J.-F. Hanocq

The soil line concept in remote sensing

Abstract The soil line concept, a linear relationship between red and near infrared reflectance of bare soil is widely used for the interpretation of remotely sensed data over the Earths surface. The slope and intercept of soil lines presented in the literature are reviewed. They are found to be inversely related. A numerical experiment shows that part of this relationship is due to statistical artifacts occurring when estimating these parameters. However, observed variations correspond also to the various experimental conditions and to actual differences in soil types and surface status. The fundamentals of the soil line are analyzed from a simple radiative transfer model in which the bidirectional reflectance is split into its single and multiple scattering components. The slope of the soil line corresponds to the ratio of the single scattering albedos characterizing the 2 wavebands where the soil line is observed. The intercept originates from the difference in multiple scattering observed in each of ...

About the soil line concept in remote sensing

Abstract The soil line, a linear relationship between bare soil reflectance observed in two different wavebands, is widely used for interpretation of remotely sensed data. The basis on soil line was analyzed using a radiative transfer model in which reflectance was splitted into its single and multiple scattering components. The slope of the soil line corresponded to the ratio of the single scattering albedos corresponding to the two wavebands where the soil line was defined. The intercept originated from the difference in multiple scattering observed in each of the two wavelength bands used. The soil line concept was very robust over the whole optical domain as soon as soil types are separated, and when the effect of the view and source configurations as well as the surface roughness were considered. However, in the middle infrared spectral domain, the soil line concept failed when soil moisture was a factor of variation.

Modeling directional brightness temperature over a maize canopy in row structure

A study on modeling the variations of directional brightness temperature (DBT) for row-structure crops was carried out with the images captured by a large-aperture thermal infrared camera over a maize canopy. The model assumes that the DBT is a function of target component brightness temperatures and their directional fractions. The canopy has three brightness temperature components: the sunlit soil, the shaded soil, and the vegetation. Their fractions in the scene depend on the sun-view geometry and the distributions of gaps within and between plant rows. To describe canopy geometrical features, a series of porous hedgerows with a rectangular cross section is used. The directional variations of gap fractions are described by the Kuusk function. The model demonstrated how the features of DBT depend on the sun-view geometry, canopy geometrical structure, and component brightness temperatures. In the simulation of DBT over a middle-density canopy near the local noontime, the results revealed an evident row-direction-oriented hot stripe in DBT polar maps, where the hot spot appeared along the sun direction. The sensitivities of the model to the input parameters were tested. Further validation demonstrated a close correlation between predicted DBT and field observations.

Atmospheric corrections of single broadband channel and multidirectional airborne thermal infrared data: Application to the ReSeDA experiment

This study focused on atmospheric corrections of airborne thermal infrared remote sensing data acquired with a multidirectional and single broadband channel sensor during the ReSeDA experiment. For single channel sensors, atmospheric corrections are generally performed using atmospheric radiative transfer models such as MODTRAN 3.5 along with radiosoundings. A sensitivity study was performed using MODTRAN 3.5 simulations to assess the accuracy of the processing regarding the experimental context. It was shown that the local topography and the atmosphere spatial variability could affect significantly the radiosounding representativeness, whereas the fluctuations of the flight altitude around the nominal value induced non-negligible inaccuracies. Moreover, using a broadband sensor induced non-linear effects that required a second order correction and also the use of look-up tables to reduce computation time. A theoretical error was next proposed to account for both the sensor accuracy and the experimental uncertainties considered when performing the sensitivity study. This theoretical error was about 1°C, and agreed well with the results obtained when validating against field measurements.

Geometrical modelling of soil bidirectional reflectance incorporating specular effects

Abstract A geometrical model, taking into account the diffuse, as well as the specular component of energy leaving soil surfaces in the visible and near-infrared, is discussed here. The model computes the bidirectional, reflectance of soils illuminated by a single source. A rough soil surface is simulated by equal-sized spheroids regularly spaced on a horizontal surface. The model was tested using soil bidirectional reflectance data obtained in laboratory conditions by Jacquemoud et al in 1992. Two parameters describing soil surface geometry were used for modelling the soil relalive reflectance in laboratory conditions: the relative distance (d/ a) between spheroids (relative to their horizontal radii ( a )and the shape of spheroids (b/ a) ( as proportion of their vertical (b) to horizontal radii (a)) The simulation of reflectance for soil surfaces of pebbles and sand, containing simple dense particles with rounded edges, can be carried out using the d/ a and b/ a ratios which nearly described their aclua...

Study on thermal infrared emission directionality over crop canopies with TIR camera imagery

In order to investigate directionality of thermal infrared emission from crop canopies, a wide-angle thermal video camera (INFRAMETRICS) equipped with an 80° FOV lens was mounted on a small aircraft and used to acquire thermal imagery along several different flight traces. Accordingly, multi-angle directional brightness temperatures were acquired at different view angles for individual pixel. The flight experiment was carried out from January 1997 to October 1997 over a 5 km×5 km flat agricultural area, located near Avignon, southeastern France.This paper presents results from analyses performed using these data including instrument calibration, radiometric correction, atmospheric correction, temperature temporal adjustment, geometric matching and registration of images. Results are presented for different thermal infrared emission patterns of different surface types including bare soil, wheat, maize and sunflower at different growth stages.

Influence of agricultural practices on micrometerological spatial variations at local and regional scales

Soil–vegetation–atmosphere transfers significantly influence interactions and feedbacks between vegetation and boundary layer, in relation with plant phenology and water status. The current study focused on linking micrometeorological conditions to cultural practices at the local and regional scales (lower than 100 km2), over an agricultural region in South Western France. This was achieved considering observation and modelling tools designed for characterising spatial variabilities over land surfaces. These tools were the ASTER high spatial resolution optical remote sensing data, and the SEBAL spatialised surface energy balance model. Surface bidirectional reflectance and brightness temperature were first derived from ASTER data through solar and thermal atmospheric radiative transfer codes, and next used to infer surface radiative properties required for model simulations. Assessing model consistency in terms of air temperature simulations gave satisfactory results when intercomparing against weather station data, although basic model assumptions were not systematically verified in terms of spatial variability. Next, spatialised simulations of evapotranspiration and air temperature were analysed at the regional and local scales, in relation with pedology, land use, and cultural practices. It was shown model estimates were consistent with the considered crops and the related cultural practices. Irrigation appeared as the main factor amongst others (soil, landuse, sowing date…) explaining the micrometeorological variability. Although interesting and promising in terms of linking micrometeorological conditions to cultural practices, the results reported here emphasised several difficulties, specially about capturing subfield scale variability and monitoring the considered processes at an appropriate temporal sampling.

Classification of brightness temperature components for a maize canopy

In order to modeling directional thermal radiation and energy balance for a partially covered canopy, surface brightness temperature is usually classified into several components. This paper researches the methodology for brightness temperature component classification and temporal variations of component number and values by an in situ experiment, dedicated to analyze maize canopy brightness temperature distribution. The measurement was carried out by using a TIR camera and a visible camera mounted on an industrial crane, the experiment lasted 3 months throughout a maize growth cycle. In the analysis of the brightness temperature, a Gaussian distribution has been assumed. Results show the number of components and their brightness temperature values vary with time of day and biomass density. Three brightness temperature components of vegetation, sunlit and shaded soil could be identified at midday during the measurement period. In the daytime, temperature variability of sunlit soil is much larger than the other two components when the canopys density is not high. When the canopy is fully covered, vegetation brightness temperature has a wider range.

Modeling field of view effect on the ground observations of directional brightness temperature over a maize canopy

Composite scene of row crops induced an unavoidable error in ground measurements of directional brightness temperature (DBT) due to the use of wide field of view (FOV). The measurement results vary with sample size and position, detector height and view direction, and bias due to project principle. This is called FOV effect. The study focuses on the estimation of FOV effect on the measurements of maize canopy using a computational geometric 2D model. The model was developed to simulate the fractional variations of canopy brightness temperature components. The simulation results revealed that the errors caused by FOV effect have a complex feature. Generally, vegetation fraction is always over counted in the nadir view, errors increase dramatically with the decrease of detector height as well as the enlargement of sample size, the deviation of the error corresponding to detect position is small; in oblique view, the errors are limited to a low level due to an effect called compensation effect. However, the deviation of the error keeps large when the sample size is small. Nevertheless, the best approach to reduce FOV effect in ground observation is levering the detector to a higher altitude as the model suggested.

Using night TIR images to model the gap fraction of a dense maize canopy

In order to estimate directional variation of gap fraction over a high dense maize canopy, a geometric optical and radiative transfer (GORT) model was improved to simulate the hemispherical gap fraction, the model was validated by a crane borne experiment using a narrow FOV thermal infrared camera conducted at night. The research revealed that the path length is a function of canopy geometrical structure, view direction and position, which leads to the row effect on hemispherical gap fraction: azimuthal variation of gap fraction is insignificant except for the observations parallel to the rows. For dense canopies, the value of gap fraction declined quickly in small view zenith rather than in large view zenith range, which leads the curves to show a concave shape. The experiment was conducted at 22 h local time on July 26 in 1999 (LAI=5) for the validation. At the time, brightness temperatures of leaves and soil had Gaussian distribution, their mean values presented a significant difference (24.3/spl deg/C and 26.5/spl deg/C) comparing to their small standard deviations (0.52 degC and 0.44 degC), gap fraction could be discriminated from canopy background. Observations showed that most gaps appeared between the adjacent rows, which lead the high dense canopy still to keep row feature in thermal infrared images. As conclusion of the comparison, the model could capture the main features of the measured gap fraction. With a proper adjustment of input leaf optical parameters, the simulated gap fraction showed a fairly good agreement with observed gap fraction.