S. Jacquemoud

Detecting Vegetation Leaf Water Content Using Reflectance in the Optical Domain.

This paper outlines the first part of a series of research studies to investigate the potential and approaches for using optical remote sensing to assess vegetation water content. It first analyzes why most methods used as approximations of vegetation water content (such as vegetation stress indices, estimation of degree of curing and chlorophyll content) are not suitable for retrieving water content at leaf level. It then documents the physical basis supporting the use of remote sensing to directly detect vegetation water content in terms of Equivalent Water Thickness (EWT) at leaf level. Using laboratory measurements, the radiative transfer model PROSPECT and a sensitivity analysis, it shows that shortwave infrared (SWIR) is sensitive to EWT but cannot be used alone to retrieve EWT because two other leaf parameters (internal structure and dry matter) also influence leaf reflectance in the SWIR. A combination of SWIR and NIR (only influenced by these two parameters) is necessary to retrieve EWT at leaf level. These results set the basis towards establishing operational techniques for the retrieval of EWT at top-of-canopy and top-of-atmospheric levels.

Estimating Leaf Biochemistry Using the PROSPECT Leaf Optical Properties Model

Abstract The biophysical, biochemical, and optical properties of 63 fresh leaves and 58 dry leaves were measured to investigate the potential of remote sensing to estimate the leaf biochemistry from space. Almost 2000 hemispherical reflectance and transmittance spectra were acquired from 400 nm to 2500 nm using a laboratory spectrophotometer. The amount of chlorophyll, water, protein, cellulose, hemicellulose, lignin, and starch was determined on these leaves using standard wet chemistry techniques. These experimental data were used to improve the PROSPECT model, a simple but effective radiative transfer model that calculates the leaf optical properties with a limited number of input parameters: a structure parameter and the leaf biochemistry. The new model construction mainly consisted in providing specific absorption coefficients for the biochemical constituents; the comparison with absorption spectra of pure materials derived from the literature showed good agreement. In the inversion, however, it was necessary to group some leaf components in order to estimate leaf biochemistry with reasonable accuracy. Predictive power varied with the chemistry variable, wavelengths used in analysis, and whether leaves were fresh or dry. r2 ranged from 0.39 to 0.88 for predictions on dry leaves; on fresh leaves, water and chlorophyll had high r2 values, 0.95 and 0.68 respectively, carbon based compounds reasonable r2, from 0.50 to 0.88, while the estimation of protein is still at issue.

Comparison of Four Radiative Transfer Models to Simulate Plant Canopies Reflectance: Direct and Inverse Mode

Four one-dimensional radiative transfer models are compared in direct and inverse modes. These models are combinations of the PROSPECT leaf optical properties model and the SAIL (Scattering by Arbitrarily Inclined Leaves), IAPI, KUUSK, and NADI (New Advanced Discrete Model) canopy reflectance models. To evaluate their ability to estimate canopy biophysical parameters, inversions were first performed on synthetic reflectance spectra (10 wavelengths in the visible and near-infrared). The simulated spectral and directional reflectances showed good agreement among the four models. A 1997 airborne experiment in the United States was used to test their performance on real data. This experiment gathered a unique data set composed primarily of 200 reflectance spectra acquired over corn (Zea mays L.) and soybean (Glycine max) fields, and the corresponding ground truth (chlorophyll a+b content and leaf area index). Only the first three models, which ran fast enough to allow the processing of a large data set, were actually inverted by iterative optimization techniques. Inversions were conducted in successive stages where the number of retrieved parameters was reduced. No significant difference can be observed between the three models. Globally, the leaf mesophyll structure parameter and leaf dry matter content couldnt be estimated. The chlorophyll content, the leaf area index, and the mean leaf inclination angle yielded better results, although the latter wasnt validated due to missing ground data. Assuming that model inversion by iterative optimization techniques is a promising method to extract information on plant canopies, the SAIL and KUUSK models, which perform well in terms of accuracy and running time, proved to be good candidates for remote sensing application in ecology or agriculture (precision farming).

Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors

The PROSPECT leaf optical properties and SAIL canopy reflectance models were coupled and inverted using a set of 96 AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) equivalent spectra gathered in afield experiment on sugar beet plots expressing a large range in leaf area index, chlorophyll concentration, and soil color. In a first attempt, the model accurately reproduced the spectral reflectance of vegetation, using six variables: chlorophyll a + b concentration (Cab), water depth (Cw), leaf mesophyll structure parameter (N), leaf area index (LAI), mean leaf inclination angle (θl), and hot-spot size parameter (s). The four structural parameters (N, LAI, τl, and s) were poorly estimated, indicating instability in the inversion process; however, the two biochemical parameters (Cab and Cw) were evaluated reasonably well, except over very bright soils. In a second attempt, three of the four structure variables were assigned a fixed value corresponding to the average observed in the experiment. Inversions performed to retrieve the remaining structure variable, leaf area index, and the two biochemical variables showed large improvements in the accuracy of LAI, but slightly poorer performance for Cab and Cw. Here again, poor results were obtained with very bright soils. The compensations observed between the LAI and Cab or Cw led us to evaluate the performance of two more-synthetic variables, canopy chlorophyll content or canopy water content, for these the inversions produced reasonable estimates. The application of this approach to Landsat TM (Thematic Mapper) data provided similar results, both for the spectrum reconstruction capability and for the retrieval of canopy biophysical characteristics.

Leaf optical properties with explicit description of its biochemical composition: Direct and inverse problems

Abstract This study presents a methodology to estimate the leaf biochemical compounds specific absorption coefficients and to use them to predict leaf biochemistry. A wide range of leaves was collected including variations in species and leaf status. All the leaves were dried out. The biochemical composition was measured using classical wet chemistry techniques to determine lignin, cellulose, hemicellulose, starch, and protein contents. Concurrently, leaf reflectance and transmittance were measured with a high spectral resolution spectrophotometer in the 800–2500 nm range with approximately 1 nm spectral resolution and sampling interval. In addition, infinite reflectance achieved by stacking leaves was also measured. The PROSPECT leaf optical properties model was first inverted over a selection of wavebands in the 800–2400 nm domain to provide estimates of the scattering characteristics using leaf reflectance, transmittance, and infinite reflectance data. Then, the model was inverted again over all the wavelengths to estimate the global absorption coefficient, using the previously estimated scattering properties. The global absorption coefficient was eventually explained using the measured biochemical composition by fitting the corresponding specific absorption coefficients after substraction of the measured contribution of the residual structural water absorption. Results show that the derived specific absorption coefficients are quite robustly estimated. Further, they are in good agreement with known absorption features of each biochemical compound. The average contribution of each biochemical compound to leaf absorption feature is also evaluated. Sugar, cellulose, and hemicellulose are the main compounds that contribute to absorption. Results demonstrate the possibility of modeling leaf optical properties of dry leaves with explicit description of leaf biochemistry. Estimates of the detailed biochemical composition obtained by model inversion over the 1300–2400 nm spectral domain show poor predictive performances. In particular, the protein content is very poorly retrieved. The retrieval performances of several combinations of the biochemical compounds are investigated. Results show that the total amount of dry matter per unit leaf area is the only variable to be accurately retrieved. Possible improvements of these results are discussed.

Estimating Canopy Water Content of Chaparral Shrubs Using Optical Methods

Predicting fire hazard in fire-prone ecosystems in urbanized landscapes, such as the chaparral systems of California, is critical to risk assessment and mitigation. Understanding the dynamics of fire spread, topography and vegetation condition are necessary to increase the accuracy of fire risk assessment. One vital input to fire models is spatial and temporal estimates of canopy water content. However, timely estimates of such a dynamic ecosystem property cannot be provided for more than periodic point samples using ground based methods. This study examined the potential of three quasiphysical methods for estimating water content using remotely sensed Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data of chaparral systems in the Santa Monica Mountains, California. We examined estimates of water content at the leaf, canopy, and image level and compared them to each other and to ground-based estimates of plant water content. These methods predicted water content (with R2 between 0.62 and 0.95) but differ in their ease of use and the need for ancillary data inputs. The prospect for developing regional estimates for canopy water content at high spatial resolution (20 m) from high resolution optical sensors appears promising.

Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data

Abstract This study examined the use of stepwise multiple linear regression to quantify leaf carbon, nitrogen, lignin, cellulose, dry weight, and water compositions from leaf level reflectance ( R ). Two fresh leaf and one dry leaf datasets containing a broad range of native and cultivated plant species were examined using unconstrained stepwise multiple linear regression and constrained regression with wavelengths reported from other leaf level studies and wavelengths derived from chemical spectroscopy. Although stepwise multiple linear regression explained large amounts of the variation in the chemical data, the bands selected were not related to known absorption bands, varied among datasets and expression bases for the chemical [concentration (g g −1 ) or content (g m −2 )], did not correspond to bands selected in other studies, and were sensitive to the samples entered into the regression. Stepwise multiple regression using artificially constructed datasets that randomized the association between nitrogen concentration and reflectance spectra produced coefficients of determination ( R 2 s) between 0.41 and 0.82 for first and second derivative log(1/ R ) spectra. The R 2 s for correctly-paired nitrogen data and first and second derivative log (1/ R ) only exceeded the average randomized R 2 s by 0.02–0.42. Replication of this randomization experiment on a larger dry ground leaf data set from the Harvard Forest showed the same trends but lower R 2 s. All of these results suggest caution in the use of stepwise multiple linear regression on fresh leaf reflectance spectra. Band selection does not appear to be based upon the absorption characteristics of the chemical being examined.

Radiation transfer model intercomparison (RAMI) exercise

The community involved in modeling radiation transfer over terrestrial surfaces designed and implemented the first phase of a radiation transfer model intercomparison (RAMI) exercise. This paper discusses the rationale and motivation for this endeavor, presents the intercomparison protocol as well as the evaluation procedures, and describes the principal results. Participants were asked to simulate the transfer of radiation for a variety of precisely defined terrestrial environments and illumination conditions. These were abstractions of typical terrestrial systems and included both homogeneous and heterogeneous scenes. The differences between the results generated by eight different models, including both one-dimensional and three-dimensional approaches, were then documented and analyzed. RAMI proposed a protocol to quantitatively assess the consequences of the model discrepancies with respect to application, such as those motivating the development of physically based inversion procedures. This first phase of model intercomparison has already proved useful in assessing the ability of the modeling community to generate similar radiation fields despite the large panoply of models that were tested. A detailed analysis of the results also permitted to identify apparent “outliers” and their main deficiencies. Future undertakings in this intercomparison framework must be oriented toward an expansion of RAMI into other and more complex geophysical systems as well as the focusing on actual inverse problems.

Three-dimensional radiation transfer modeling in a dicotyledon leaf

The propagation of light in a typical dicotyledon leaf is investigated with a new Monte Carlo ray-tracing model. The three-dimensional internal cellular structure of the various leaf tissues, including the epidermis, the palisade parenchyma, and the spongy mesophyll, is explicitly described. Cells of different tissues are assigned appropriate morphologies and contain realistic amounts of water and chlorophyll. Each cell constituent is characterized by an index of refraction and an absorption coefficient. The objective of this study is to investigate how the internal three-dimensional structure of the tissues and the optical properties of cell constituents control the reflectance and transmittance of the leaf. Model results compare favorably with laboratory observations. The influence of the roughness of the epidermis on the reflection and absorption of light is investigated, and simulation results confirm that convex cells in the epidermis focus light on the palisade parenchyma and increase the absorption of radiation.

Investigation of leaf biochemistry by statistics

Abstract The biochemical concentration (total protein, cellulose, lignin, and starch) of 73 plant leaves has been related to their optical properties through statistical relationships. Both fresh and dry plant material, leaves and needles, were used in this study. Stepwise multiple regression analyses have been performed on reflectance, transmittance, and absorptance values (individual leaves) as well as on reflectance values of optically thick samples (stacked leaves + needles), on measured values and on transformations of them such as the first derivative or the logarithm of the reciprocal of the reflectance. They underscored good prediction performances for protein, cellulose, and lignin with high squared multiple correlation coefficients (r2) values. Starch, whose concentration in the leaf was smaller compared to the other components, was estimated with less accuracy. As expected, dry material and optically thick samples provided respectively stronger correlations than fresh material and individual leaves.