Analysis and quantification of seabed adjacency effects in the subsurface upward radiance in shallow waters.

The estimation of the bathymetry and the detection of targets located on the seabed of shallow waters using remote sensing techniques is of great interest for many environmental applications in coastal areas such as benthic habitat mapping, monitoring of seabed aquatic plants and the subsequent management of littoral zones. For that purpose, knowledge of the optical effects induced by the neighborhood of a given seabed target and by the water column itself is required to better interpret the subsurface upward radiance measured by satellite or shipborne radiometers. In this paper, the various sources of photons that contribute to the subsurface upward radiance are analyzed. In particular, the adjacency effects caused by the neighborhood of a given seabed target are quantified for three water turbidity conditions, namely clear, moderately turbid and turbid waters. Firstly, an analytical expression of the subsurface radiance is proposed in order to make explicit the radiance terms corresponding to these effects. Secondly, a sensitivity study is performed using radiative transfer modeling to determine the influence of the seabed adjacency effects on the upward signal with respect to various parameters such as the bathymetry or the bottom brightness. The results show that the highest contributions of the adjacency effects induced by the neighborhood of a seabed target to the subsurface radiance could reach 26%, 18% and 9% for clear, moderately turbid and turbid water conditions respectively. Therefore, the detection of a seabed target could be significantly biased if the seabed adjacency effects are ignored in the analysis of remote sensing measurements. Our results could be further used to improve the performance of inverse algorithms dedicated to the retrieval of bottom composition, water optical properties and/or bathymetry.

Estimation of soil moisture content from the spectral reflectance of bare soils in the 0.4-2.5 µm domain.

This work aims to compare the performance of new methods to estimate the Soil Moisture Content (SMC) of bare soils from their spectral signatures in the reflective domain (0.4-2.5 µm) in comparison with widely used spectral indices like Normalized Soil Moisture Index (NSMI) and Water Index SOIL (WISOIL). Indeed, these reference spectral indices use wavelengths located in the water vapour absorption bands and their performance are thus very sensitive to the quality of the atmospheric compensation. To reduce these limitations, two new spectral indices are proposed which wavelengths are defined using the determination matrix tool by taking into account the atmospheric transmission: Normalized Index of Nswir domain for Smc estimatiOn from Linear correlation (NINSOL) and Normalized Index of Nswir domain for Smc estimatiOn from Non linear correlation (NINSON). These spectral indices are completed by two new methods based on the global shape of the soil spectral signatures. These methods are the Inverse Soil semi-Empirical Reflectance model (ISER), using the inversion of an existing empirical soil model simulating the soil spectral reflectance according to soil moisture content for a given soil class, and the convex envelope model, linking the area between the envelope and the spectral signature to the SMC. All these methods are compared using a reference database built with 32 soil samples and composed of 190 spectral signatures with five or six soil moisture contents. Half of the database is used for the calibration stage and the remaining to evaluate the performance of the SMC estimation methods. The results show that the four new methods lead to similar or better performance than the one obtained by the reference indices. The RMSE is ranging from 3.8% to 6.2% and the coefficient of determination R2 varies between 0.74 and 0.91 with the best performance obtained with the ISER model. In a second step, simulated spectral radiances at the sensor level are used to analyse the sensitivity of these methods to the sensor spectral resolution and the water vapour content knowledge. The spectral signatures of the database are then used to simulate the signal at the top of atmosphere with a radiative transfer model and to compute the integrated incident signal representing the spectral radiance measurements of the HYMAP airborne hyperspectral instrument. The sensor radiances are then corrected from the atmosphere by an atmospheric compensation tool to retrieve the surface reflectances. The SMC estimation methods are then applied on the retrieve spectral reflectances. The adaptation of the spectral index wavelengths to the HyMap sensor spectral bands and the application of the convex envelope and ISER models to boarder spectral bands lead to an error on the SMC estimation. The best performance is then obtained with the ISER model (RMSE of 2.9% and R2 of 0.96) while the four other methods lead to quite similar RMSE (from 6.4% to 7.8%) and R² (between 0.79 and 0.83) values. In the atmosphere compensation processing, an error on the water vapour content is introduced. The most robust methods to water vapour content variations are WISOIL, NINSON, NINSOL and ISER model. The convex envelope model and NSMI index require an accurate estimation of the water vapour content in the atmosphere.

OSIS: remote sensing code for estimating aerosol optical properties in urban areas from very high spatial resolution images.

The achievement of new satellite or airborne remote sensing instruments enables the more precise study of cities with metric spatial resolutions. For studies such as the radiative characterization of urban features, knowledge of the atmosphere and particularly of aerosols is required to perform first an atmospheric compensation of the remote sensing images. However, to our knowledge, no efficient aerosol characterization technique adapted both to urban areas and to very high spatial resolution images has yet been developed. The goal of this paper is so to present a new code to characterize aerosol optical properties, OSIS, adapted to urban remote sensing images of metric spatial resolution acquired in the visible and near-IR spectral domains. First, a new aerosol characterization method based on the observation of shadow/sun transitions is presented, offering the advantage to avoid the assessment of target reflectances. Its principle and the modeling of the signal used to solve the retrieval equation are then detailed. Finally, a sensitivity study of OSIS from synthetic images simulated by the radiative transfer code AMARTIS v2 is also presented. This study has shown an intrinsic precision of this tool of Δτ(a)=0.1.τ(a) ± (0.02 + 0.4.τ(a)) for retrieval of aerosol optical thicknesses. This study shows that OSIS is a powerful tool for aerosol characterization that has a precision similar to satellite products for the aerosol optical thicknesses retrieval and that can be applied to every very high spatial resolution instrument, multispectral or hyperspectral, airborne or satellite.

A nonlinear unmixing method in the infrared domain.

This paper presents the physical principle of a new (to our knowledge) unmixing method to retrieve optical properties (reflectance and emissivity) and surface temperatures over a heterogeneous and a folded landscape using hyperspectral and multiangular airborne images acquired with high spatial resolution. In fact, over such a complex scene, the linear mixing model of the reflectance commonly used in the reflective domain is no longer valid in the IR range for the two following reasons: multiple reflections due to the three-dimensional (3D) structure and the radiative phenomenon introduced by the temperature by way of the black body law. Thus, to solve this nonlinear unmixing problem, a new physical model of aggregation is used. Our model requires as inputs knowledge of the 3D scene structure and the spatial contribution of each material in the scene. Each elementary scene element is characterized by its optical properties, and its temperature, spectral, and multiangular acquisitions are required. This paper focuses only on the theoretical feasibility of such a method. In addition, an analysis is conducted evaluating the impact of the misregistration between the radiometric image and its digital terrain model, estimating a threshold of the relative importance of every elementary material to retrieve its corresponding optical properties and temperature. The results show that the 3D geometry must be accurately known (accuracy of 1 m for a spatial resolution of 20 m), and the relative contribution of material in the mixed area must be above 15% to retrieve its surface temperature with an accuracy better than 1 K. So, this method is applied on three different landscapes (heterogeneous flat surface, V shape, and urban canyon), and the results exhibit performances better than 1% for optical properties and 1 K for surface temperatures.

Aggregation process of optical properties and temperature over heterogeneous surfaces in infrared domain.

We propose a modeling of the aggregation processes of optical properties and temperature over the heterogeneous landscape in the infrared domain (3-14 microm). The main objectives of the modeling are to understand how these parameters aggregate and to study their links at different spatial scales. As the landscape is described at each scale by its radiative parameters, general equations linking the radiative parameters at a given high spatial scale to those at a rough scale are proposed. Then these equations are applied to several synthetic landscapes. An analysis based on a design of experiments is conducted to point out the influence of each of the input factors. The results show the importance of the intrinsic parameters (reflectance, emissivity, and surface temperature) of each surface element and also the directional and spectral behaviors of the aggregated parameters.

Remote sensing of aerosol plumes: a semianalytical model.

A semianalytical model, named APOM (aerosol plume optical model) and predicting the radiative effects of aerosol plumes in the spectral range [0.4,2.5 microm], is presented in the case of nadir viewing. It is devoted to the analysis of plumes arising from single strong emission events (high optical depths) such as fires or industrial discharges. The scene is represented by a standard atmosphere (molecules and natural aerosols) on which a plume layer is added at the bottom. The estimated at-sensor reflectance depends on the atmosphere without plume, the solar zenith angle, the plume optical properties (optical depth, single-scattering albedo, and asymmetry parameter), the ground reflectance, and the wavelength. Its mathematical expression as well as its numerical coefficients are derived from MODTRAN4 radiative transfer simulations. The DISORT option is used with 16 fluxes to provide a sufficiently accurate calculation of multiple scattering effects that are important for dense smokes. Model accuracy is assessed by using a set of simulations performed in the case of biomass burning and industrial plumes. APOM proves to be accurate and robust for solar zenith angles between 0 degrees and 60 degrees whatever the sensor altitude, the standard atmosphere, for plume phase functions defined from urban and rural models, and for plume locations that extend from the ground to a height below 3 km. The modeling errors in the at-sensor reflectance are on average below 0.002. They can reach values of 0.01 but correspond to low relative errors then (below 3% on average). This model can be used for forward modeling (quick simulations of multi/hyperspectral images and help in sensor design) as well as for the retrieval of the plume optical properties from remotely sensed images.

Thermal infrared radiance simulation with aggregation modeling (TITAN): an infrared radiative transfer model for heterogeneous three-dimensional surface--application over urban areas.

The thermal infrared radiance simulation with aggregation modeling (TITAN) model, presented here, is an innovative transfer radiative code in the infrared domain (3-14 microm). It takes into account the three-dimensional (3D) structure of the landscape and simulates all the radiative components introduced by this 3D structure, which are due to the reflection and emission of walls and sloping roofs. Examples are given to illustrate the new opportunities offered by TITAN over urban areas. First, a phenomenological study is conducted at four wavelengths analyzing the relative effect of all the radiative contributors to the total signal. The same analysis is performed at bottom of atmosphere, which reveals an error occurring when a flat assumption is made (between 1% and 5%). In a second example, the directional effects at sensor level are simulated and show that the radiative temperature can vary by up to 10 K.

Sensor radiance physical model for rugged heterogeneous surfaces in the 3-14 mum region.

We present a physical model describing the radiance acquired by an infrared sensor over a rugged heterogeneous surface. This model predicts the radiance seen over complex landscapes like urban areas and provides an accurate analysis of the signal, as each component is available at ground and sensor level. Plus, it allows data comparison from different instruments. Two representative cases (natural and urban) are analysed to show the composition and the construction of the sensor signal and to highlight the importance of having a 3D model, especially for rugged surfaces where environment weights in the overall spectral domain.