Jean-Philippe Gastellu-Etchegorry

Directional Viewing Effects on Satellite Land Surface Temperature Products Over Sparse Vegetation Canopies—A Multisensor Analysis

Thermal infrared satellite observations of the Earths surface are key components in estimating the surface skin temperature over global land areas. However, depending on sun illumination and viewing directional configurations, satellites measure different surface radiometric temperatures, particularly over sparsely vegetated regions where the radiometric contributions from soil and vegetation vary with the sun and viewing geometry. Over an oak tree woodland located near the town of Evora, Portugal, we compare different satellite-based land surface temperature (LST) products from the Moderate Resolution Imaging Spectroradiometer on board the Terra and Aqua polar-orbiting satellites and from the Spinning Enhanced Visible and Infrared Imager on board the geostationary Meteosat satellite with ground-based LST. The observed differences between LSTs derived from polar and geostationary satellites are up to 12 K due to directional effects. In this letter, we develop a methodology based on a radiative transfer model and dedicated field radiometric measurements to interpret and validate directional remote sensing measurements. The methodology is used to estimate the quantitative uncertainty in LST products derived from polar-orbiting satellites over a sparse vegetation canopy.

DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence

To better understand the life-essential cycles and processes of our planet and to further develop remote sensing (RS) technology, there is an increasing need for models that simulate the radiative budget (RB) and RS acquisitions of urban and natural landscapes using physical approaches and considering the three-dimensional (3-D) architecture of Earth surfaces. Discrete anisotropic radiative transfer (DART) is one of the most comprehensive physically based 3-D models of Earth-atmosphere radiative transfer, covering the spectral domain from ultraviolet to thermal infrared wavelengths. It simulates the optical 3-D RB and optical signals of proximal, aerial, and satellite imaging spectrometers and laser scanners, for any urban and/or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. In this paper, we briefly introduce DART theory and present recent advances in simulated sensors (LiDAR and cameras with finite field of view) and modeling mechanisms (atmosphere, specular reflectance with polarization and chlorophyll fluorescence). A case study demonstrating a novel application of DART to investigate urban landscapes is also presented.

Biomass prediction in tropical forests: the canopy grain approach

The challenging task of biomass prediction in dense and heterogeneous tropical forest requires a multi-parameter and multi-scale characterization of forest canopies. Completely different forest structures may indeed present similar above ground biomass (AGB) values. This is probably one of the reasons explaining why tropical AGB still resists accurate mapping through remote sensing techniques. There is a clear need to combine optical and radar remote sensing to benefit from their complementary responses to forest characteristics. Radar and Lidar signals are rightly considered to provide adequate measurements of forest structure because of their capability of penetrating and interacting with all the vegetation strata. However, signal saturation at the lowest radar frequencies is observed at the midlevel of biomass range in tropical forests (Mougin et al. 1999; Imhoff, 1995). Polarimetric Interferometric (PolInsar) data could improve the inversion algorithm by injecting forest interferometric height into the inversion of P-band HV polarization signal. Within this framework, the TROPISAR mission, supported by the Centre National d’Etudes Spatiales (CNES) for the preparation of the European Space Agency (ESA) BIOMASS program is illustrative of both the importance of interdisciplinary research associating forest ecologists and physicists and the importance of combined measurements of forest properties. Lidar data is a useful technique to characterize the vertical profile of the vegetation cover, (e.g. Zhao et al. 2009) which in combination with radar (Englhart et al. 2011) or optical (e.g. Baccini et al. 2008; Asner et al. 2011) and field plot data may allow vegetation carbon stocks to be mapped over large areas of tropical forest at different resolution scales ranging from 1 hectare to 1 km2. However, small-footprint Lidar data are not yet accessible over sufficient extents and with sufficient revisiting time because its operational use for tropical studies remains expensive. At the opposite, very-high (VHR) resolution imagery, i.e. approximately 1-m resolution, provided by recent satellite like Geoeye, Ikonos, Orbview or Quickbird as well as the forthcoming Pleiades becomes widely available at affordable costs, or even for free in certain regions of the world through Google Earth®. Compared to coarser resolution imagery with

DART: A 3D Model for Remote Sensing Images and Radiative Budget of Earth Surfaces

Modeling the radiative behavior and the energy budget of land surfaces is relevant for many scientific domains such as the study of vegetation functioning with remotely acquired information. DART model (Discrete Anisotropic Radiative Transfer) is developed since 1992. It is one of the most complete 3D models in this domain. It simulates radiative transfer (R.T.) in the optical domain: 3D radiative budget and remote sensing images (i.e., radiance, reflectance, brightness temperature) of vegetation and urban Earth surfaces, for any atmosphere, wavelength, sun/view direction, altitude and spatial resolution. It uses an innovative multispectral approach (flux tracing, exact kernel, discrete ordinate techniques) over the whole optical domain. Here, its potential is illustrated with the case of urban and tropical forest canopies. Moreover, three recent improvements in terms of functionality and operability are presented: account of Earth/Atmosphere curvature for oblique remote sensing measurements, importation of 3D objects simulated as the juxtaposition of triangles with the possibility to transform them into 3D turbid objects, and R.T. simulation in landscapes that have a continuous topography and landscapes that are non repetitive. Finally, preliminary results concerning two application domains are presented. 1) 2D distribution of the reflectance, brightness temperature and radiance measured by a geostationary satellite over a whole continent. 2) 3D radiative budget of natural and urban surfaces with a DART energy budget (EB) component that is being developed. This new model, called DARTEB, is intended to simulate the energy budget of land surfaces.

Using multi-directional high-resolution imagery from POLDER sensor to retrieve leaf area index

Multi‐directional satellite optical imagery collected at high spatial resolution potentially allows improving the accuracy of biophysical variable retrieval. The improvements result from the inclusion of the directional anisotropy of the target, which provides additional information related to vegetation structural properties. The research presented here analyses airborne imagery and ground reference data in order to quantify the accuracy of the retrieval methods for LAI (leaf area index). Both variables are estimated through processing of airborne POLDER (POLarization and Directionality of Earth Reflectances) sensor images from an agricultural area. In a first step, the BRDF (Bi‐directional Reflectance Distribution Function) of the surface is estimated using a simple parametric model, whose parameters where derived from fitting POLDER BRF (Bi‐directional Reflectance Factor) measurements. LAI estimation was performed using two different approaches, both based on an artificial neural network designed to invert a 1D soil‐vegetation radiative transfer model. The difference between the two methods is that one of them uses only the isotropic component of the BRDF parametric model and the other the full BRDF information, i.e. adding the anisotropic components. The algorithm using isotropic information shows a clear improvement when compared to semi‐empirical approaches. Root mean square error between estimated and ground measured LAI values is 0.87. However, the method using the full BRDF information yielded poorer estimates, pointing out the difficulty of fully exploiting the multi-directional information. The performance decrease is partially explained by the incoherence between real and modelled BRDF measurements.

Physically-based retrievals of Norway spruce canopy variables from very high spatial resolution hyperspectral data

This study was conducted to answer two research questions: (1) what is the spatial variability of the leaf optical properties between 400-1600 nm (hemispherical-directional reflectance, transmittance, absorption) within young Norway spruce crowns, and (2) how to design a suitable physically-based approach retrieving the total chlorophyll content of a complex coniferous canopy from very high spatial resolution (0.4 m) hyperspectral data? It was proved that sun-exposed needles of current age-class statistically differ (alpha-level = 0.01) from rest of the needles in reflectance between 510-760 nm. Last four age-classes of sun-exposed needles were also found to be significantly different from almost all age-classes of sun-shaded needles in transmittance from 760-1350 nm. An operational estimation of chlorophyll a+b content (Cab) from an airborne AISA Eagle hyperspectral image was proposed by means of a PROSPECT-DART inversion employing an artificial neural network (ANN). A spatial pattern of estimated Cab was successfully validated against the Cab map produced by a vegetation index ANCB650-720. Coefficients of determination (R2) between ground measured and retrieved Cab were 0.81 and 0.83, respectively, with root mean square errors (RMSE) of 2.72 mug cm-2 for ANN and 3.27 mug cm-2 for ANCB650-720.

Tree crown detection in high resolution optical and lidar images of tropical forest

Tropical forests are complex ecosystems where the potential of remote sensing has not yet been fully realized. The increasing availability of satellite metric imagery along with canopy altimetry from airborne LiDAR open new prospects to detect individual trees. For this objective, we optimized, calibrated and applied a model based on marked point processes to detect trees in high biomass mangroves of French Guiana by considering a set of 1m pixel images including 1) panchromatic images from the IKONOS sensor 2) LiDAR-derived canopy 2D altimetry and 3) reflectance panchromatic images simulated by the DART-model. The relevance of detection is then discussed considering: (i) the agreement in space of detected crown centers locations with known true locations for the DART images and also the detection agreement for each pair of IKONOS and LiDAR images, and (ii) the comparison between the frequency distributions of the diameters of the detected crowns and of the tree trunks measured in the field. Both distributions are expected to be related due to the allometry relationships between trunk and crown. Results are encouraging provided that crown sizes sufficiently large compared to 1m pixels.

Simulating satellite waveform Lidar with DART model

DART model was extended for simulating satellite Lidar data of 3D Earth scenes with Monte Carlo based methods. 2 major modeling methods were developed. (1) Monte Carlo method for efficiently handling complex phase functions: once scattering directions with close occurrence probabilities are grouped within classes, a 1st random pulling gives the class of scattering directions, and a 2nd random pulling gives the scattering direction within the class. (2) A so-called RayCarlo method combines the classical Monte Carlo forward photon tracing method and the flux tracking method, which allows one to decrease computer time of classical Monte Carlo method by factors that can reach 108. Simulation results are very encouraging. Validation tests are being conducted.

Lidar radiative transfer modeling in the Atmosphere

DART model was extended for simulating satellite lidar signal of Earth-Atmosphere systems. The adopted approach combines Monte Carlo and flux tracking methods. It improves a lot signal to noise ratios of simulated waveforms. For accurate simulation of atmosphere photon tracing, the atmosphere modelling was modified for obtaining continuous vertical distribution of extinction coefficients. It leads to a much better accuracy than the use of atmosphere layers with constant extinction coefficients. This improvement is valid with any type of atmosphere, exponential or not.

Attenuating the Absorption Contribution on \({C_{n^{2}}}\) Estimates with a Large-Aperture Scintillometer

Large-aperture scintillometers (LAS) are often used to characterize atmospheric turbulence by measuring the structure parameter of the refractive index \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}