Frédéric Baret

PROSPECT: A Model of Leaf Optical Properties Spectra

Abstract PROSPECT is a radiative transfer model based of Allens generalized “plate model” that represents the optical properties of plant leaves from 400 nm to 2500 nm. Scattering is described by a spectral refractive index (n) and a parameter characterizing the leaf mesophyll structure (N). Absorption is modeled using pigment concentration (Ca+b), water content (Cw), and the corresponding specific spectral absorption coefficients (Ka+b and Kw). The parameters n, Ka+b, and Kw have been fitted using experimental data corresponding to a wide range of plant types and status. PROSPECT has been tested successfully on independent data sets. Its inversion allows one to reconstruct, with reasonable accuracy, leaf reflectance, and transmittance features in the 400–2500 nm range by adjusting the three input variables N, Ca+b, and Cw.

Potentials and limits of vegetation indices for LAI and APAR assessment

Abstract Most vegetation indices (VI) combine information contained in two spectral bands: red and near-infrared. These indices are established in order to minimize the effect of external factors on spectral data and to derive canopy characteristics such as leaf area index (LAI) and fraction of absorbed photosynthetic active radiation (P). The potentials and limits of different vegetation indices are discussed in this paper using the normalized difference (NDVI), perpendicular vegetation index (PVI), soil adjusted vegetation index (SAVI), and transformed soil adjusted vegetation index (TSAVI). The discussion is based on a sensitivity analysis in which the effect of canopy geometry (LAI and leaf inclination) and soil background are analyzed. The calculation is performed on data derived from the SAIL reflectance model. General semiempirical models, describing the relations between VI and LAI or P, are elaborated and used to derive the relative equivalent noise (REN) for the determination of LAI and P. The performances of VIs are discussed on the basis of the REN concept.

Optimization of soil-adjusted vegetation indices☆

The sensitivity of the normalized difference vegetation index (NDVI) to soil background and atmospheric effects has generated an increasing interest in the development of new indices, such as the soil-adjusted vegetation index (SAVI), transformed soil-adjusted vegetation index (TSAVI), atmospherically resistant vegetation index (AR VI), global environment monitoring index (GEMI), modified soil-adjusted vegetation index (MSAVI), which are less sensitive to these external influences. These indices are theoretically more reliable than NDVI, although they are not yet widely used with satellite data. This article focuses on testing and comparing the sensitivity of NDVI, SAVI, TSAVI, MSAVI and GEMI to soil background effects. Indices are simulated with the SAIL model for a large range of soil reflectances, including sand, clay, and dark peat, with additional variations induced by moisture and roughness. The general formulation of the SAVI family of indices with the form VI = (NIR - R) / (NIR + R + X) is also reexamined. The value of the parameter X is critical in the minimization of soil effects. A value of X = 0.16 is found as the optimized value. Index performances are compared by means of an analysis of variance.

Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem

Estimation of canopy biophysical variables from remote sensing data was investigated using radiative transfer model inversion. Measurement and model uncertainties make the inverse problem ill posed, inducing difficulties and inaccuracies in the search for the solution. This study focuses on the use of prior information to reduce the uncertainties associated to the estimation of canopy biophysical variables in the radiative transfer model inversion process. For this purpose, lookup table (LUT), quasi-Newton algorithm (QNT), and neural network (NNT) inversion techniques were adapted to account for prior information. Results were evaluated over simulated reflectance data sets that allow a detailed analysis of the effect of measurement and model uncertainties. Results demonstrate that the use of prior information significantly improves canopy biophysical variables estimation. LUT and QNT are sensitive to model uncertainties. Conversely, NNT techniques are generally less accurate. However, in our conditions, its accuracy is little dependent significantly on modeling or measurement error. We also observed that bias in the reflectance measurements due to miscalibration did not impact very much the accuracy of biophysical estimation.

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.

Relating soil surface moisture to reflectance

Abstract The objective of this study was to explore the relationship between soil reflectance in the solar domain (400–2500 nm) and soil moisture. Ten soils covering a large range of composition have been sampled. To decrease the large dimensionality of the data set, we reduced the number of wavebands investigated thanks to a simple stepwise linear regression. Seven wavebands were selected, which represent the whole spectral domain for the 10 soils and all moisture conditions with a root mean square error (RMSE) better than 0.002, close to the experimental uncertainties. Each soil reflectance spectrum was normalized by the corresponding reflectance spectrum observed under the driest condition. This allows to minimize effects due to soil type, as well as those of other undesirable multiplicative factors such as roughness and measurement configuration. The relationship between the normalized soil reflectance and moisture was then investigated. For all the wavelengths and all the soils, results show that for low soil moisture levels, the reflectance decreased when the moisture increased. Conversely, after a critical point, soil reflectance increased with soil moisture. For some soils, the reflectance of the wettest conditions can overpass that of the driest conditions. The position of the critical point was related to soil hydrodynamic properties. For both low and high soil moisture levels, and the seven wavelengths selected, the relative reflectance was strongly correlated with moisture. Adjustment of the relationships over individual soil types provides better soil moisture retrieval performances. It also shows that the relationships are generally nonlinear. These results are discussed with regards to the underlying physical processes, as well as for application to soil moisture estimates from reflectance measurements.

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.

Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.

The robustness of canopy gap fraction estimates from red and near-infrared reflectances : a comparison of approaches

Abstract The estimation of canopy gap fraction [Po(0)] from red and near-infrared reflectance is investigated. Several approaches are compared, using classical vegetation indices (VI) and backpropagation neural networks. The parameters of the VI-Po(0) relationships and the synaptic weights and biases of two-layer neural networks were successfully adjusted on an experimental data set and on data derived from radiative transfer model simulations. Three experimental data sets were acquired over sugar beet canopies. They expressed a large range of canopy architecture and soil background reflectance. Two of them that correspond to similar experimental procedures were merged together and then randomly split into two subsets: one for fitting the parameters of the VI-Po(0) relationships or to train the neural network, and the other for the evaluation of the predictive performances. The third experiment is used as an additional independent data set to test the robustness of the approaches. The model-simulated data set was generated using the SAIL and PROSPECT radiative transfer models, with input parameter values that were chosen to have similar distributions as observed over sugar beet canopies. As for the experimental data set, the model-simulated data set was split into a training (calibration) and a test (validation) data set. Results show that the gap fraction can be accurately estimated from the red and near-infrared reflectance without any external information except maybe the crop type (here sugar beet) and the soil line characteristics required for some of the vegetation indices. The best predictive performances were observed for the SAVI-like vegetation indices (SAVI, TSAVI, MSAVI) and the poorest for the NDVI, PVI, and GEMI having intermediate although satisfactory results. Neural networks trained on the simulated data set appeared to be the most robust approach. It allows us to implicitly incorporate our knowledge about the physics of the radiative transfer in the interpretation of remote sensing data.

Hyperspectral remote sensing of foliar nitrogen content

A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact—it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423–855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710–790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.