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The identification of crop diversity in today's world is very crucial to ensure adaptation of the crop with changing climate for better productivity as well as food security. Towards this, Hyperspectral Remote Sensing (HRS) is an efficient technique that offers the opportunity to discriminate crop types based on morphological as well as physiological features due to availability of contiguous spectral bands. The current work utilized the benefits of Airborne Visible Infrared Imaging Spectrometer- New Generation (AVIRIS-NG) data and explored the techniques for classification and identification of crop types. The endmembers were identified using the Geo-Stat Endmember Extraction (GSEE) algorithm for pure pixels identification and to generate the spectral library of the different crop types. Spectral feature comparison was done among AVIRIS-NG, Analytical Spectral Device (ASD)-Spectroradiometer and Continuum Removed (CR) spectra. The best-fit spectra obtained with the Reference ASD-Spectroradiometer and Pure Pixel spectral library were then used for crop discrimination using the ten supervised classifiers namely Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Support Vector Machine (SVM), Minimum Distance Classifier (MDC), Binary Encoding, deep learning-based Convolution Neural Network (CNN) and different algorithms of Ensemble learning such as Tree Bag, AdaBoost (Adaptive Boosting), Discriminant and RUSBoost (Random Under Sampling). In total, nine crop types were identified, namely, wheat, maize, tobacco, sorghum, linseed, castor, pigeon pea, fennel and chickpea. The performance evaluation of the classifiers was made using various metrics like Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score. The classifier 2D-CNN was found to be the best with Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score values of 89.065 %, 0.871, 87.565%, 89.541% and 88.678% respectively. The output of this work can be utilized for large scale mapping of crop types at the species level in a short interval of time with high accuracy. [ABSTRACT FROM AUTHOR]

Accurate and rapid estimation of leaf nitrogen content (LNC) in winter wheat using hyperspectral techniques is important for growth monitoring and accurate fertilization. Based on field experiments conducted over four years at three ecological sites with four different nitrogen (N) application treatments and four different N-efficient wheat varieties in Henan Province, the canopy spectra and LNC of winter wheat were obtained simultaneously in the main growth stages. The original spectrum was subjected to continuum removal (CR), the correlation between LNC and various vegetation indices was systematically analysed, and the optimal vegetation index estimation model for LNC was constructed. Simultaneously, discrete wavelet transform (DWT) was used to further compress and extract the CR spectrum. The model was combined with partial least squares regression (PLSR) and K-nearest neighbour (KNN) algorithms to estimate LNC in winter wheat. The results showed that the spectrum treated with CR was significantly improved in its correlation with LNC and that the model established based on normalized vegetation index NDVI (CR728, CR977) combined with the CR spectrum was better compared to existing vegetation indices. The calibration and validation coefficients of determination (R2) and root mean square error (RMSE) were 0.816 and 0.799, and 0.352% and 0.342%, respectively. DWT was used to transform the CR spectrum, and the results showed that a combination of the CR spectrum and a sym8 wavelet function with PLSR based on the approximation coefficients at decomposition level 2 predicted LNC most accurately. The coefficient of determination RC2, root mean square error RMSEC, and relative percent deviation (RPDC) of the calibration set were 0.884, 0.279%, and 2.932, respectively, and RV2, RMSEV, and RPDV of the validation set were 0.855, 0.291%, and 2.619, respectively, indicating that the model had good stability and predictive ability. Combination of CR and DWT improved the modelling accuracy of wheat LNC and showed better prediction results for multi-year and multi-point samples. The results of the study can provide a basis and reference for rapid monitoring of LNC in winter wheat. [ABSTRACT FROM AUTHOR]

Species-level classification of mangroves provides important inputs for conservation, rehabilitation and understanding of ecosystem functions. The hyperspectral sensor, Airborne Visible InfraRed Imaging Spectrometer-New Generation (AVIRIS-NG), holds promises for species-level discrimination by virtue of its coverage across a wider spectrum at very high spatial resolution. Using the continuum removal (CR) technique and absorption band depth (ABD), this study applied Random Forest (RF) model to classify the distribution of three species (Heritiera fomes, Excoecaria agallocha and Avicennia officinalis) and two of their combinations (Heritiera fomes-Excoecaria agallocha and Avicennia officinalis-Excoecaria agallocha). The classified map demonstrated good accuracy (overall accuracy = 88%; kappa coefficient = 0.84) using ABD as an independent variable. The important wavelengths (972, 1172, 1177 nm) identified for mangrove species discrimination correspond to water absorption bands. This characteristic may be replicated for species-level classification of other mangrove forests with similar species. [ABSTRACT FROM AUTHOR]

Salt marshes are one of the most productive ecosystems and provide numerous ecosystem services. However, they are seriously threatened by human activities and sea level rise. One of the main characteristics of this environment is the distribution of specialized plant species. The environmental conditions governing the distribution of this vegetation, as well as its variation over time and space, still need to be better understood. In this way, these ecosystems will be managed and protected more effectively. Low-altitude remote sensing techniques are excellent for rapidly assessing salt marsh vegetation coverage. By applying a high-resolution hyperspectral imaging system onboard a UAV (UAV-HS), this study aims to differentiate between plant species and determine their distribution in salt marshes, using the salt marshes of Cadiz Bay as a case study. Hyperspectral processing techniques were used to find the purest spectral signature of each species. Continuum removal and second derivative transformations of the original spectral signatures highlight species-specific spectral absorption features. Using these methods, it is possible to differentiate salt marsh plant species with adequate precision. The elevation range occupied by these species was also estimated. Two species of Sarcocornia spp. were identified on the Cadiz Bay salt marsh, along with a class for Sporobolus maritimus. An additional class represents the transition areas from low to medium marsh with different proportions of Sarcocornia spp. and S. maritimus. S. maritimus can be successfully distinguished from soil containing microphytobenthos. The final species distribution map has up to 96% accuracy, with 43.5% of the area occupied by medium marsh species (i.e., Sarcocornia spp.) in the 2.30–2.80 m elevation range, a 29% transitional zone covering in 1.91–2.78 m, and 25% covered by S. maritims (1.22–2.35 m). Basing a method to assess the vulnerability of the marsh to SLR scenarios on the relationship between elevation and species distribution would allow prioritizing areas for rehabilitation. UAV-HS techniques have the advantage of being easily customizable and easy to execute (e.g., following extreme events or taking regular measurements). The UAV-HS data is expected to improve our understanding of coastal ecosystem responses, as well as increase our capacity to detect small changes in plant species distribution through monitoring. [ABSTRACT FROM AUTHOR]

Spectrally mixed pixels are the rule, not the exception, in decameter terrestrial imaging. By definition, the reflectance spectrum of a mixed pixel is a function of more than one generative process. Physically based surface biology and geology retrievals must therefore isolate the component of interest from a myriad of unrelated processes, each occurring with unknown presence and abundance across the hundreds of square meters sampled by each pixel. Foliar traits, for example, must be isolated from canopy structure and substrate composition. In many cases, these unrelated processes can dominate overall variance of spatially integrated reflectance. We propose a new approach to isolate low‐variance spectral signatures in mixed pixels. The aggregate effects of (high‐variance) spatial mixing processes within each pixel are modeled by treating the observed reflectance as a linear mixture of a small set of generic endmember spectra. Spatial mixing effects are removed by computing the (low‐variance) difference between the modeled and observed spectra, named the Mixture Residual (MR). The MR, a residual reflectance spectrum that is presumed to carry the subtler and variable signals of interest, is then leveraged as a source of signal. We illustrate the approach using three independent collections of reflectance spectra: synthetic composites computed from field measurements, NEON AOP airborne image compilations, and DESIS satellite data. The MR is found to discriminate between land cover versus plant trait signals, and to accentuate subtle absorption features. Mean band‐to‐band correlations within the visible, NIR, and SWIR wavebands decrease from 0.97, 0.94, and 0.97 to 0.95, 0.04, and 0.31 (respectively). The number of dimensions required to explain 99% of image variance increases from 4 to 13. We focus on vegetation as an illustrative example, but note that the concept can be extended to other classes of spectra and used as an input to other algorithms. Plain Language Summary: Imaging spectroscopy measures a rich superposition of signals generated by a wide range of biogeophysical processes and materials. In many cases, subpixel mixing—variations in the abundance of broad land cover types like soil, foliage, and shadow—can dominate these spectra at the expense of subtler signals like leaf chemistry and soil composition. This spatial mixing problem can seriously confound surface biology and geology retrieval generalizability and accuracy. We propose a way to isolate the subtle signatures of interest from strong land cover mixing signals through a new approach called the mixture residual (MR). This approach models and removes the land cover mixing signal in a highly general, computationally efficient, and invertible way. We show the effectiveness of the method using synthetic imagery computed from mixtures of field spectra, a broad compilation of ecologically diverse airborne data, and satellite imagery. This approach can complement established algorithms by adding a degree of explicability, as well as improving generalizability and classification and regression results in some cases. Key Points: Mixture residual spectrum can contain informative low‐variance signalsAverage band‐to‐band correlations drop from 0.94 and 0.97 to 0.04 and 0.31 for near infrared and shortwave infrared, respectivelyLinear approach offers feature accentuation, interpretability, generalizability, and computational efficiency [ABSTRACT FROM AUTHOR]

Se ha realizado el análisis mineralógico a través de las propiedades espectrales desarrolladas por muestras de suelos y sedimentos del borde noroccidental de la Cuenca del Duero. Las absorciones producidas por los óxidos y oxihidróxidos de hierro (hematites y goethita, principalmente) están localizadas en zonas del VNIR (400-1200 nm), mientras que las bandas de absorción presentes en los espectros del SWIR (1200-2500 nm) están relacionadas con la composición química de minerales arcillosos. Los espectros de reflectancia medidos en laboratorio han sido normalizados aplicando los métodos del Continuum Removal (CR) y la segunda derivada (SD). Este último puede resolver la superposición de bandas al cuantificar sutiles inflexiones de la curva. Esto ha permitido examinar las bandas de absorción por separado midiendo los parámetros geométricos desarrollados en ellas. La proporción de los minerales influye en la respuesta espectral y, por tanto, en los valores de los parámetros. Se han realizado correlaciones lineales entre estos valores y la proporción de las diferentes fases minerales obtenidas por difracción de rayos X. De los parámetros estudiados, la correlación entre la posición del centro de banda (BC) en la máxima absorción alrededor de longitudes de onda de 890-960 nm y la profundidad del rasgo de absorción a 470 nm (D470) ha permitido realizar una estimación relativa de la proporción de hematites/goethita. En cuanto a la distribución de los diferentes minerales de arcilla, se ha podido establecer una correlación entre la proporción de caolinita y la profundidad de las bandas de absorción a 1415 y 2210 nm, y en los rasgos de absorción cercanos a 1390 y 2160 nm, analizados en SD.

Salt marshes are one of the most productive ecosystems and provide numerous ecosystem services. However, they are seriously threatened by human activities and sea level rise. One of the main characteristics of this environment is the distribution of specialized plant species. The environmental conditions governing the distribution of this vegetation, as well as its variation over time and space, still need to be better understood. In this way, these ecosystems will be managed and protected more effectively. Low-altitude remote sensing techniques are excellent for rapidly assessing salt marsh vegetation coverage. By applying a high-resolution hyperspectral imaging system onboard a UAV (UAV-HS), this study aims to differentiate between plant species and determine their distribution in salt marshes, using the salt marshes of Cadiz Bay as a case study. Hyperspectral processing techniques were used to find the purest spectral signature of each species. Continuum removal and second derivative transformations of the original spectral signatures highlight species-specific spectral absorption features. Using these methods, it is possible to differentiate salt marsh plant species with adequate precision. The elevation range occupied by these species was also estimated. Two species of Sarcocornia spp. were identified on the Cadiz Bay salt marsh, along with a class for Sporobolus maritimus. An additional class represents the transition areas from low to medium marsh with different proportions of Sarcocornia spp. and S. maritimus. S. maritimus can be successfully distinguished from soil containing microphytobenthos. The final species distribution map has up to 96% accuracy, with 43.5% of the area occupied by medium marsh species (i.e., Sarcocornia spp.) in the 2.30–2.80 m elevation range, a 29% transitional zone covering in 1.91–2.78 m, and 25% covered by S. maritims (1.22–2.35 m). Basing a method to assess the vulnerability of the marsh to SLR scenarios on the relationship between elevation and species distribution would allow prioritizing areas for rehabilitation. UAV-HS techniques have the advantage of being easily customizable and easy to execute (e.g., following extreme events or taking regular measurements). The UAV-HS data is expected to improve our understanding of coastal ecosystem responses, as well as increase our capacity to detect small changes in plant species distribution through monitoring.

Reflectance spectroscopy can provide an alternate approach to traditional method for estimation of a large scale of major soil parameter. In the present study, an airborne high-resolution hyperspectral data Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) was used to spatially predict topsoil calcium carbonate (CaCO3) and clay content in parts of Karnataka, India. A total of 24 locations were selected over agricultural and waste land, and soil samples were collected, and surface reflectance was measured using an ASD Field Spec Pro Spectroradiometer in the laboratory. Continuum Removal (CR) method was used to normalize the reflectance spectra. Continuum Removal Absorption Depth (CRAD) of bands 2205 and 2340 nm was used to predict clay and CaCO3 content in soils through linear regression. The mean clay and CaCO3 content of 35.6% (95%CI 28.07–43.16%) and 4.16% (95%CI 3.61–4.71%) for surface soils were predicted by continuum removal linear regression (CRLR) method with estimation errors of RMSE = 11.0 and 2.19, R2 = 0.51 and 0.58, respectively. The 95% confidence intervals were used to calculate the uncertainty of the prediction which showed 42.3% and 26.4% for clay and CaCO3 prediction, respectively. The uncertainty assessment shows that CRLR approach is not a very promising tool for quantitative spatial prediction of soil clay; however, it can be used fairly for CaCO3 prediction from airborne hyperspectral data. The higher uncertainties in the clay estimates may be due to the nature and types of various clay minerals present in the studied soils. The regression models developed may or may not be utilized for other regions with similar variability. [ABSTRACT FROM AUTHOR]

Real-time non-destructive monitoring of nitrogen accumulation by hyperspectral remote sensing is important for crop nitrogen management. In this study, winter wheat field experiment incorporating several varieties and exogenous nitrogen treatments was performed at multiple sites. Using hyperspectral readings of the experimental crops, the continuum removal method was used to expand the chlorophyll absorption characteristic band. The correlation among the spectral reflectance of the wheat canopy, the continuum removal spectrum, and leaf nitrogen accumulation (LNA) were systematically analyzed. The correlations between LNA and spectral parameters (e.g., original spectral reflectance, two-band combination parameters, and common vegetation indices) and continuum-removed absorption feature parameters were all compared. Three nonlinear modeling methods were considered (partial least squares regression, SVM regression, and random forest regression) and their relative ability to predict LNA was compared. Continuum removal treatment significantly improved the correlation between the continuum-removed spectra of the chlorophyll absorption regions (550–750 nm) and LNA. Results also show that RSI (NBDI743, NBDI703) could be used to estimate LNA using univariate linear regression (R2 and root mean square error were 0.806 and 1.231 g m−2, respectively). The SVM regression was found to be the most accurate regression model when chlorophyll absorption characteristic band reflectivity values normalized by the continuum removal process were taken as an input (R2 and root mean square error values were 0.895 and 0.903 g m−2, respectively). This approach was able to predict LNA of wheat using continuum-removed absorption features through hyperspectral measurements, which provide technical support for nitrogen diagnosis and precise crop production management. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing is a promising tool for a variety of applications including ecology, geology, analytical chemistry and medical research. This article presents the new hsdar package for R statistical software, which performs a variety of analysis steps taken during a typical hyperspectral remote sensing approach. The package introduces a new class for efficiently storing large hyperspectral data sets such as hyperspectral cubes within R. The package includes several important hyperspectral analysis tools such as continuum removal, normalized ratio indices and integrates two widely used radiation transfer models. In addition, the package provides methods to directly use the functionality of the caret package for machine learning tasks. Two case studies demonstrate the package's range of functionality: First, plant leaf chlorophyll content is estimated and second, cancer in the human larynx is detected from hyperspectral data.

Spectral mixing models for minerals can be complex, and choosing the right unmixing model is indispensable to ensure the accuracy of spectral unmixing. Continuum removal (CR) and natural log operation have the potential to eliminate nonlinear effects in spectral mixing, and have already been used in nonlinear spectral unmixing applications. In this study, the newly proposed log and CR (LCR) model and four other spectral unmixing models for mineral analysis (the linear model, CR model, natural log model, and simplified Hapke model) were summarized and applied to both laboratory mineral powder spectra and hyperspectral data of Cuprite, Nevada, USA. This paper summarizes the concept of mixing reflectance reconstruction (MRR), along with a comprehensive method to determine accuracy based on MRR results, which can be performed in different dimensions, such as spatial dimension and spectral dimension. The LCR model performed the best in both laboratory experiments and image data analysis, indicating its strong potential for practical application. The level of atmospheric correction’s influence on unmixing accuracy varied for different spectral unmixing models, among which LCR model also had the best performance.

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High accuracy soil classification and model-based mapping can provide a better evaluation for sustainable management in intelligent agriculture. Nevertheless, most previous studies on the type classification and mapping were derived mainly from hyperspectral images that captured indoor measurement, or airborne hyperspectral and spaceborne multispectral images. Only a few studies were associated with the satellite hyperspectral images in soil classification and mapping, due partly to the lack of satellite hyperspectral data. Thanks to the Gaofen-5 (GF-5) satellite, the high-resolution hyperspectral data can currently be returned, and thereby can make it possible to realize soil classification and mapping using satellite hyperspectral images. However, it has become a great challenge to achieve a high accuracy mapping only by using soil spectral characteristics. Alternatively, the introduction of soil formation factors can effectively improve the accuracy of soil classification. In this study, taking the Baiquan and Mingshui county as the study areas, the GF-5 satellite hyperspectral data was selected to verify whether the combination of satellite hyperspectral data and terrain data (one of the five factors of soil formation) can enhance the performance accuracy of soil classification and mapping. The continuum removed (CR) analysis was performed on the hyperspectral reflectance data (OR), and then the obtained CR and OR data were processed by a principal component analysis, in order to gain the inputs of OR-PCA (Principal Component Analysis) and CR-PCA. Subsequently, some terrain (TA) factors were added to the program of OR-PCA and CR-PCA, particularly including elevation, slope, aspect, curvature, and relief degree of land surface. As such, the soil hyperspectral remote sensing classification models were established in combination with a random forest classifier. The results are as follows: 1) The spectral phase differences among various soil types increased significantly after the continuum removal treatment, indicating a great improvement on the accuracy of the hyperspectral soil classification. Compared with that of OR as input data, the correct classification number of Phaeozems, Chernozems and Cambisols increased by 20, 17 and 27, respectively. The total accuracy and Kappa coefficient were also improved by 5.48% and 0.12, respectively. The findings demonstrated that the CR treatment can be used to greatly increase the accuracy of soil classification and mapping. 2) The PCA method can be used to dramatically reduce the redundancy data of hyperspectral images, while improve the classification efficiency and the accuracy of model. In the mapping data, the misclassification of pixels was significantly reduced after the PCA treatment. Compared with that of OR as the input, the total accuracy of soil classification in the OR-PCA method was improved by 1.71%, and the Kappa coefficient was enhanced by 0.02. Compared with that of CR as the input, the total accuracy of soil classification in the CR-PCA method was improved by 3.67%, and the Kappa coefficient was improved by 0.02. 3) The obtained results indicated that the addition of TA factors can significantly improve the accuracy of soil classification. Among the different input combinations, the CR-PCA-TA group showed the highest accuracy of soil classification when using as the input data, with the classification total accuracy of 81.61% and the Kappa coefficient of 0.72. Compared with that of CR-PCA as the input, the total accuracy of soil classification based on the combination of CR-PCA-TA was improved by 13.01%, and the Kappa coefficient was improved by 0.20, indicating that the high precision soil classification model and soil mapping were realized during this time. The findings can provide new insightful ideas for soil classification and mapping with a high accuracy in a wide range for intelligent agriculture. [ABSTRACT FROM AUTHOR]

Many factors could influence simultaneously soil spectra. We aimed to study the single effect of organic carbon and total iron in soil visible and short-wave near-infrared spectra and to quantify their contents. Two datasets of soil mixture samples were prepared by mixing, in various fractions, an organic carbon-rich material with a total iron-rich material and then with a total iron-poor material. For these two datasets, contents in organic carbon are quite similar but contents in total iron are significantly different. Results show that samples of the same dataset have the same overall spectral shape. Organic carbon has a decreasing effect that affects the whole spectral range without showing any specific absorption peaks. By contrast, total iron has specific absorption peaks. Spectra of the second dataset characterized by soil mixtures with higher total iron contents were more compact within the spectral bands 400–440 and 920–950 nm. Besides, continuum removal enables to exaggerate absorption peaks of wavelengths linked to total iron content. Partial Least Squares Regression (PLS R) models of both total organic carbon and total iron assign high coefficients to the wavelengths that are considered relevant and conversely low coefficients to those that are considered irrelevant. Both organic carbon content and total iron content were well predicted. For these models, coefficients of determination were superior to 0.9 and RMSE was closed to zero. The global models calibrated on all the samples demonstrated that PLS R was able to integrate sample heterogeneity. [ABSTRACT FROM AUTHOR]

Monitoring and management of plant water status over the critical period between flowering and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R2 = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.

Drylands, one of the planet's largest terrestrial biomes, are suggested to be greatly threatened by climate change. Drylands are usually sparsely vegetated, and biological soil crusts (biocrusts) – that is, soil surface communities of cyanobacteria, mosses and/or lichens – can cover up to 70% of dryland cover. As they control key ecosystem processes, monitoring their spatial and temporal distribution can provide highly valuable information. In this study, we examine the potential of European Space Agency's (ESA) Sentinel-2 (S2) data to characterize the spatial and temporal development of biocrust and vascular plant greening along a rainfall gradient of the Negev Desert (Israel). First, the chlorophyll a absorption feature in the red region (CRred) was identified as the index mostly sensitive to changes in biocrust greening but minimally affected by changes in soil moisture. This index was then computed on the S2 images and enabled monitoring the phenological dynamics of different dryland vegetation components from August 2015 to August 2017. The analysis of multi-temporal S2 images allowed us to successfully track the biocrust greening within 15 days from the first seasonal rain events in the north of Negev, and to identify the maximum development of annual vascular plants and greening of perennial ones. These results show potential for monitoring arid and semi-arid environments using the newly available S2 images, allowing new insights into dryland vegetation dynamics. [ABSTRACT FROM AUTHOR]

Highlights • N, P and K concentrations were significantly higher (p < 0.05) in summer than in winter; whilst the opposite was true for Ca concentration. • N was highly correlated with spectral of ground leaf samples: R2 = 0.72 in winter; R2 = 0.88 for summer. • The coefficient of variation (CV) for nitrogen was higher during winter (CV = 23.1%) compared to summer (CV = 21.7%). • Using Sentinel-2 data, the prediction accuracy for nitrogen was higher compared to that of potassium, phosphorus and calcium. • Estimation of leaf nutrients using Sentinel-2 data yielded low performances. Abstract Trees provide low-cost organic inputs, with the potential to improve livelihoods for rural communities. Understanding foliar nutrients of tree species is crucial for integration of trees into agroecosystems. The study explored nitrogen (N), phosphorus (P), potassium (K) and calcium (Ca) concentrations of nine browse species collected from the bushveld region of South Africa using wet analysis and laboratory spectroscopy in the region 400–2500 nm, along with partial least squares (PLS) regression. We further explore the relationship between canopy reflectance of Sentinel-2 image and foliar N, P, K & Ca. Laboratory spectroscopy was significant for N estimation, while satellite imagery also revealed useful information about the estimation of nitrogen at landscape level. Nitrogen was highly correlated with spectral reflectance (R2 = 0.72, p < 0.05) for winter and (R2 = 0.88, p < 0.05) for summer, whilst prediction of phosphorus potassium and calcium were considered not accurate enough to be of practical use. Modelling the relationship using Sentinel-2 data showed lower correlations for nitrogen (R2 = 0.44, p < 0.05) and the other nutrients when compared to the dried samples. The findings indicate that there is potential to assess and monitor resource quality of indigenous trees using nitrogen as key indicator. This multi-level remote sensing approach has promise for providing rapid plant nutrient analyses at different scales. [ABSTRACT FROM AUTHOR]

Spectral mixing models for minerals can be complex, and choosing the right unmixing model is indispensable to ensure the accuracy of spectral unmixing. Continuum removal (CR) and natural log operation have the potential to eliminate nonlinear effects in spectral mixing, and have already been used in nonlinear spectral unmixing applications. In this study, the newly proposed log and CR (LCR) model and four other spectral unmixing models for mineral analysis (the linear model, CR model, natural log model, and simplified Hapke model) were summarized and applied to both laboratory mineral powder spectra and hyperspectral data of Cuprite, Nevada, USA. This paper summarizes the concept of mixing reflectance reconstruction (MRR), along with a comprehensive method to determine accuracy based on MRR results, which can be performed in different dimensions, such as spatial dimension and spectral dimension. The LCR model performed the best in both laboratory experiments and image data analysis, indicating its strong potential for practical application. The level of atmospheric correction's influence on unmixing accuracy varied for different spectral unmixing models, among which LCR model also had the best performance. [ABSTRACT FROM AUTHOR]

Irrigation-induced salinization is an important land degradation process that affects crop yield in the Brazilian semi-arid region, and gypsum has been used as a corrective measure for saline soils. Fluvent soil samples (180) were treated with increasing levels of salinization of NaCl, MgCl2 and CaCl2. The salinity was gauged using electrical conductivity (EC). Gypsum was added to one split of these samples before they were treated by the saline solutions. Laboratory reflectance spectra were measured at nadir under a controlled environment using a FieldSpec spectrometer, a 250-W halogen lamp and a Spectralon panel. Variations in spectral reflectance and brightness were evaluated using principal component analysis, as well as the continuum-removed absorption depths of major features at 1450, 1950, 1750 and 2200 nm for both the gypsum-treated (TG) and non-treated (NTG) air-dried soil samples as a function of EC. Pearson’s correlation coefficients of reflectance and the band depth with EC were also obtained to establish the relationships with salinity. Results showed that NTG samples presented a decrease in reflectance and brightness with increasing CaCl2 and MgCl2 salinization. The reverse was observed for NaCl. Gypsum increased the spectral reflectance of the soil. The best negative correlations between reflectance and EC were observed in the 1500–2400 nm range for CaCl2 and MgCl2, probably because these wavelengths are most affected by water absorption, as Ca and Mg are much more hygroscopic than Na. These decreased after chemical treatment with gypsum. The most prominent features were observed at 1450, 1950 and 1750 nm in salinized-soil spectra. The 2200-nm clay mineral absorption band depth was inversely correlated with salt concentration. From these features, only the 1750 and 2200 nm ones are within atmospheric absorption windows and can be more easily measured using hyperspectral sensors.

Water status controls plant physiology and is key to managing vineyard grape quality and yield. Water status is usually estimated by leaf water potential (LWP), which is measured using a pressure chamber; however, this method is difficult, time-consuming, and error-prone. While traditional spectral methods based on leaf reflectance are faster and non-destructive, most are based on vegetation indices derived from satellite imagery (and so only take into account discrete bandwidths) and do not take full advantage of modern hyperspectral sensors that capture spectral reflectance for thousands of wavelengths. We used partial least squares regression (PLSR) to predict LWP from reflectance values (wavelength 350−2500 nm) captured with a field spectroradiometer. We first identified wavelength ranges that minimized regression error. We then tested several common data pre-processing methods to analyze the impact on PLSR prediction precision, finding that derivative pre-processing increased the determination coefficients of our models and reduced root mean squared error (RMSE). The models fitted with raw data obtained their best results at around 1450 nm, while the models with derivative pre-processed achieved their best estimates at 826 nm and 1520 nm.

As soil oxidizable carbon (C ox ) has several different absorptions in the visible– and near infrared (vis–NIR) region due to its complex composition, multivariate calibration techniques such as multiple linear regression (MLR), partial least squares regression (PLSR), support vector machines (SVM) or random forest (RF) can be advantageously used to obtain a good prediction. Besides that, the content of C ox is often affected by the character of the terrain, mainly due to prevailing water regime with associated transport and sedimentation processes. Therefore, the question arises; if predictive models calibrated by combining vis–NIR diffuse reflectance spectroscopy (vis–NIR DRS, 350–2500 nm) and the digital elevation model (DEM) derivatives will provide a more accurate estimate of C ox . Focused on a sloping arable land (100 ha) affected by distinct water erosion, we tested for this purpose two conceptually different predictive approaches that differ in the nature of the spectroscopic predictor variables. In Approach A that relied on absorption feature (AF) parameters, the inclusion of DEM derivatives resulted in improved C ox prediction using all the tested calibration techniques, i.e. MLR, RF, PLSR and SVM. The MLR prediction that was the most accurate among all others improved from R 2 cv = 0.81 (vis–NIR DRS dataset) or 0.50 (DEM derivatives dataset) to 0.84 (combination of both). For that prediction especially AF centered at 500, 700, 900, 1800, 1900, 2200 and 2400 nm, as well as elevation, LS factor and plan curvature were important. In contrast, in the Approach B that relied on reflectance (RF and PLSR) or normalized reflectance (SVM) values at each wavelength, no positive effect of inclusion of DEM derivatives was observed. [ABSTRACT FROM AUTHOR]

Proximal sensing provides an alternative method to physical and chemical laboratory soil analyses. The aim of this study is to predict soil organic carbon (SOC), clay, sand, and silt content using reduced spectral features as covariables selected by two spectral preprocessing. A total of 299 soil samples were collected in Santa Catarina state, Brazil. Two preprocessing techniques, detrend transformation and continuum removal (CR), were applied to isolate particular absorption features in the reflectance spectrum. Two techniques were used to select the spectral features in the spectrum: hand and mathematical selection. Partial least squares regression (PLSR) and Support vector machines (SVM) were applied to predict the soil properties. The reduction of predictor covariables by hand selection technique contributed in developing a high-level prediction model for SOC. PLSR and SVM presented no statistical difference between the RMSE results, except for clay content, where SVM presented superior performance. The preprocessing techniques were statistically identical based on RMSE results. Overall, the prediction of SOC, clay, sand and silt presented suitable results using reduced spectral features as covariables in modeling process. [ABSTRACT FROM AUTHOR]

Ten different wheat genotypes were studied for understanding their differential behaviour to different water-deficit stress levels. Hyperspectral data (350–2500 nm) and relative water content (RWC) of plants were measured at different stress level for identifying optimal spectral bands, indices and multivariate models to develop non-invasive phenotyping protocols. Evaluation of water sensitive existing spectral indices, proposed indices and band depth analysis at selected wavelengths was done with respect to RWC and prediction models were developed. The prediction models developed were efficient in predicting RWC with the R 2 values ranging from 0.73 to 0.88 for spectral indices and 0.74–0.85 with continuum depth. Then, the ratio spectral indices (RSI) and normalised difference spectral indices (NDSI) were obtained in all possible combinations within 350–2500 nm and their correlations with RWC were quantified to identify the best indices. The best spectral indices for estimating RWC in wheat were RSI ( R 1391 , R 1830 ) and NDSI ( R 1391 , R 1830 ) with R 2 of 0.86 and 0.81, respectively. Spectral reflectance data were also used to develop partial least squares regression (PLSR) followed by multiple linear regression (MLR), support vector machine regression (SVR), multivariate adaptive regression spline (MARS) and random forest (RF) model to calculate plant RWC. Among these multivariate models, PLSR was the best model for prediction of RWC ( R 2 and RMSE of 0.96 and 3.88%; 0.91 and 6.52% for calibration and validation, respectively). The methodology developed would help for its further use in high-throughput phenomics of different crops for drought stress. [ABSTRACT FROM AUTHOR]

Above ground biomass (AGB) is a biophysical variable in vegetation studies .Biomass is considered as fresh matter weight or dry matter weight. The main objective of this study is to explore the potential of narrow band vegetation indices and band depth analysis method in biomass estimation. We will calculate three vegetation indices in this study i.e. soil adjusted vegetation indices (SAVI), normalized difference vegetation indices (NDVI), normalized difference water indices (NDWI) from field spectrometer data. We will use ASD field spec 4 for field spectrometer data. We will also apply band depth analysis method on field spectrometer data using continuum removed indices. The second objective of this study compares the performance of narrow band vegetation indices and band depth analysis method in aboveground biomass estimation by calculating root mean square error. [ABSTRACT FROM AUTHOR]

The laboratory soil analysis is traditionally used to establish elements, such as those related with fertility. It is costly and time consuming, which insures issues for future of precision agriculture, which is stagnated in some countries. Reflectance spectroscopy has recently emerged as a potential tool to reduce these issues. However, chemical elements are usually only underlined with spectra by a “coincidently” statistics and doesn't provide real detection. This study aims to investigate to interaction of K + and Mg 2 + with electromagnetic energy for decrease the demand of soil analysis and rationalize the use of fertilizers. The experiment was carried out using three major soil classes with different textures from tropical environment in Brazil. To reach K + and Mg 2 + saturation in different levels, the soils were saturated with concentrated solutions of KCl and MgCl 2 in vertical columns and then washed with distilled and deionized water to extract the residual elements. A second experiment was made by incubation of these elements in soils during four days in room temperature around 30 °C. Laboratory spectral sensing was carried out in the VIS-NIR-SWIR regions (350–2500 nm). Spectra were processed by the Continuum Removal, the Principal Components Analysis (PCA) and Partial Least Squares regression. The PCA showed a high degree of association between spectral and chemical variations. There was no alteration on mineralogy, texture, organic matter, moisture and effective cation exchange capacity (CEC) after the experiment occurs. On the other hand, differences on spectral, mainly where occurs CEC around 2200 nm, did change. Thus, the incident energy interacts with K + and Mg 2 + which promoted these alterations, mostly for Arenosol and Ferrasol. In Cambisol (2:1 mineralogy) we had a double effect due to effective CEC and cations alteration. Were encountered specific bands which altered features due to K + and Mg 2 + content mainly in 2186, 2189 and 2200 nm. Based on the results, the identified bands (related to K + and Mg 2 + contents in the soil) were extracted from the spectral data of soil samples obtained from a Brazilian soil spectral library. Indeed calibrations of K + and Mg 2 + models allowed to quantify these elements with 0.66 R 2 for the selected bands and 0.64 for the entire spectrum. Thus, the results indicate that it is possible to detect chemical elements, such K + and Mg 2 + in VIS-NIR-SWIR, looking forward on to assist soil analysis and all inherent approaches. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is one of the strategic biophysical variables of interest in vegetation studies. The main objective of this study was to evaluate the Support Vector Machine (SVM) and Partial Least Squares Regression (PLSR) for estimating the AGB of grasslands from field spectrometer data and to find out which data pre-processing approach was the most suitable. The most accurate model to predict the total AGB involved PLSR and the Maximum Band Depth index derived from the continuum removed reflectance in the absorption features between 916–1,120 nm and 1,079–1,297 nm (R2 = 0.939, RMSE = 7.120 g/m2). Regarding the green fraction of the AGB, the Area Over the Minimum index derived from the continuum removed spectra provided the most accurate model overall (R2 = 0.939, RMSE = 3.172 g/m2). Identifying the appropriate absorption features was proved to be crucial to improve the performance of PLSR to estimate the total and green aboveground biomass, by using the indices derived from those spectral regions. Ordinary Least Square Regression could be used as a surrogate for the PLSR approach with the Area Over the Minimum index as the independent variable, although the resulting model would not be as accurate.

The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical–conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing.

This study examined the relationship between the leaf reflectance of different seasons and the concentration of heavy metal elements in leaves, such as Co, Cu, Mo, and Ni in a post-mining area. The reflectance spectra and leaf samples of three typical plants were measured and collected in a whole growth cycle (June, July, August, and September). The Red Edge Position (REP), Readjustment Normalized Difference Vegetation Index (RE-NDVI), and Photochemical Reflectance Index (PRI) were extracted and used to explore its relation with the heavy metals concentrations in leaves between different seasons. The results show that all three Vegetation Indices (VIs) were insensitive indicators for monitoring the metal effects of vegetation in different seasons, which showed similar trends. Based on this, the Continuum Removal Indices (CRIs) were proposed from the continuum removed approach and extended for detecting the effects of heavy metal pollution over a full growth cycle. The relationship between the metal concentrations and CRIs of different plants was respectively analyzed by Stepwise Multiple Linear Regression (SMLR) and Partial Least Squares Regression (PLSR). It is found that a significant correlation exists between the band depth and the concentration of Cu and Ni based on the White birch data sets using the PLSR, resulting in a small deviation from the established relationships. Compared with VIs, the approach of coupling CRIs and multiple regressions was effective for improving the estimation accuracy. The presented study provides a detection model of leaf heavy metals that can be adapted to different growing cycles, even an arbitrary growing cycle.

It is demonstrated that hyperspectral imagery can be used, without atmospheric correction, to determine the presence of accessory phytoplankton pigments in coastal waters using derivative techniques. However, care must be taken not to confuse other absorptions for those caused by the presence of pigments. Atmospheric correction, usually the first step to making products from hyperspectral data, may not completely remove Fraunhofer lines and atmospheric absorption bands and these absorptions may interfere with identification of phytoplankton accessory pigments. Furthermore, the ability to resolve absorption bands depends on the spectral resolution of the spectrometer, which for a fixed spectral range also determines the number of observed bands. Based on this information, a study was undertaken to determine under what circumstances a hyperspectral sensor may determine the presence of pigments. As part of the study a hyperspectral imager was used to take high spectral resolution data over two different water masses. In order to avoid the problems associated with atmospheric correction this data was analyzed as radiance data without atmospheric correction. Here, the purpose was to identify spectral regions that might be diagnostic for photosynthetic pigments. Two well proven techniques were used to aid in absorption band recognition, the continuum removal of the spectra and the fourth derivative. The findings in this study suggest that interpretation of absorption bands in remote sensing data, whether atmospherically corrected or not, have to be carefully reviewed when they are interpreted in terms of photosynthetic pigments.

This project examines the causes, effects, and optimization of continuum removal in laser-induced breakdown spectroscopy (LIBS) to produce the best possible prediction accuracy of elemental composition in geological samples. We compare prediction accuracy resulting from several different techniques for baseline removal, including asymmetric least squares (ALS), adaptive iteratively reweighted penalized least squares (Air-PLS), fully automatic baseline correction (FABC), continuous wavelet transformation, median filtering, polynomial fitting, the iterative thresholding Dietrich method, convex hull/rubber band techniques, and a newly-developed technique for Custom baseline removal (BLR). We assess the predictive performance of these methods using partial least-squares analysis for 13 elements of geological interest, expressed as the weight percentages of SiO 2 , Al 2 O 3 , TiO 2 , FeO, MgO, CaO, Na 2 O, K 2 O, and the parts per million concentrations of Ni, Cr, Zn, Mn, and Co. We find that previously published methods for baseline subtraction generally produce equivalent prediction accuracies for major elements. When those pre-existing methods are used, automated optimization of their adjustable parameters is always necessary to wring the best predictive accuracy out of a data set; ideally, it should be done for each individual variable. The new technique of Custom BLR produces significant improvements in prediction accuracy over existing methods across varying geological data sets, instruments, and varying analytical conditions. These results also demonstrate the dual objectives of the continuum removal problem: removing a smooth underlying signal to fit individual peaks (univariate analysis) versus using feature selection to select only those channels that contribute to best prediction accuracy for multivariate analyses. Overall, the current practice of using generalized, one-method-fits-all-spectra baseline removal results in poorer predictive performance for all methods. The extra steps needed to optimize baseline removal for each predicted variable and empower multivariate techniques with the best possible input data for optimal prediction accuracy are shown to be well worth the slight increase in necessary computations and complexity. [ABSTRACT FROM AUTHOR]

The estimation of crop nitrogen status in fresh vegetation leaf using field spectroscopy is challenging due to the weak responses on leaf/canopy reflectance and the overlapping with the absorption features of other compounds. Although the spectral indices were proposed in the literature to predict leaf nitrogen content (LNC), the performance of selected spectral indices to estimate the LNC is often inconsistent. Moreover, the models for nitrogen content estimation changed with the growth stage. The goal of this study was to evaluate the performance of published indices, ratio of data difference index (RDDI) and ratio of data index (RDI) developed by band iterative-optimization algorithm in LNC estimation. The correlation analysis, linear regression and cross validation were used to analyze the relationship between spectral data and LNC and construct the best performed estimation model. The study was conducted by the data of five growing seasons of litchi from the orchards in Guangdong Province of China. Results showed that the relationship between chlorophyll (Chl) related spectral indices and LNC varied with the growth stage. Even in flower bud morphological differentiation stage and autumn shoot maturation stage, there were not significant correlations between the proposed spectral indices and LNC. Besides it is difficult to estimate the LNC by the general model across the growth stages due to the integrated effects of cultivar, biochemical, canopy structure, etc. The band iterative-optimization algorithm can improve the sensitivity of spectral data to LNC to some extent. The optimal RDDI performed better than other indices for the synthetic dataset and the dataset in each growth stage. And the sensitive bands selected in the optimal indices at each growth stage are not consistent, which are not only related to the Chl absorption but also other biochemical components, such as starch, lignin, cellulose, protein, etc. In general, the LNC can be estimated by the optimized CR-based RDDI indices in autumn shoot maturation stage, flower spike stage, fruit maturation stage, and flowering stage with the R 2 > 0.50 and RMSE < 0.14. Although there were the significant relationship between RDIs and RDDIs in flower bud morphological differentiation stage, the highest R 2 s of the model developed by RDDIs and RDIs were less than 0.50 in cross validation. This study indicated that the applicability of canopy reflectance to estimate litchi LNC was closely related to the growth stage of litchi. Growth stage-specific models will be preferred for estimating litchi LNC estimation. [ABSTRACT FROM AUTHOR]

This study aims at discriminating eight mangrove species ofRhizophoraceaefamily 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. [ABSTRACT FROM AUTHOR]

In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning method - support vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 - 710 nm, c) CR 705 - 780 nm, and d) CR 680 - 800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab < 10 μg cm-2 and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 - 710 nm, whereas CR bands in range of 680 - 800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view. [ABSTRACT FROM AUTHOR]

Fast and precisely estimating total nitrogen (TN) content in soil helps to promote carrying out prescription fertilization. And soil moisture is a severe interference factor in forecasting soil nitrogen content based on real-time NIR spectroscopy. This paper aims at predicting soil nitrogen content based on real-time soil spectrum through exploring pretreatment method without artificial drying and sieving soil samples. Firstly, the real-time near infrared absorbance spectra of soil samples were measured and their characteristics were analyzed. Then 1st–7th level wavelet decompositions were carried out for each soil sample’s real-time spectrum. RSNR (Relative Signal-to-Noise Ratio) was constructed to evaluate wavelet filtering quality at different levels, and the results indicated that low-frequency signals obtained after the 3rd level wavelet decomposing had the best performance. And then 5 soil sample groups (each group had the same moisture content but different nitrogen contents) were selected and continuum-removal method was used for processing their filtering signals. And by using the methods combined wavelet analysis and continuum removal technology, six sensitive wavebands were determined for predicting the TN content in soil, which were 1375 nm, 1520 nm, 1861 nm, 2100 nm, 2286 nm and 2387 nm. Finally the real-time TN content detecting models were calibrated and validated based on PLSR (Partial Least Squares Regression) and SVM (Support Vectors Machine) respectively. For the PLSR model, its calibration R 2 was 0.602 and its RMSEC was 0.051 mg/Kg; the validation R 2 was 0.634, the RMSEP was 0.056 mg/Kg and its RPD = 1.838. For the SVM model, its calibration R 2 reached to 0.823, the RMSEC was 0.034 mg/Kg, the validation R 2 reached to 0.810, the RMSEP was 0.053 mg/Kg and its RPD was 2.129. It showed that, by using the proposed approach in this paper, the interference of soil moisture was mostly removed from soil real-time spectrum in the process of soil total nitrogen prediction, and the TN content regression models established by using the six sensitive wavebands had great performances in predicting soil TN content in real time. [ABSTRACT FROM AUTHOR]

Considering the important roles of carbonate rock fraction in karst rocky desertification areas and their potential for indicating damage to vegetation, improved knowledge is desired to assess the application of spectroscopy and remote sensing to characterizing and quantifying the biophysical constituents of karst landscapes. In this study, we examined the spectra of major surface constituents in karst areas for direct evidence of absorption features attributable to carbonate rock fraction. Using spectral feature analysis with continuum removal, we observed that there are overlapping spectral absorption in 2.149-2.398 µm by soils and non-photosynthetic vegetation. These overlapping features complicated the carbonate absorption feature near 2.340 µm in synthetic mixed spectra. To remove the overprint signal, two hyperspectral carbonate rock indices (HCRIs) were developed. Compared to the absorption features including depths, areas, and KRDSIs (karst rocky desertification synthesis indices), linear regression of HCRIs with carbonate rock fraction in linear synthetic mixtures resulted in higher correlations and lower errors. This study demonstrates that spectral variation of the surface constituents spectra in 2.270-2.398 µm region can indicate carbonate rock fraction and be used to quantify them. Still, additional research is needed to advance our understanding of the spectral influences from carbonate petrography relative to carbonate mineralogy, components and physical state of rock surface. [ABSTRACT FROM AUTHOR]

Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic C H bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf ( r 2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf ( r 2 = 0.79, RMSE = 2.1%, n = 56), and canopy ( r 2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin). [ABSTRACT FROM AUTHOR]

Needle-leaf chlorophyll content (Cab) of a Norway spruce stand was estimated from CHRIS-PROBA images using the canopy reflectance simulated by the PROSPECT model coupled with two canopy reflectance models: 1) discrete anisotropic radiative transfer model (DART); and 2) PARAS. The DART model uses a detailed description of the forest scene, whereas PARAS is based on the photon recollision probability theory and uses a simplified forest structural description. Subsequently, statistically significant empirical functions between the optical indices \textbfANCB\textbf{670 - \textbf{720}} and \textbfANMB\textbf{670 - \textbf{720}} and the needle-leaf Cab content were established and then applied to CHRIS-PROBA data. The Cab estimating regressions using \textbfANMB\textbf{670 - \textbf{720}} were more robust than using \textbfANCB\textbf{670 - \textbf{720}} since the latter was more sensitive to LAI, especially in case of PARAS. Comparison between Cab estimates showed strong linear correlations between PARAS and DART retrievals, with a nearly perfect one-to-one fit when using \textbfANMB\textbf{670 - \textbf{720}} (\textbfslope = \textbf1.1, \textbfoffset = \textbf11\,\upmu \textbfg \cdot \textbfcm^ - 2). Further comparison with Cab estimated from an AISA Eagle image of the same stand showed better results for PARAS (\textbfRMSE = \textbf2.7\,\upmu \textbfg \cdot \textbfcm^ - 2 for \textbfANCB\textbf{670 - \textbf{720}}; \textbfRMSE = \textbf9.5\,\upmu \textbfg \cdot \textbfcm^ - 2 for \textbfANMB\textbf{670 - \textbf{720}}) than for DART (\textbfRMSE = \textbf7.5\,\upmu \textbfg \cdot \textbfcm^ - 2 for \textbfANCB\textbf{670 - \textbf{720}}; \textbfRMSE = \textbf23\,\upmu \textbfg \cdot \textbfcm^ - 2 for \textbfANMB\textbf{670 - \mathbf{720}}). Although these results show the potential for simpler models like PARAS in estimating needle-leaf Cab from satellite imaging spectroscopy data, further analyses regarding parameterization of radiative transfer models are recommended. [ABSTRACT FROM PUBLISHER]

Quantitative evidence for the fundamental spectral shape similarity existing between different soils is presented utilizing Pearson's correlation and SAM (Spectral Angle Mapper) analysis in-between signatures representing thousands of soil samples collected from different environments around the world. A universal quadratic soil line (UQSL) was found to highly fit these representative signatures as well as spectra from the ISRIC (International Soil Reference and Information Center) global database including soil samples collected from 58 countries. K-means clustering of ISRIC spectral reflectance deviations from the UQSL facilitated identification of 5 characteristic deviation patterns. Significant correlation was found for a set of 1000 soil samples between changes in multiple chemical properties and corresponding reflectance changes between these clusters. Future availability of large global soil data bases may allow improved determination of the UQSL and generalization of its deviation patterns relationships with PBC (Physical, Biological and Chemical) properties for wide areas monitoring of soil conditions. • Quantitative evidence for high soil spectral reflectance similarity is presented. • New Universal Quadratic Soil Line (UQSL) highly fitted spectra of Global database. • Patterns of deviations from UQSL were analyzed and clustered into 5 classes. • Between classes chemical properties change correlated reflectance change. • Satellite images' based deviations from UQSL may assist regional soil's monitoring. [ABSTRACT FROM AUTHOR]

Estimation of aboveground green biomass is essential for evaluating grassland productivity and functioning. This study aimed to explore the potential of band depth indices for estimating aboveground green biomass in grassland with low canopy cover. Field spectral and biomass measurements were conducted during 2009 and 2010 growing seasons in desert steppe of Inner Mongolia. Band depth (BD), band depth ratio (BDR), normalised band depth index (NBDI), band depth normalised to area (BNA), maximum band depth (BD max ), and area of absorption region (BD area ) extracted from red absorption region (650–740 nm) were utilised as band depth indices. Results indicated that: (1) BD at individual bands between 655 and 716 nm showed good accuracy for aboveground green biomass estimation; (2) BD at 698 nm yielded the best accuracy ( R 2 = 0.7, RMSECV = 29.6 g m −2 for calibration; RMSE = 32.4 g m −2 , rRMSE = 26.9% for validation); (3) BDR, NBDI, and BNA at all bands were not reliable estimators of aboveground green biomass ( R 2 < 0.3, RMSECV > 45 g m −2 for calibration; RMSE > 46 g m −2 , rRMSE > 39% for validation); (4) although the performance of BD max ( R 2 = 0.65, RMSECV = 32.1 g m −2 for calibration; RMSE = 34.5 g m −2 , rRMSE = 28.7% for validation) and BD area ( R 2 = 0.69, RMSECV = 30.2 g m −2 for calibration; RMSE = 33.1 g m −2 , rRMSE = 27.6% for validation) was slight lower than that of BD 698nm , the performance was far better than that of BDR, NBDI, and BNA. Our results suggest that BD 698nm has good potential to estimate aboveground green biomass in grassland with low canopy cover. The performance of BD 698nm needs to be further tested using space-borne hyperspectral images. [ABSTRACT FROM AUTHOR]

This paper explores the use of spectral feature analysis to detect plant stress in visible/near infrared wavelengths. A time series of close range leaf and canopy reflectance data of two plant species grown in hydrocarbon-contaminated soil was acquired with a portable spectrometer. The ProSpecTIR-VS airborne imaging spectrometer was used to obtain far range hyperspectral remote sensing data over the field experiment. Parameters describing the chlorophyll 680 nm absorption feature (depth, width, and area) were derived using continuum removal applied to the spectra. A new index, the Plant Stress Detection Index (PSDI), was calculated using continuum-removed values near the chlorophyll feature centre (680 nm) and on the green-edge (560 and 575 nm). Chlorophyll feature’s depth, width and area, the PSDI and a narrow-band normalised difference vegetation index were evaluated for their ability to detect stressed plants. The objective was to analyse how the parameters/indices were affected by increasing degrees of plant stress and to examine their utility as plant stress indicators at the remote sensing level (e.g. airborne sensor). For leaf data, PSDI and the chlorophyll feature area revealed the highest percentage (67–70%) of stressed plants. The PSDI also proved to be the best constraint for detecting the stress in hydrocarbon-impacted plants with field canopy spectra and airborne imaging spectroscopy data. This was particularly true using thresholds based on the ASD canopy data and considering the combination of higher percentage of stressed plants detected (across the thresholds) and fewer false-positives. [ABSTRACT FROM AUTHOR]

Irrigation-induced salinization is an important land degradation process that affects crop yield in the Brazilian semi-arid region, and gypsum has been used as a corrective measure for saline soils. Fluvent soil samples (180) were treated with increasing levels of salinization of NaCl, MgCl2 and CaCl2. The salinity was gauged using electrical conductivity (EC). Gypsum was added to one split of these samples before they were treated by the saline solutions. Laboratory reflectance spectra were measured at nadir under a controlled environment using a FieldSpec spectrometer, a 250-W halogen lamp and a Spectralon panel. Variations in spectral reflectance and brightness were evaluated using principal component analysis, as well as the continuum-removed absorption depths of major features at 1450, 1950, 1750 and 2200 nm for both the gypsum-treated (TG) and non-treated (NTG) air-dried soil samples as a function of EC. Pearson's correlation coefficients of reflectance and the band depth with EC were also obtained to establish the relationships with salinity. Results showed that NTG samples presented a decrease in reflectance and brightness with increasing CaCl2 and MgCl2 salinization. The reverse was observed for NaCl. Gypsum increased the spectral reflectance of the soil. The best negative correlations between reflectance and EC were observed in the 1500-2400 nm range for CaCl2 and MgCl2, probably because these wavelengths are most affected by water absorption, as Ca and Mg are much more hygroscopic than Na. These decreased after chemical treatment with gypsum. The most prominent features were observed at 1450, 1950 and 1750 nm in salinized-soil spectra. The 2200-nm clay mineral absorption band depth was inversely correlated with salt concentration. From these features, only the 1750 and 2200 nm ones are within atmospheric absorption windows and can be more easily measured using hyperspectral sensors. [ABSTRACT FROM AUTHOR]