Canopy Nitrogen Content 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

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.

An Estimation of the Leaf Nitrogen Content of Apple Tree Canopies Based on Multispectral Unmanned Aerial Vehicle Imagery and Machine Learning Methods

Accurate nitrogen fertilizer management determines the yield and quality of fruit trees, but there is a lack of multispectral UAV-based nitrogen fertilizer monitoring technology for orchards. Therefore, in this study, a field experiment was conducted by UAV to acquire multispectral images of an apple orchard with dwarf stocks and dense planting in southern Xinjiang and to estimate the nitrogen content of canopy leaves of apple trees by using three machine learning methods. The three inversion methods were partial least squares regression (PLSR), ridge regression (RR), and random forest regression (RFR). The results showed that the RF model could significantly improve the accuracy of estimating the leaf nitrogen content of the apple tree canopy, and the validation set of the four periods of apple trees ranged from 0.670 to 0.797 for R2, 0.838 mg L−1 to 4.403 mg L−1 for RMSE, and 1.74 to 2.222 for RPD, among which the RF model of the pre-fruit expansion stage of the 2023 season had the highest accuracy. This paper shows that the apple tree leaf nitrogen content estimation model based on multispectral UAV images constructed by using the RF machine learning method can timely and accurately diagnose the growth condition of apple trees, provide technical support for precise nitrogen fertilizer management in orchards, and provide a certain scientific basis for tree crop growth.

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.

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.

Estimation of Potato Canopy Nitrogen Content Based on Hyperspectral Index Optimization

Potato canopy nitrogen content (CNC) is an imperative metric for assessing potato growth status and guiding field management. While the spectral index can be utilized to estimate CNC, its efficacy is influenced by the environment and crop type. To address this issue, we utilized hyperspectral indices (HIs) optimization for CNC estimation. Using the inverse and first-order differential (FD) transformations of the original data (OD), HIs comprising two-band combinations in 400–1000 nm, such as RSI, DSI, NDSI, SASI, and PSI, were constructed to analyze the correlation between CNC and HIs. Based on this analysis, prediction models for potato CNC were created using the most optimal HIs. The results showed that FD transformation significantly improved the correlations between CNC and HIs, among which FD−PSI(R654, R565) had the highest correlation with CNC. We further employed the optimal HIs as variables to establish univariate and multivariate regression models to estimate the potato CNC. Among the univariate models, the accuracy of the OD−DSI model was the highest, with an R2 of 0.79 and RMSE of 0.22. Meanwhile, the FD−MLR model demonstrated the highest accuracy compared to the other multivariate models, with an R2 of 0.84, an RMSE of 0.20 during validation, and a greater prediction accuracy than the OD−DSI model. FD−MLR can be used to map the CNC distribution map of monitored potato planting plots to guide precision fertilization.

Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images

Nitrogen is one of the most important nutrients affecting the growth and fruit quality of walnut trees. Rapid and accurate monitoring of nitrogen concentration in the walnut canopy can provide a digital basis for its nutritional diagnosis and precision fertilization. Consequently, the main purpose of this study was to use Unmanned Aerial Vehicle (UAV) remote sensing technology to monitor the nitrogen concentration of walnut canopies. In this study, UAV multispectral images of the canopies of nine walnut orchards with different management levels in Wensu County, South Xinjiang, China, were collected during the fast-growing (20 May), sclerotization (25 June), and near-maturity (27 August) periods of walnut fruit, and canopy nitrogen concentration data for 180 individual plants were collected during the same periods. The validity of the information extracted via the outline canopy and simulated canopy methods was compared. The accuracy of nitrogen concentration inversion for three modeling methods, partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF), was analyzed; the effects of different combinations of variables on model accuracy were compared; and the spatial distribution of the nitrogen concentration in the walnut canopy was numerically mapped using the optimal model. The results showed that the accuracy of the model created using the single plant information extracted from the outlined canopy was better than that of the simulated canopy method, but the simulated canopy method was more efficient in extracting effective information from the single plant canopy than the outlined canopy. The simulated canopy method overcame the difficulty of mismatching the spectral information of individual plants extracted, by outlining the canopy in the original image for nitrogen distribution mapping with the spectral information of image elements in the original resolution image. The prediction accuracy of the RF model was better than that of the SVM and PLSR models; the prediction accuracy of the model using a combination of waveband texture information and vegetation index texture information was better than that of the single-source model. The coefficients of determination (R2) values of the RF prediction model built using the band texture information extracted via the simulated canopy method with the vegetation index texture information were in the range of 0.61–0.84, the root mean square error (RMSE) values were in the range of 0.27–0.43 g kg−1, and the relative analysis error (RPD) values were in the range of 1.58–2.20. This study shows that it is feasible to monitor the nitrogen concentration of walnut tree canopies using UAV multispectral remote sensing. This study provides a theoretical basis and methodological reference for the rapid monitoring of nutrients in fruit trees in southern Xinjiang.

Retrieving canopy nitrogen concentration and aboveground biomass with deep learning for ryegrass and barley: Comparing models and determining waveband contribution

Retrieving canopy nitrogen concentration and aboveground biomass with deep learning for ryegrass and barley: Comparing models and determining waveband contribution

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R2=0.67–0.89, RMSE=13.63–23.71 mg g–1, MAE=10.75–17.59 mg g–1) performed better than the direct inversion models (R2=0.61–0.80, RMSE=18.01–25.12 mg g–1, MAE=12.96–18.88 mg g–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R2=0.89, RMSE=13.63 mg g–1, MAE=10.75 mg g–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

An improved light use efficiency model by considering canopy nitrogen concentrations and multiple environmental factors

Although the light use efficiency (LUE) models are widely employed to estimate ecosystem gross primary production (GPP), the majority of these models inadequately consider the effects of environmental and biological factors on GPP, resulting in considerable uncertainty. In addition, most developed LUE models have assumed that the maximum LUE (εmax) is a fixed value for different vegetation types, while εmax should be dynamic under environmental changes. The canopy nitrogen (N) concentrations were considered to have a significant linear relationship with εmax and could be estimated using various vegetation indices. In this study, we selected a vegetation index to characterize the canopy N concentrations and further simulate the dynamic εmax. We then developed an improved LUE model that simultaneously integrated the effects of canopy N concentrations, temperature, water, atmospheric carbon dioxide (CO2) and radiation components on the GPP estimates. Different forms of LUE models that partially integrate the above factors were also constructed for comparison. Our results showed that (1) the green chlorophyll index (CIgreen) correlated well with measured canopy N concentrations (R2 = 0.68), and the model using the CIgreen to characterize canopy N concentrations performed the best; (2) the GPP estimated using the improved model gave the best accuracy (R2 = 0.69, RMSE = 2.13 gC/m2/d, MAE=1.36 gC/m2/d, IOA = 0.915) and performed well for different vegetation types when validated against the FLUXNET GPP; and (3) the estimated GPP had the best accuracy compared with MOD17 GPP and the revised EC-LUE GPP on a both daily and yearly scale. Overall, this study was an attempt to integrate N into the LUE model to obtain the spatiotemporally dynamic εmax while simultaneously taking into account the impacts of multiple environmental variables on the GPP estimates. The proposed model has the potential for satisfactory GPP simulations on a global or regional scale.

Identification of photosynthetic parameters for superior yield of two super hybrid rice varieties: A cross-scale study from leaf to canopy.

Enhancing photosynthetic capacity is widely accepted as critical to advancing crop yield. Therefore, identifying photosynthetic parameters positively related to biomass accumulation in elite cultivars is the major focus of current rice research. In this work, we assessed leaf photosynthetic performance, canopy photosynthesis, and yield attributes of super hybrid rice cultivars Y-liangyou 3218 (YLY3218) and Y-liangyou 5867 (YLY5867) at tillering stage and flowering stage, using inbred super rice cultivars Zhendao11(ZD11) and Nanjing 9108 (NJ9108) as control. A diurnal canopy photosynthesis model was applied to estimate the influence of key environmental factors, canopy attributes, and canopy nitrogen status on daily aboveground biomass increment (AMDAY). Results showed that primarily the light-saturated photosynthetic rate at tillering stage contributed to the advancing yield and biomass of super hybrid rice in comparison to inbred super rice, and the light-saturated photosynthetic rate between them was similar at flowering stage. At tillering stage, the higher CO2 diffusion capacity, together with higher biochemical capacity (i.e., maximum carboxylation rate of Rubisco, maximum electron transport rate (J max), and triose phosphate utilization rate) favored leaf photosynthesis of super hybrid rice. Similarly, AMDAY in super hybrid rice was higher than inbred super rice at tillering stage, and comparable at flowering stage partially due to increased canopy nitrogen concentration (SLNave) of inbred super rice. At tillering stage, model simulation revealed that replacement of J max and g m in inbred super rice by super hybrid rice always had a positive effect on AMDAY, and the averaged AMDAY increment was 5.7% and 3.4%, respectively. Simultaneously, the 20% enhancement of total canopy nitrogen concentration through the improvement of SLNave (TNC-SLNave) resulted in the highest AMDAY across cultivars, with an average increase of 11.2%. In conclusion, the advancing yield performance of YLY3218 and YLY5867 was due to the higher J max and g m at tillering stage, and TCN-SLNave is a promising target for future super rice breeding programs.

Estimation of Cotton Nitrogen Content Based on Multi-Angle Hyperspectral Data and Machine Learning Models

For crop growth monitoring and agricultural management, it is important to use hyperspectral remote sensing techniques to estimate canopy nitrogen content in a timely and accurate manner. The traditional nadir method has limited ability to assess the nitrogen trophic state of cotton shoots, which is not conducive to high-precision nitrogen inversion, whereas the multi-angle remote sensing monitoring method can effectively extract the canopy’s physicochemical information. However, multi-angle spectral information is affected by a variety of factors, which frequently causes shifts in the band associated with nitrogen uptake, and lowers the estimation accuracy. The capacity of the spectral index to estimate aerial nitrogen concentration (ANC) in cotton was therefore investigated in this work under various observation zenith angles (VZAs), and the Relief−F method was employed to select the best spectral band with weight for ANC that is insensitive to VZA. Therefore, in this study, the ability of the spectral index to estimate ANC in cotton was explored under different VZAs, and the Relief-F algorithm was used to optimize the optimal spectral band with weight for ANC that is insensitive to VZA. The angle insensitive nitrogen index (AINI) for various VZAs was calculated using the expression (R530 − R704)/(R1412 + R704). The results show that the correlation between the spectral index and the ANC chosen in this study is stronger than the correlation between off-nadir observations, and the correlation coefficients between Photochemical Reflectance Index (PRI), AINI, and ANC are highest when VZA is −20° and −50° (r = 0.866 and 0.893, respectively). Compared with the traditional vegetation index, AINI had the best correlation with ANC under different VZAs (r > 0.84), and the performance of ANC in the backscatter direction was estimated to be better than that in the forward-scatter direction. At the same time, the ANC estimation model of the optimal indices AINI and PRI was combined with the machine learning method to achieve better accuracy, and the prediction accuracy of the random forest (RF) model was R2 = 0.98 and RMSE = 0.590. This study shows that the AINI index can estimate cotton ANC under different VZAs. Simultaneously, the backscattered direction is revealed to be more conducive to cotton ANC estimation. The findings encourage the use of multi-angle observations in crop nutrient estimation, which will also help to improve the use of ground-based and satellite sensors.

Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors

The terrestrial gross primary productivity (GPP) plays a crucial role in regional or global ecological environment monitoring and carbon cycle research. Many previous studies have produced multiple products using different models, but there are still significant differences between these products. This study generated a global GPP dataset (NI-LUE GPP) with 0.05° spatial resolution and at 8 day-intervals from 2001 to 2018 based on an improved light use efficiency (LUE) model that simultaneously considered temperature, water, atmospheric CO2 concentrations, radiation components, and nitrogen (N) index. To simulate the global GPP, we mapped the global optimal ecosystem temperatures (Topteco) using satellite-retrieved solar-induced chlorophyll fluorescence (SIF) and applied it to calculate temperature stress. In addition, green chlorophyll index (CIgreen), which had a strong correlation with the measured canopy N concentrations (r = 0.82), was selected as the vegetation index to characterize the canopy N concentrations to calculate the spatiotemporal dynamic maximum light use efficiency (εmax). Multiple existing global GPP datasets were used for comparison. Verified by FLUXNET GPP, our product performed well on daily and yearly scales. NI-LUE GPP indicated that the mean global annual GPP is 129.69 ± 3.11 Pg C with an increasing trend of 0.53 Pg C/yr from 2001 to 2018. By calculating the SPAtial Efficiency (SPAEF) with other products, we found that NI-LUE GPP has good spatial consistency, which indicated that our product has a reasonable spatial pattern. This product provides a reliable and alternative dataset for large-scale carbon cycle research and monitoring long-term GPP variations.

Sentinel-2 estimation of CNC and LAI in rice cropping system through hybrid approach modelling

ABSTRACT Earth observation techniques represent a reliable and faster alternative to in-situ measurements by providing spatio-temporal information on crop status. In this framework, a study was conducted to assess the performance of hybrid approaches, either standard (HYB) or exploiting an active learning optimisation strategy (HYB-AL), to estimate leaf area index (LAI) and canopy nitrogen content (CNC) from Sentinel–2 (S2) data, in rice crops. To achieve this, the PROSAIL-PRO Radiative Transfer Model (RTM) was tested. Results demonstrate that a wide range of rice spectra, simulated according to realistic crop parameters, are reliable when appropriate field background conditions are considered. Simulations were used to train a Gaussian Process Regression (GPR) algorithm. Both cross-validation and validation results showed that HYB-AL approach resulted the best performing retrieval schema. LAI estimation achieved good performance (R2=0.86; RMSE=0.54) and resulted very promising for model application in operational monitoring systems. CNC estimations showed moderate performance (R2=0.63; RMSE=0.89) due to a saturation behaviour limiting the retrieval accuracy for moderate/high CNC values, approximately above 4 [g m−2]. S2 maps of LAI and CNC provided spatio-temporal information in agreement with crop growth, nutritional status and agro-practices applied to the study area, resulting in an important contribution to precision farming applications.

Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling

ABSTRACT The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R 2 = 0.82 and R 2 = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages.

HSI-PP: A flexible open-source software for hyperspectral imaging-based plant phenotyping

Hyperspectral imaging has become one of the most popular techniques for high-throughput plant phenotyping. Extracting and analyzing useful plant phenotypic traits from hyperspectral images represents a major bottleneck for plant science and breeding communities. This study aims to present a stand-alone easy-to-use software platform called HSI-PP to process and analyze hyperspectral images for high-throughput plant phenotyping. The HSI-PP software integrates pre-processing, feature extraction, and modeling functions. The application of HSI-PP is exemplified by investigating the response of different Arabidopsis thaliana genotypes to drought stress, and the impact of various imaging angles on predicting the canopy nitrogen content (CNC) of oilseed rape (Brassica napus L.). The results showed that HSI-PP can process 10 GB on an ordinary PC in time ranging from 30 to 73 min according to image size and the complexity of the pipeline. HSI-PP extracted multiple phenotyping traits (spectral, textural, and morphological) of Arabidopsis thaliana from a large image dataset (104 GB) within five hours. The fusion of these features achieved higher accuracy (94%) than only using spectral information (85%) as early as day 4 after drought stress treatment. For oilseed rape, about 384 GB image data was processed within eighteen hours, and it was found that the tilted imaging angle of 75° had the optimized PLSR fitting (0.83) to the ground truth. The results demonstrate that HSI-PP is a stand-alone, automated, and open-source hyperspectral image processing platform adapted to various applications in plant phenotyping without requiring professional programming skills to serve the plant research community.