Mapping plant nitrogen concentration within Luvuvhu River Catchment using Sentinel-2 imagery

Plant nitrogen (N) estimation using real-time and non-convection methods is very crucial for ecosystem management. This study aimed at estimating plant N concentration of indigenous vegetation in Luvuvhu River Catchment (LRC) using both field and remotely sensed data from Sentinel-2 imagery. The study used three different categories of spectral indices to fulfil its objectives. Red edge based, nitrogen related, and combined spectral indices based on Sentinel-2 data were subjected to stepwise regression on R studio software to determine which category of spectral indices is more efficient in estimating plant N concentration. Results have shown that combined spectral indices performed better with R² = 0.59, RMSE = 0.47% and MAE = 0.38%, followed by N based spectral indices with R² = 0.44, RMSE = 0.65% and MAE = 0.48%, and the last category is red edge based spectral indices with R² = 0.35, RMSE = 0.81% and MAE = 0.65%. The coefficients of the best performing model obtained from stepwise regression were used to compute multiple linear regression on QGIS to produce a map showing the concentration of plant N across the study area. Plant N varies with plant species and, the thematic map created show how plant N is distributed across the study area. With the help of this study, forest managers can better manage the natural vegetation by collaborating with forest communities. This possible partnership will create green jobs in addition to revenue. The link of the natural regeneration, reforestation, agroforestry and quantification by Sentinel-2 images for emission reduction will be beneficial for their livelihood. On a broader scale, participatory management is a good way to mitigate and adapt to climate change. On the other hand, the study suggests that more in-depth research should be conducted to explore further properties of red-edge indices for vegetation parameters prediction.

1 Introduction

Nitrogen (N) is a main regulator of several leaf physiological processes, such as photosynthesis, respiration, and transpiration (Reich et al., 2006), and it correlates well with chlorophyll (Chl) content, light use efficiency (LUT), and net primary production (Jiang et al., 2021). N is the principal component which restrains productivity of various terrestrial ecosystems (Richards et al., 2012). This is influenced by the fact that N is a well-known resource that limits plants’ productivity and growth (Ustin, 2013). Spatial patterns of N at leaf level help us to understand the part that terrestrial ecosystems play in the larger Earth system since they are connected to fluctuations of carbon, water, and energy (Ollinger et al., 2008). Canopy N is also associated with the capacity of vegetation to hold atmospheric N deposition (Lindsay, 2017), which has increased together with atmospheric carbon dioxide (CO₂) over the years because of increased use of fossil fuel and production of artificial fertilizer using harmful chemicals (Galloway et al., 2008). The distribution of plant N varies across the landscape due to many variables.

Plant N concentration among plants found in natural ecosystems differs by over 32% (Hobbie et al., 2005) and this varies with topographic gradients and cycling of nutrients, among species (Craine et al., 2009), and in response to manipulations of resource obtainability for experimental purposes (Amundson et al., 2003). Plant N may also be affected by other factors such as land-use history, changes in CO₂ concentration, acid deposition, and limitation of another nutrient element supply (Goodale and Aber, 2001; Hallett and Hornbeck, 1997). Overall, plant N can be used as a measure to generalize patterns of N cycling globally (Lindsay, 2017) as well as a vital indicator to assess changes in N cycling that might accompany anthropogenic influence on ecosystems such as rising atmospheric CO₂ (Huber et al., 2011). As the main nutrient that limits plants’ growth, plant N availability, the way it fluctuates in ecosystems, and how it responds to global change will have long term significant impacts on carbon (C) sequestration in various ecosystems (Luo et al., 2004). Estimations of N at leaf level and canopy N concentration of vegetation allow for a better understanding of ecosystem functioning and biochemical processes (Lee and Nguyen, 2005).

Traditionally, chemical analysis has been used to obtain canopy and foliar N (Serrano et al., 2002). This approach is accurate, but destructive, time-consuming, and costly (Sáez-Plaza et al., 2013). Thus, make it impractical to measure canopy and leaf N for a large area of forests consisting of different vegetation types using the chemical analysis method. To our advantage, remote sensing has turned things around, such assessments can be carried out a low cost, and within a very short space of time across large spatial scales (Walshe et al., 2020). Remote sensing has been used for various applications in vegetation monitoring such as land cover change detection, species classification, land cover estimation, and defoliation (Walshe et al., 2020). Leaf N has been determined in forests (Singh et al., 2015), grassland and crop ecosystems using remote sensing techniques over the years (Yao et al., 2015).

Airborne and spaceborne serve as exceptional tools for temporal and spatial monitoring of leaf N since they provide continuous information in manifold spectral and directional settings (Ustin et al., 2009; Cohen and Goward, 2004). A growing number of hyperspectral imagery reports have been exceptional when it comes to leaf and canopy N prediction for various plants community (Wang and Wei, 2016). However, hyperspectral data are often affected by high dimensionality and are relatively costly to acquire. On the other hand, multispectral data such as Sentinel-2 and field spectroscopy can be used as primary data sources since they are relatively cheaper and easily accessible (Chemura et al., 2018). Launched in 2015, Sentinel-2 is the European Space Agency’s (ESA) state-of-the-art sensor. It has a high spatial resolution (10–60 m range) and 13 bands that span from visible to near-infrared and shortwave infrared (Gholizadeh et al., 2016). Many vegetation characteristics, including the Leaf Area Index (LAI), nitrogen and chlorophyll, biophysical parameters, and Red-Edge Position, can be estimated with the use of Sentinel-2 (Frampton et al., 2013; Clevers and Gitelson, 2013; Verrelst et al., 2013). However, leaf trait contents are distributed differently across vegetation canopies along vertical canopy profile of different species which makes it complex to estimate them (Gara et al., 2018). Other factors such canopy structure, mask of the strong water absorption (Dash and Curran, 2004), viewing geometry and background make it a challenge to retrieve plant N at the canopy level (Grogan and Chapin, 2000).

Nonetheless, retrieval of many vegetation parameters such as plant N, leaf Chl, and others are dominated by empirical techniques such as spectral indices (Gitelson et al., 2003), traditional regression techniques such as stepwise multiple linear regression and partial least square regression (PLSR), to several machine learning approaches such as support vector regression (SVR), neural network and random forest (RF) (Zhao et al., 2013). Spectral indices are one of the simplest and frequently utilized methods to estimate leaf biochemical contents such as N, Chl and vegetation leaf water content (Gitelson A. A. et al., 1996; Wang B. et al., 2018). Spectral indices calculated from the Sentinel-2 product were investigated in this study. The Normalized Difference Vegetation Index, Modified Simple Ratio, and Atmospherically Resistant Vegetation Index (ARVI) are among the broadband indices that are obtained from Sentinel-2 imagery. Normally, the Normalized Difference Vegetation Index (NDVI) is the most widely used vegetation index, and it is typically used to analyse vegetation and its changes over time at both local and regional levels (Ali et al., 2022). The Ratio Vegetation indicator (RVI), the most fundamental indicator used in vegetation study and monitoring, has been refined into the Modified Simple Ratio (MSR) (Calvão and Palmeirim, 2011). N in leaf cells mostly occurs in proteins and chlorophylls (Kokaly et al., 2009). Plant N and Chl correlate well across various plant species (Homolova et al., 2013), therefore, spectral indices formulated for Chl have been employed as a means of N estimation (Le Maire et al., 2008). This study aimed to map plant N concentration using different categories of spectral indices and stepwise multiple linear regression in the Luvuvhu River Catchment.

2 Materials and methods

2.1 Description of the study area

The Luvuvhu River Catchment is in the northeastern region of Limpopo province, South Africa as shown in Figure 1. The catchment is situated between the latitudes 22⁰ 17′ 33″ S and 23⁰ 17′ 57″ S and longitudes 29⁰ 49′ 46″ E and 31⁰ 23′ 32″ E. The Luvuvhu River Catchment covers an area of approximately 331,602 447 ha with an elevation in the catchment ranging between 200 and 1,700 m resulting in shallow storage dams that are exposed to excess evaporation due to large water surfaces (Odiyo et al., 2012). The catchment is also characterized by topographic features such as the Soutpansberg mountain range in the eastern side of the catchment. This topographic feature rises to a height of approximately 1 700 m above mean sea level prior to reaching the low-lying surface of the Limpopo River Valley (Kundu et al., 2013). The landscape in other parts of the catchment has a mean ridge height of about 800–1 200 m while some places within the catchment reach a peak above 1 500 m (Kundu et al., 2013).

Figure 1

Figure 1. Map showing the study area.

Precipitation in some parts of the catchment is influenced by orographic uplift due to east-west orientation of Soutpansberg mountain range where the western movement of moisture from the Indian Ocean results into 1 200 mm to 1 500 mm/year in the forested mountain headwaters (Shongwe, 2007). Semi-arid forest and bushland receive only receive 200 mm–600 mm/year as the terrain drops across the study area. Extreme topographic diversity and altitude changes over short distances within the Soutpansberg result in dramatic climatic variance within the LRC (Volenzo and Odiyo, 2018). Luvuvhu River Catchment is characterized by subtropical climate with distinct wet conditions during November to March, and little rainfall from April to October during winter with little precipitation. High temperatures in the study area influence the gradual increase in evaporation rates ranging between 1 400 mm and 1 900 mm/year (Nethononda, 2018).

2.2 Field data collection

Fieldwork was conducted between the 2nd and 11th of December 2021 using simple random sampling. This period was chosen because it is the growing period in the subtropical environment of South Africa and the peak season for plant N in vegetation. The study region had both broadleaf and needle leaf plant species, and their distribution varied across the study area. To incorporate various plant species and canopy structures, dominating species within the sampled point was the one considered for this study. However, other plant species around the sampled points were also taken into consideration to supplement plant data collected. A total of 82 sampling points was generated and spread out within the study area with at least 2 km between the points to minimize pixel overlapping on satellite imagery. Data were randomly split into 60% training ($n=$ 49) and 40% validation ($n=$ 33) data sets. This reasonable discrepancy between the output values and the validation set will improve the credibility of the model while a large discrepancy may invalidate the model (Croft et al., 2014). Standard Garmin eTrex 10 Handheld GPS with the maximum spatial accuracy of approximately of 3 m was used to record coordinates at the center of each plot. At each sample point, depending on its heterogeneity, one dominating overstory plant species was selected for sampling. Each sample was composed of five leaflets taken from the sunlit branches of an individual tree. Wet weight of the leaves was measured in the field using Digital Portable Kitchen Scale of accuracy of approximately 0.1 g. Leaf samples were then stored in coded brown paper bags which were folded to avoid leaves drying out. Wet leaf samples were placed in the oven for 24 h. After 24 h when the leaves have dried out, dry weight of the leaves was measured to obtain dry mass of the leaves. The oven dried samples of leaf N content (expressed in percentage) were sent to the laboratory for nitrogen analysis.

2.3 Image acquisition and pre-processing

Satellite images from Sentinel-2 were downloaded from (https://scihub.copernicus.eu/) between 02^nd December and 09 December 2021 leaf nitrogen estimation. The centre positions and spatial resolutions of all bands from the Sentinel-2 images are summarized in Table 1. All bands were resampled to 10 m spatial resolution using the resampling tool in the Sentinel Applications Platform (SNAP) software 4.0 and the nearest method. This study utilized ten bands (490, 560, 665, 705, 740, 783, 842, 865, 1,610, and 2,190 nm) from the Sentinel-2 images. Like other spaceborne optical sensors, Sentinel-2 is also affected by atmospheric, topographic, shadows, and cloud cover (Pflug et a., 2016; Main-Knorn et al., 2017). These effects have the potential of reducing classification accuracy during a land cover/use mapping (Phiri et al., 2018).

Table 1

Table 1. List of sentinel-2 bands (Herman et al., 2018).

The atmospheric correction of the Sentinel-2 images was performed using the Sen2Cor atmospheric correction toolbox in the Sentinel Application Platform (SNAP) software (version 4.0). The atmosphere contains several components that influence the spectral information. The aim of atmospheric correction is the conversion of at-sensor radiance with atmospheric influences to at-ground reflectance with reduced atmospheric influences. Atmospheric correction improves the quality of the image, and consequently, the relationship between field measured N and spectral reflectance, by minimizing the influence of the atmosphere’s brightness on the image spectral value (Hadjimitsis et al., 2010).

2.4 Derivation of spectral variables

Vegetation indices reduce the total amount of reflectance information obtained from a remote sensing platform into a smaller number of optical properties that are highly correlated with the trait of interest. Therefore, identifying and extracting critical bands from Sentinel-2 imagery is crucial for enhancing the accuracy of estimating plant N (Rao et al., 2008; Houborg et al., 2013). This study used 27 vegetation indices that were listed in the literature (Baret, 2016; (Kemenova and Dimitrov, 2021). Selected optical indices based on Sentinel-2 sensor are developed from visible NIR, red-edge and SWIR part of the spectrum. Chlorophyll-related spectral indices that use the red-edge part of the spectrum were used, and since chlorophyll serves as a proxy to nitrogen, such spectral indices can be explored for indirect nitrogen estimation in the forest (Houles et al., 2007), which has yielded good results in many ecosystems such as grassland and for agricultural crops (Clevers and Gitelson, 2013). The SWIR-related vegetation indices were also explored since Perich et al. (2021) suggested that the SWIR was the best region for plant N status. This may be because protein and amino acid molecules in plants contain N (Perchlik and Tegeder, 2017). Proteins typically absorb short-wave infrared light (Yu et al., 2024), hence, when using the SWIR to monitor plant growth, light in this band is absorbed by proteins in the plant, resulting in absorption peaks in the spectrum (He et al., 2023), therefore, combining the SWIR band and RE can effectively monitor the plant N concentration (Yu et al., 2024). Vegetation indices were calculated using formulas on Tables 2, 3.

Table 2

Table 2. Summary of selected optical narrowband spectral indices used in this study based on Sentinel-2.

Table 3

Table 3. Summary of selected optical broadband spectral indices used in this study based on Sentinel-2.

2.5 Model calibration

For this study, n = 82 samples were collected from the field and dataset was randomly split into 60% (n = 49) training data and 40% (n = 33) calibration data in R studio software. Training data set was used to train the model for plant nitrogen concentration estimation in R studio software. The study used stepwise multiple linear regression for each data set to determine variables that are significant and insignificant for the final model. Variables that were not significant for the respective models were eliminated, and models remained with variables that were significant.

2.6 Model validation

Validation data set (n = 33) was used to assess the validity of the model. Three data sets that were calibrated using stepwise multiple linear regression were validated by comparing observed values and predicted values from the validation data set. The coefficient of determination (R²), root mean square error (RMSE) given by Equation 1, and mean absolute error (MAE) given by Equation 2 were used to describe and compute the relationship between observed and predicted plant N, and the two metrics can be expressed as follows:

$\text{RMSE}=\sqrt{\frac{{\mathrm{\Sigma }}_{t=1}^T{\left(x_t-y_t\right)}^2}{T}}(1)$

$\text{MAE}=\frac{{\sum }_{\dot{i}=1}^n\left|y_i-x_i\right|}{n}(2)$

Where xi and yi are the measured and predicted plant nitrogen, x is the mean of measured plant nitrogen, and n is the total number of samples.

3 Results

3.1 Plant species from the sampled point

The study sampled variety of natural vegetation species as listed in Table 4, those species included both needle leaf and broadleaved vegetation in the study area.

Table 4

Table 4. Sampled indigenous species with important statistical information.

3.2 Stepwise regression results

Table 5 shows that the stepwise regression was repeated for narrowband spectral indices, broadband spectral indices, and combined bands spectral indices where insignificant variables were eliminated from the models. Combined spectral indices have produced higher correlation between observed and predicted values of plant N with R² = 0.59, followed by broadband indices with R² = 0.44 and the narrowband indices with R² = 0.35.

Table 5

Table 5. Significant variables resulting from stepwise logistic regression analysis.

The final model selected explanatory variables based on significance level as probability to be selected was≤0.05 and probability for exclusion was≥0.100. Results showed that from this model, spectral indices from the red edge were the most insignificant indices with all of them producing results at p > 0.05. From this model, the most significant spectral indices come from the broadband part of the spectrum with OSAVI, MSI1 and NDVI having p < 0.05. More than 60% of broadband spectral indices have shown negative relationship regarding plant nitrogen concentration in the study area. OSAVI, MSI1 and NDII3 have showed significant negative relationship with NDVI and MSI3 showing positive relation regarding plant nitrogen concentration. About 40% of spectral indices selected from combined spectral indices model have shown to be significant with p < 0.05, with SWIR3 and NDII3 being most significant at p < 0.001 and positive relationship regarding plant nitrogen concentration.

3.3 Performance of vegetation indices in mapping plant nitrogen

Figures 2–4 show the results of indigenous plant nitrogen concentration models which were constructed based on Sentinel-2 data. These models randomly selected 49 training points to train the model and used 33 as verification points. The narrowband model produced an accuracy of R² = 0.35, RMSE = 0.81% and MAE = 0.65, broadband model produced an accuracy of R² = 0.44, RMSE = 0.65% and MAE = 0.48, and the model containing both narrowband and broadband produced an accuracy of R² = 0.59, RMSE = 0.47% and MAE = 0.35%. Based on these results, the study has selected the model with both narrowband and broadband indices to estimate plant nitrogen across the study area.

Figure 2

Figure 2. Relationship between observed and predicted values of plant nitrogen using selected red-edge indices from stepwise regression and 33 validation points.

Figure 3

Figure 3. Relationship between observed and predicted values of plant nitrogen using selected nitrogen related indices from stepwise regression and 33 validation points.

Figure 4

Figure 4. Relationship between observed and predicted values of plant nitrogen using selected combined bands indices from stepwise regression and 33 validation points.

3.4 Plant N mapping using multiple linear regression and vegetation indices

The first activity regarding the mapping of plant N across the study areas was to resample Sentinel-2 image strips to 30 m spatial resolution, then mosaic them to a single image. The regression model from stepwise regression was used to compute multiple linear regression (equation) using raster calculator on QGIS which was then applied to the masked image and resulted in the map showing plant N concentration across Luvuvhu River Catchment. The spatial distribution of plant N correlates well with the distribution of various plants species across the catchment, this was observed during field visits. Three models, i.e., narrowband, broadband and combined (stepwise selection) were selected for plant N prediction based on their performance (R² values). Results in Table 6 have shown that combined spectral indices performed better with R² = 0.59, RMSE = 0.47% and MAE = 0.38%, followed by N based spectral indices with R² = 0.44, RMSE = 0.65% and MAE = 0.48%, and the last category is red edge based spectral indices with R² = 0.35, RMSE = 0.81% and MAE = 0.65%. The R² evaluation showed that the combined indices model should be used to map spatial distribution of plant N over Luvuvhu Catchment Area.

Table 6

Table 6. Accuracy assessment of regression models.

The resultant map was shown on Figure 5. From results density of natural vegetation is higher at southwestern part of study area hence there is relatively higher plant N concentration while moving towards northwestern and some areas at the center of the study area the natural vegetation is less resulting in plant N concentration. The southwestern and eastern aspects are cool hence has more vegetation cover and plant N as compared to the northwestern aspect which is warmer. The plant N of needleleaf species which dominates the northwestern and eastern of the study area will be less while compared to broadleaved species due to low wood density, volume and age of broadleaved species than that of needleleaf species.

Figure 5

Figure 5. Interpolation map of sampling points.

3.5 Kriging mapping of plant N in LRC

This study used ordinary Kriging to create the interpolation of plant N in Luvuvhu Catchment Area as shown in Figure 6. Kriging is a geostatistical interpolation with multistep technique that considers both the distance and the degree of variation between known points when estimating the unknown points (Paramasivam and Venkatramanan, 2019). In Kriging, interpolated values are modelled by a Gaussian process governed by prior covariance, and it is used to forecast geographic area. This method of interpolation was considered for this study to gain a general overview of how plant N concentration is like across the entire catchment since sampled poigurents are not spread throughout the study area. Data obtained from the interpolation map helps to improve the accuracy of the study since it gives general overview of plant N concentration.

Figure 6

Figure 6. Plant nitrogen concentration distribution map in the study area based on Sentinel-2 data and 11 indices.

In this study, the coefficient of determination (R²), the root-mean-square error (RMSE), and the percentage rootmean-square error (RMSE%) were used to evaluate the performance of the models and the accuracy of the plant N map as shown in Table 7 (Li et al., 2019). Besides the R², the residual sums of squares for the regression of actual and modelled values (RSS) were also used to evaluate the accuracy of the semivariogram models using Equation 3. (Robertson et al., 2014):

$RSS={\displaystyle \sum _{i=1}^n}{\left(s_i-{\widehat{s}}_i\right)}^2(3)$

where si represents the actual semivariance values, Osi represents the predicted semivariance values by the semivariogram model, and n is the number of semivariance values. A lower RSS value indicates a relatively better estimation performance of the semivariogram model.

Table 7

Table 7. Accuracy assessment of Kriging models.

4 Discussion

The aim of this study was to estimate and map plant nitrogen concentration of indigenous vegetation in the Luvuvhu River Catchment in Limpopo province using Sentinel-2 data. Remote sensing makes it feasible to monitor plant N concentration at a large scale effectively and at a low cost. However, remote sensing mostly obtains spectral reflectance of the canopy of vegetation which requires the need to understand spectral traits of vegetation to be mapped. This allows for the establishment of the relationship between physiological, ecological variables, and spectral reflectance which will allow rapid N estimation and vegetation health monitoring (Vigneau et al., 2011; Osbome et al., 2011). Spectral reflectance of vegetation is influenced by many factors which result in changes in spectral reflectance across the study area (Liu et al., 2019). Therefore, this presents a challenge to come up with spectral indices which are simple and applicable for plant N estimation.

However, Sentinel-2 allows for the construction of chlorophyll-related spectral indices using the red-edge part of the spectrum, and since chlorophyll serves as a proxy to nitrogen, such spectral indices can be explored for indirect nitrogen estimation in the forest, which has yielded good results in many ecosystems such as grassland and for agricultural crops (Clevers and Gitelson, 2013). However, spectral indices also have many factors that mostly affect them (Zheng et al., 2018). For vegetation parameters estimations, some of spectral indices have excessive capability for soil noise reduction (Rondeaux et al., 1996), while others are very useful by avoiding at saturation for vegetation spectra (Jordan, 1969). In cases where the ecosystem is nitrogen-rich, the correlation between chlorophyll and nitrogen becomes weak (Hallik et al., 2009). Hence, it is very crucial to be considerate when choosing spectral indices that are related to chlorophyll for nitrogen estimation, and to consider the features that are sensitive to nitrogen when selecting spectral indices to estimate plant N (Osco et al., 2020; Zheng et al., 2018). NIR serves as a principal constituent for various spectral indices (Rondeaux et al., 1996). However, in practice, the red edge is one of the most utilized spectral features for vegetation health evaluation (Vigneau et al., 2011).

Therefore, in this study, three groups of spectral indices linked to the biochemical and physical properties of vegetation such as nitrogen, structure, and chlorophyll were used with Sentinel-2 imagery to estimate plant N in the study area. Three models were built for plant N concentration in this study, namely, red edge, nitrogen based and combined spectral indices to explore the relationship between predicted and observed N values for plant N concentration estimation. Spectral indices constructed from the combination of bands (red edge and nitrogen based) in Sentinel-2 data have the potential to extract vegetation properties and be utilised for a wide range of application such as plant N, chlorophyll, and water content estimation (Li et al., 2014a; Hunt Jr et al., 2013). A combination of spectral regression models which are suitable for plant N estimation are very useful for vegetation health monitoring. The SWIR and the red edge region had the greatest impact on plant N estimation among the evaluated spectral indices. The red edge is a useful band for tracking vegetative metrics, as is the case with most of the research (Jiang et al., 2021). A sensitive area for tracking the amount of chlorophyll in plants is the red edge, which exhibits abrupt variations in reflectance. The degree of chlorophyll absorption determines the red edge zone (Li et al., 2014b). Nevertheless, the study’s findings also show that SWIR can successfully raise plant N estimation accuracy. Similarly, the SWIR was proposed by Perich et al. (2021) as the optimal zone for plant N status. This could be because plants have nitrogen in their protein and amino acid molecules. Generally, short-wave infrared light is absorbed by proteins. Proteins in the plant absorb light in this band, which causes absorption peaks to appear in the spectrum when using the SWIR to track plant growth.

Furthermore, stepwise regression was used to determine the category of spectral indices with better performance when it comes to predicting plant nitrogen. Regression model of combined spectral indices from the red edge and the broadband was used in this study since it showed higher correlation between the observed and predicted values of plant N which presents reference for the plant N monitoring using spectral indices in the subtropical environment. The combination of broadband and narrowband spectral indices performed best for this study with R² = 0.59. According to the Darvishzadeh et al. (2008) study, foliar N and indices have been found to correlate, and both narrowband and broadband regions appear to be significant predictors of foliar N. The method of using the whole spectrum has yielded the best results since it was able to leverage important regions across the spectrum and discern differences in foliar N both between and within varying biomes (Darvishzadeh et al., 2008). The broadband spectral indices came second with R² = 0.44. Foody et al. (2001) study supported our results according to which there is poor correlation between the observed and predicted plant N using the broadband spectral indices. Main problem is saturation hence the accuracy is less; saturation can be due to complex structure of forest in result of which broadband VIs did not observe the plant N increase. Finally, the narrowband spectral indices with R² = 0.35. Wang et al. (2016) studied that reason of saturation is the computation of indices through NIR and red spectral band and the red band absorbs electromagnetic radiation and remains constant while the canopy cover increases hence in result at 100% canopy cover red energy absorption reaches to peak. Also, the soil reflectance and topography influence plant N estimation using spectral indices therefore assessment of different spectral indices is very useful (Heiskanen, 2006). However, the study by Imran et at., (2020) has shown that a relationship of plant N with these vegetation indices is best due to the reason that red-edge band is located in high chlorophyll reflectance and absorption area which is between red and NIR region due to which the change in chlorophyll as well as leaf properties have greater effect on the red-edge spectral band. It was found that red edge indices have more accuracy and correlation as compared to broadband indices (Xie et al., 2018). The NIR region can contain spectral variation due to structural variables that covary with foliar N concentrations and can influence overall reflectance and absorption from this region (Knyazikhin et al., 2013; Lepine et al., 2016; Ollinger et al., 2008). For this investigation, summed reflectance values over the whole NIR area did not account for more than 35% of the variance in foliar N, even though narrowband within this region consistently emerged as significant predictors of foliar N. Since only specific portions of the NIR were important, this might be due to atmospheric effects rather than broad structural changes with changing N concentrations. Variation of plant nitrogen concentration is well explained by functional and species composition in the study area. This is dependable looking into studies from various ecosystems, such as temperate, tropical, boreal, and Mediterranean ecosystems (Asner and Martin, 2009).

The resultant map shown in Figure 6 Indicates density of forest is higher at northern part of study area since have higher biomass as well as towards southwestern and northeastern side vegetation is less hence the plant N is relatively higher compared to other parts. The southwestern aspect is cool hence have more vegetation cover and plant N as compared to northern aspect which is warmer.

5 Conclusion

This study compared the performance of three groups of spectral indices which are mostly related to nitrogen, chlorophyll, and structural properties, derived from Sentinel-2 imagery to estimate the concentration of plant N in a subtropical environment of South Africa. Further, it was found that there are still very few studies in South Africa dedicated to utilizing Sentinel-2 data for plant N estimation. Plant N varies greatly across the study area depending on species composition. The resultant map shown in Figure 6. Indicates density of forest is higher at northern part of study area since have higher biomass as well as towards southwestern and northeastern side vegetation is less hence the plant N is relatively higher compared to other parts. The southwestern aspect is cool hence have more vegetation cover and plant N as compared to northern aspect which is warmer.

Results from stepwise regression showed that showed that plant N can be estimated using different groups of indices with acceptable accuracy in the subtropical environment. Results have shown that combined spectral indices performed better with R² = 0.59, RMSE = 0.47% and MAE = 0.38%, followed by N related spectral indices with R² = 0.44, RMSE = 0.65% and MAE = 0.48%, and the last category is red edge based spectral indices with R² = 0.35, RMSE = 0.81% and MAE = 0.65%. Most of the structural related indices could estimate nitrogen at a moderate to good accuracy, due to the functional link between canopy structure and nitrogen resulting from functional type and species differences. Improvements could be accomplished by using multiple VIs with differing feature relevance according to different methods and by using hyperspectral data instead of Sentinel-2 imagery. Although the VIs from different ways could have different feature relevance, stacking them while considering different methodologies could prevent overfitting and should be used to monitor vegetation biophysical parameters and improve classification accuracy. We expect that these predictions will also be very helpful in limiting the uncertainty of biogeochemical models for a variety of terrestrial ecosystems around the world.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

UT: Writing–original draft, Writing–review and editing. OM: Writing–original draft, Writing–review and editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study has been funded by the University of Venda.

Acknowledgments

The authors thank the editor and reviewers for their helpful comments and valuable suggestions. We also like to thank the United States Geological Survey, and the R Development Core Team for making data and processing software freely available.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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