Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture

The retrieval of canopy biophysical variables is known to be affected by confounding factors such as plant type and background reflectance. The effects of soil type and plant architecture on the retrieval of vegetation leaf area index (LAI) from hyperspectral data were assessed in this study. In situ measurements of LAI were related to reflectances in the red and near-infrared and also to five widely used spectral vegetation indices (VIs). The study confirmed that the spectral contrast between leaves and soil background determines the strength of the LAI–reflectance relationship. It was shown that within a given vegetation species, the optimum spectral regions for LAI estimation were similar across the investigated VIs, indicating that the various VIs are basically summarizing the same spectral information for a given vegetation species. Cross-validated results revealed that, narrow-band PVI was less influenced by soil background effects (0.15 ≤ RMSE_cv ≤ 0.56). The results suggest that, when using remote sensing VIs for LAI estimation, not only is the choice of VI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using hyperspectral imagery for large-scale mapping of vegetation biophysical variables.     

Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

The research evaluated the information content of spectral reflectance (laboratory and airborne data) for the estimation of needle chlorophyll (C_AB) and nitrogen (C_N) concentration in Norway spruce (Picea abies L. Karst.) needles. To identify reliable predictive models different types of spectral transformations were systematically compared regarding the accuracy of prediction. The results of the cross-validated analysis showed that C_AB can be well estimated from laboratory and canopy reflectance data. The best predictive model to estimate C_AB was achieved from laboratory spectra using continuum-removal transformed data (R²cv = 0.83 and a relative RMSEcv of 8.1%, n = 78) and from hyperspectral HyMap data using band-depth normalised spectra (R²cv = 0.90, relative RMSEcv = 2.8%, n = 13). Concerning the nitrogen concentration, we observed somewhat weaker relations, with however still acceptable accuracies (at canopy level: R²cv = 0.57, relative RMSEcv = 4.6%). The wavebands selected in the regression models to estimate C_AB were typically located in the red edge region and near the green reflectance peak. For C_N, additional wavebands related to a known protein absorption feature at 2350 nm were selected. The portion of selected wavebands attributable to known absorption features strongly depends on the type of spectral transformation applied. A method called “water removal” (WR) produced for canopy spectra the largest percentage of wavebands directly or indirectly related to known absorption features. The derived chlorophyll and nitrogen maps may support the detection and the monitoring of environmental stressors and are also important inputs to many bio-geochemical process models.     

冠层反射光谱对植被理化参数的全局敏感性分析

植被理化参数与许多有关植物物质能量交换的生态过程密切相关,定量分析植被反射光谱对理化参数的敏感性是遥感反演理化参数含量的前提。本文采用EFAST(Extended Fourier Amplitude Sensitivity Test)全局敏感性分析方法,利用PROSAIL辐射传输模型分析了冠层疏密程度对叶片生化组分含量、冠层结构以及土壤背景等多种参数敏感性的影响,并对植被理化参数反演所需先验知识的精度问题进行了初步探讨。研究表明:(1)对于较为稠密的冠层,可见光波段的冠层反射率主要受叶绿素含量的影响,近红外和中红外波段的冠层反射率主要受干物质量和含水量的影响;(2)对于稀疏的冠层,LAI是影响400—2500 nm波段范围内冠层反射率的最重要参数,土壤湿度次之,叶片生化参数对冠层反射率的敏感性较低;(3)在已知稀疏冠层LAI的情况下进一步确定土壤的干湿状态,可显著提高冠层反射率对叶绿素含量的敏感度,有助于稀疏冠层叶绿素含量的反演。     

Estimation of gross primary production in wheat from in situ measurements

Gross primary production (GPP) is a parameter of significant importance for carbon cycle and climate change research. Remote sensing combined with other climate and meteorological data offers a convenient tool for large scale GPP estimation. This paper presents a study of GPP estimation using three methods with in situ measurements of canopy reflectance, LAI, and the photosynthetically active radiation (PAR). First, because LAI is considered as an indicator of the factor of absorbed PAR (f_APAR), it provides reasonable estimates of GPP for all types of wheat with coefficient of determination R² of 0.7353. The second method uses four kinds of vegetation indices (VIs) to estimate GPP because these indices are suggested to be reliable candidates in the estimation of light use efficiency (LUE). Good determination coefficients were acquired in estimating GPP with R² ranging from the lowest of 0.7604 for NDVI to the highest of 0.8505 for EVI. A new method was proposed for the estimation of GPP following the Monteith logic, which considering GPP as a product of VI × VI × PAR. Results indicated that this method can provide the best estimates of GPP as determination coefficient R² increased largely compared to the other two methods. EVI × EVI × PAR was demonstrated to be the most suitable for the estimation of GPP with the highest R² of 0.9207, which was about 10% larger as compared to GPP estimated from the single EVI. These results will be helpful for the development of new models of GPP estimation with all remote sensing inputs.     

Non-destructive estimation of potato leaf chlorophyll from canopy hyperspectral reflectance using the inverted PROSAIL model

Optimizing nitrogen (N) fertilization in crop production by in-season measurements of crop N status may improve fertilizer N use efficiency. Hyperspectral measurements may be used to assess crop N status by estimating leaf chlorophyll content. This study evaluated the ability of the PROSAIL canopy-level reflectance model to predict leaf chlorophyll content. Trials were conducted with two potato cultivars under different N fertility rates (0–300 kg N ha⁻¹). Canopy reflectance, leaf area index (LAI) and leaf chlorophyll and N contents were measured. The PROSAIL model was able to predict leaf chlorophyll content with reasonable accuracy later in the growing season. The low estimation accuracy earlier in the growing season could be due to model sensitivity to non-homogenous canopy architecture and soil background interference before full canopy closure. Canopy chlorophyll content (leaf chlorophyll content × LAI) was predicted less accurately than leaf chlrophyll content due to the low estimation accuracy of LAI for values higher than 4.5.     

A visible band index for remote sensing leaf chlorophyll content at the canopy scale

Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. The triangular greenness index (TGI) was developed based on the area of a triangle surrounding the spectral features of chlorophyll with points at (670 nm, R₆₇₀), (550 nm, R₅₅₀), and (480 nm, R₄₈₀), where R_λ is the spectral reflectance at wavelengths of 670, 550 and 480, respectively. The equation is TGI = −0.5[(670 − 480)(R₆₇₀ − R₅₅₀) − (670 − 550)(R₆₇₀ − R₄₈₀)]. In 1999, investigators funded by NASA's Earth Observations Commercialization and Applications Program collaborated on a nitrogen fertilization experiment with irrigated maize in Nebraska. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and Landsat 5 Thematic Mapper (TM) data were acquired along with leaf chlorophyll meter and other data on three dates in July during late vegetative growth and early reproductive growth. TGI was consistently correlated with plot-averaged chlorophyll-meter values at the spectral resolutions of AVIRIS, Landsat TM, and digital cameras. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high LAI and canopy closure, TGI was only affected by leaf chlorophyll content. Therefore, TGI may be the best spectral index to detect crop nitrogen requirements with low-cost digital cameras mounted on low-altitude airborne platforms.     

RTM-based dynamic absorption integrals for the retrieval of biochemical vegetation traits

Information about pigment and water contents provides comprehensive insights for evaluating photosynthetic potential and activity of agricultural crops. In this study, we present the concept of using spectral integral ratios (SIR) to retrieve three biochemical traits, namely chlorophyll a and b (C_ab), carotenoids (C_cx), and water (C_w) content, simultaneously from hyperspectral measurements in the wavelength range 460−1100 nm. The SIR concept is based on automatic separation of respective absorption features through local peak and intercept analysis between log-transformed reflectance and convex hulls. The algorithm was tested on two synthetically established databases using a physiologically constrained look-up-table (LUT) generated by (i) the leaf optical properties model PROSPECT and (ii) the canopy radiative transfer model (RTM) PROSAIL. LUT constraints were realized based on natural C_cx-C_ab relations and green peak locations identified in the leaf optical database ANGERS. Linear regression between obtained SIRs and model parameters resulted in coefficients of determination (R²) of 0.66 (i and ii) for C_cx, R² = 0.85 (i) and 0.53 (ii) for C_ab, and R² = 0.97 (i) and 0.67 (ii) for C_w, respectively. Using the model established from the PROSPECT LUT, leaf level validation was carried out based on ANGERS data with reasonable results both in terms of goodness of fit and root mean square error (RMSE) (C_cx: R² = 0.86, RMSE = 2.1 μg cm⁻²; C_ab: R² = 0.67, RMSE = 12.5 μg cm^-2; C_w: R² = 0.89, RMSE = 0.007 cm). The algorithm was applied to airborne spectrometric HyMap data acquired on 12th July 2003 in Barrax, Spain and to AVIRIS-NG data recorded on 2nd July 2018 southwest of Munich, Germany. Mapping of the SIR results as multiband images (3-segment SIR) allows for intuitive visualization of dominant absorptions with respect to the three considered biochemical variables. Barrax in situ validation using linear regression models derived from PROSAIL LUT showed satisfactory results regarding C_ab (R² = 0.84; RMSE = 9.06 μg cm^-2) and canopy water content (CWC, R² = 0.70; RMSE = 0.05 cm). Retrieved C_cx values were reasonable according to C_ab-C_cx-dependence plausibility analysis. Hence, the presented SIR algorithm allows for computationally efficient and RTM supported robust retrievals of the two most important vegetation pigments as well as of water content and is ready to be applied on satellite imaging spectroscopy data available in the near future. The algorithm is publicly available as an interface supported tool within the 'Agricultural Applications' of the EnMAP-Box 3 hyperspectral remote sensing software suite.     

Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat

Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf N accumulation per unit soil area (LNA, g N m⁻²) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350–2500 nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆), and the regression models based on the above four spectral indices were formulated as Y = 26.34x^1.887, Y = 5.095x − 6.040, Y = 0.609 e^3.008x and Y = 0.388x^1.260, respectively, with R² greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R₈₆₀, R₇₂₀) and RSI (R₉₉₀, R₇₂₀) from 1 nm to 100 nm at 1 nm interval produced the LNA monitoring models with similar performance within about 33 nm and 23 nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) gave R² over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) can be reliably used for estimating LNA in winter wheat.     

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

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

Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery

Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest (Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI_(i,j) = (R_i − R_j)/(R_i + R_j), where R_i and R_j = reflectance in any two bands) were evaluated using calibration (n = 33) and test (n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756–820 nm) and the water absorption feature centred at 1200 nm (1172–1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R² = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH map could be useful to forest management in many ways, e.g. thinning of coppice to promote diameter growth, to assess the effects of management on forest structure or to detect changes in the forest structure caused by anthropogenic and natural factors.     

Leaf to canopy upscaling approach affects the estimation of canopy traits

In remote sensing applications, leaf traits are often upscaled to canopy level using sunlit leaf samples collected from the upper canopy. The implicit assumption is that the top of canopy foliage material dominates canopy reflectance and the variability in leaf traits across the canopy is very small. However, the effect of different approaches of upscaling leaf traits to canopy level on model performance and estimation accuracy remains poorly understood. This is especially important in short or sparse canopies where foliage material from the lower canopy potentially contributes to the canopy reflectance. The principal aim of this study is to examine the effect of different approaches when upscaling leaf traits to canopy level on model performance and estimation accuracy using spectral measurements (in-situ canopy hyperspectral and simulated Sentinel-2 data) in short woody vegetation. To achieve this, we measured foliar nitrogen (N), leaf mass per area (LMA), foliar chlorophyll and carbon together with leaf area index (LAI) at three vertical canopy layers (lower, middle and upper) along the plant stem in a controlled laboratory environment. We then upscaled the leaf traits to canopy level by multiplying leaf traits by LAI based on different combinations of the three canopy layers. Concurrently, in-situ canopy reflectance was measured using an ASD FieldSpec-3 Pro FR spectrometer, and the canopy traits were related to in-situ spectral measurements using partial least square regression (PLSR). The PLSR models were cross-validated based on repeated k-fold, and the normalized root mean square errors (nRMSE_cv) obtained from each upscaling approach were compared using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. Results of the study showed that leaf-to-canopy upscaling approaches that consider the contribution of leaf traits from the exposed upper canopy layer together with the shaded middle canopy layer yield significantly (p < 0.05) lower error (nRMSE_cv < 0.2 for canopy N, LMA and carbon) as well as high explained variance (R² > 0.71) for both in-situ hyperspectral and simulated Sentinel-2 data. The widely-used upscaling approach that considers only leaf traits from the upper illuminated canopy layer yielded a relatively high error (nRMSE_cv>0.2) and lower explained variance (R² < 0.71) for canopy N, LMA and carbon. In contrast, canopy chlorophyll upscaled based on leaf samples collected from the upper canopy and total canopy LAI exhibited a more accurate relationship with spectral measurements compared with other upscaling approaches. Results of this study demonstrate that leaf to canopy upscaling approaches have a profound effect on canopy traits estimation for both in-situ hyperspectral measurements and simulated Sentinel-2 data in short woody vegetation. These findings have implications for field sampling protocols of leaf traits measurement as well as upscaling leaf traits to canopy level especially in short and less foliated vegetation where leaves from the lower canopy contribute to the canopy reflectance.     

Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat5-TM and EO-1Hyperion data

Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill.) plantation in southern Australia. This technique combines an iterative mixture analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral mixture analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-spectral (R²_{no age correction} = 0.55 vs R²_{age correction} = 0.73 for Landsat) and hyperspectral (R²_{no age correction} = 0.12 vs R²_{age correction} = 0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for analysis of multi-spectral datasets such as MODIS and SPOT-VEGETATION.     

Modelling the spectral response of the desert tree Prosopis tamarugo to water stress

In this paper, we carried out a laboratory experiment to study changes in canopy reflectance of Tamarugo plants under controlled water stress. Tamarugo (Prosopis tamarugo Phil.) is an endemic and endangered tree species adapted to the hyper-arid conditions of the Atacama Desert, Northern Chile. Observed variation in reflectance during the day (due to leaf movements) as well as changes over the experimental period (due to water stress) were successfully modelled by using the Soil-Leaf-Canopy (SLC) radiative transfer model. Empirical canopy reflectance changes were mostly explained by the parameters leaf area index (LAI), leaf inclination distribution function (LIDF) and equivalent water thickness (EWT) as shown by the SLC simulations. Diurnal leaf movements observed in Tamarugo plants (as adaptation to decrease direct solar irradiation at the hottest time of the day) had an important effect on canopy reflectance and were explained by the LIDF parameter. The results suggest that remote sensing based assessment of this desert tree should consider LAI and canopy water content (CWC) as water stress indicators. Consequently, we tested fifteen different vegetation indices and spectral absorption features proposed in literature for detecting changes of LAI and CWC, considering the effect of LIDF variations. A sensitivity analysis was carried out using SLC simulations with a broad range of LAI, LIDF and EWT values. The Water Index was the most sensitive remote sensing feature for estimating CWC for values less than 0.036 g/cm², while the area under the curve for the spectral range 910–1070 nm was most sensitive for values higher than 0.036 g/cm². The red-edge chlorophyll index (CI_red-edge) performed the best for estimating LAI. Diurnal leaf movements had an effect on all remote sensing features tested, particularly on those for detecting changes in CWC.     

Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation

Accurate representation of leaf area index (LAI) from high resolution satellite observations is obligatory for various modelling exercises and predicting the precise farm productivity. Present study compared the two retrieval approach based on canopy radiative transfer (CRT) method and empirical method using four vegetation indices (VI) (e.g. NDVI, NDWI, RVI and GNDVI) to estimate the wheat LAI. Reflectance observations available at very high (56 m) spatial resolution from Advanced Wide-Field Sensor (AWiFS) sensor onboard Indian Remote Sensing (IRS) P6, Resourcesat-1 satellite was used in this study. This study was performed over two different wheat growing regions, situated in different agro-climatic settings/environments: Trans-Gangetic Plain Region (TGPR) and Central Plateau and Hill Region (CPHR). Forward simulation of canopy reflectances in four AWiFS bands viz. green (0.52–0.59 μm), red (0.62–0.68 μm), NIR (0.77–0.86 μm) and SWIR (1.55–1.70 μm) were carried out to generate the look up table (LUT) using CRT model PROSAIL from all combinations of canopy intrinsic variables. An inversion technique based on minimization of cost function was used to retrieve LAI from LUT and observed AWiFS surface reflectances. Two consecutive wheat growing seasons (November 2005–March 2006 and November 2006–March 2007) datasets were used in this study. The empirical models were developed from first season data and second growing season data used for validation. Among all the models, LAI-NDVI empirical model showed the least RMSE (root mean square error) of 0.54 and 0.51 in both agro-climatic regions respectively. The comparison of PROSAIL retrieved LAI with in situ measurements of 2006–2007 over the two agro-climatic regions produced substantially less RMSE of 0.34 and 0.41 having more R² of 0.91 and 0.95 for TGPR and CPHR respectively in comparison to empirical models. Moreover, CRT retrieved LAI had less value of errors in all the LAI classes contrary to empirical estimates. The PROSAIL based retrieval has potential for operational implementation to determine the regional crop LAI and can be extendible to other regions after rigorous validation exercise.     

Terrestrial laser scanning to estimate plot-level forest canopy fuel properties

This paper evaluates the potential of a terrestrial laser scanner (TLS) to characterize forest canopy fuel characteristics at plot level. Several canopy properties, namely canopy height, canopy cover, canopy base height and fuel strata gap were estimated. Different approaches were tested to avoid the effect of canopy shadowing on canopy height estimation caused by deployment of the TLS below the canopy. Estimation of canopy height using a grid approach provided a coefficient of determination of R² = 0.81 and an RMSE of 2.47 m. A similar RMSE was obtained using the 99th percentile of the height distribution of the highest points, representing the 1% of the data, although the coefficient of determination was lower (R² = 0.70). Canopy cover (CC) was estimated as a function of the occupied cells of a grid superimposed upon the TLS point clouds. It was found that CC estimates were dependent on the cell size selected, with 3 cm being the optimum resolution for this study. The effect of the zenith view angle on CC estimates was also analyzed. A simple method was developed to estimate canopy base height from the vegetation vertical profiles derived from an occupied/non-occupied voxels approach. Canopy base height was estimated with an RMSE of 3.09 m and an R² = 0.86. Terrestrial laser scanning also provides a unique opportunity to estimate the fuel strata gap (FSG), which has not been previously derived from remotely sensed data. The FSG was also derived from the vegetation vertical profile with an RMSE of 1.53 m and an R² = 0.87.     

Vegetation chlorophyll estimates in the Amazon from multi-angle MODIS observations and canopy reflectance model

As a preparatory study for future hyperspectral missions that can measure canopy chemistry, we introduce a novel approach to investigate whether multi-angle Moderate Resolution Imaging Spectroradiometer (MODIS) data can be used to generate a preliminary database with long-term estimates of chlorophyll. MODIS monthly chlorophyll estimates between 2000 and 2015, derived from a fully coupled canopy reflectance model (ProSAIL), were inspected for consistency with eddy covariance fluxes, tower-based hyperspectral images and chlorophyll measurements. MODIS chlorophyll estimates from the inverse model showed strong seasonal variations across two flux-tower sites in central and eastern Amazon. Marked increases in chlorophyll concentrations were observed during the early dry season. Remotely sensed chlorophyll concentrations were correlated to field measurements (r² = 0.73 and r² = 0.98) but the data deviated from the 1:1 line with root mean square errors (RMSE) ranging from 0.355 μg cm⁻² (Tapajós tower) to 0.470 μg cm⁻² (Manaus tower). The chlorophyll estimates were consistent with flux tower measurements of photosynthetically active radiation (PAR) and net ecosystem productivity (NEP). We also applied ProSAIL to mono-angle hyperspectral observations from a camera installed on a tower to scale modeled chlorophyll pigments to MODIS observations (r² = 0.73). Chlorophyll pigment concentrations (Chl_A+B) were correlated to changes in the amount of young and mature leaf area per month (0.59 ≤  r² ≤  0.64). Increases in MODIS observed Chl_A+B were preceded by increased PAR during the dry season (0.61 ≤ r² ≤  0.62) and followed by changes in net carbon uptake. We conclude that, at these two sites, changes in LAI, coupled with changes in leaf chlorophyll, are comparable with seasonality of plant productivity. Our results allowed the preliminary development of a 15-year time series of chlorophyll estimates over the Amazon to support canopy chemistry studies using future hyperspectral sensors.     

Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC)

The Normalized Area Over reflectance Curve (NAOC) is proposed as a new index for remote sensing estimation of the leaf chlorophyll content of heterogeneous areas with different crops, different canopies and different types of bare soil. This index is based on the calculation of the area over the reflectance curve obtained by high spectral resolution reflectance measurements, determined, from the integral of the red–near-infrared interval, divided by the maximum reflectance in that spectral region. For this, use has been made of the experimental data of the SPARC campaigns, where in situ measurements were made of leaf chlorophyll content, LAI and fCOVER of 9 different crops – thus, yielding 300 different values with broad variability of these biophysical parameters. In addition, Proba/CHRIS hyperspectral images were obtained simultaneously to the ground measurements. By comparing the spectra of each pixel with its experimental leaf chlorophyll value, the NAOC was proven to exhibit a linear correlation to chlorophyll content. Calculating the correlation between these variables in the 600–800 nm interval, the best correlation was obtained by computing the integral of the spectral reflectance curve between 643 and 795 nm, which practically covers the spectral range of maximum chlorophyll absorption (at around 670 nm) and maximum leaf reflectance in the infrared (750–800 nm). Based on a Proba/CHRIS image, a chlorophyll map was generated using NAOC and compared with the land-use (crops classification) map. The method yielded a leaf chlorophyll content map of the study area, comprising a large heterogeneous zone. An analysis was made to determine whether the method also serves to estimate the total chlorophyll content of a canopy, multiplying the leaf chlorophyll content by the LAI. To validate the method, use was made of the data from another campaign ((SEN2FLEX), in which measurements were made of different biophysical parameters of 7 crops, and hyperspectral images were obtained with the CASI imaging radiometer from an aircraft. Applying the method to a CASI image, a map of leaf chlorophyll content was obtained, which on, establishing comparisons with the experimental data allowed us to estimate chlorophyll with a root mean square error of 4.2 μg/cm², similar or smaller than other methods but with the improvement of applicability to a large set of different crop types.     

Estimating canopy water content using hyperspectral remote sensing data

Hyperspectral remote sensing has demonstrated great potential for accurate retrieval of canopy water content (CWC). This CWC is defined by the product of the leaf equivalent water thickness (EWT) and the leaf area index (LAI). In this paper, in particular the spectral information provided by the canopy water absorption feature at 970 nm for estimating and predicting CWC was studied using a modelling approach and in situ spectroradiometric measurements. The relationship of the first derivative at the right slope of the 970 nm water absorption feature with CWC was investigated with the PROSAIL radiative transfer model and tested for field spectroradiometer measurements on two test sites. The first site was a heterogeneous floodplain with natural vegetation like grasses and various shrubs. The second site was an extensively grazed fen meadow.     

Retrieval of remotely sensed LAI using Landsat ETM+ data and ground measurements of solar radiation and vegetation structure: Implication of leaf inclination angle

A time series of leaf area index (LAI) of a managed birch forest in Germany (near Dresden) has been developed based on 16-day normalized difference vegetation index (NDVI) data from the Landsat ETM+ sensor at 30 m resolution. The Landsat ETM+ LAI was retrieved using a modified physical radiative transfer (RTM) model which establishes a relationship between LAI, fractional vegetation cover (fC), and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. In situ measurements of photosynthetically active radiation (PAR) and vegetation structure parameters using hemispherical photography (HSP) served for calibration of model parameters, while data from litter collection at the study site provided the ground-based estimates of LAI for validation of modelling results. Influence of view-illumination conditions on optical properties of canopy was simulated by a view angle geometry model incorporating the solar zenith angle and the sensor viewing angle. Effects of intra-annual and inter-annual variability of structural properties of the canopy on the light extinction coefficient were simulated by implementing variability of the leaf inclination angle (LIA), which was confirmed in the study site. The results revealed good compatibility of the produced Landsat ETM+ LAI data set with the litter-estimated LAI. The results also showed high sensitivity of the LAI retrieval algorithm to variability of structural properties of the canopy: the implementation of LIA dynamics into the LAI retrieval algorithm significantly improved the model accuracy.     

Leaf area index retrieval using gap fractions obtained from high resolution satellite data: Comparisons of approaches,scales and atmospheric effects

This study is aimed at demonstrating the feasibility of the large scale LAI inversion algorithms using red and near infrared reflectance obtained from high resolution satellite imagery. Radiances in digital counts were obtained in 10 m resolution acquired on cloud free day of August 23, 2007, by the SPOT 5 high resolution geometric (HRG) instrument on mostly temperate hardwood forest located in the Great Lakes – St. Lawrence forest in Southern Quebec. Normalized difference vegetation index (NDVI), scaled difference vegetation index (SDVI) and modified soil-adjusted vegetation index (MSAVI) were applied to calculate gap fractions. LAI was inverted from the gap fraction using the common Beer–Lambert's law of light extinction under forest canopy. The robustness of the algorithm was evaluated using the ground-based LAI measurements and by applying the methods for the independently simulated reflectance data using PROSPECT + SAIL coupled radiative transfer models. Furthermore, the high resolution LAI was compared with MODIS LAI product. The effects of atmospheric corrections and scales were investigated for all of the LAI retrieval methods. NDVI was found to be not suitable index for large scale LAI inversion due to the sensitivity to scale and atmospheric effects. SDVI was virtually scale and atmospheric correction invariant. MSAVI was also scale invariant. Considering all sensitivity analysis, MSAVI performed best followed by SDVI for robust LAI inversion from high resolution imagery.