentration Across C3 and C4 Grasslands Using WorldView-2 Multispectral Data

This paper assesses the potential of multispectral data attained using WorldView-2 (WV2) satellite to estimate and map the variability in canopy nitrogen (N) concentration across C3 and C4 grasslands. The WV2 satellite image was acquired for the Cathedral Peak region of the Drakensberg Mountain range, South Africa. The random forest (RF) regression algorithm was used to develop a relationship between two-band normalized ratio indices (RIs), including the normalized difference vegetation index (NDVI), computed from the WV2 image data and N concentration. The RF-based variable importance (VIP) scores calculated using the training dataset (n = 150) showed that the RI computed involving the costal-blue (400-450 nm) and yellow (585-625 nm) band is the most important, when predicting canopy N concentration in the area. Using the validation dataset (n = 64), the RF explained 71% of the variation, with a Nash-Sutcliffe efficiency NSE = 0.68, in predicting N across the C3 grass, Festuca costata, and C4 grasses, Themeda triandra and Rendlia altera grasslands. Overall, results indicate that predictions of canopy N using new multispectral data with unique band setting, such as WV2 spectra are possible for C3 and C4 grasslands.

Data-driven diagnostics of terrestrial carbon dynamics over North America

The exchange of carbon dioxide is a key measure of ecosystem metabolism and a critical intersection between the terrestrial biosphere and the Earth's climate. Despite the general agreement that the terrestrial ecosystems in North America provide a sizeable carbon sink, the size and distribution of the sink remain uncertain. We use a data-driven approach to upscale eddy covariance flux observations from towers to the continental scale by integrating flux observations, meteorology, stand age, aboveground biomass, and a proxy for canopy nitrogen concentrations from AmeriFlux and Fluxnet-Canada Research Network as well as a variety of satellite data streams from the MODIS sensors. We then use the resulting gridded flux estimates from March 2000 to December 2012 to assess the magnitude, distribution, and interannual variability of carbon fluxes for the U.S. and Canada. The mean annual gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP) of the U.S. over the period 2001–2012 were 6.84, 5.31, and 1.10PgCyr−1, respectively; the mean annual GPP, ER, and NEP of Canada over the same 12-year period were 3.91, 3.26, and 0.60PgCyr−1, respectively. The mean nationwide annual NEP of natural ecosystems over the period 2001–2012 was 0.53PgCyr−1 for the U.S. and 0.49PgCyr−1 for the conterminous U.S. Our estimate of the carbon sink for the conterminous U.S. was almost identical with the estimate of the First State of the Carbon Cycle Report (SOCCR). The carbon fluxes exhibited relatively large interannual variability over the study period. The main sources of the interannual variability in carbon fluxes included drought and disturbance. The annual GPP and NEP were strongly related to annual evapotranspiration (ET) for both the U.S. and Canada, showing that the carbon and water cycles were closely coupled. Our gridded flux estimates provided an independent, alternative perspective on ecosystem carbon exchange over North America.

Elevated carbon dioxide and ozone alter productivity and ecosystem carbon content in northern temperate forests.

Three young northern temperate forest communities in the north-central United States were exposed to factorial combinations of elevated carbon dioxide (CO2) and tropospheric ozone (O3) for 11 years. Here, we report results from an extensive sampling of plant biomass and soil conducted at the conclusion of the experiment that enabled us to estimate ecosystem carbon (C) content and cumulative net primary productivity (NPP). Elevated CO2 enhanced ecosystem C content by 11%, whereas elevated O3 decreased ecosystem C content by 9%. There was little variation in treatment effects on C content across communities and no meaningful interactions between CO2 and O3. Treatment effects on ecosystem C content resulted primarily from changes in the near-surface mineral soil and tree C, particularly differences in woody tissues. Excluding the mineral soil, cumulative NPP was a strong predictor of ecosystem C content (r2 = 0.96). Elevated CO2 enhanced cumulative NPP by 39%, a consequence of a 28% increase in canopy nitrogen (N) content (g N m−2) and a 28% increase in N productivity (NPP/canopy N). In contrast, elevated O3 lowered NPP by 10% because of a 21% decrease in canopy N, but did not impact N productivity. Consequently, as the marginal impact of canopy N on NPP (ΔNPP/ΔN) decreased through time with further canopy development, the O3 effect on NPP dissipated. Within the mineral soil, there was less C in the top 0.1 m of soil under elevated O3 and less soil C from 0.1 to 0.2 m in depth under elevated CO2. Overall, these results suggest that elevated CO2 may create a sustained increase in NPP, whereas the long-term effect of elevated O3 on NPP will be smaller than expected. However, changes in soil C are not well-understood and limit our ability to predict changes in ecosystem C content.

Estimating Canopy Nitrogen Content in a Heterogeneous Grassland with Varying Fire and Grazing Treatments: Konza Prairie, Kansas, USA

Quantitative, spatially explicit estimates of canopy nitrogen are essential for understanding the structure and function of natural and managed ecosystems. Methods for extracting nitrogen estimates via hyperspectral remote sensing have been an active area of research. Much of this research has been conducted either in the laboratory, or in relatively uniform canopies such as crops. Efforts to assess the feasibility of the use of hyperspectral analysis in heterogeneous canopies with diverse plant species and canopy structures have been less extensive. In this study, we use in situ and aircraft hyperspectral data to assess several empirical methods for extracting canopy nitrogen from a tallgrass prairie with varying fire and grazing treatments. The remote sensing data were collected four times between May and September in 2011, and were then coupled with the field-measured leaf nitrogen levels for empirical modeling of canopy nitrogen content based on first derivatives, continuum-removed reflectance and ratio-based indices in the 562–600 nm range. Results indicated that the best-performing model type varied between in situ and aircraft data in different months. However, models from the pooled samples over the growing season with acceptable accuracy suggested that these methods are robust with respect to canopy heterogeneity across spatial and temporal scales.

江苏省农作物最大光能利用率时空特征及影响因子分析

基于遥感数据的光能利用率模型被广泛应用于计算陆地生态系统的生产力，但其结果对最大光能利用率（<em>ε</em>_max）参数非常敏感。由于该参数存在明显的时空变异性，在区域尺度上难以确定。利用MODIS遥感数据、分县产量统计数据和VPM模型推算了2001-2010年江苏省各县逐年的农田<em>ε</em>_max，分析了其时空变化特征及其可能原因。结果表明，在2001-2010年期间，江苏省61个县区农田<em>ε</em>_max平均值的变化范围为0.757-3.435 g C/MJ，呈现北高南低、中间高四周较低的空间分布特征；各县区农田的<em>ε</em>_max都呈现出上升趋势，但在2001-2006年期间存在明显的年际波动，2002年、2004年和2006年的<em>ε</em>_max相对较低，2007年后全省农田<em>ε</em>_max稳定上升；<em>ε</em>_max的年际波动呈现由北向南递减的趋势；全省大部分地区<em>ε</em>_max的年际变化与单位耕地面积农用化肥施用量呈正相关性，苏北北部尤为明显；同时也与C₄作物产量所占比例相关。研究表明在利用光能利用率模型计算农田生产力时，需要发展能考虑<em>ε</em>_max时空变化的参数化方案。;The productivity is a critical component of the terrestrial carbon cycle. Remote sensing driven light use efficiency models have been widely utilized to calculate the productivity of terrestrial ecosystems. They calculate the productivity as a linear product of maximum light use efficiency <em>ε</em>_max, scaling factors of environmental stress, and absorbed photosynthetically active radiation (APAR). The outputs of these models are very sensitive to t maximum light use efficiency (<em>ε</em>_max), which represents the utilization rate of photosynthetically active radiation under ideal conditions without restrictions. However, it is difficult to determine this parameter properly since it is affected by numerous factors, including canopy nitrogen content, atmospheric concentration of CO₂, plant types and species, and exhibit significant spatial and temporal variations. In currently used light use efficiency models, this parameter only changes with plant types. Such simplified parameterization might induce large uncertainties in calculated productivity of terrestrial ecosystems at regional and global scales. In this study, MODIS reflectance data, county\level yield census data, locally observed meteorological data, and the vegetation photosynthesis model (VPM) were employed to derive annual mean county\level cropland <em>ε</em>_max in Jiangsu province for the period from 2001 to 2010. The county\level yield census data was converted into total net primary productivity of each county for estimating the <em>ε</em>_max parameter in the VPM model. Then, the spatial and temporal variations of <em>ε</em>_max and possible driving factors were analyzed were analyzed. The results show that the county\level means of cropland <em>ε</em>_max in 61 counties of Jiangsu province varied between 0.757-3.435 g C/MJ during the period from 2001 to 2010, with an overall gradient decreasing from the north to the south. It is higher in the central areas than in the surrounding areas. Annual mean cropland <em>ε</em>_max increased during the period from 2001 to 2010 in all 61 counties, but showed obvious interannual fluctuations during the period from 2001 to 2006. It was relatively lower in years 2002, 2004 and 2006 and steadily increased in all counties since 2007. The interannual fluctuations of <em>ε</em>_max were normally higher in the north than in the south. The annual means of <em>ε</em>_max were positively related to the amount of fertilizer used in per unit area of cultivated cropland in most counties, particularly in the northern areas of Jiangsu Province. The increasing rates of <em>ε</em>_max were positively correlated with the increasing rates of fertilizer utilization per unit area. The interannual fluctuations of <em>ε</em>_max were also related to the yield fraction of C₄ crops (corn). This study proves that it is of importance to develop a parameterization scheme accounting for the temporal and spatial variations of <em>ε</em>_max for improving the calculation of productivity in croplands using light use efficiency models and remote sensing data. It should be kept in mind that some factors might induce uncertainties estimated <em>ε</em>_max, such as the uncertainties in yield census data, the parameters used to converts yield into net primary productivity, methodologies used to calculate APAR and scaling factors of environmental stress. The underlying mechanisms for driving significant spatial and temporal variations of <em>ε</em>_max need further thorough investigation. Nevertheless, the findings in this study can provide a value reference for optimizing the calculation of carbon fluxes between the atmosphere and terrestrial ecosystems.

Random forest algorithm and regional applications of spectral inversion model for estimating canopy nitrogen concentration in rice

Random forest algorithm and regional applications of spectral inversion model for estimating canopy nitrogen concentration in rice