Equivalent Water Thickness Estimation Research Articles

Inversion of Leaf Water Content of Cinnamomum camphora Based on Preferred Spectral Index and Machine Learning Algorithm

Plant leaf water content significantly influences photosynthetic efficiency and crop yield. Leaf water content (LWC) and equivalent water thickness (EWT) are indicators that reflect the water state within plant tissues, and they play a crucial role in assessing plant water supply and usage. In recent years, there has been a growing focus on the rapid and precise determination of plant water content. In this study, Cinnamomum camphora (C. camphora) was chosen as the subject of investigation. After acquiring spectral data, three types of vegetation indices were computed: the empirical vegetation index, the random combination dual-band vegetation index, and the ‘trilateral’ parameter. Four groups of optimal spectral index screening strategies were established, namely an empirical vegetation index group (G1), a random combination dual-band vegetation index group (G2), a ‘trilateral’ parameter group (G3), and a mixed group (G4). Three algorithms, specifically random forest (RF), radial basis function neural network (RBFNN), and support vector machine (SVM), were employed for the estimation of leaf water content (LWC) and equivalent water thickness (EWT) in mature C. camphora. The results demonstrated that the G4 group displayed superior performance, yielding five optimal spectral indices for LWC: water index (WI), optimized soil-adjusted vegetation index (OSAVI), difference vegetation index (DVI) at wavelengths 734 and 956 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1009 and 774 nm, and red-edge amplitude (Dr). With regard to EWT estimation, the five optimal spectral indices encompassed the red-edge normalized difference vegetation index (RE-NDVI), simple ratio water index (SRWI), difference vegetation index (DVI) at wavelengths 700 and 1167 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1182 and 1514 nm, and red-edge area (SDr). Utilizing these indices as inputs significantly enhanced the accuracy of the models, with the RF model emerging as the most effective for estimating LWC and EWT in C. camphora. Based on the LWC estimation model of the G4 group and the RF algorithm, the determination coefficient (R2) for both the training and test sets reached 0.848 and 0.871, respectively. The root mean square error (RMSE) was 0.568% for the training set and 0.582% for the test set, while the average relative error (MRE) stood at 0.806% and 0.642%, respectively. Regarding the EWT estimation model, R2 values of 0.887 and 0.919 were achieved for the training and test sets, accompanied by RMSE values of 0.6 × 10−3 g·cm−2 and 0.7 × 10−3 g·cm−2, and MRE values of 3.198% and 2.901%, respectively. These findings lay a solid foundation for hyperspectral moisture monitoring in C. camphora and offer valuable reference for the rapid assessment of crop growth status.

PROSPECT-GPR: Exploring spectral associations among vegetation traits in wavelength selection for leaf mass per area and water contents

Leaf mass per area (LMA) and equivalent water thickness (EWT) are key indicators providing information on plant growth status and agricultural management, and their retrieval is commonly done through radiative transfer models (RTMs) such as the PROSPECT model. However, the PROSPECT model is frequently hampered by the ill-posed problem as a consequence of measurement and model uncertainties. Here, we propose a wavelength selection method to improve the inversion of EWT and LMA by integrating PROSPECT with a machine learning algorithm (Gaussian process regression (GPR); PROSPECT-GPR for short). The GPR model conducted sorting of wavelengths and the PROSPECT-D was used to determine the optimal number of characteristic wavelengths. The results demonstrated that the estimation of EWT (R2 = 0.80; RMSE = 0.0021) and LMA (R2 = 0.71; RMSE = 0.0021) using the proposed wavelengths and PROSPECT inversion all exhibited superior accuracy in comparison with those from previous studies. The efficacy of PROSPECT-GPR in exploring the spectral linkage among vegetation traits was demonstrated by selecting wavelengths associated with leaf structure parameter N and EWT (1368 nm) that turn out to contribute to the estimation of LMA. The findings lay a strong foundation for understanding the spectral linkage among vegetation traits, and the proposed wavelength selection method provides valuable insights for selecting informative spectral wavelengths for RTMs inversion and designing future remote sensors.

Improving the Selection of Vegetation Index Characteristic Wavelengths by Using the PROSPECT Model for Leaf Water Content Estimation

Equivalent water thickness (EWT) is a major indicator for indirect monitoring of leaf water content in remote sensing. Many vegetation indices (VIs) have been proposed to estimate EWT based on passive or active reflectance spectra. However, the selection of the characteristics wavelengths of VIs is mainly based on statistical analysis for specific vegetation species. In this study, a characteristic wavelength selection algorithm based on the PROSPECT-5 model was proposed to obtain characteristic wavelengths of leaf biochemical parameters (leaf structure parameter (N), chlorophyll a + b content (Cab), carotenoid content (Car), EWT, and dry matter content (LMA)). The effect of combined characteristic wavelengths of EWT and different biochemical parameters on the accuracy of EWT estimation is discussed. Results demonstrate that the characteristic wavelengths of leaf structure parameter N exhibited the greatest influence on EWT estimation. Then, two optimal characteristics wavelengths (1089 and 1398 nm) are selected to build a new ratio VI (nRVI = R1089/R1398) for EWT estimation. Subsequently, the performance of the built nRVI and four optimal published VIs for EWT estimation are discussed by using two simulation datasets and three in situ datasets. Results demonstrated that the built nRVI exhibited better performance (R2 = 0.9284, 0.8938, 0.7766, and RMSE = 0.0013 cm, 0.0022 cm, 0.0030 cm for ANGERS, Leaf Optical Properties Experiment (LOPEX), and JR datasets, respectively.) than that the published VIs for EWT estimation. It is demonstrated that the built nRVI based on the characteristic wavelengths selected using the physical model exhibits desirable universality and stability in EWT estimation.

Improving characteristic band selection in leaf biochemical property estimation considering interrelations among biochemical parameters based on the PROSPECT-D model.

At present, many studies have mainly focused on analyzing the sensitivity and correlation to select characteristic bands. However, the interrelations between biochemical parameters were ignored, which may significantly influence the accuracy of biochemical concentration retrieval. The study aims to propose a new band selection method and to focus on the improving magnitude of characteristic band combination in leaf trait estimation when taking interrelations among different traits into consideration. Thus, in this study, firstly a ranking- and searching-based method considering the sensitivity and correlation between different wavelengths, which can enhance the reliability of spectral band selection, was proposed to select a subset of characteristic bands for leaf structure index and five leaf biochemical parameters (including chlorophyll (Chl), carotenoid (Car), leaf dry matter per area (LMA), equivalent water thickness (EWT), and anthocyanin (Anth)) based on the PROSPECT-D model. These characteristic bands were then validated based on a physical model for retrieving five biochemical properties using one synthetic dataset and six experimental datasets on leaf-level spectra. Secondly, and more innovatively, to explore interrelations among different biochemical parameters, trait-trait band combinations were adopted to retrieve and analyze how the five biochemical participants above affected each other. The results demonstrated that the combination of LMA (809 and 2278 nm), EWT (1386, 1414, and 1894 nm) is more beneficial in LMA and EWT estimation than respective retrieval: LMA-EWT band combination retrieval improves R2 by 0.5782 and 0.1824 in two datasets, respectively, compared with solely LMA characteristic bands retrieval. What's more, the accuracy of Chl, EWT, Car, and Anth estimation can be also improved when considering interrelations between biochemical parameters. The experimental results show that the ranking- and searching-based method is an effective and efficient way to select a set of spectral bands related to the foliar information about plant traits, and trait-trait combinations, which focus on exploring latent interrelations between leaf traits, are useful in furthering improve retrieval accuracy. This research will provide notably advanced insight into identifying the spectral responses of biochemical traits in foliage and canopies.

Data-Driven Methods for the Estimation of Leaf Water and Dry Matter Content: Performances, Potential and Limitations.

Leaf equivalent water thickness (EWT) and dry matter content (expressed as leaf mass per area (LMA)) are two critical traits for vegetation function monitoring, crop yield estimation, and precise agriculture management. Data-driven methods are widely used for remote sensing of leaf EWT and LMA because of their simplicity, satisfactory accuracy, and computation efficiency, such as the vegetation indices (VI)-based and machine learning (ML)-based methods. However, most of the data-driven methods are utilized at the canopy level, comparison of the performances of the data-driven methods at the leaf level has not been well documented. Moreover, the ML-based data-driven methods generally adopt leaf optical properties directly as their inputs, which may subsequently decrease their ability in remote sensing of leaf biochemical constituents. Performances of the ML-based methods cooperating with VI are rarely evaluated. Using the independent LOPEX and ANGERS datasets, we compared the performances of three data-driven methods: VI-based, ML-reflectance-based, and ML-VI-based methods, for the estimation of leaf EWT and LMA. Three sampling strategies were also utilized for evaluation of the generalization of these data-driven methods. Our results evidenced that ML-VI-based methods were the most accurate among these data-driven methods. Compared to the ML-reflectance-based and VI-based methods, the ML-VI-based model with support vector regression overall reduced errors by 5.7% (41.5%) and 1.8% (12.4%) for the estimation of leaf EWT (LMA), respectively. The ML-VI-based model inherits advantages of vegetation indices and ML techniques, which made it sensitive to changes of leaf biochemical constituents and capable of solving nonlinear tasks. It is thus recommended for the estimation of EWT and LMA at the leaf level. Moreover, its performance can further be enhanced by improving its generalization ability, such as adopting techniques on the selection of better wavelengths and definition of new vegetation indices. These results thus provided a prior knowledge of the data-driven methods and can be helpful for future studies on the remote sensing of leaf biochemical constituents.

DUAL-WAVELENGTH TERRESTRIAL LASER SCANNING AS A CALIBRATION TOOL FOR SATELLITE ESTIMATION OF FOREST CANOPY MOISTURE CONTENT

Abstract. Satellites can estimate forest canopy moisture content at the landscape level, and thus have been widely utilized in forest health monitoring. However, the calibration and validation of the estimation models can be challenging. Collecting a sufficient number of leaf samples from the canopy top layers in sampling plots that match the pixel size of the sensor is needed, which is a cost, time and effort consuming process. Dual-wavelength Terrestrial Laser Scanning (TLS) has been successfully used in estimating canopy moisture content of individual trees in three dimensions (3D) in several recent studies. Such 3D estimates, if produced at the plot level, can be used in the calibration and validation of satellite forest canopy moisture content estimation models. In this study, forest canopy moisture content, quantified as the leaf Equivalent Water Thickness (EWT), was estimated in 3D at the plot level in a mixed-species deciduous broadleaf forest plot using dual-wavelength TLS intensity data (808 nm near infrared and 1550 nm shortwave infrared wavelengths). The relative error in the EWT estimation was 6%, and the EWT point cloud revealed vertical heterogeneity in the EWT distribution. EWT was 37% higher in the canopy top layers than in the canopy bottom layers. The results obtained in this study showed that dual-wavelength TLS has the potential to be used in operational landscape-scale EWT estimation, and can be a useful tool for the calibration and validation of satellite EWT estimation models.

Three dimensional mapping of forest canopy equivalent water thickness using dual-wavelength terrestrial laser scanning

Globally, forests are being subjected to numerous threats, including climate change, wildfires, and insect and disease outbreaks, among others. Satellite optical remote sensing data have been widely utilized in early detection of tree and forest stress by estimating water status metrics such as the leaf Equivalent Water Thickness (EWT). This estimate, however, is affected by soil characteristics and understory vegetation and often ignores the effects of the fine-scale heterogeneity of canopy structure and leaf water content. Such effects can be better understood by studying the EWT distribution in three dimensions. In this study, Terrestrial Laser Scanning (TLS) intensity data from the commercially-available Leica P20 and P40 instruments (808 nm and 1550 nm respectively) were combined in a Normalized Difference Index (NDI). NDI was used to map EWT of 12 trees in three dimensions from floor to canopy in a mixed broadleaf forest plot (Wytham Woods, UK). The average error in EWT estimates across three species was less than 8%. The three dimensional point clouds revealed that, in this snapshot, EWT changes vertically, usually increasing towards canopy top. The proposed method has the potential to provide predawn EWT measurements, is independent of solar illumination, and can lead to a better understanding of the factors affecting satellite estimation of EWT.

Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning

Leaf mass per area (LMA) and leaf equivalent water thickness (EWT) are key leaf functional traits providing information for many applications including ecosystem functioning modeling and fire risk management. In this paper, we investigate two common conclusions generally made for LMA and EWT estimation based on leaf optical properties in the near-infrared (NIR) and shortwave infrared (SWIR) domains: (1) physically-based approaches estimate EWT accurately and LMA poorly, while (2) statistically-based and machine learning (ML) methods provide accurate estimates of both LMA and EWT.Using six experimental datasets including broadleaf species samples of >150 species collected over tropical, temperate and boreal ecosystems, we compared the performances of a physically-based method (PROSPECT model inversion) and a ML algorithm (support vector machine regression, SVM) to infer EWT and LMA based on leaf reflectance and transmittance. We assessed several merit functions to invert PROSPECT based on iterative optimization and investigated the spectral domain to be used for optimal estimation of LMA and EWT. We also tested several strategies to select the training samples used by the SVM, in order to investigate the generalization ability of the derived regression models.We evidenced that using spectral information from 1700 to 2400 nm leads to strong improvement in the estimation of EWT and LMA when performing a PROSPECT inversion, decreasing the LMA and EWT estimation errors by 55% and 33%, respectively.The comparison of various sampling strategies for the training set used with SVM suggests that regression models show limited generalization ability, particularly when the regression model is applied on data fully independent from the training set. Finally, our results demonstrate that, when using an appropriate spectral domain, the PROSPECT inversion outperforms SVM trained with experimental data for the estimation of EWT and LMA. Thus we recommend that estimation of LMA and EWT based on leaf optical properties should be physically-based using inversion of reflectance and transmittance measurements on the 1700 to 2400 nm spectral range.

Using VNIR and SWIR field imaging spectroscopy for drought stress monitoring of beech seedlings

Drought stress is expected to become a recurrent problem for central European forests due to regional climate change. In order to study the effects on one of the most common tree species in Germany, the European beech (Fagus sylvatica), young potted beech trees were exposed to drought stress in a controlled experiment and their reaction was observed using visible/near-infrared (VNIR) and shortwave infrared (SWIR) field imaging spectroscopy cameras mounted on a platform. Equivalent water thickness (EWT) and leaf chlorophyll content (LCC) were measured and partial least squares (PLS) regression models were trained using these reference measurements and reflectance spectra of the trees. The models were applied to create maps of these properties with a spatial resolution in the millimetre range. These maps can be used to study the spatial distribution of EWT and LCC for single leaves or even for intra-leaf variability. Both properties can be estimated using only the VNIR sensor, but EWT estimation improves considerably by also incorporating SWIR data. LCC estimations with SWIR data alone do not work satisfactorily.

A MODIS-based perpendicular moisture index to retrieve leaf moisture content of forest canopies

Moisture dictates vegetation susceptibility to fire ignition and propagation. Various spectral indices have been proposed for the estimation of equivalent water thickness (EWT), which is defined as the mass of liquid water per unit of leaf surface. However, fire models use live fuel moisture content (LFMC) as a measure of vegetation moisture. LFMC is defined as the ratio of the mass of the liquid water in a leaf over the mass of dry matter, and traditional spectral indices are not as effective as with EWT in capturing LFMC variability. The aim of this research was to explore the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra and Aqua satellites in retrieving LFMC from top of the canopy reflectance, and to develop a new spectral index sensitive to this parameter. All the analyses were based on synthetic canopy spectra constructed by coupling the PROSPECT (leaf optical properties model) and SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer models. Simulated top of the canopy spectra were then convolved to MODIS ‘land’ channels 1–7 spectral response functions. All band pairs were evaluated to determine the subspace of MODIS measurements where the separability of points based on their value of LFMC was the highest. This led to the identification of isolines of LFMC in the plane defined by MODIS reflectance measurements in channels 2 and 5; the isolines are straight and parallel, and ordered from lower to higher values of LFMC. This observation allowed the construction of a novel spectral index that is directly related to LFMC – the perpendicular moisture index (PMI). This index measures the distance of a point in the plane spanned by reflectance measurements in MODIS channels 2 and 5 from a reference line, that of completely dry vegetation. Validation against simulated data showed that PMI exhibits a linear relationship with LFMC. When the vegetation cover is dense, the LFMC explains most of the variability in the PMI (R2 = 0.70 when LAI > 2; R2 = 0.87 when LAI > 4). When the LAI is lower, the contribution of soil background to the measured reflectance increases, and the index underestimates LFMC. The PMI was also validated against the LOPEX93 (Leaf Optical Properties Experiment 1993) data set of leaf optical and biophysical measurements, scaled to canopy reflectance with SAIL, showing acceptable results (R2 = 0.56 when LAI > 2; R2 = 0.63 when LAI > 4).

Monitoring the leaf water content and specific leaf weight of cotton (Gossypium hirsutum L.) in saline soil using leaf spectral reflectance

The objectives of the present study are to determine the relationships of equivalent water thickness (EWT), fuel moisture content (FMC) and specific leaf weight (SLW) of cotton leaves with leaf spectra reflectance, and to find sensitive spectral bands and best spectral indices to establish quantitative models for the quick and accurate estimation of EWT, FMC and SLW in cotton plants under different salinity levels. Plot experiments were conducted at different levels of salinity and cotton cultivars during three consecutive growing seasons. Time-course measurements of the leaf spectral reflectance, leaf fresh weight, leaf dry weight, leaf area, and leaf ion content of cotton were recorded under various treatments. Then, the normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the leaf spectrum were obtained within 350–2500nm, and their correlation with EWT, FMC and SLW were quantified. The results show that the EWT, FMC and SLW of cotton leaves increased with increasing soil salinity levels and that the changes in leaf spectral reflectance under varied salinity levels are highly significant, with consistent patterns across the two cultivars tested. As the soil salinity levels increased, the Na+, Cl−, and SO42− content in the cotton leaves increased, whereas K+ and Ca2+ decreased at the same growth stage. In addition, the relationships among ion content, EWT, FMC and SLW were significant (P<0.01). The sensitive spectral bands for EWT, FMC and SLW occurred mainly within the near infrared (NIR) and short-wave infrared (SWIR) ranges. The best spectral indices for estimating EWT, FMC and SLW in cotton were found to be NDSI (R1347, R2307), RSI (R2307, R1347); NDSI (R1650, R1801), RSI (R1801, R1650); NDSI (R1300, R2308), RSI (R2307, R1347) and 1650/2220nm ratio, and the regression models based on the above spectral indices were identified as the best equations for the effective estimation of EWT and SLW in cotton. After testing these derived equations, the models for EWT and SLW estimation based on NDSI and RSI yielded an R2 of over 0.73, with more satisfactory performance under different ecological conditions, but the estimated accuracies of FMC models were very low and may not be suitable for estimating vegetation water content in saline conditions from leaf-level reflectance. The high fit between the measured and estimated values indicates that the EWT and SLW models based on new spectral indices from leaf-level reflectance could be used for the indirect estimation of plant salinity status by monitoring the changes in EWT and SLW caused by soil salinity in cotton plants.

Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling

This study investigates the applicability of empirical and radiative transfer models to estimate water content at leaf and landscape level. The main goal is to evaluate and compare the accuracy of these two approaches for estimating leaf water content by means of laboratory reflectance/transmittance measurements and for mapping leaf and canopy water content by using airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired over intensive poplar plantations (Ticino, Italy). At leaf level, we tested the performance of different spectral indices to estimate leaf equivalent water thickness (EWT) and leaf gravimetric water content (GWC) by using inverse ordinary least squares (OLS) regression, and reduced major axis (RMA) regression. The analysis showed that leaf reflectance is related to changes in EWT rather than GWC, with best results obtained by using RMA regression by exploiting the spectral index related to the continuum removed area of the 1200 nm water absorption feature with an explained variance of 61% and prediction error of 6.6%. Moreover, we inverted the PROSPECT leaf radiative transfer model to estimate leaf EWT and GWC and compared the results with those obtained by means of empirical models. The inversion of this model showed that leaf EWT can be successfully estimated with no prior information with mean relative errors of 14% and determination coefficient of 0.65. Inversion of the PROSPECT model showed some difficulties in the simultaneous estimation of leaf EWT and dry matter content, which led to large errors in GWC estimation. At landscape level with MIVIS data, we tested the performance of different spectral indices to estimate canopy water per unit ground area (EWT canopy). We found a relative error of 20% using a continuum removed spectral index around 1200 nm. Furthermore, we used a model simulation to evaluate the possibility of applying empirical models based on appositely developed MIVIS double ratios to estimate mean leaf EWT at landscape level ( EWT ― ). It is shown that combined indices (double ratios) yielded significant results in estimating leaf EWT at landscape level by using MIVIS data (with errors around 2.6%), indicating their potential in reducing the effects of LAI on the recorded signal. The accuracy of the empirical estimation of EWT canopy and EWT ― was finally compared with that obtained from inversion of the PROSPECT + SAILH canopy reflectance model to evaluate the potential of both methods for practical applications. A relative error of 27% was found for EWT canopy and an overestimation of leaf EWT ― with relative errors around 19%. Results arising from this remote sensing application support the robustness of hyperspectral regression indices for estimating water content at both leaf and landscape level, with lower relative errors compared to those obtained from inversion of leaf and 1D canopy radiative transfer models.

Estimating equivalent water thickness in a conifer forest using Landsat TM and ASTER data: a comparison study

Remotely sensed data from the Landsat thematic mapper (TM) and the advanced spaceborne thermal emission and reflection radiometer (ASTER) can be used to make physically based estimates of equivalent water thickness (EWT) in a Pinus ponderosa forest ecosystem. EWT is an important parameter for studying ecosystem dynamics and fire potential and is likely to be of interest in monitoring biological responses to climate change. Near infrared (NIR) and shortwave infrared (SWIR) reflectances were simulated using the LIBERTY and GeoSAIL leaf and canopy reflectance models; the results were used to calculate a NIR/SWIR ratio and a normalized NIR/SWIR index. Index–EWT relationships were modeled and inverted for EWT derivation. Landsat TM and ASTER images were able to produce EWT estimates with average errors of ±17.3% and 19.4%, respectively. TM band 5 and ASTER band 4 contained the most valuable information among the SWIR bands for EWT estimation. Exclusion of plots with dense understory vegetation significantly reduced point scatter, especially with Landsat (r2 = 0.85, ±13% average error), indicating that this method can provide robust EWT quantification where conifer trees predominate.

Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level

Fire danger models identify fuel moisture content (FMC) of live vegetation as a critical variable, since it affects fire ignition and propagation. FMC can be calculated by dividing equivalent water thickness (EWT) by dry matter content (DM). The optical properties spectra (PROSPECT) model was inverted to estimate EWT and DM separately using the Leaf Optical Properties Experiment (LOPEX) database, based on 490 measurements of leaf optical and biochemical properties. DM estimations were poor when leaf samples were fresh (r/sup 2/ = 0.38). Results of a sensitivity analysis conducted on the spectral response of samples demonstrated that water absorption masks the effects of DM on the spectral response. This causes a poor estimation of FMC (r/sup 2/ = 0.33), even though EWT estimation was good (r/sup 2/ = 0.94). However, DM of dry samples was accurately estimated (r/sup 2/ = 0.84), since no water was present. FMC estimation in fresh leaf material improved considerably (r/sup 2/ = 0.89) by accounting for DM in fresh samples (r/sup 2/ = 0.71), when a constant species-dependent DM value is used. A similar approach was taken on a canopy level by linking the PROSPECT leaf model with the Lillesaeter infinitive reflectance canopy model using data from laboratory measurements under controlled conditions. As expected, results indicate grater difficulty to estimate DM and FMC on a canopy level, although similar trends were observed. DM estimation improved from r/sup 2/ = 0.12 to r/sup 2/ = 0.39 when considering measurements of dry samples, which when used for FMC estimation, correlations increase from r/sup 2/ = 0.62 to r/sup 2/ = 0.81. Therefore, DM can be accurately estimated only when plant material is dry, and it is a necessary measurement in order to estimate FMC accurately. DM remains stable over annual time periods although lower values are expected during the drought season. However, time variations in DM are smaller than DM variations among species. Because there is also a decrease in water content with low EWT values during phenologically dormant periods, multitemporal data could be used to estimate FMC. Further research must be applied on real canopies to confirm these results.