Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery

Microwave-based remote sensing algorithms for mapping soil moisture are sensitive to water contained in surface vegetation at moderate levels of canopy cover. Correction schemes require spatially distributed estimates of vegetation water content at scales comparable to that of the microwave sensor footprint (10¹ to 10⁴ m). This study compares the relative utility of high-resolution (1.5 m) aircraft and coarser-resolution (30 m) Landsat imagery in upscaling an extensive set of ground-based measurements of canopy biophysical properties collected during the Soil Moisture Experiment of 2002 (SMEX02) within the Walnut Creek Watershed. The upscaling was accomplished using expolinear relationships developed between spectral vegetation indices and measurements of leaf area index, canopy height, and vegetation water content. Of the various indices examined, a Normalized Difference Water Index (NDWI), derived from near- and shortwave-infrared reflectances, was found to be least susceptible to saturation at high levels of leaf area index. With the aircraft data set, which did not include a short-wave infrared water absorption band, the Optimized Soil Adjusted Vegetation Index (OSAVI) yielded best correlations with observations and highest saturation levels. At the observation scale (10 m), LAI was retrieved from both NDWI and OSAVI imagery with an accuracy of 0.6, vegetation water content at 0.7 kg m⁻², and canopy height to within 0.2 m. Both indices were used to estimate field-scale mean canopy properties and variability for each of the intensive soil-moisture-sampling sites within the watershed study area. Results regarding scale invariance over the SMEX02 study area in transformations from band reflectance and vegetation indices to canopy biophysical properties are also presented.     

A novel restoration approach for vegetation reflectance spectra at noisy bands using the principal component analysis method

ABSTRACT

Due to the signal-to-noise ratio (SNR) of sensors, as well as atmospheric absorption and illumination conditions, etc., hyperspectral data at some bands are of poor quality. Data restoration for noisy bands is important for many remote sensing applications. In this paper, we present a novel data-driven Principal Component Analysis (PCA) approach for restoring leaf reflectance spectra at noisy bands using the spectra at effective bands. The technique decomposes the leaf reflectance spectra into their principal components (PCs), selects the leading PCs that describe the most variance in the data, and restores the data from these components. First, the first 10 PCs were determined from a training dataset simulated by the leaf optical properties model (PROSPECT-5) that contained 99.998% of the total information in the 3636 training samples. Then, the performance of the PCA method for restoration of the reflectance at noisy bands was investigated using the ANGERS leaf optical properties dataset; the results showed the spectral root mean squared error (RMSE) is in the range 6.46 × 10^?4 to 6.44 × 10^?2, which is about 3 ? 34 times more accurate than the stepwise regression method and partial least squares method (PLSR) for the ANGERS dataset. The results also showed that if the noisy bands are far away from the effective bands, the accuracy of the restored leaf reflectance spectra will decrease. Thirdly, the reliability of the restored reflectance spectra for retrieving leaf biochemical contents was assessed using the ANGERS dataset and leaf optical properties dataset established by the Beijing Academy of Agriculture and Forestry Sciences (BAAFS). Three water-sensitive vegetation indices ? normalized difference water index (NDWI), normalized difference infrared index (NDII) and Datt water index (DWI), derived from the restored leaf spectra ? were employed to retrieve the equivalent water thickness (EWT). The results showed that the leaf water content can be accurately retrieved from the restored leaf reflectance spectra. In addition, the PCA method to restore vegetation spectral reflectance can be applied on canopy level as well. The results showed that the spectral root mean squared error (RMSE) is in the range 8.22 × 10^?4 to 1.87 × 10^?2. The performance of the restored canopy spectra was investigated according to the results of retrieving canopy equivalent water thickness (CEWT) using the five spectral indices NDWI, NDWI₁₃₇₀, NDWI₁₈₉₀, NDII and DWI. The results indicated that the restored canopy spectra can be used for retrieving CEWT reliably and improve the accuracy of retrieval compared to the results using original canopy reflectance spectra.     

Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions

Irrigated agriculture is an important strategic sector in arid and semi-arid regions. Given the large spatial coverage of irrigated areas, operational tools based on satellite remote sensing can contribute to their optimal management. The aim of this study was to evaluate the potential of two spectral indices, calculated from SPOT-5 high-resolution visible (HRV) data, to retrieve the surface water content values (from bare soil to completely covered soil) over wheat fields and detect irrigation supplies in an irrigated area. These indices are the normalized difference water index (NDWI) and the moisture stress index (MSI), covering the main growth stages of wheat. These indices were compared to corresponding in situ measurements of soil moisture and vegetation water content in 30 wheat fields in an irrigated area of Morocco, during the 2012–2013 and 2013–2014 cropping seasons. NDWI and MSI were highly correlated with in situ measurements at both the beginning of the growing season (sowing) and at full vegetation cover (grain filling). From sowing to grain filling, the best correlation (R² = 0.86; p < 0.01) was found for the relationship between NDWI values and observed soil moisture values. These results were validated using a k-fold cross-validation methodology; they indicated that NDWI can be used to estimate and map surface water content changes at the main crop growth stages (from sowing to grain filling). NDWI is an operative index for monitoring irrigation, such as detecting irrigation supplies and mitigating wheat water stress at field and regional levels in semi-arid areas.     

NDWI与NDVI指数在区域干旱监测中的比较分析——以2003年江西夏季干旱为例

植被水分指数NDWI是基于短波红外（SWIR）与近红外（NIR）的归一化比值指数。与NDVI相比，它能有效地提取植被冠层的水分含量；在植被冠层受水分胁迫时，NDWI指数能及时地响应，这对于旱情监测具有重要意义。结合2003年夏季MODIS影像数据和地面气象数据，以江西省内一片农田和一片林地为试验区域，分析比较了NDWI与NDVI距平值在短期旱情监测中的有效性。监测结果表明， NDWI对植被冠层水分信息比NDVI更为敏感；在短期干旱监测中，NDWI指数能准确地反映旱情的时空变化。     

Evaluating variations of physiology-based hyperspectral features along a soil water gradient in a Eucalyptus grandis plantation

Remote sensing is viewed as a cost-effective alternative to intensive field surveys in assessing site factors that affect growth of Eucalyptus grandis over broad areas. The objective of this study was to assess the utility of hyperspectral remote sensing to discriminate between site qualities in E. grandis plantation in KwaZulu-Natal, South Africa. The relationships between physiology-based hyperspectral indicators and site quality, as defined by total available water (TAW), were assessed for E. grandis plantations through one-way analysis of variance (ANOVA). Canopy reflectance spectra for 68 trees (25 good, 25 medium and 18 poor sites) were collected on clear-sky days using an Analytical Spectral Device (ASD) spectroradiometer (350–2500 nm) from a raised platform. Foliar macronutrient concentrations for N, P, K, S, Ca, Mg and Na and their corresponding spectral features were also evaluated. The spectral signals for leaf water – normalized difference water index (NDWI), water band index (WBI) and moisture stress index (MSI) – exhibited significant differences (p < 0.05) between sites. The magnitudes of these indices showed distinct gradients from the poor to the good sites. Similar results were observed for chlorophyll indices. These results show that differences in site quality based on TAW could be detected via imaging spectroscopy of canopy water or chlorophyll content. Among the macronutrients, only K and Ca exhibited significant differences between sites. However, a Tukey post-hoc test showed differences between the good and medium or medium and poor sites, a trend not consistent with the TAW gradient. The study also revealed the capability of continuum-removed spectral features to provide information on the physiological state of vegetation. The normalized band depth index (NBDI), derived from continuum-removed spectra in the region of the red-edge, showed the highest potential to differentiate between sites in this study. The study thus demonstrated the capability of hyperspectral remote sensing of vegetation canopies in identifying the site factors that affect growth of E. grandis in KwaZulu Natal, South Africa.     

MODIS spectral signals at a flux tower site: Relationships with high-resolution data, and CO2 flux and light use efficiency measurements

In this study, spectral indices were calculated from single date HyMap (3 m; 126 bands), Hyperion (30 m; 242 bands), ASTER (15/30 m; 9 bands), and a time series of MODIS nadir BRDF-adjusted reflectance (NBAR; 1 km, 7 bands) for a study area surrounding the Tumbarumba flux tower site in eastern Australia. The study involved: a) the calculation of a range of physiologically-based vegetation indices from ASTER, HyMap, Hyperion and MOD43B NBAR imagery over the flux tower site; b) comparison across scales between HyMap, Hyperion and MODIS for the normalized difference water index (NDWI) and the Red-Green ratio; c) analysis of relationships between tower-based flux and light use efficiency (LUE) measurements and seasonal and climatic constraints on growth; and d) examination of relationships between fluxes, LUE and time series of NDVI, NDWI and Red-Green ratio. Strong seasonal patterns of variation were observed in NDWI and Red/Green ratio from MODIS NBAR. Correlations between fine (3 and 30 m) and coarse (1 km) scale indices for a small region around the flux tower site were moderately good for Red/Green ratio, but poor for NDWI. Hymap NDWI values for the understorey canopy were much lower than values for the tree canopy. MODIS NDWI was negatively correlated with CO₂ fluxes during warm and cool seasons. The correlation indicated that surface reflectance, affected by a spectrally bright grassland understorey canopy, was decoupled from growth of trees with access to deep soil moisture. The application of physiologically-based indices at earth observation scale requires careful attention to applicability of band configurations, contribution of vegetation components to reflectance signals, mechanistic relationships between biochemical processes and spectral indexes, and incorporation of ancillary information into any analysis.     

Evaluation of hyperspectral indices for retrieval of canopy equivalent water thickness and gravimetric water content

There are two main parameters describing the amount of water in vegetation: the gravimetric water content (GWC) and the equivalent water thickness (EWT). In this study, we investigated the applicability of hyperspectral water-sensitive indices from canopy spectra for estimating canopy EWT (CEWT) and GWC. First, the spectral reflectance’s response to different levels of canopy water content was analysed and a noticeable increase in the slope of the near-infrared (NIR) shoulder of the canopy spectrum was observed. Next, the correlation between the CEWT and various hyperspectral water-sensitive indices was investigated. It was found that all of the indices could retrieve the CEWT of winter wheat well, with the coefficients of determination (R²) all being higher than 0.80. Finally, the retrieval performance of these indices for canopy GWC was evaluated and no significant correlation was observed between canopy GWC and the water-sensitive indices except for the spectral ratio index in the NIR shoulder region (NSRI). These results showed that the traditional water-sensitive vegetation indices are more sensitive to CEWT than to GWC, especially when the LAI is not highly correlated with the GWC, and that the NSRI is a potential vegetation index for use in the retrieval of GWC.     

Relationship between spatio-temporal characteristics of leaf-fall phenology and seasonal variations in near surface- and satellite-observed vegetation indices in a cool-temperate deciduous broad-leaved forest in Japan

We examined the relationship between the spatio-temporal distribution of leaf litter for each species and the seasonal patterns of in situ and satellite-observed daily vegetation indices in a cool-temperate deciduous broad-leaved forest. The timing and distribution of leaf-fall revealed spatio-temporal relationships with species and topography. Values of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and green–red vegetation index (GRVI), measured both in situ and by satellite, and those of the in situ-measured leaf area index (LAI), rapidly declined at the peak of leaf-fall. At the late stage of leaf-fall, in situ-measured values of NDVI, EVI, and LAI declined but those of GRVI changed from decreasing to increasing. The peak timing of leaf-fall, when 50–73% of the leaf litter had fallen, corresponds to LAI = 1.80–0.81, NDVI = 0.61–0.54, EVI = 0.29–0.25, and GRVI = 0.01 ～ ?0.07. Although the distribution of leaf litter among species displayed spatial characteristics at the peak of leaf-fall, spatial heterogeneity of amount of leaf litter at the peak timing of leaf-fall was less than that at the beginning and end. These facts suggest that the criterion for determining the timing of leaf-fall from vegetation indices should be a value corresponding to the peak of leaf-fall rather than its end. In a high-biodiversity forest, such as this study forest, the effect of spatial heterogeneity on the timing and patterns of leaf-fall on vegetation indices can be reduced by observing only the seasonal variation in colour on the canopy surface by using GRVI, which consists of visible reflectance bands, rather than that of both leaf area and colour of the canopy surface by using NDVI and EVI, which consist of visible and near-infrared reflectance bands.     

Foliar nutrient and water content in subtropical tree islands: A new chemohydrodynamic link between satellite vegetation indices and foliar δN values

We examined the relationships between two satellite-derived vegetation indices and foliar δ¹⁵N values obtained from dominant canopy species in a set of tree islands located in the Everglades National Park in South Florida, USA. These tree islands constitute important nutrient hotspots in an otherwise P-limited wetland environment. Foliar δ¹⁵N values obtained from a previous study of 17 tree islands in both slough (perennially wet) and prairie (seasonally wet) locations served as a proxy of P availability at the stand level. We utilized five cloud-free SPOT 4 multispectral images (20 m spatial resolution) from different times of the seasonal cycle to derive two atmospherically corrected vegetation indices: the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), averaged for each tree island. NDWI, which incorporates a shortwave infrared (SWIR) band that provides information on leaf water content, showed consistently higher linear fits with island foliar δ¹⁵N values than did NDVI. In addition, NDWI showed greater variation throughout the seasonal cycle than did NDVI, and was significantly correlated with average water stage, which suggests that the SWIR band captures important information on seasonally variable water status. Tree islands in slough locations showed higher NDWI than prairie islands during the dry season, which is consistent with higher levels of transpiration and nutrient harvesting and accumulation for perennially wet locations. Overall, the results suggest that water availability is closely related to P availability in subtropical tree islands, and that NDWI may provide a robust indicator of community-level water and nutrient status.     

Yield prediction and waterlogging assessment for tea plantation land using satellite image‐based techniques

Motivated by the operational use of remote sensing in various agricultural crop studies, this study evaluates the application and utility of remote sensing‐based techniques in yield prediction and waterlogging assessment of tea plantation land in the Assam State of India. The potential of widely used vegetation indices like NDVI and SR (simple ratio) and the recently proposed TVI has been evaluated for the prediction of green leaf tea yield and made tea yield based on image‐derived leaf area index (LAI), along with weather parameters. It was observed that the yield model based on the TVI showed the highest correlation (R² = 0.83) with green leaf tea yield. The NDVI‐ and SR‐based models suffered non‐responsiveness when the yield approached maximum. The NDVI and SR showed saturation when the LAI exceeded a magnitude of 4. However, the TVI responded well, even when the LAI exceeded 5, and thus has potential use in the estimation of the LAI of dense vegetation such as some crops and forest where it generally exceeds the threshold value of 4.

An attempt was made for the innovative application of TCT and NDWI in the mapping of waterlogging in tea plantation land. The NDWI in conjunction with TCT offered fairly good accuracy (87%) in the delineation of tea areas prone to waterlogging. This observation indicates the potential of NDWI and TCT in mapping waterlogged areas where the soil has considerable vegetation cover.     

Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method

A hybrid inversion method was developed to estimate the leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) of crops. Fifty hyperspectral vegetation indices (VIs), such as the photochemical reflectance index (PRI) and canopy chlorophyll index (CCI), were compared to identify the appropriate VIs for crop LCC and CCC inversion. The hybrid inversion models were then generated from different modelling methods, including the curve-fitting and least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms, by using simulated Compact High Resolution Imaging Spectrometer (CHRIS) datasets that were generated by a radiative transfer model. Finally, the remote-sensing mapping of a CHRIS image was completed to test the inversion accuracy. The results showed that the remote-sensing mapping of the CHRIS image yielded an accuracy of R² = 0.77 and normalized root mean squared error (NRMSE) = 17.34% for the CCC inversion, and an accuracy of only R² = 0.33 and NRMSE = 26.03% for LCC inversion, which indicates that the remote-sensing technique was more appropriate for obtaining chlorophyll content at the canopy scale (CCC) than at the leaf scale (LCC). The estimated results of various VIs and algorithms suggested that the PRI and CCI were the optimal VIs for LCC and CCC inversion, respectively, and RFR was the optimal method for modelling.     

Leaf area and clumping indices for a boreal mixed-wood forest: lidar,hyperspectral, and Landsat models

The leaf area index (LAI) and the clumping index (CI) provide valuable insight into the spatial patterns of forest canopies, the canopy light regime and forest productivity. This study examines the spatial patterns of LAI and CI in a boreal mixed-wood forest, using extensive field measurements and remote sensing analysis. The objectives of this study are to: (1) examine the utility of airborne lidar (light detection and ranging) and hyperspectral data to model LAI and clumping indices; (2) compare these results to those found from commonly used Landsat vegetation indices (i.e. the normalized difference vegetation index (NDVI) and the simple ratio (SR)); (3) determine whether the fusion of lidar data with Landsat and/or hyperspectral data will improve the ability to model clumping and LAI; and (4) assess the relationships between clumping, LAI and canopy biochemistry.

Regression models to predict CI were much stronger than those for LAI at the site. Lidar was the single best predictor of CI (r ² > 0.8). Landsat NDVI and SR also had a moderately strong predictive performance for CI (r ² > 0.68 with simple linear and non-linear regression forms), suggesting that canopy clumping can be predicted operationally from satellite platforms, at least in boreal mixed-wood environments. Foliar biochemistry, specifically canopy chlorophyll, carotenoids, magnesium, phosphorus and nitrogen, was strongly related to the clumping index. Combined, these results suggest that Landsat models of clumping could provide insight into the spatial distribution of foliar biochemistry, and thereby photosynthetic capacity, for boreal mixed-wood canopies. LAI models were weak (r ² < 0.4) unless separate models were used for deciduous and coniferous plots. Coniferous LAI was easier to model than deciduous LAI (r ² > 0.8 for several indices). Deciduous models of LAI were weaker for all remote sensing indices (r ² < 0.67). There was a strong, linear relationship between foliar biochemistry and LAI for the deciduous plots. Overall, our results suggest that broadband satellite indices have strong predictive performance for clumping, but that airborne hyperspectral or lidar data are required to develop strong models of LAI at this boreal mixed-wood site.     

Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments

In this study, various hyperspectral indices were evaluated for estimation of leaf area index (LAI) and crop discrimination under different irrigation treatments. The study was conducted for potato crop using the spectral reflectance values measured by a hand‐held spectro‐radiometer. Three categories of hyperspectral indices, such as ratio/difference indices, multivariate indices and derivative based indices were computed. It was found that, among various band combinations for NDVI (normalized difference vegetation index) and SAVI (soil adjusted vegetation index), the band combination of the 780～680, produced highest correlation coefficient with LAI. Among all the forms of LAI and VI empirical relationships, the power and exponential equations had highest R ² and F values. Analysis of variance showed that, hyperspectral indices were found to be more efficient than the LAI to detect the differences among crops under different irrigation treatments. The discriminant analysis produced a set of five most optimum bands to discriminate the crops under three irrigation treatments.     

In situ examination of the relationship between various vegetation indices and canopy phenology in an evergreen coniferous forest,Japan

We examined the relationship between four vegetation indices and tree canopy phenology in an evergreen coniferous forest in Japan based on observations made using a spectral radiometer and a digital camera at a daily time step during a 4 year period. The colour of the canopy surface of Japanese cedar (Cryptomeria japonica) changed from yellowish-green to whitish-green from late May to July and turned reddish-green in winter. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and plant area index (PAI) showed no seasonality. In contrast, the green–red ratio vegetation index (GRVI) increased from March to June and then decreased gradually from July to December, resulting in a bell-shaped curve. GRVI revealed seasonal changes in the colour of the canopy surface. GRVI correlated more positively with the evaluated maximum photosynthetic rate for the whole forest canopy, A _max, than did NDVI or EVI. These results suggest the possibility that GRVI is more useful than NDVI and EVI for capturing seasonal changes in photosynthetic capacity, as the green and red reflectances are strongly influenced by changes in leaf pigments in this type of forest.     

Comparison of different vegetation indices for the remote assessment of green leaf area index of crops

Many algorithms have been developed for the remote estimation of biophysical characteristics of vegetation, in terms of combinations of spectral bands, derivatives of reflectance spectra, neural networks, inversion of radiative transfer models, and several multi-spectral statistical approaches. However, the most widespread type of algorithm used is the mathematical combination of visible and near-infrared reflectance bands, in the form of spectral vegetation indices. Applications of such vegetation indices have ranged from leaves to the entire globe, but in many instances, their applicability is specific to species, vegetation types or local conditions. The general objective of this study is to evaluate different vegetation indices for the remote estimation of the green leaf area index (Green LAI) of two crop types (maize and soybean) with contrasting canopy architectures and leaf structures. Among the indices tested, the chlorophyll Indices (the CI_Green, the CI_Red-edge and the MERIS Terrestrial Chlorophyll Index, MTCI) exhibited strong and significant linear relationships with Green LAI, and thus were sensitive across the entire range of Green LAI evaluated (i.e., 0.0 to more than 6.0 m²/m²). However, the CI_Red-edge was the only index insensitive to crop type and produced the most accurate estimations of Green LAI in both crops (RMSE = 0.577 m²/m²). These results were obtained using data acquired with close range sensors (i.e., field spectroradiometers mounted 6 m above the canopy) and an aircraft-mounted hyperspectral imaging spectroradiometer (AISA). As the CI_Red-edge also exhibited low sensitivity to soil background effects, it constitutes a simple, yet robust tool for the remote and synoptic estimation of Green LAI. Algorithms based on this index may not require re-parameterization when applied to crops with different canopy architectures and leaf structures, but further studies are required for assessing its applicability in other vegetation types (e.g., forests, grasslands).     

Effects of adaxial and abaxial surface on the estimation of leaf chlorophyll content using hyperspectral vegetation indices

Vegetation indices are frequently used for the non-destructive assessment of leaf chemistry, especially chlorophyll content. However, most vegetation indices were developed based on the statistical relationship between the spectral reflectance of the adaxial leaf surface and chlorophyll content, even though abaxial leaf surfaces may influence reflectance spectra because of canopy structure or the inclination of leaves. In the present study, reflectance spectra from both adaxial and abaxial leaf surfaces of Populus alba and Ulmus pumila var. pendula were measured. The results showed that structural differences of the two leaf surfaces may result in differences in reflectance and hyperspectral vegetation indices. Among 30 vegetation indices tested, R₆₇₂/(R₅₅₀ × R₇₀₈) had the smallest difference (4.66% for P. alba, 2.30% for U. pumila var. pendula) between the two blade surfaces of the same leaf in both species. However, linear regression analysis showed that several vegetation indices (R₈₅₀ ? R₇₁₀)/(R₈₅₀ ? R₆₈₀), VOG₂, D₇₃₀, and D₇₄₀, had high coefficients of determination (R² > 0.8) and varied little between the two leaf surfaces of the plants we sampled. This demonstrated that these four vegetation indices had relatively stable accuracy for estimating leaf chlorophyll content. The coefficients of determination (R²) for the calibration of P. alba leaves were 0.92, 0.98, 0.93, and 0.95 on the adaxial surfaces, and 0.88, 0.87, 0.88, and 0.92 on the abaxial surfaces. The coefficients of determination (R²) for the calibration of U. pumila var. pendula leaves were 0.85, 0.91, 0.86, and 0.90 on adaxial surface, and 0.80, 0.80, 0.84, and 0.88 on abaxial surface. These four vegetation indices were readily available and were little influenced by the differences in the two leaf surfaces during the estimation of leaf chlorophyll content.     

Modelling spectral reflectance of open cork oak woodland: a simulation analysis of the effects of vegetation structure and background

This study analyses the influence of vegetation structure (i.e. leaf area index and canopy cover) and seasonal background changes on moderate-resolution imaging spectrometer (MODIS)-simulated reflectance data in open woodland. Approximately monthly spectral reflectance and transmittance field measurements (May 2011 to October 2013) of cork oak tree leaves (Quercus suber) and of the herbaceous understorey were recorded in the region of Ribatejo, Portugal. The geometric-optical and radiative transfer (GORT) model was used to simulate MODIS response (red, near-infrared) and to calculate vegetation indices, investigating their response to changes in the structure of the overstorey vegetation and to seasonal changes in the understorey using scenarios corresponding to contrasting phenological status (dry season vs. wet season). The performance of normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) is discussed. Results showed that SAVI and EVI were very sensitive to the emergence of background vegetation in the wet season compared to NDVI and that shading effects lead to an opposing trend in the vegetation indices. The information provided by this research can be useful to improve our understanding of the temporal dynamic of vegetation, monitored by vegetation indices.     

Grape quality assessment in vineyards affected by iron deficiency chlorosis using narrow-band physiological remote sensing indices

The present study investigated the use of physiological indices calculated from hyperspectral remote sensing imagery as potential indicators of wine grape quality assessment in vineyards affected by iron deficiency chlorosis. Different cv. Tempranillo/110 Richter vineyards located in northern Spain, affected and non-affected by iron chlorosis, were identified for field and airborne data collection. Airborne campaigns imaged a total of 14 study areas in both 2004 and 2005 using the AHS hyperspectral sensor, which acquired 20 spectral bands in the VIS-NIR region. Field measurements were conducted in each study site to obtain leaf and grape physiological parameters potentially linked to wine quality. Simulations carried out with the rowMCRM radiative transfer model demonstrated the feasibility of estimating leaf chlorophyll a + b (C_ab) content using TCARI/OSAVI from AHS spectral bands. In addition to traditional structural vegetation indices (NDVI) and successful canopy-level chlorophyll indices (TCARI/OSAVI), other innovative physiological indices sensitive to changes in carotenoid (Car) and anthocyanin (Anth) content in leaves were assessed from the imagery. The rowMCRM model simulations were used to evaluate canopy structural effects on these physiological indices as a function of the typical row-structured canopy variables in vineyards (LAI, crown width, row distances, C_ab content and soil background effects). Modeling results concluded that Car (Gitelson-Car2) and Anth (Gitelson-Anth) indices were highly affected by canopy structure (C_w, V_s) and soil background (ρ_s). Field measurements of grape composition and quality were used to assess potential relationships with physiological indices sensitive to foliar pigment content (C_ab, Car and Anth). NDVI and TCARI/OSAVI indices yielded lower relationships for CIRG and IMAD must quality parameters than Car and Anth physiological indices. These results suggest that the increase in carotenes and anthocyanins due to drought, thermal damage or micronutrient deficiencies is a better indicator to detect phenolic ripening difficulties for vines affected by iron chlorosis than chlorosis detection. Therefore, the potential use of physiological remote sensing indices related to carotene and anthocyanin pigments demonstrates their importance as grape quality indicators in vineyards affected by iron chlorosis.     

Responses of the reflectance indices PRI and NDVI to experimental warming and drought in European shrublands along a north-south climatic gradient

The aim of this study was to evaluate the use of ground-based canopy reflectance measurements to detect changes in physiology and structure of vegetation in response to experimental warming and drought treatment at six European shrublands located along a North-South climatic gradient. We measured canopy reflectance, effective green leaf area index (green LAIe) and chlorophyll fluorescence of dominant species. The treatment effects on green LAIe varied among sites. We calculated three reflectance indices: photochemical reflectance index PRI [531 nm; 570 nm], normalized difference vegetation index NDVI₆₈₀ [780 nm; 680 nm] using red spectral region, and NDVI₅₇₀ [780 nm; 570 nm] using the same green spectral region as PRI. All three reflectance indices were significantly related to green LAIe and were able to detect changes in shrubland vegetation among treatments. In general warming treatment increased PRI and drought treatment reduced NDVI values. The significant treatment effect on photochemical efficiency of plants detected with PRI could not be detected by fluorescence measurements. However, we found canopy level measured PRI to be very sensitive to soil reflectance properties especially in vegetation areas with low green LAIe. As both soil reflectance and LAI varied between northern and southern sites it is problematic to draw universal conclusions of climate-derived changes in all vegetation types based merely on PRI measurements. We propose that canopy level PRI measurements can be more useful in areas of dense vegetation and dark soils.