Canopy Nitrogen Research Articles

From Spectral Characteristics to Index Bands: Utilizing UAV Hyperspectral Index Optimization on Algorithms for Estimating Canopy Nitrogen Concentration in Carya Cathayensis Sarg

Employing drones and hyperspectral imagers for large-scale, precise evaluation of nitrogen (N) concentration in Carya cathayensis Sarg canopies is crucial for accurately managing nitrogen fertilization in C. cathayensis Sarg cultivation. This study gathered five sets of hyperspectral imagery data from C. cathayensis Sarg plantations across four distinct locations with varying environmental stresses using drones. The research assessed the canopy nitrogen concentration of C. cathayensis Sarg trees both during singular growth periods and throughout their entire growth cycles. The objective was to explore the influence of band combinations and spectral index formula configurations on the predictive capability of the hyperspectral indices (HIs) for canopy N concentration (CNC), optimize the performance between HIs and machine learning approaches, and validate the efficacy of optimized HI algorithms. The findings revealed the following: (i) Optimized HIs demonstrated optimal predictive performance during both singular growth periods and the full growth cycles of C. cathayensis Sarg. The most effective HI model for singular growth periods was the optimized–modified–normalized difference vegetation index (opt-mNDVI), achieving an adjusted coefficient of determination (R2) of 0.96 and a root mean square error (RMSE) of 0.71. For the entire growth cycle, the HI model, also opt-mNDVI, attained an R2 of 0.75 and an RMSE of 2.11; (ii) optimized band combinations substantially enhanced HIs’ predictive performance by 16% to 71%, while the choice between three-band and two-band combinations influenced the predictive capacity of optimized HIs by 4% to 46%. Hence, utilizing optimized HIs combined with Unmanned Aerial Vehicle (UAV) hyperspectral imaging to evaluate nitrogen concentration in C. cathayensis Sarg trees under complex field conditions offers significant practical value.

Estimating the Canopy Nitrogen Content in Maize by Using the Transform-Based Dynamic Spectral Indices and Random Forest

Estimating the Canopy Nitrogen Content in Maize by Using the Transform-Based Dynamic Spectral Indices and Random Forest

Leaf Nitrogen and Phosphorus Variation and Estimation of Citrus Tree under Two Labor-Saving Cultivation Modes Using Hyperspectral Data

Understanding canopy nitrogen (N) and phosphorus (P) differences is crucial for optimizing plant nutrient distribution and management. This study evaluated leaf N and P content in citrus trees across three cultivation modes: traditional mode (TM), wide-row and narrow-plant mode (WRNPM), and fenced mode (FM). We used hyperspectral data for non-destructive quantification and compared 1080 leaf samples from upper, middle, and lower canopy layers. Four models—Random Forest (RF), Backpropagation Neural Network (BPNN), Partial Least Squares (PLS), and Support Vector Machine (SVM)—were employed for leaf N and P estimation. Results showed that the TM had significantly lower N content compared to the WRNPM and FM, while the WRNPM exhibited higher P content. The canopy layer had minimal impact on N and P in the FM, and leaves in the upper layer had higher nutrient content in the WRNPM and TM. RF provided the best estimation accuracy, with R2 values of 0.66 for N and 0.72 for P. The cultivation mode and canopy layer significantly influenced the estimation accuracy, with the TM yielding the highest R2, followed by the WRNPM and FM obtaining the lowest accuracy. The labor-saving cultivation mode had different nutrient utilization efficiency compared to the TM. The cultivation mode and canopy layer should be considered when hyperspectral data were used for estimating the leaf N and P content. The study can offer new insights for precise fertilization strategies in fruit trees.

Determinants of Deadwood Biomass under the Background of Nitrogen and Water Addition in Warm Temperate Forests

Climate change is exacerbating the vulnerability of temperate forests to severe disturbances, potentially increasing tree mortality rates. Despite the significance of this issue, there has been a lack of comprehensive research on tree survival across extensive forest areas under the background of global climate change. To fill this gap, we conducted a detailed analysis of tree survival within a canopy nitrogen and water addition experimental platform in central China, utilizing data from two censuses and evaluating contributing factors. Our findings revealed 283 dead trees within the plots, predominantly of very small diameters (1–10 cm). The distribution of these dead trees varied among subplots, influenced by both biotic and abiotic factors. Notably, three dominant tree species were responsible for 64.8% of the deadwood biomass. The study determined that both the breast diameter and the quantity of dead trees, affected by surrounding trees and environmental conditions, played a critical role in deadwood biomass accumulation. This research offers an in-depth examination of deadwood biomass patterns in a temperate forest, highlighting the need to consider both experiment treatments and abiotic elements like topography in studies of forest ecosystem carbon. The insights gained from this study enhance our understanding of warm temperate forests’ role in the global carbon cycle and offer valuable guidance for forest conservation and management strategies.

Correction: Canopy nitrogen application effects on Quercus petraea L. and Fagus sylvatica L. ring width and wood density

Correction: Canopy nitrogen application effects on Quercus petraea L. and Fagus sylvatica L. ring width and wood density

Effects of Canopy Nitrogen Addition and Understory Vegetation Removal on Nitrogen Transformations in a Subtropical Forest

The management of understory vegetation and anthropogenic nitrogen (N) deposition has significantly resulted in a nutrient imbalance in forest ecosystems. However, the effects of canopy nitrogen addition and understory vegetation removal on N transformation processes (mineralization, nitrification, ammonification, and leaching) along with seasonal variations (spring, summer, autumn, and winter) remain unclear in subtropical forests. To fill this research gap, a field manipulation experiment was conducted with four treatments, including: (i) CK, control; (ii) CN, canopy nitrogen addition (25 kg N ha−1 year−1); (iii) UR, understory vegetation removal; and (iv) CN+UR, canopy nitrogen addition plus understory vegetation removal. The results revealed that CN increased net mineralization and nitrification by 294 mg N m−2 month−1 in the spring and 126 mg N m−2 month−1 in the winter, respectively. UR increased N mineralization and nitrification rates by 618 mg N m−2 month−1 in the summer. In addition, CN effectively reduced N leaching in the spring, winter, and autumn, while UR increased it in the spring and winter. UR increased annual nitrification rates by 93.4%, 90.3%, and 38.9% in the winter, spring, and summer, respectively. Additionally, both net N ammonification and annual nitrification rates responded positively to phosphorus availability during the autumn. Overall, UR potentially boosted nitrification rates in the summer and ammonification in the spring and winter, while CN reduced N leaching in the spring, winter, and autumn. Future research should integrate canopy nitrogen addition, understory vegetation removal, and phosphorus availability to address the global N deposition challenges in forest ecosystems.

Canopy nitrogen application effects on Quercus petraea L. and Fagus sylvatica L. ring width and wood density

AbstractAs an essential nutrient, Nitrogen (N) availability is fundamental in evaluating forest productivity, and as such, understanding the effects of changing atmospheric N inputs in forest ecosystems is of high significance. While most field experiments have been employing ground fertilization as a method to simulate N deposition, two experimental forest sites in Italy have adopted the more advanced canopy N application approach. Here we present findings from a case study of wood core analyses of predominantly pure, even aged, Sessile oak (Quercus petraea L.) and European beech (Fagus sylvatica L.) forest stands, treated with either below or above canopy N fertilization, comparing between the two simulation pathways of increased N deposition. The potential effects of elevated N availability on total ring width, mean ring density, and their corresponding earlywood and latewood fractions are examined. Our results indicate inconclusive effects of the treatments on the ring width traits of both Q. petraea or F. sylvatica, although basal area increment patterns appeared to be affected divergently between the species and treatments. Mean and earlywood, but not latewood, densities on the other hand, exhibited a decrease in certain years of the treatment period in Q. petraea as result of the above canopy N application only, whereas F. sylvatica wood density showed no clear response to any of the treatments. Thus, we are describing distinct reactions of the two broadleaved species to the different experimental N deposition approaches, discussing potential growth patterns under increased N availability, and emphasizing the importance of considering wood density in assessments of tree biomass accumulation and essentially Carbon storage capacities.

Integrating UAV hyperspectral data and radiative transfer model simulation to quantitatively estimate maize leaf and canopy nitrogen content

Crop nitrogen (N) content reflects crop nutrient status and plays an important role in precision nutrient management. Accurate crop N content estimation from remote sensing has been well documented. However, the robustness (i.e., the ability of a model to perform consistently across various conditions) of these methods under varied soil conditions or different growth stages has rarely been considered. We proposed a hybrid method that integrates in-situ measurements and the data simulated by a mechanistic model to improve the estimation of maize N content. In-situ data included hyperspectral images collected by Unmanned Aerial Vehicle (UAV), and leaf and canopy N content (LNC and CNC). A mechanistic radiative transfer model (PROSAIL-PRO) was used to generate simulated data, i.e., canopy reflectance paired with target crop traits (i.e., LNC, CNC). We compared the performance from the hybrid method with a machine learning method (Gaussian Process Regression) and six different vegetation indices (VIs) on four in-situ datasets collected at three study sites from 2021 to 2022. Results show that the hybrid method consistently performed the best for LNC estimation across four testing datasets (RRMSE ranging from 10.08% to 10.84%). For CNC estimation, the hybrid method had the best estimation results on two out of the four testing datasets and performed comparably to the best method on the other two datasets (RRMSE ranging from 13.89% to 25.21%). Next, we assessed the estimation robustness of the hybrid method, the machine learning, and the best-VI by comparing the mean (µ) and standard deviation (σ) of RRMSE across diverse water and N treatments (condition #1) and different growth stages (condition #2). Among 16 total cases (two crop traits by four study sites by two conditions), the hybrid method had 11 cases of smallest µ and seven cases of smallest σ, outperforming the machine learning (0/16 for µ, 4/16 for σ) and the best-VI (5/16 for µ, 5/16 for σ). These results underscore the greater robustness of the hybrid method. This study highlights the potential of integrating in-situ measurements and simulated data to improve estimation accuracy and robustness for maize LNC and CNC. The promising performance of the hybrid method suggests its applicability to a broader range of crops and various crop traits.

Linkages among leaf nutrient concentration, resorption efficiency, litter decomposition and their stoichiometry to canopy nitrogen addition and understory removal in subtropical plantation

BackgroundThe prevalence of understory removal and anthropogenic nitrogen (N) deposition has significantly altered the ecological processes of forest ecosystems at both regional and global scales. However, it remains a pressing challenge to understand how N deposition and understory removal affect leaf nutrient dynamics, nutrient resorption, litter decomposition, and their linkages for better managing forest ecosystems under nutrient imbalances induced by N enrichment. To address this research gap, a field manipulation experiment was carried out in a subtropical Cunninghamia lanceolata plantation with four treatments including: control (CK), canopy N addition (CN), understory removal (UR), and canopy N addition plus understory removal (CN × UR). Green and senesced leaf N and phosphorus (P) concentrations, N and P resorption efficiencies, litter decomposition, and their correlations were measured.ResultsThe results revealed that the average N concentrations of green early and late leaves in UR were increased by 6.61 and 18.89% compared to CK. UR had the highest whereas CN had the lowest P concentrations in green leaves across the two sampling seasons. Following this, UR, leaf type, season, and their interactions significantly affected leaf N, P, and N:P (P < 0.05). The highest leaf N resorption (32.68%) and P resorption efficiencies (63.96%) were recorded in UR. Litter decomposition was significantly retarded in UR (P < 0.01) relative to CN. The regression analysis demonstrated that leaf nutrient status was significantly interconnected with leaf nutrient resorption efficiencies. In addition, leaf nutrient dynamics were strongly correlated with litter nutrients, indicating that both were coupled.ConclusionThese findings can deepen our knowledge of biogeochemical cycling and reveal contrasting nutrient-acquisition strategies on N and P limitation in response to UR and CN. Considering the P limitation, it is important to note that P was resorbed more efficiently, illustrating a remarkable nutrient preservation approach for nutrient-limitations. Resorption may be a crucial mechanism for keeping nutrients in these forests, so better understory management practices are required to prevent reliance on external nutrient pools. Overall, this study sheds meaningful insights into the ability of forest adaptation in response to global climatic change.

Canopy nitrogen deposition enhances soil ecosystem multifunctionality in a temperate forest.

Nitrogen (N) deposition affects ecosystem functions crucial to human health and well-being. However, the consequences of this scenario for soil ecosystem multifunctionality (SMF) in forests are poorly understood. Here, we conducted a long-term field experiment in a temperate forest in China, where N deposition was simulated by adding N above and under the canopies. We discover that canopy N addition promotes SMF expression, whereas understory N addition suppresses it. SMF was regulated by fungal diversity in canopy N addition treatments, which is largely due to the strong resistance to soil acidification and efficient resource utilization characteristics of fungi. While in understory N addition treatments, SMF is regulated by bacterial diversity, which is mainly because of the strong resilience to disturbances and fast turnover of bacteria. Furthermore, rare microbial taxa may play a more important role in the maintenance of the SMF. This study provides the first evidence that N deposition enhanced SMF in temperate forests and enriches the knowledge on enhanced N deposition affecting forest ecosystems. Given the divergent results from two N addition approaches, an innovative perspective of canopy N addition on soil microbial diversity-multifunctionality relationships is crucial to policy-making for the conservation of soil microbial diversity and sustainable ecosystem management under enhanced N deposition. In future research, the consideration of canopy N processes is essential for more realistic assessments of the effects of atmospheric N deposition in forests.

Eleven-Year Canopy Nitrogen Addition Enhances the Uptake of Phosphorus by Plants and Accelerates Its Depletion in Soil

Soil phosphorus (P) is a critical factor that limits plant productivity. Enhanced nitrogen (N) deposition has the potential to modify P transformation and availability, thereby potentially affecting the long-term productivity of forests. Here, we conducted an 11-year-long field experiment to simulate N deposition by adding N to the forest canopy in a N-limited northern subtropical forest in central China and assessed the changes in soil organic P mineralization, P fractions, microbial biomass P content, phosphatase activity, and plant P content under N deposition. Our objective was to establish a theoretical framework for addressing the P supply and sustaining plant productivity in soils with low P availability, particularly in a changing global setting. The results demonstrated a substantial reduction in the levels of total, organic, and available P owing to the canopy addition of N. Furthermore, there was a marked decrease in the proportion of organic P in the total P pool. However, no substantial changes were observed in the soil inorganic P content or the proportion of inorganic P within the total P across different treatments. Canopy N addition significantly enhanced the microbial biomass P content, phosphatase activity, and organic P mineralization rate, suggesting that in soils with limited P availability, the primary source of P was derived from the mineralization of organic P. Canopy N addition substantially increased the P content in leaves and fine roots while concurrently causing a considerable decrease in the N:P ratio. This indicates that N deposition increases P demand in plants. Correlation analysis revealed a significant negative association among the total, organic, and available P levels in the soil and plant P concentrations (p &lt; 0.05). This suggests that the primary cause of the reduced fractions of P was plant uptake following canopy N addition. Various studies have demonstrated that N deposition induces an augmented P demand in plants and expedites the utilization of available P. A substantial reduction in potentially accessible soil P caused by N deposition is likely to exacerbate regional P depletion, thereby exerting adverse impacts on forest ecosystem productivity.

Canopy nitrogen addition and understory removal destabilize the microbial community in a subtropical Chinese fir plantation

Subtropical Chinese fir plantations have been experiencing increased nitrogen deposition and understory management because of human activities. Nevertheless, effect of increased nitrogen deposition and understory removal in the plantations on microbial community stability and the resulting consequences for ecosystem functioning is still unclear. We carried out a 5-year experiment of canopy nitrogen addition (2.5 g N m−2 year−1), understory removal, and their combination to assess their influences on microbial community stability and functional potentials in a subtropical Chinese fir plantation. Nitrogen addition, understory removal, and their combination reduced soil bacterial diversity (OUT richness, Inverse Simpson index, Shannon index, and phylogenetic diversity) by 11–18%, 15–24%, and 19–31%; reduced fungal diversity indexes by 3–5%, 5–6%, and 5–7%, respectively. We found that environmental filtering and interspecific interactions together determined changes in bacterial community stability, while changes in fungal community stability were mainly caused by environmental filtering. Fungi were more stable than bacteria under disturbances, possibly from having a more stable network structure. Furthermore, we found that microbial community stability was linked to changes in microbial community functional potentials. Importantly, we observed synergistic interactions between understory removal and nitrogen addition on bacterial diversity, network structure, and community stability. These findings suggest that understory plants play a significant role in promoting soil microbial community stability in subtropical Chinese fir plantations and help to mitigate the negative impacts of nitrogen addition. Hence, it is crucial to retain understory vegetation as important components of subtropical plantations.

Quantifying canopy nitrogen of Aman rice utilizing multi-temporal unmanned aerial systems

Timely and precise monitoring of canopy nutrient status is critical for rice production. Advancement in unmanned aerial vehicle (UAV) sensing technology has great potential in monitoring and assessing crops. This study quantified canopy nitrogen (N) using a multispectral sensing in a field experiment. The experiment comprised seven treatments including different doses and sources of N. The UAV data was collected at different phenological stages from three consecutive rice seasons e.g., Boro (Winter, 2020–21), Aman (monsoon 2021), and Boro (Winter, 2021–22). The popular rice varieties Binadhan-7 and BRRI dhan29 were cultivated for Aman and Boro rice, respectively. The vegetation indices (VIs) increased with the growth of crops and were the highest at 50 DAT in all the treatments. The RD with 25 % more N showed significantly higher values for normalized difference vegetation index (NDVI), chlorophyll vegetation index (CVI), simple ratio (SR), optimized soil adjusted vegetation index (OSAVI), and green normalized difference vegetation index (GNDVI) leading to excessive vegetative growth. The model for estimating canopy N percent was based on NDVI and observed nitrogen with a R2 value of 0.74. The model was validated against N percent of Boro rice (2020–21) with a R2 value of 0.78 and RMSE 0.09 and N percent of Boro rice (2021–22) R2 value of 0.75 and RMSE 0.10. In biochar treatment, the highest level of canopy N was observed, followed by cow dung and USG treatments. The study revealed that 25 % more N than RD instigates vegetative growth and canopy nitrogen was highest in biochar treatment.

Higher yields of modern maize cultivars are not associated with coordinated light and N distribution within the canopy

ContextCanopy architecture and associated light distribution, nitrogen distribution, and leaf physiological characteristics drive canopy photosynthesis, and ultimately determine grain yields. Breeding has increased those yields during the past decades of breeding history but it is not clear if this also improved efficiency in nitrogen allocation for optimal light use. Research questionsTo what extent has selection for yields affected maize canopy architecture and the vertical distribution of light and nitrogen? MethodsA 2-year field experiment was conducted in Jilin province, Northeast China, involving six maize cultivars released between 1950 and 2004. The canopy architecture, the vertical distribution patterns of light and nitrogen, and grain yields of these cultivars were quantified and analysed. ResultsThe genetic gain in grain yield through breeding was 109 kg ha−1 year−1, and the concomitant increase in aboveground biomass was 16.1 g m−2 ground year −1 at maturity stage. Modern cultivars had more erect leaves compared to older cultivars, especially the leaves above the ear leaf, showing a decrease of 0.31° year−1. The changes in leaf area index (LAI) and leaf angle contributed to the improved light distribution within the canopy over generations of selection, as the light extinction coefficient KL decreased significantly by 0.67% year−1. The canopy nitrogen content increased by 0.09 g N m−2 ground year−1, but the average canopy specific leaf nitrogen (canopy N/LAI) tended to decrease. The relationship between light and nitrogen distribution within the canopy differed among cultivars but did not relate to year of release(YOR), indicating that breeding of maize cultivars from the 1950–2004 primarily led to changes in canopy N and canopy architecture and did not improve the coordination between light and nitrogen in maize canopy. ConclusionYield gain through breeding history from 1950 to 2004 was strongly associated with increases in canopy N and LAI linked to increasing erectness of leaves, which improved light distribution under these conditions. In contrast, the coordination of light and nitrogen distribution was not changed much. We conclude that the higher yields that are obtained with modern cultivars do not appear to rely on a coordinated light and N distribution within the canopy.

Estimation of above ground biomass, biophysical and quality parameters of spinach (Spinacia Oleracea L.) using Sentinel-2 to support the supply chain

The estimation of biophysical and biochemical parameters using Sentinel-2 satellite imagery provides crucial information to support agronomic management and logistics for the processing industry, yet this approach has been poorly explored for vegetable crops, such as spinach (Spinacia oleracea L.). For this reason, Sentinel-2 satellite images were used to estimate biophysical and biochemical parameters of open field spinach crops (Above Ground Biomass (AGB), Canopy Nitrogen Content (CNC), Leaf Area Index (LAI), Canopy Chlorophyll Content (CCC), Leaf Dry Matter Content (LDMC), Leaf Nitrogen Content (LNC), Leaf Nitrate Content (LNO3−C) and Leaf Chlorophyll Content (LCC)). Spinach samples were collected in northern Italy in two different growing seasons (2020 – 2021) to measure biophysical and biochemical parameters which were linearly regressed via linear regression k-fold to five vegetation indices retrieved from Sentinel-2 images (MCARI, NDRE, NDVI, NDWI and SR). The AGB estimation models were also validated at field scale using historical data, totally independent from those used for the model calibration, collected by the processing industry between 2018 and 2021. Overall, canopy-level parameters (AGB, LAI, CNC, CCC) were estimated more accurately than leaf-level parameters (LDMC, LNC, LNO3−C, LCC). The highest accuracy was observed for the estimation of the canopy-scale parameter AGB (nRMSE = 9.48 %, R2 = 0.87) using MCARI, while the lowest accuracy was observed for the estimation of the leaf-scale parameter LCC (nRMSE = 29.24 %, R2 = 0.01) using NDWI. At field level, the validation of the AGB estimation models showed the highest performance using SR (nRMSE = 19.69 %, R2 = 0.21). This work demonstrates that using Sentinel-2 satellite images, it is feasible to estimate biophysical and biochemical parameters useful for monitoring the health of spinach crops for both agronomic management and the industrial supply chain.

A new multispectral index for canopy nitrogen concentration applicable across growth stages in ryegrass and barley

Accurately monitoring Canopy Nitrogen Concentration (CNC) is a prerequisite for precision nitrogen (N) fertiliser management at the farm scale with carbon and N budgeting across the landscape and ecosystems. While many spectral indices have been proposed for CNC monitoring, their applicability and accuracy are often adversely affected by confounding factors such as aboveground biomass (AGB), crop type, growth stages, and environmental conditions, limiting their broader application and adoption; with AGB being one of the most dominant signals and confounding factors at canopy scale. The confounding effect can become more challenging as AGB is also physiologically linked with CNC across the growth stages. Additionally, the interplay between index form, selection of optimal wavebands and their bandwidths remains poorly understood for CNC index design. This study proposes robust and cost-effective 2- and 4-waveband multispectral (MS) CNC indices applicable across a wide range of crop conditions. We collected 449 canopy reflectance spectra (400–980 nm) together with corresponding CNC and AGB measurements across four growth stages of ryegrass (winter and summer), and five growth stages of barley (winter-spring) in Victoria, Australia, in 2018 and 2019. All possible waveband (400–980 nm) combinations revealed that the best combination varied between seasons and crop types. However, the visible spectrum, particularly the blue region, presented high and consistent performance. Bandwidths of 10–40 nm outperformed either very narrow (2 nm) or very broad bandwidths (80 nm). The newly developed 2-waveband index (416 and 442 nm with 10-nm bandwidth; R2 = 0.75 and NRMSE = 0.2) and 4-waveband index (512, 440, 414 and 588 nm with 40-nm bandwidth; R2 = 0.81 and NRMSE = 0.17) exhibited the best performance, while validation with an independent dataset (from a different growing period to those used in the model development) obtained NRMSE values of 0.25 and 0.24, respectively. The 4-waveband index provides enhanced performance and permits use of broader bandwidths than its 2-waveband counterpart.

A general methodology for the quantification of crop canopy nitrogen across diverse species using airborne imaging spectroscopy

Accurate monitoring of crop nitrogen (N) across spatial and temporal scales is a fundamental goal for meeting precision agriculture requirements and promoting sustainable agriculture. The planning and implementation of several spaceborne imaging spectroscopy missions in recent years holds great promise for such large scale and intricate monitoring. Several N retrieval models have been developed for specific crop species, but a generalized model across diverse species is lacking. By leveraging imaging spectroscopy data collected by the Global Airborne Observatory (GAO), and leaf samples collected from commercial and research farms, we used partial least squares regression to calibrate and validate the retrieval of mass-based crop canopy N concentrations across diverse species and several major agricultural regions in the contiguous United States. The performance statistics indicated high precision and accuracy of the model results, suggesting that the development of a generalized N retrieval model is possible (R2: 0.78; RMSE: 0.49% N). Maps derived from GAO data provided quantitative crop N information at fine spatial resolution (i.e., 0.6 m), capturing both inter- and intra-species variations across agricultural locations. The algorithm was also successfully tested on simulated moderate resolution (i.e., 30 m) imagery, corresponding to data to be collected by forthcoming spaceborne imaging spectroscopy missions. Imaging spectroscopy offers an effective approach to quantify crop N concentration that could be incorporated to promote sustainable agriculture and improve global food security.

Utilization of the Fusion of Ground-Space Remote Sensing Data for Canopy Nitrogen Content Inversion in Apple Orchards

Utilizing multi-source remote sensing data fusion to achieve efficient and accurate monitoring of crop nitrogen content is crucial for precise crop management. In this study, an effective integrated method for inverting nitrogen content in apple orchard canopies was proposed based on the fusion of ground-space remote sensing data. Firstly, ground hyper-spectral data, unmanned aerial vehicles (UAVs) multi-spectral data, and apple leaf samples were collected from the apple tree canopy. Secondly, the canopy spectral information was extracted, and the hyper-spectral and UAV multi-spectral data were fused using the Convolution Calculation of the Spectral Response Function (SRF-CC). Based on the raw and simulated data, the spectral feature parameters were constructed and screened, and the canopy abundance parameters were constructed using simulated multi-spectral data. Thirdly, a variety of machine-learning models were constructed and verified to identify the optimal inversion model for spatially inverting the canopy nitrogen content (CNC) in apple orchards. The results demonstrated that SRF-CC was an effective method for the fusion of ground-space remote sensing data, and the fitting degree (R2) of raw and simulated data in all bands was higher than 0.70; the absolute values of the correlation coefficients (|R|) between each spectral index and the CNC increased to 0.55–0.68 after data fusion. The XGBoost model established based on the simulated data and canopy abundance parameters was the optimal model for the CNC inversion (R2 = 0.759, RMSE = 0.098, RPD = 1.855), and the distribution of the CNC obtained from the inversion was more consistent with the actual distribution. The findings of this study can provide the theoretical basis and technical support for efficient and non-destructive monitoring of canopy nutrient status in apple orchards.

Estimation of canopy nitrogen nutrient status in lodging maize using unmanned aerial vehicles hyperspectral data

Rapid and non-destructive monitoring of the temporal dynamic changes in canopy nitrogen in lodging maize is essential to explore the influence on plant nutrient transport and yield loss. This study aims to monitor the canopy nitrogen status and lodging severity in maize using unmanned aerial vehicle (UAV) hyperspectral technology. The lodging maize experiments, involving different types, were conducted at the vegetative tasseling (VT) and reproductive milk (R3) stages. Agronomic traits and hyperspectral images were collected at 1, 7, 14, 21 and 28 days after lodging (DAL). Lodging intensity was quantified using canopy nitrogen concentration (CNC) and canopy nitrogen volumetric density (CNVD). Firstly, the variation in CNC and CNVD among different lodging types was analyzed, and the correlation between CNC, CNVD and the canopy hyperspectrum were assessed. Subsequently, the recursive feature elimination with cross-validation (RFECV) algorithm was used to screen wavelengths sensitive to CNC and CNVD. Next, the CNC and CNVD estimation models were developed using random forest regression (RFR) and gradient boosting regression (GBR) algorithms. Finally, the spatial distribution maps of CNC and CNVD in lodging maize were generated based on UAV images. The key findings were as follows: (1) CNVD exhibited more significant variation than CNC in different lodging types, with greater lodging severity corresponding to higher CNVD values; (2) the correlation between CNVD and canopy hyperspectrum was stronger than that of CNC; (3) there were 12 CNC-sensitive and 16 CNVD-sensitive wavelengths selected by RFECV algorithm; (4) the CNVD estimation model performed the best (R2 = 0.77, RMSE = 69.61 g/m3 in testing set) using the GBR algorithm. Therefore, the combination of feature selection with regression models effectively reduces hyperspectral data dimensionality. This enhances the estimation accuracy and computational efficiency for assessing canopy nitrogen nutrient status in lodging maize.

Nitrogen addition changes the canopy biological characteristics of dominant tree species in an evergreen broad-leaved forest

Many studies have focused on the impact of nitrogen deposition on plants, but due to technical limitations, research on the responses of forest canopy to manipulated nitrogen deposition is relatively scarce. Based on a canopy nitrogen addition (CN) platform, this study used laboratory analysis and unmanned aerial vehicle (UAV) observations to assess the impact of CN on the canopy traits of dominant tree species (Engelhardia roxburghiana, Schima superba, and Castanea henryi) in an evergreen broad-leaved forest in China. The results showed that nitrogen application at 25 kg N ha−1 y−1 (CN25) and 50 kg N ha−1 y−1 (CN50) significantly increased the actual net photosynthetic rate (An) of all the three tree species. CN25 significantly increased superoxide dismutase (SOD), catalase (CAT), and ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) activities in C. henryi. CN50 significantly increased the leaf area of all the three tree species and significantly reduced the leaf thickness of C. henryi, and significantly increased the POD and Rubisco activities in S. superba and C. henryi. CN significantly changed the number of forest gaps, but did not significantly change the area of forest gaps within the sample plots. CN25 significantly decreased the vertical projection area but increased the canopy flowering coverage of S. superba in dominant directions. CN25 and CN50 significantly increased the flowering coverage of C. henryi in favorable directions. It is found that under long-term (10-year) nitrogen addition, the balance between carbon fixation and antioxidant defense functions of E. roxburghiana may be broken down, but the carbon assimilation, antioxidant capacity and reproduction potential of S. superba and C. henryi may be well coordinated, which will have a potential impact on the species composition and ecological functions of the evergreen broad-leaved forest. This study may also provide scientific basis for forest management in the context of enhanced atmospheric nitrogen deposition.