R. Darvishzadeh

Leaf Area Index derivation from hyperspectral vegetation indices and the red edge position.

The aim of this study was to compare the performance of various narrowband vegetation indices in estimating Leaf Area Index (LAI) of structurally different plant species having different soil backgrounds and leaf optical properties. The study uses a dataset collected during a controlled laboratory experiment. Leaf area indices were destructively acquired for four species with different leaf size and shape. Six widely used vegetation indices were investigated. Narrowband vegetation indices involved all possible two band combinations which were used for calculating RVI, NDVI, PVI, TSAVI and SAVI2. The red edge inflection point (REIP) was computed using three different techniques. Linear regression models as well as an exponential model were used to establish relationships. REIP determined using any of the three methods was generally not sensitive to variations in LAI (R 2 < 0.1). However, LAI was estimated with reasonable accuracy from red/near-infrared based narrowband indices. We observed a significant relationship between LAI and SAVI2 (R 2 = 0.77, RMSE = 0.59 (cross validated)). Our results confirmed that bands from the SWIR region contain relevant information for LAI estimation. The study verified that within the range of LAI studied (0.3 ≤ LAI ≤ 6.1), linear relationships exist between LAI and the selected narrowband indices.

Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach

Optical remote sensing provides information on important vegetation variables such as leaf area index (LAI), biomass, and chlorophyll content. In this study, rice crops, which are rarely studied, were selected because of their high economic importance and the role they play in food security in the study area. The aim was to obtain a reliable estimate of canopy chlorophyll content as an important indicator for the evaluation of the plant status. PROSAIL radiative transfer model and the multispectral image data of ALOS AVNIR-2 were used. A field campaign was carried out in July 2010 in the northern part of Iran, Amol. Sixty sample plots of 20 × 20 m-2 were randomly selected, and their chlorophyll content was measured using a SPAD-502 chlorophyll meter. The PROSAIL was inverted using a lookup-table (LUT) approach. The LUTs were generated in different sizes. The effect of the LUT size on the retrieval accuracy of the canopys chlorophyll content was studied using analysis of variance (ANOVA). The outcome of the inversion was evaluated using the calculated R2 and RMSE values with the field measurements. The obtained results demonstrate the ability of PROSAIL to estimate rice plant chlorophyll content using ALOS AVNIR-2 multispectral data (R2= 0.65; RMSE = 0.45). The results also confirmed the usefulness of such an approach for crop monitoring and ecological applications.

Estimating leaf functional traits by inversion of PROSPECT: Assessing leaf dry matter content and specific leaf area in mixed mountainous forest

Assessments of ecosystem functioning rely heavily on quantification of vegetation properties. The search is on for methods that produce reliable and accurate baseline information on plant functional traits. In this study, the inversion of the PROSPECT radiative transfer model was used to estimate two functional leaf traits: leaf dry matter content (LDMC) and specific leaf area (SLA). Inversion of PROSPECT usually aims at quantifying its direct input parameters. This is the first time the technique has been used to indirectly model LDMC and SLA. Biophysical parameters of 137 leaf samples were measured in July 2013 in the Bavarian Forest National Park, Germany. Spectra of the leaf samples were measured using an ASD FieldSpec3 equipped with an integrating sphere. PROSPECT was inverted using a look-up table (LUT) approach. The LUTs were generated with and without using prior information. The effect of incorporating prior information on the retrieval accuracy was studied before and after stratifying the samples into broadleaf and conifer categories. The estimated values were evaluated using R2 and normalized root mean square error (nRMSE). Among the retrieved variables the lowest nRMSE (0.0899) was observed for LDMC. For both traits higher R2 values (0.83 for LDMC and 0.89 for SLA) were discovered in the pooled samples. The use of prior information improved accuracy of the retrieved traits. The strong correlation between the estimated traits and the NIR/SWIR region of the electromagnetic spectrum suggests that these leaf traits could be assessed at canopy level by using remotely sensed data.

Leaf Nitrogen Content Indirectly Estimated by Leaf Traits Derived From the PROSPECT Model

Leaf nitrogen content has so far been quantified through empirical techniques using hyperspectral remote sensing. However, it remains a challenge to estimate the nitrogen content in fresh leaves through inversion of physically based models. Leaf nitrogen has been found to correlate with leaf traits (e.g., leaf chlorophyll, dry matter, and water) well through links to the photosynthetic process, which provides potential to estimate nitrogen indirectly. We therefore set out to estimate leaf nitrogen content by using its links to leaf traits that could be retrieved from a physically based model (PROSPECT) inversion. Leaf optical (directional-hemispherical reflectance and transmittance between 350 and 2500 nm) and leaf biochemical (nitrogen, chlorophyll, dry matter, and water) properties were measured. Correlation analysis showed that the area-based nitrogen correlations with leaf traits were higher than mass-based correlations. Hence, simple and multiple linear regression models were established for area-based nitrogen using three leaf traits (leaf chlorophyll content, leaf mass per area, and equivalent water thickness). In addition, the traits were retrieved by the inversion of PROSPECT using an iterative optimization algorithm. The established empirical models and the leaf traits retrieved from PROSPECT were used to estimate leaf nitrogen content. A simple linear regression model using only retrieved equivalent water thickness as a predictor produced the most accurate estimation of nitrogen (R2 = 0.58, normalized RMSE = 0.11). The combination of empirical and physically based models provides a moderately accurate estimation of leaf nitrogen content, which can be transferred to other datasets in a robust and upscalable manner.

Effects of Canopy Structural Variables on Retrieval of Leaf Dry Matter Content and Specific Leaf Area From Remotely Sensed Data

Leaf dry matter content (LDMC) and specific leaf area (SLA) are two important traits in measuring biodiversity. To use remote sensing for the estimation of these traits, it is essential to understand the underlying factors that influence their relationships with canopy reflectance. The effect of canopy structures-particularly stem density (SD), leaf area index (LAI), stand height (SH), crown diameter (CD), and average leaf angle (ALA)-on the relationship between LDMC and SLA with the canopy reflectance were investigated using a canopy reflectance dataset simulated by the invertible forest reflectance model (INFORM) radiative transfer model. The parameterization of the model was based on the range of the field parameters collected in the Bavarian National Park in July 2013 and the configuration of the HYSpex hyperspectral sensor. Strong correlations were observed between the two leaf traits and indices derived from simulated canopy spectra in the NIR and SWIR region (R2 values of 0.87 for LDMC and 0.85 for SLA). Among the tested HYSpex wavelengths, the bands most sensitive to variation were 2298.69 nm for LDMC and 2280.71 nm for SLA. The effects of the stated structural variables on the relationships were best controlled by the modified normalized difference (mND) vegetation index (VI): ([R2275 - R1920]/[R2275 + R1920 - 2 * R1520]). The structural variables that most affected the relationship were forest SD and CD. The modeling results suggest that the spectral variation due to changes in LDMC and SLA is best captured for stands with SD > 400 trees/ha and CD ≥ 5 m. The influence of LAI and SH on the relationships can be greatly reduced using VIs. We conclude that LDMC and SLA can be accurately estimated from canopy reflectance, irrespective of the heterogeneity of structural variables, providing that canopy cover exceeds 50%.

Suitability and adaptation of PROSAIL radiative transfer model for hyperspectral grassland studies

Methods are presented testing the suitability of PROSAIL radiative transfer model for analysing HyMap imaging spectroscopy data over grassland. The presented methods include forward modelling and cross-checks of 2D correlation plots. In the forward modelling, it is taken into account that the in situ data are not error free. To increase the predictive power of PROSAIL, a simple and fully automatic feature selection (FS) algorithm is presented identifying and discarding poorly modelled wavebands, yielding more reliable parameter retrievals.

Simple and robust methods for remote sensing of canopy chlorophyll content : a comparative analysis of hyperspectral data for different types of vegetation

Canopy chlorophyll content (CCC) is an essential ecophysiological variable for photosynthetic functioning. Remote sensing of CCC is vital for a wide range of ecological and agricultural applications. The objectives of this study were to explore simple and robust algorithms for spectral assessment of CCC. Hyperspectral datasets for six vegetation types (rice, wheat, corn, soybean, sugar beet and natural grass) acquired in four locations (Japan, France, Italy and USA) were analysed. To explore the best predictive model, spectral index approaches using the entire wavebands and multivariable regression approaches were employed. The comprehensive analysis elucidated the accuracy, linearity, sensitivity and applicability of various spectral models. Multivariable regression models using many wavebands proved inferior in applicability to different datasets. A simple model using the ratio spectral index (RSI; R815, R704) with the reflectance at 815 and 704 nm showed the highest accuracy and applicability. Simulation analysis using a physically based reflectance model suggested the biophysical soundness of the results. The model would work as a robust algorithm for canopy-chlorophyll-metre and/or remote sensing of CCC in ecosystem and regional scales. The predictive-ability maps using hyperspectral data allow not only evaluation of the relative significance of wavebands in various sensors but also selection of the optimal wavelengths and effective bandwidths.

Why confining to vegetation indices? exploiting the potential of improved spectral observations using radiative transfer models

Vegetation indices (VI) combine mathematically a few selected spectral bands to minimize undesired effects of soil background, illumination conditions and atmospheric perturbations. In this way, the relation to vegetation biophysical variables is enhanced. Albeit numerous experiments found close relationships between vegetation indices and several important vegetation biophysical variables, well known shortcomings and drawbacks remain. Important limitations of VIs are illustrated and discussed in this paper. As most of the limitations can be overcome using physically-based radiative transfer models (RTM), advantages and limits of RTM are also presented.

Spatially detailed retrievals of spring phenology from single-season high-resolution image time series

Vegetation indices derived from satellite image time series have been extensively used to estimate the timing of phenological events like season onset. Medium spatial resolution (≥250 m) satellite sensors with daily revisit capability are typically employed for this purpose. In recent years, phenology is being retrieved at higher resolution (≤30 m) in response to increasing availability of high-resolution satellite data. To overcome the reduced acquisition frequency of such data, previous attempts involved fusion between high- and medium-resolution data, or combinations of multi-year acquisitions in a single phenological reconstruction. The objectives of this study are to demonstrate that phenological parameters can now be retrieved from single-season high-resolution time series, and to compare these retrievals against those derived from multi-year high-resolution and single-season medium-resolution satellite data. The study focuses on the island of Schiermonnikoog, the Netherlands, which comprises a highly-dynamic saltmarsh, dune vegetation, and agricultural land. Combining NDVI series derived from atmospherically-corrected images from RapidEye (5 m-resolution) and the SPOT5 Take5 experiment (10m-resolution) acquired between March and August 2015, phenological parameters were estimated using a function fitting approach. We then compared results with phenology retrieved from four years of 30 m Landsat 8 OLI data, and single-year 100 m Proba-V and 250 m MODIS temporal composites of the same period. Retrieved phenological parameters from combined RapidEye/SPOT5 displayed spatially consistent results and a large spatial variability, providing complementary information to existing vegetation community maps. Retrievals that combined four years of Landsat observations into a single synthetic year were affected by the inclusion of years with warmer spring temperatures, whereas adjustment of the average phenology to 2015 observations was only feasible for a few pixels due to cloud cover around phenological transition dates. The Proba-V and MODIS phenology retrievals scaled poorly relative to their high-resolution equivalents, indicating that medium-resolution phenology retrievals need to be interpreted with care, particularly in landscapes with fine-scale land cover variability.

Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects

A statistical relationship between canopy mass-based foliar nitrogen concentration (%N) and canopy bidirectional reflectance factor (BRF) has been repeatedly demonstrated. However, the interaction between leaf properties and canopy structure confounds the estimation of foliar nitrogen. The canopy scattering coefficient (the ratio of BRF and the directional area scattering factor, DASF) has recently been suggested for estimating %N as it suppresses the canopy structural effects on BRF. However, estimation of %N using the scattering coefficient has not yet been investigated for longer spectral wavelengths (>855 nm). We retrieved the canopy scattering coefficient for wavelengths between 400 and 2500 nm from airborne hyperspectral imagery, and then applied a continuous wavelet analysis (CWA) to the scattering coefficient in order to estimate %N. Predictions of %N were also made using partial least squares regression (PLSR). We found that %N can be accurately retrieved using CWA (R2 = 0.65, RMSE = 0.33) when four wavelet features are combined, with CWA yielding a more accurate estimation than PLSR (R2 = 0.47, RMSE = 0.41). We also found that the wavelet features most sensitive to %N variation in the visible region relate to chlorophyll absorption, while wavelet features in the shortwave infrared regions relate to protein and dry matter absorption. Our results confirm that %N can be retrieved using the scattering coefficient after correcting for canopy structural effect. With the aid of high-fidelity airborne or upcoming space-borne hyperspectral imagery, large-scale foliar nitrogen maps can be generated to improve the modeling of ecosystem processes as well as ecosystem-climate feedbacks.