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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]

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]

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]

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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]

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 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]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Capturing both the shape of the spectral continuum and the positions/widths of absorption bands is essential to accurately measure similarity between hyperspectral signatures. Furthermore, the relative importance of these features is data dependent. In this letter, we present an adaptive version of our recently proposed continuum-intact (CI)/continuum-removed (CR) similarity measure which automatically determines a convex weighting between similarity measurements of CI and CR signatures according to input data. We describe an efficient technique to calculate an optimal weight for a linear combination of CI and CR similarity measurements. We evaluate the technique on the Airborne Visible/Infrared Imaging Spectrometer spectra sampled from a well-studied urban scene and show that our technique yields improved classification accuracy in comparison to CI or CR similarity measurements alone and performs comparably to calculating the weight via brute-force search, at a much reduced computational cost. A source code implementation of our algorithm is provided online. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents a combination of techniques suitable for remotely sensing foliar Nitrogen (N) in semiarid shrublands – a capability that would significantly improve our limited understanding of vegetation functionality in dryland ecosystems. The ability to estimate foliar N distributions across arid and semi-arid environments could help answer process-driven questions related to topics such as controls on canopy photosynthesis, the influence of N on carbon cycling behavior, nutrient pulse dynamics, and post-fire recovery. Our study determined that further exploration into estimating sagebrush canopy N concentrations from an airborne platform is warranted, despite remote sensing challenges inherent to open canopy systems. Hyperspectral data transformed using standard derivative analysis were capable of quantifying sagebrush canopy N concentrations using partial least squares (PLS) regression with an R 2 value of 0.72 and an R 2 predicted value of 0.42 (n =35). Subsetting the dataset to minimize the influence of bare ground (n =19) increased R 2 to 0.95 (R 2 predicted=0.56). Ground-based estimates of canopy N using leaf mass per unit area measurements (LMA) yielded consistently better model fits than ground-based estimates of canopy N using cover and height measurements. The LMA approach is likely a method that could be extended to other semiarid shrublands. Overall, the results of this study are encouraging for future landscape scale N estimates and represent an important step in addressing the confounding influence of bare ground, which we found to be a major influence on predictions of sagebrush canopy N from an airborne platform. [Copyright &y& Elsevier]

Abstract: The absorption feature approach was used in CHRIS multiangular hyperspectral data in order to investigate its potential for ecosystem remote sensing. For that purpose, CHRIS images in mode 1 were acquired throughout a two-year period for a Mediterranean ecosystem dominated by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident in situ Leaf spectra and ecophysiological measurements (Leaf Area Index, leaf pigment content and leaf water potential) were conducted. After data preprocessing, absorption feature information was calculated for both CHRIS and Leaf spectra for the whole spectrum. Three common characteristic absorption features within the spectral areas 450–550nm, 550–750nm and 900–1000nm were detected. Each spectral area was then examined separately and four characteristic parameters were calculated that described the pattern, magnitude and position of the maximum absorption. Correlations between CHRIS and Leaf spectra for each date and viewing angle (VA) were then conducted. All correlations, either on full continuum removed spectra or on spectral areas, showed high coefficients of determination, especially (i) in higher observation angles (VA +55), (ii) during the wet season and (iii) in strong absorptions such as the “red absorption”. Subsequently, correlations between CHRIS and Leaf absorption parameters of selected spectral areas with field-measured ecophysiological parameters were examined. Ecophysiological parameters proved to be highly correlated to CHRIS and Leaf absorption parameters in magnitude and/or pattern of the absorption feature and less in wavelength of the maximum absorption. CHRIS VAs +/− 36 showed the highest correlations although the type of relation, linear or nonlinear, was not conclusive. Finally, a first comparison between narrowband spectral indices and absorption features in correlations with ecophysiological parameters showed that both methods provide significant and comparable results, with oblique angles showing best performance. However, ecophysiological parameters are generally better predicted linearly by narrowband spectral indices issued from CHRIS, with most significant differences appearing on pigments absorbing mainly within 450–550nm. [Copyright &y& Elsevier]

Abstract: Remotely sensing instruments, both airborne and spaceborne, of sufficient spectral resolution, can be used to identify absorptions that occur at 2.20µm, which are indicative of AlOH minerals. In a pixel containing green and/or dry vegetation the depth of the AlOH feature at 2.20µm is decreased. Since the depth of the feature at 2.20µm is an indicator of the AlOH content it is desirable to correct the depth of the 2.20µm feature in such a manner as to remove, or negate, the obscuring effect of the vegetation. This is achieved by using a multiple linear regression model where the coefficients of the linear model are produced via forward modeling, and where the independent variables are continuum removed band depth (CRBD) that are used to detect the presence of green and dry vegetation and the uncorrected AlOH CRBD. The proposed vegetation corrected continuum depth (VCCD) method was tested with synthetic datasets as well as hyperspectral data (HyMap) collected at Mount Isa in Queensland, Australia. The results of using the VCCD method on the uncorrected HyMap data were validated with vegetation free samples collected from the Mount Isa region. Improvements in the R squared statistics of the corrected 2.20µm CRBD to the vegetation free CRBD, after application of the VCCD, were found to be 2–4 times greater than the uncorrected 2.20µm CRBD. Visual inspection of large survey areas demonstrated that the uncorrected CRBD in vegetated areas were lower, and did not match adjacent vegetation free areas, and also produced false positives of high AlOH content. [Copyright &y& Elsevier]

Abstract: This paper demonstrates the potential of hyperspectral remote sensing to predict the chemical composition (i.e., nitrogen, phosphorous, calcium, potassium, sodium, and magnesium) of three tree species (i.e., willow, mopane and olive) and one shrub species (i.e., heather). Reflectance spectra, derivative spectra and continuum-removed spectra were compared in terms of predictive power. Results showed that the best predictions for nitrogen, phosphorous, and magnesium occur when using derivative spectra, and the best predictions for sodium, potassium, and calcium occur when using continuum-removed data. To test whether a general model for multiple species is also valid for individual species, a bootstrapping routine was applied. Prediction accuracies for the individual species were lower then prediction accuracies obtained for the combined dataset for all except one element/species combination, indicating that indices with high prediction accuracies at the landscape scale are less appropriate to detect the chemical content of individual species. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Vegetation growth and physiological functioning, including carbon uptake and transpiration, can be strongly affected by drought. During water-limited conditions, urban vegetation may be limited in its ability to provide ecosystem services, such as shading and evaporative cooling. While there has been extensive research on drought response in natural vegetation and agricultural systems using remote sensing, drought impacts on urban vegetation have remained understudied. With >50% of people worldwide living in cities and the increased potential for future droughts, it is important to understand how urban vegetation will respond and to evaluate potential impacts. Here, we used repeat flights of NASA airborne imaging spectroscopy (AVIRIS and AVIRIS-NG) to quantify the responses of common urban tree species and turfgrass in downtown Santa Barbara, California, USA to the 2012–2016 California drought. The effects of the drought on vegetation were quantified using several spectral indicators: fractional cover from multiple endmember spectral mixture analysis (MESMA), vegetation indices, and continuum removed absorption features. We used existing tree crown object polygons that were classified to species and had associated estimates of pre-drought leaf area index and biomass. We also included polygons of turfgrass parks and recreational fields. All tree species and turfgrass had four or more spectral indicators with significantly lower mean values (p < 0.05) during the drought in 2014, and evidence of recovery was observed for some species in 2017, which was after the drought had ended. In general, needleleaf and native trees were more affected than broadleaf and non-native trees, respectively, but with considerable interspecific variability. Based on the spectral indicators, turfgrass cover senesced in the middle of the drought but recovered soon after the drought ended, whereas the majority of tree species had not fully recovered. Mean green vegetation cover declined in most tree canopies during the drought, primarily exposing paved surfaces and/or non-photosynthetic vegetation, depending on the site type and tree patch size. Drought impacts as expressed in spectral indicators were generally more severe in tree patches with high pre-drought leaf area index, suggesting that denser, leafier canopies were more difficult for trees to maintain during the drought. Most spectral indicators were correlated with one another (R > 0.4), suggesting co-occuring adjustments of greenness, water content, and plant pigments. • Quantified turfgrass and 19 urban tree species' spectral response to drought • Spectral indicators showed changes in greenness, water content, and pigments • All tree species had significant response, with large species differences • Trees with higher pre-drought leaf area index had greater drought response overall • Turfgrass recovered after drought, but the majority of tree species had not [ABSTRACT FROM AUTHOR]