Spectral discrimination of giant reed (Arundo donax L.): A seasonal study in riparian areas

The giant reed (Arundo donax L.) is amongst the one hundred worst invasive alien species of the world, and it is responsible for biodiversity loss and failure of ecosystem functions in riparian habitats. In this work, field spectroradiometry was used to assess the spectral separability of the giant reed from the adjacent vegetation and from the common reed, a native similar species.The study was conducted at different phenological periods and also for the giant reed stands regenerated after mechanical cutting (giant reed_RAC). A hierarchical procedure using Kruskal–Wallis test followed by Classification and Regression Trees (CART) was used to select the minimum number of optimal bands that discriminate the giant reed from the adjacent vegetation. A new approach was used to identify sets of wavelengths – wavezones – that maximize the spectral separability beyond the minimum number of optimal bands. Jeffries Matusita and Bhattacharya distance were used to evaluate the spectral separability using the minimum optimal bands and in three simulated satellite images, namely Landsat, IKONOS and SPOT.Giant reed was spectrally separable from the adjacent vegetation, both at the vegetative and the senescent period, exception made to the common reed at the vegetative period. The red edge region was repeatedly selected, although the visible region was also important to separate the giant reed from the herbaceous vegetation and the mid infrared region to the discrimination from the woody vegetation. The highest separability was obtained for the giant reed_RAC stands, due to its highly homogeneous, dense and dark-green stands. Results are discussed by relating the phenological, morphological and structural features of the giant reed stands and the adjacent vegetation with their optical traits. Weaknesses and strengths of the giant reed spectral discrimination are highlighted and implications of imagery selection for mapping purposes are argued based on present results.     

Spectral response of two diverse pigeonpea (Cajanus cajan L.) cultivars

Spectral-plant parametric functional relations of two widely cultivated and diverse pigeonpea cultivars (GT-100 and BDN-2) revealed a decline in red radiance values from 10 weeks after sowing (WAS) onwards in both the cultivars and an increase in red radiance at 16 WAS associated with leaf senescence. Increased IR radiance was noticed at peak leaf area indices. IR/R ratio attained peak values by 16 WAS and higher IR/R ratio in GT-100 was associated with high LAI. ND attained a value of 0.87 in GT-100 and 0.84 in BDN-2 during first flowering stage. IR/R ratio and ND values were positively and red radiance values negatively correlated with LAI and dry matter. Based on coefficient of determination values, the regression models between plant parameters and ND for GT-100 and IR/R ratio for BDN-2 were suggested for estimating pigeonpea development.     

Age discrimination among eruptives of Menengai Caldera,Kenya, using vegetation parameters from satellite imagery

This paper reports an investigation to determine the degree to which digitally processed Landsat TM imagery can be used to discriminate among vegetated lava flows of different ages in the Menengai Caldera, Kenya. Since Landsat data display vegetation parameters well, and plant communities vary with type and depth of soil development, selective digital processing techniques were applied to take advantage of these characteristics for discriminating relative age differences of the underlying volcanics. A selective series of five images, consisting of a color‐coded Landsat 5 classification and four color composites, were compared with geologic maps. These included a color coded, modified, unsupervised classification and contrast enhanced, color composite images using TM bands 3–2–1, 4–3–2 and 7–5–3, and the first 3 Karhunen‐Loeve transformation axes that had been generated using 7 Landsat TM bands.

The most recent of more than 70 post‐caldera flows within the caldera are trachytes, which are variably covered by shrubs and subsidiary grasses. Soil development evolves as a function of time, and as such, supports a changing plant community. Progressively older flows exhibit the increasing dominance of grasses over bushes. It was found that the Landsat images correlated well with geologic maps, but that the two mapped age classes could be further subdivided on the basis of different vegetation communities. It is concluded that field maps can be modified, and in some cases corrected by use of such imagery, and that digitally enhanced Landsat imagery can be a useful aid to field mapping in similar terrains.     

Spectral discrimination of hydrothermally altered materials using ASTER short-wave infrared bands: Evaluation in a tropical savannah environment

ASTER short-wave infrared bands were used to investigate the spectral discrimination of hydrothermally altered materials, based on the presence of minerals with diagnostic spectral features in wavelengths around 2200 nm (e.g. kaolinite and K-micas). Due to the presence of widespread albitized-greisenized materials, the Serra do Mendes granitoid, located in area of tropical savannah environment in Central Brazil, was selected for this study. The Spectral Angle Mapper (SAM) technique was used as an attempt to detect the presence of hydroxyl-bearing minerals in the domain of the hydrothermally altered materials. Results indicated that areas of altered materials were discriminated from the surrounding mainly due to the high overall reflectance of the whitish lithosols in these areas. The detection of hydroxyl-bearing minerals was blurred by the presence of a sparse grass cover in the alteration zone, which caused a slight increase in the SAM classification angles. As a consequence, the remote detection of hydroxyl-bearing minerals was restricted to a small number of pixels from barren areas. Results indicate that, for the environmental conditions of the study area, ASTER data are more efficacious for spectral characterization of rock–soil-vegetation associations than for the detection of alteration-derived minerals.     

Multiple statistical approaches for the discrimination of mangrove species of Rhizophoraceae using transformed field and laboratory hyperspectral data

This study aims at discriminating eight mangrove species of Rhizophoraceae family of Indian east coast using field and laboratory spectra in spectral range (350–2500 nm). Parametric and non-parametric statistical analyses were applied on spectral data in four spectral modes: (i) reflectance (ii) continuum removed, (iii) additive inverse and (iv) continuum removed additive inverse. We introduced continuum removal of inverse spectra to utilize the advantage of continuum removal in reflectance region. Non-parametric test gave better separability than parametric test. Principal component analysis and stepwise discriminant analysis were applied for feature reduction and to identify optimal wavelengths for species discrimination. To quantify the separability, Jeffries–Matusita distance measure was derived. Green (550 nm), red edge (680–720 nm) and water absorption region (1470 and 1850 nm) were found to be optimal wavelengths for species discrimination. The continuum removal of additive inverse spectra gave better separability than the continuum removed spectra.     

Spectral characterization of coastal sediments using Field Spectral Libraries,Airborne Hyperspectral Images and Topographic LiDAR Data (FHyL)

Beach dune systems are important for coastal zone ecosystems as they provide natural sea defences that dissipate wave energy. Geomorphological models of this near-shore topography require site-specific sediment composition, grain size and moisture content as inputs. Hyperspectral, field radiometry and LiDAR remote sensing can be used as tools by providing synoptic maps of these properties. However, multi-remote sensing of near-shore beach images can only be interpreted if there are adequate bio-geophysical or empirical models for information extraction. Our aim was thus to model the effects of varying sediment properties on the reflectance in both field and laboratory conditions within the FHyL (Field Spectral Libraries, Airborne Hyperspectral Images and Topographic LiDAR) procedure, using a multisource dataset (airborne Hyperspectral – MIVIS and topographic LiDAR – Hawk-eye II and field radiometry). The methodology consisted of (i) acquisition of simultaneous multi-source datasets (airborne Hyperspectral – MIVIS and topographic LiDAR – Hawk-eye) (ii) hyperspectral measurements of sediment mixtures with varying physical characteristics (moisture, grain size and minerals) in field and laboratory conditions, (iii) determination and quantification of specific absorption features, and (iv) correlation between the absorption features and physical parameters cited above.Results showed the potential of hyperspectral signals to assess the effect of moisture, grain-size and mineral composition on sediment properties.     

Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data

Integrating the Red Edge channel in satellite sensors is valuable for plant species discrimination. Sentinel-2 MSI and Rapid Eye are some of the new generation satellite sensors that are characterized by finer spatial and spectral resolution, including the red edge band. The aim of this study was to evaluate the potential of the red edge band of Sentinel-2 and Rapid Eye, for mapping festuca C3 grass using discriminant analysis and maximum likelihood classification algorithms. Spectral bands, vegetation indices and spectral bands plus vegetation indices were analysed. Results show that the integration of the red edge band improved the festuca C3 grass mapping accuracy by 5.95 and 4.76% for Sentinel-2 and Rapid Eye when the red edge bands were included and excluded in the analysis, respectively. The results demonstrate that the use of sensors with strategically positioned red edge bands, could offer information that is critical for the sustainable rangeland management.     

Identification and classification of geographically isolated wetlands in North Alabama using geographic object based image analysis (GeOBIA)

Due to recent Supreme Court rulings, there has been an increased interest in the isolated wetlands of the United States. These types of wetlands serve vital ecological roles such as water quality regulation and as a habitat of biological diversity. This study focuses specifically on mapping of geographically isolated wetlands, or those that are separated from traditional wetlands by a given spatial extent, using Geographic Object-Based Image Analysis (GeOBIA). GeOBIA is a type of remote sensing analysis that identifies objects and features in data-sets via automated methodologies. This type of analysis offers the opportunity to increase the efficiency of what has traditionally been a very labour intensive process of manual photo-interpretation. This analysis resulted in the delineation of 26,424 areas as geographically isolated wetlands. These results were assessed for accuracy through both manual inspection of aerial imagery and field verification which yielded accuracies of 83.7 and 87.7%, respectively.     

Retrieval of leaf area index in alpine wetlands using a two-layer canopy reflectance model

In this paper, we focused on the retrieval of the LAI in an alpine wetland located in western part of China in late August and early July 2011. A two-layer canopy reflectance model (ACRM) was used to establish the relationships between the LAI and the reflectance of near-infrared (NIR) and red (RED) wavebands. The reflectance data were derived from Landsat TM L1T product and the Terra and Aqua MODIS 16-day and 8-day composite reflectance products (MOD/MYD09) at 250 m resolution. Due to the lack of the information about some major input parameters for ACRM, which are sensitive to model outputs in the reflectance of NIR and RED wavebands, the inverse problem was ill-posed. To overcome this problem, a method of increasing the sensitivity of the LAI while reducing the influence of other model free parameters based on the study of free parameters’ sensitivity to the ACRM outputs and the region’s features was studied. The area of interest was divided into two parts using the approximately statistic normalized difference vegetation index (NDVI) value around 0.5. One part was sparse vegetation (0.1 < NDVI < 0.5), which is more sensitive to soil background effects and less sensitive to the canopy biophysical and biochemical variables. The other part was dense vegetation (0.5 ≤ NDVI < 1.0), which is less sensitive to soil background effects and more sensitive to plant canopies and leaf parameters. Then, the relationships of ρ_nir–LAI and ρ_red–LAI were established using a look-up table algorithm for the two parts. Furthermore, a regularization technique for fast pixel-wise retrieval was introduced to reduce the elements of LUT sets while maintaining a relatively high accuracy. The results were very promising compared to the field measured LAI values that the correlation (R²) of the measured LAI values and retrieved LAI values reached 0.95, and the root-mean-square deviation (RMSD) was 0.33 for late August, 2011, while the R² reached 0.82 and RMSD was 0.25 for early July 2011.     

Developing detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor

Coffee is a commodity of international trade significance, and its value chain can benefit from age-specific thematic maps. This study aimed to assess the potential of Landsat 8 OLI to develop these maps. Using field-collected samples with the random forest classifier, splitting coffee into three age classes (Scheme A) was compared with running the classification with one compound coffee class (Scheme B). Higher overall classification accuracy was obtained in Scheme B (90.3% for OLI and 86.8% for ETM+) than in Scheme A (86.2% for OLI and 81.0% for ETM+). The NIR band of OLI was the most important band in intra-class discrimination of coffee. Landsat 8 OLI mapped area closely matched farm records (R^2?=?0.88) compared to that of Landsat 7 ETM+ (R^2?=?0.78). It was concluded that Landsat 8 OLI data can be used to produce age-specific thematic maps in coffee production areas although disaggregating coffee classes reduces overall accuracy.     

Visual discrimination of surface features of salt affected soils using satellite images in arid region of Rajasthan (India)

The study has been carried for visual discrimination of natural salt affected soils on FCC images of IRS 1 B in Pali district of Rajasthan. The salt affected soils show wide variations in salinity (EC_2.53.7 to 28 dSm^-1), alkalinity (pH 8.5-9.8), cover ofP. juliflora (10-90%), salt tolerant grasses (10–55%) and gravelly surface (20–35%). ThoughP. juliflora and grasses were present at most of the observation points their cover decreased with soil EC_2.5 values more than 10 and 13 dSm^-1, respectively. Five darkness categories derived as the result of visual interpretation of FCCs; and ground and laboratory studies revealed that the darkness category 1 represented fewer plant community with high salinity (EC 28.7 dSm^-1) and gravelly surface, categories 2 and 3 were characterised by grass cover and moderate salt affected soils (EC 3-10 dSm^-1) whereas category 4 was dominated by thicket ofP. juliflora. The derived numerical darkness categories of the FCC images were slightly low for February images. The darkness values of observation pixel on February images correlated positively withP. juliflora cover and negatively with grass cover and soil pH indicating that surface features on FCC were related with the immediate observation pixels.     

Assessing Metal-Induced Changes in the Visible and Near-Infrared Spectral Reflectance of Leaves: A Pot Study with Sunflower (<Emphasis Type="Italic">Helianthus annuus</Emphasis> L.)

The aim of this study was to monitor changes in leaf spectral reflectance due to phytoaccumulation of trace elements (Cd, Pb, and As) in sunflower mutant (M5 mutant line 38/R4-R6/15-35-190-04-M5) grown in spiked and in situ metal-contaminated potted soils. Reflectance spectra (350–2500 nm) of leaves were collected using portable ASD spectroradiometer, and respective leaves sample were analyzed for total metal contents. The spectral changes were quite noticeable and showed increased visible and decreased NIR reflectance for sunflower grown in soil spiked with 900 mg As kg^?1, and in in situ metal-contaminated soils. These changes also involved a blue-shift feature of red-edge position in the first derivatives spectra, studied vegetation indices and continuum removed absorption features at 495, 680, 970, 1165, 1435, 1780, and 1925 nm wavelength. Correlograms of leaf-metal concentration and reflectance values show highest degrees of overall correlation for visible, near-infrared, and water-sensitive wavelengths. Partial least square and multiple linear regression statistical models (cross-validated), respectively, based on Savitzky–Golay filter first-order derivative spectra and combination of spectral feature such as vegetation indices and band depths yielded good prediction of leaf-metal concentrations.     

Scaling estimates of vegetation structure in Amazonian tropical forests using multi-angle MODIS observations

Detailed knowledge of vegetation structure is required for accurate modelling of terrestrial ecosystems, but direct measurements of the three dimensional distribution of canopy elements, for instance from LiDAR, are not widely available. We investigate the potential for modelling vegetation roughness, a key parameter for climatological models, from directional scattering of visible and near-infrared (NIR) reflectance acquired from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). We compare our estimates across different tropical forest types to independent measures obtained from: (1) airborne laser scanning (ALS), (2) spaceborne Geoscience Laser Altimeter System (GLAS)/ICESat, and (3) the spaceborne SeaWinds/QSCAT. Our results showed linear correlation between MODIS-derived anisotropy to ALS-derived entropy (r² = 0.54, RMSE = 0.11), even in high biomass regions. Significant relationships were also obtained between MODIS-derived anisotropy and GLAS-derived entropy (0.52 ≤ r² ≤ 0.61; p < 0.05), with similar slopes and offsets found throughout the season, and RMSE between 0.26 and 0.30 (units of entropy). The relationships between the MODIS-derived anisotropy and backscattering measurements (σ⁰) from SeaWinds/QuikSCAT presented an r² of 0.59 and a RMSE of 0.11. We conclude that multi-angular MODIS observations are suitable to extrapolate measures of canopy entropy across different forest types, providing additional estimates of vegetation structure in the Amazon.     

Classification of vegetation in an open landscape using full-waveform airborne laser scanner data

Airborne laser scanning (ALS) is increasingly being used for the mapping of vegetation, although the focus so far has been on woody vegetation, and ALS data have only rarely been used for the classification of grassland vegetation. In this study, we classified the vegetation of an open alkali landscape, characterized by two Natura 2000 habitat types: Pannonic salt steppes and salt marshes and Pannonic loess steppic grasslands. We generated 18 variables from an ALS dataset collected in the growing (leaf-on) season. Elevation is a key factor determining the patterns of vegetation types in the landscape, and hence 3 additional variables were based on a digital terrain model (DTM) generated from an ALS dataset collected in the dormant (leaf-off) season. We classified the vegetation into 24 classes based on these 21 variables, at a pixel size of 1 m. Two groups of variables with and without the DTM-based variables were used in a Random Forest classifier, to estimate the influence of elevation, on the accuracy of the classification. The resulting classes at Level 4, based on associations, were aggregated at three levels — Level 3 (11 classes), Level 2 (8 classes) and Level 1 (5 classes) — based on species pool, site conditions and structure, and the accuracies were assessed. The classes were also aggregated based on Natura 2000 habitat types to assess the accuracy of the classification, and its usefulness for the monitoring of habitat quality. The vegetation could be classified into dry grasslands, wetlands, weeds, woody species and man-made features, at Level 1, with an accuracy of 0.79 (Cohen’s kappa coefficient, κ). The accuracies at Levels 2–4 and the classification based on the Natura 2000 habitat types were κ: 0.76, 0.61, 0.51 and 0.69, respectively. Levels 1 and 2 provide suitable information for nature conservationists and land managers, while Levels 3 and 4 are especially useful for ecologists, geologists and soil scientists as they provide high resolution data on species distribution, vegetation patterns, soil properties and on their correlations. Including the DTM-based variables increased the accuracy (κ) from 0.73 to 0.79 for Level 1. These findings show that the structural and spectral attributes of ALS echoes can be used for the classification of open landscapes, especially those where vegetation is influenced by elevation, such as coastal salt marshes, sand dunes, karst or alluvial areas; in these cases, ALS has a distinct advantage over other remotely sensed data.     

基于地形调节植被指数估算长汀县植被覆盖度

植被覆盖度遥感估算最常用的方法是基于植被指数构建模型,但大部分的植被指数没有考虑地形的影响。以福建省长汀县作为研究区,引入能消除地形影响的地形调节植被指数(topography adjusted vegetation index,TAVI),利用像元二分模型估算植被覆盖度,旨在研究TAVI对植被覆盖度估算结果的影响,并与基于归一化差值植被指数(normalized difference vegetation index,NDVI)估算的结果进行比较。根据目视效果和统计指标的分析表明:基于TAVI估算的植被覆盖度精度高于基于NDVI的估算结果,并能有效降低阴坡阳坡间的差异,提高阴坡区域植被覆盖度的估算精度。     

Geomorphological study of Bulandshahr District (U.P.) using remotely sensed data

Remote sensing data pertaining to LANDSAT TM FCC of bands 2, 3 and 4 of 9th May 1991 and IRS-1A LISS II digital data of 3rd May 1991, have been utilized for the study of geomorphology of Bulandshahr district, U.P. Visual interpretation technique has been followed for geomorphological mapping and the area has been separated into four broadly defined geomorphic zones, namely Varahasi Older Alluvial Plain, Aligarh Older Alluvial Plain, Terrace Zone and Recent Flood Plains of Ganga and Yamuna, each characterized by its own geomorphic/landform elements discernable on remote sensing data. The Varanasi Older Alluvial Plain represents the oldest geomorphic surface occurring at highest tectonic level in the Gangetic plain. The Aligarh Older Alluvial Plain represents a palaeo-flood plain of a north flowing palaeo-drainage in the area. The Terrace zone represents the older flood plain of Ganga and its tributaries. The Recent Flood Plains of Ganga and Yamuna rivers, which get periodically inundated, constitute the youngest geomorphic surface in the study area. Digital image processing outputs, particularly ratio images have been found to be helpful in identifying certain geomorphic landforms (old/abandoned channels, scars etc.) due to greater contrast within ratio images.     

Prediction of L-band signal attenuation in forests using 3D vegetation structure from airborne LiDAR

In this study, we propose a novel method to predict microwave attenuation in forested areas by using airborne Light Detection and Ranging (LiDAR). While propagating through a vegetative medium, microwave signals suffer from reflection, absorption, and scattering within vegetation, which cause signal attenuation and, consequently, deteriorate signal reception and information interpretation. A Fresnel zone enveloping the radio frequency line-of-sight is applied to segment vegetation structure occluding signal propagation. Return parameters and the spatial distribution of vegetation from the airborne LiDAR inside Fresnel zones are used to weight the laser points to estimate directional vegetation structure. A Directional Vegetation Density (DVD) model is developed through regression that links the vegetation structure to the signal attenuation at the L-band using GPS observations in a mixed forest in North Central Florida. The DVD model is compared with currently-used empirical models and obtained better R² values of 0.54 than the slab-based models. Finally, the model is evaluated by comparing with GPS observations of signal attenuation. An overall root mean square error of 3.51 dB and a maximum absolute error of 9.38 dB are found. Sophisticated classification algorithms and full-waveform LiDAR systems may significantly improve the estimation of signal attenuation.     

Mapping the functional dimension of vegetation series in the Mediterranean region using multitemporal MODIS data

ABSTRACT

Monitoring the structural and functional dimensions of natural vegetation is a critical issue to ensure effective management of biodiversity. While coarse-resolution satellite image time-series have been used extensively to monitor vegetation physiognomies, their potential to describe plant species composition remains understudied. The objective of this study is to assess the potential of annual time-series of MODIS images to discriminate combinations of plant communities, called “vegetation series,” and characterize their structural and functional dimensions at the landscape scale. Twelve vegetation series were mapped in a 16 574 ha study area in a Mediterranean context located in Corsica (France). First, the structural dimension of vegetation series was examined using a random forest (RF) model calibrated with a reference field map to (i) measure the importance of each MODIS image in discriminating vegetation series; (ii) quantify the influence of the number of dates on model accuracy; and (iii) map the vegetation series with the optimal subset of MODIS images. Second, the functional dimension of vegetation series was analyzed by ordinating three functional indices through principal component analysis. These indices were the annual sum of normalized difference vegetation index (NDVI), the annual amplitude of NDVI, and the date of maximum NDVI, considered as a proxy for annual primary production, seasonality of carbon fluxes, and vegetation phenology, respectively. Results showed that (i) vegetation series were mapped accurately (median Kappa index 0.70, median overall accuracy 0.76), preferably using images acquired from February to August; (ii) at least 10 MODIS images were required to achieve sufficient accuracy; and (iii) a functional gradient was detected, ranging from high annual net primary production with low seasonality of carbon fluxes and early phenology in Mediterranean vegetation series to low annual net primary production with high seasonality of carbon fluxes and late phenology in alpine vegetation series.     

A shadow- eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains

ABSTRACT

The effect of terrain shadow, including the self and cast shadows, is one of the main obstacles for accurate retrieval of vegetation parameters by remote sensing in rugged terrains. A shadow- eliminated vegetation index (SEVI) was developed, which was computed from only red and near-infrared top-of-atmosphere reflectance without other heterogeneous data and topographic correction. After introduction of the conceptual model and feature analysis of conventional wavebands, the SEVI was constructed by ratio vegetation index (RVI), shadow vegetation index (SVI) and adjustment factor (f (Δ)). Then three methods were used to validate the SEVI accuracy in elimination of terrain shadow effects, including relative error analysis, correlation analysis between the cosine of solar incidence angle (cosi) and vegetation indices, and comparison analysis between SEVI and conventional vegetation indices with topographic correction. The validation results based on 532 samples showed that the SEVI relative errors for self and cast shadows were 4.32% and 1.51% respectively. The coefficient of determination between cosi and SEVI was only 0.032 and the coefficient of variation (std/mean) for SEVI was 12.59%. The results indicate that the proposed SEVI effectively eliminated the effect of terrain shadows and achieved similar or better results than conventional vegetation indices with topographic correction.     

Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy

The direct estimation of nitrogen (N) in fresh vegetation is challenging due to its weak influence on leaf reflectance and the overlaps with absorption features of other compounds. Different empirical models relate in this work leaf nitrogen concentration ([N]_Leaf) on Holm oak to leaf reflectance as well as derived spectral indices such as normalized difference indices (NDIs), the three bands indices (TBIs) and indices previously used to predict leaf N and chlorophyll. The models were calibrated and assessed their accuracy, robustness and the strength of relationship when other biochemicals were considered. Red edge was the spectral region most strongly correlated with [N]_Leaf, whereas most of the published spectral indexes did not provide accurate estimations. NDIs and TBIs based models could achieve robust and acceptable accuracies (TBI_{1310,1720,730}: R² = 0.76, [0.64,0.86]; RMSE (%) = 9.36, [7.04,12.83]). These models sometimes included indices with bands close to absorption features of N bonds or nitrogenous compounds, but also of other biochemicals. Models were independently and inter-annually validated using the bootstrap method, which allowed discarding those models non-robust across different years. Partial correlation analysis revealed that spectral estimators did not strongly respond to [N]_Leaf but to other leaf variables such as chlorophyll and water, even if bands close to absorption features of N bonds or compounds were present in the models.