Leaf Equivalent Water Thickness Research Articles

Developing Vegetation Health Index from biophysical variables derived<br>using MODIS satellite data in the Trans-Gangetic plains of India

Basic requirement for crop growth monitoring is the efficient tool for retrieving different plant biophysicalvariables and its accuracy or reliability. In the present study, statistical approach is used to retrieve the threebiophysical parameters i.e. leaf area index (LAI), Chlorophyll content (Cab), and leaf equivalent waterthickness (Cw). Normalized Difference Vegetation Index (NDVI), Red Index (RI) and Normalized DifferenceWater Index (NDWI) were used to retrieve LAI, Cab and Cw respectively. Study area was Trans-Gangetic plaincomprising of whole Punjab, Haryana, Chandigarh, Delhi and some parts of Rajasthan. Field measurementswere done at 190 locations. Spectral measurements (wheat crop and soil) with ground held spectroradiometer(FieldSpec3), LAI using Canopy Analyzer (LICOR-2000) and leaf samples were collected for further analysisin laboratory. Satellite data used was MODIS Surface Reflectance Product (MOD 09). Vegetation Health Index(VHI) was developed using the retrieved LAI, Cab and Cw and study area was categorized into four groupsbased on VHI.

Estimating canopy water content from spectroscopy

Canopy water content is a dynamic quantity that depends on the balance between water losses from transpiration and water uptake from the soil. Absorption of short-wave radiation by water is determined by various frequencies that match overtones of fundamental bending and stretching molecular transitions. Leaf water potential and relative water content are important variables for determining water deficit and drought effects; however, these variables may only be indirectly estimated from leaf and canopy spectral reflectance. We review the state of understanding in remote sensing measurements of leaf equivalent water thickness and canopy water content. Indexes using different combinations of spectral bands estimate leaf and canopy water contents, albeit sometimes with large errors caused by differences in canopy structure and soil surface reflectance. Inversion of leaf and canopy radiative transfer models, such as PROSPECT and SAIL, or learning algorithms, like artificial neural networks and genetic algorit...

Measuring cotton water status using water-related vegetation indices at leaf and canopy levels

Drought is one of the major environmental threats in the world. In recent years, the damage from droughts to the environment and economies of some countries has been extensive, and drought monitoring has caused widespread concerns. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties, and it offers an opportunity for the quantitative assessment of drought indicators such as the vegetation water content at different levels. In this study, sites of cotton field in Shihezi, Xinjiang, Northwest China were sampled. Four classical water content parameters, namely the leaf equivalent water thickness (EWTleaf), the fuel moisture content (FMC), the canopy equivalent water thickness (EWTcanopy) and vegetation water content (VWC) were evaluated against seven widely-used water-related vegetation indices, namely the NDII (normalized difference infrared index), NDWI2130 (normalized difference water index), NDVI (normalized difference vegetation index), MSI (moisture stress index), SRWI (simple ratio water index), NDWI1240 (normalized difference water index) and WI (water index), respectively. The results proved that the relationships between the water-related vegetation indices and EWTleaf were much better than that with FMC, and the relationships between vegetation indices and EWTcanopy were better than that with VWC. Furthermore, comparing the significance of all seven water-related vegetation in- dices, WI and NDII proved to be the best candidates for EWT detecting at leaf and canopy levels, with R 2 of 0.262 and 0.306 for EWTleaf-WI and EWTcanopy-NDII linear models, respectively. Besides, the prediction power of linear regression technique (LR) and artificial neural network (ANN) were compared using calibration and validation dataset, respectively. The results indicated that the performance of ANN as a predictive tool for water status meas- uring was as good as LR. The study should further our understanding of the relationships between water-related vegetation indices and water parameters.

Detection of leaf and canopy EWT by calculating REWT from reflectance spectra

The spectral characteristics of and the interaction between leaves and light were analysed based on the optical absorption coefficients of foliar water and biochemical components. The equations for calculating the radiative-equivalent water thickness (REWT) of leaves and canopy were presented based on the difference in reflectance at 945 and 975 nm. Because of the direct reflection on leaf surface and the leaf internal scattering, the REWT derived from the Beer–Lambert principle was different from the leaf or canopy equivalent water thickness (EWT). Two independent datasets at canopy or leaf scales were designed to calibrate and validate the relationships between EWT and REWT. The results show that (1) the leaf or canopy REWT can be calculated from the reflectance difference between 945 and 975 nm; (2) the leaf REWT was 3.3 times larger than the EWT with a significant determination coefficient (R 2) of 0.80 for our dataset and 0.86 for the Leaf Optical Properties Experiment (LOPEX'93) dataset; (3) the canopy REWT was 1.4 times larger than the EWT with a significant R 2 of 0.56 for the winter wheat canopy spectral dataset in 2002, and 0.61 for the 2004 dataset. Therefore, the leaf or canopy EWT can be detected by calculating REWT from the difference in reflectance at 945 and 975 nm. Furthermore, because the relationship between REWT and EWT reflected the interaction of light with leaves or canopy, the multiple scattering optical pathlength in the near-infrared (NIR) bands can also be calculated by the ratio of REWT to EWT.

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 970nm 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 970nm 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.PROSAIL simulations (using coupled SAIL/PROSPECT-5 models) showed a linear relationship between the first derivative over the 1015–1050nm spectral interval and CWC (R2=0.97). For 8 plots at the floodplain site the spectral derivative over the 1015–1050nm interval obtained with an ASD FieldSpec spectroradiometer yielded an R2 of 0.51 with CWC. For 40 plots at the fen meadow ASD FieldSpec spectral measurements yielded an R2 of 0.68 for the derivative over the 1015–1050nm interval with CWC. Consistency of the results confirmed the potential of using simulation results for calibrating the relationship between this first derivative and CWC.

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 live fuel moisture content from remotely sensed reflectance

Fuel moisture content (FMC) is used in forest fire danger models to characterise the moisture status of the foliage. FMC expresses the amount of water in a leaf relative to the amount of dry matter and differs from measures of leaf water content which express the amount of water in a leaf relative to its area. FMC is related to both leaf water content and leaf dry matter content, and the relationships between FMC and remotely sensed reflectance will therefore be affected by variation in both leaf biophysical properties. This paper uses spectral reflectance data from the Leaf Optical Properties EXperiment (LOPEX) and modelled data from the Prospect leaf reflectance model to examine the relationships between FMC, leaf equivalent water thickness (EWT) and a range of spectral vegetation indices (VI) designed to estimate leaf and canopy water content. Significant correlations were found between FMC and all of the selected vegetation indices for both modelled and measured data, but statistically stronger relationships were found with leaf EWT; overall, the water index (WI) was found to be most strongly correlated with FMC. The accuracy of FMC estimation was very low when the global range of FMC was examined, but for a restricted range of 0–100%, FMC was estimated with a root-mean-square error (RMSE) of 15% in the model simulations and 51% with the measured data. The paper shows that the estimation of live FMC from remotely sensed vegetation indices is likely to be problematic when there is variability in both leaf water content and leaf dry matter content in the target leaves. Estimating FMC from remotely sensed data at the canopy level is likely to be further complicated by spatial and temporal variations in leaf area index (LAI). Further research is required to assess the potential of canopy reflectance model inversion to estimate live fuel moisture content where a priori information on vegetation properties may be used to constrain the inversion process.

Water content estimation in vegetation with MODIS reflectance data and model inversion methods

Statistical and radiative-transfer physically based studies have previously demonstrated the relationship between leaf water content and leaf-level reflectance in the near-infrared spectral region. The successful scaling up of such methods to the canopy level requires modeling the effect of canopy structure and viewing geometry on reflectance bands and optical indices used for estimation of water content, such as normalized difference water index (NDWI), simple ratio water index (SRWI) and plant water index (PWI). This study conducts a radiative transfer simulation, linking leaf and canopy models, to study the effects of leaf structure, dry matter content, leaf area index (LAI), and the viewing geometry, on the estimation of leaf equivalent water thickness from canopy-level reflectance. The applicability of radiative transfer model inversion methods to MODIS is studied, investigating its spectral capability for water content estimation. A modeling study is conducted, simulating leaf and canopy MODIS-equivalent synthetic spectra with random input variables to test different inversion assumptions. A field sampling campaign to assess the investigated simulation methods was undertaken for analysis of leaf water content from leaf samples in 10 study sites of chaparral vegetation in California, USA, between March and September 2000. MODIS reflectance data were processed from the same period for equivalent water thickness estimation by model inversion linking the PROSPECT leaf model and SAILH canopy reflectance model. MODIS reflectance data, viewing geometry values, and LAI were used as inputs in the model inversion for estimation of leaf equivalent water thickness, dry matter, and leaf structure. Results showed good correlation between the time series of MODIS-estimated equivalent water thickness and ground measured leaf fuel moisture (LFM) content ( r 2=0.7), demonstrating that these inversion methods could potentially be used for global monitoring of leaf water content in vegetation.

Airborne remote sensing of canopy water thickness scaled from leaf spectrometer data

The reflectance ratio of the middle-infrared band (MIR) to the near-infrared band (NIR) is linearly related to the log(10) equivalent water thickness (EWT) for single leaves of different morphologies, whereas the MIR/NIR radiance ratio is correlated with the leaf area index (LAI). The hypothesis that the MIR/NIR ratio measures canopy EWT was tested by reanalyzing airborne Thematic Mapper Simulator and field data obtained across a large gradient of LAI in western Oregon, U.S.A. The measured airborne MIR/NIR reflectance ratios for canopies were not significantly different from the predicted ratios using leaf data for canopy EWT, except for two desert woodland sites. The interpretation of the MIR/NIR ratio is scale-dependent, because leaf EWT is determined primarily by variations in LAI.