Abstract

This work addressed the validation of the MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared (MIR) channel, proposed by Tang and Li [Int. J. Remote Sens. 29, 4907 (2008)], with ground-measured data, which were collected from a field campaign that took place in June 2004 at the ONERA (Office National d’Etudes et de Recherches Aérospatiales) center of Fauga-Mauzac, on the PIRRENE (Programme Interdisciplinaire de Recherche sur la Radiométrie en Environnement Extérieur) experiment site [Opt. Express 15, 12464 (2007)]. The leaving-surface spectral radiances measured by a BOMEM (MR250 Series) Fourier transform interferometer were used to calculate the ground brightness temperatures with the combination of the inversion of the Planck function and the spectral response functions of MODIS channels 22 and 23, and then to estimate the ground brightness temperature without the contribution of the solar direct beam and the bidirectional reflectivity by using Tang and Li’s proposed algorithm. On the other hand, the simultaneously measured atmospheric profiles were used to obtain the atmospheric parameters and then to calculate the ground brightness temperature without the contribution of the solar direct beam, based on the atmospheric radiative transfer equation in the MIR region. Comparison of those two kinds of brightness temperature obtained by two different methods indicated that the Root Mean Square Error (RMSE) between the brightness temperatures estimated respectively using Tang and Li’s algorithm and the atmospheric radiative transfer equation is 1.94 K. In addition, comparison of the hemispherical-directional reflectances derived by Tang and Li’s algorithm with those obtained from the field measurements showed that the RMSE is 0.011, which indicates that Tang and Li’s algorithm is feasible to retrieve the bidirectional reflectivity in MIR channel from MODIS data.

© 2012 OSA

1. Introduction

It’s well known that the mid-infrared (MIR) spectral region (3-5μm) has many advantages of favorable atmospheric penetration compared with the visible and thermal-infrared (TIR), and is therefore of increasing interest for the study of the terrestrial environment and its change [15]. However, the radiation in the MIR observed at satellite altitude during the daytime is composed of a combination of reflected radiance due to sun irradiance and emitted radiance from both the surface and the atmosphere, which makes it a very difficult task to take advantage of the MIR data. Consequently, many efforts have been taken to retrieve the reflectivity for separating the reflected and emitted radiance in this spectral region [69].

Tang and Li [9] proposed a method to retrieve the land surface bidirectional reflectivity in the MIR from MODIS channels 22 (wavelength centered at 3.97 μm) and 23 (wavelength centered at 4.06 μm). A split-window-like algorithm has been developed to determine the MIR ground brightness temperature without the contribution of the solar direct beam from ground brightness temperatures measured at those two adjacent MIR channels, with the assumptions that the surface bidirectional reflectivities in those two channels are equal and both the ground brightness temperatures in channels 22 and 23 are the same if the contribution of the direct solar radiation is not considered. Note that the algorithm was developed on the basis of simulation data simulated with the radiative transfer model MODTRAN 4. It needs to be further validated at ground level under outdoor conditions.

To this end, this paper aims to validate and assess the algorithm with ground-measured data. Section 2 describes briefly the bidirectional reflectivity retrieval algorithm. Section 3 depicts the field campaign and measurements. Section 4 presents the data processing and algorithm validation and gives some validation results. The conclusions are drawn in section 5.

2. Algorithm

A detailed description and discussion of the algorithm are given in Tang and Li’s paper [9], but for completeness a brief description is given here. Based on the difference in the solar reflection in MODIS MIR channels 22 (wavelength centered at 3.97 μm) and 23 (wavelength centered at 4.06 μm), and assuming that the surface bidirectional reflectivities are equal in these two MIR channels, and that the ground brightness temperatures in these two adjacent channels are the same if the contribution of the direct solar radiation is not considered, Tang and Li developed a method to retrieve the bidirectional reflectivity (ρb) in the MIR channel from MODIS channels 22 and 23 with

ρb=B(Tg_22)B(Tg0)R22s,
where B is the Planck function, Tg_22 is the daytime ground brightness temperature of MODIS channel 22, R22s is the solar irradiance at ground level in MODIS channel 22, Tg0 is the MIR ground brightness temperature without the contribution of the solar direct beam and can be estimated from the ground brightness temperatures Tg_22 and Tg_23 in the channels 22 and 23.

A split-window-like algorithm has been developed to determine the MIR ground brightness temperature Tg0 with Eq. (2)

Tg0=Tg_22+a1+a2(Tg_22Tg_23)+a3(Tg_22Tg_23)2
in which the coefficients a1a3 are dependent only on the Solar Zenith Angle (SZA) and can be estimated using Eq. (3)
ai=b1i+b2icos(SZA)+b3icos2(SZA),
where b1ib3i are constant coefficients which can be found in Tang and Li’s paper.

3. Description of ground measurements

The ground-measured data were collected from a field campaign that took place in June 2004 at the ONERA (Office National d’Etudes et de Recherches Aérospatiales) center of Fauga-Mauzac, on the PIRRENE (Programme Interdisciplinaire de Recherche sur la Radiométrie en Environnement Extérieur) experiment site [10].

The leaving-surface spectral radiances were measured by a BOMEM (MR250 Series) Fourier transform interferometer with spectral range from 750 to 3000 cm−1. The spectral resolution of the data was 4 cm−1 and the sampling interval was 2 cm−1. The acquisition time was 6 s (averaging 100 scans). The spectroradiometer, which has a horizontal line of sight, was held at 1.2 m above the ground and pointed to a horizontal surface via a 45° gold mirror. The total path length from the surface to the sensor was about 2 m and the footprint was 20 cm in diameter. To increase the temporal radiometric stability of the spectroradiometer in an outdoor environment, the instrument was put into an insulated chamber cooled by an air conditioning device and shielded from wind. In addition, the interferometer was purged with dry nitrogen. More details of the experimental setup can be found in Kanani et al.’s paper [10].

Eleven samples including soils, rocks, water, wood, and manmade materials were tested in the experiment, but nine of them were selected to validate our method, because there were some errors in the measured emissivity of the excluded materials in the laboratory. Table 1 depicts the main characteristics and composition information of the used nine surface materials.

Tables Icon

Table 1. Nine surface materials used as the validation data

In addition, meteorological measurements were acquired to provide ancillary data for environmental terms evaluation. They consist in temperature and water vapor vertical profiles, provided by the ARPEGE climate model of the CNRM (Centre National de Recherche Météorologique). These profiles, extrapolated to ground measurements provided by local humidity and temperature probes, were used in the atmospheric radiative transfer calculations.

4. Results and analysis

It should be kept in mind that the daytime observation of MIR consists of a combination of reflected radiances due to sun irradiance and emitted radiance from both the surface and the atmosphere. It makes a little difficult to obtain the bidirectional reflectivity (ρb) directly from field ground measurements. In order to validate the MODIS-derived bidirectional reflectivity retrieval algorithm in MIR channel, the algorithm of determining the MIR ground brightness temperature Tg0 was validated in the first step, and then the validation of the bidirectional reflectivity was conducted in this work.

From Eq. (1), we can see that the bidirectional reflectivity (ρb) in the MIR channel can be determined accurately, assuming that the ground brightness temperature Tg0 without the contribution of solar direct beam is known and the atmospheric correction is done previously. Consequently, validating the accuracy of the ground brightness temperature Tg0 determined with Eq. (2) can indirectly validate the accuracy of the bidirectional reflectivity (ρb) retrieval in the MIR channel.

The leaving-surface spectral radiances, measured by the BOMEM (MR250 Series) Fourier transform interferometer, were used to calculate the ground brightness temperatures Tg_22 and Tg_23 with the combination of the inversion of the Planck function and the spectral response functions of MODIS channels 22 and 23, and then to estimate the ground brightness temperature Tg0 without the contribution of the solar direct beam using Eqs. (2) and (3).

On the other hand, the simultaneously measured atmospheric profiles were used to obtain the atmospheric parameters with the atmospheric radiative transfer model MODTRAN 4 and then to calculate the brightness temperature Tg0 using Eq. (4), combined with the inversion of the Planck function and emissivity εi measured in the laboratory.

Bi(Tg_i0)=εiBi(Ts)+(1εi)(Ratm_i+Ratm_is)
where Ratm_i is the channel downward atmospheric radiance, defined as 1/π times the total downward atmospheric irradiance, Ratm_is is the channel downward solar diffusion radiation over a hemisphere divided by π.

Then, we compared these two kinds of brightness temperature Tg0 obtained by two different methods. Figure 1 shows the scatter diagram of the comparison. From this figure we can see that the Root Mean Square Error (RMSE) between the brightness temperatures estimated respectively using Tang and Li’s [9] algorithm and the atmospheric radiative transfer equation is 1.94 K for the nine surface materials with 101 times measurements at different atmospheric conditions and the Solar Zenith Angle (SZA) ranging from 20° to 60°.

 

Fig. 1 Comparison of brightness temperature Tg0 estimated using Tang and Li’s algorithm and the atmospheric radiative transfer equation, respectively.

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In addition, Table 2 gives the statistical variation range of calculated brightness temperature Tg0, the RMSE, maximum absolute error and minimum absolute error between the modeled and calculated Tg0 for those nine surface materials in different atmospheric conditions, respectively. From this table we can see that the maximum absolute error is 6.75 K for stone#1 with RMSE of 4.81K. The wood also has a relative large absolute error of 4.41 K with RMSE of 2.92 K. Except for these two kinds of materials, the errors for other materials are relative small. The RMSE for the total statistics is less than 2 K. On the basis of Tang and Li’s [9] analysis, this error affects the retrieval accuracy of the land surface bidirectional reflectivity in the MIR less than 0.006.

Tables Icon

Table 2. Statistical error of Tg0 between the modeled and calculated brightness temperature and the variation range of calculated Tg0

After determining of brightness temperature Tg0, the bidirectional reflectivity ρb can be obtained using Eq. (1). Figure 2 shows the angular variation of the MIR bidirectional reflectivity with the SZA for different samples. From this figure, we can see that the bidirectional reflectivity of water varies slightly with the increase of SZA and the bidirectional reflectivity of soil increases with the increase of SZA. The bidirectional reflectances of the remained seven types of materials decrease with the increase of SZA.

 

Fig. 2 Angular variation of the MIR bidirectional reflectivity with the solar zenith angle.

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Taking into account that the laboratory measurements are based on the principle of the interferometry, the illumination angle is hemispherical and the view zenith angle is approximately at-nadir direction. The reflectance measured in the laboratory is consequently hemispherical-directional reflectance. In order to compare with the hemispherical-directional reflectance, the calculated bidirectional reflectivity has to be integrated hemispherically at the illumination direction. Assuming that the Bidirectional Reflectance Distribution Functions (BRDF) shapes in the MIR spectral region are the same as the ones in visible and near-infrared regions [11], the hemispherical-directional reflectances can be calculated with the bidirectional reflectivity for different View Zenith Angles (VZAs). Figure 3 shows the comparison of the calculated and measured hemispherical-directional reflectances for the nine samples. From this figure, we can see that the RMSE between the calculated and measured hemispherical-directional reflectances is 0.011. Except for the stone#1 and wood, the calculated reflectances of the remained samples are agreed well with the measured ones, which indicate that the accuracy of the retrieved bidirectional reflectivity in MIR is acceptable.

 

Fig. 3 Comparison of the calculated and measured hemispherical-directional reflectance for the nine samples.

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It should be pointed out that the validation of Tang and Li’s algorithm [9] in this work is only conducted for homogeneous land cover. Actually, based on the theory that land surface reflectance typically consists of three components: the isotropic scattering, the volumetric scattering and the geometric-optical surface scattering [11]. Once the three components are determined, the non-Lambertian reflective behavior of land surface can be better described. Consequently, Tang and Li’s algorithm [9] also suits to heterogeneous land covers, which will be further studied in the near future.

5. Conclusions

In this work, the MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared (MIR) channel, proposed by Tang and Li [9], was validated and assessed with ground-measured data, which were collected from a field campaign that took place in June 2004 at the ONERA (Office National d’Etudes et de Recherches Aérospatiales) center of Fauga-Mauzac, on the PIRRENE (Programme Interdisciplinaire de Recherche sur la Radiométrie en Environnement Extérieur) experiment site.

Based on the calculation of the ground brightness temperature Tg0 with Tang and Li’s (2008) algorithm and the atmospheric radiative transfer equation respectively, the accuracy of the MODIS-derived bidirectional reflectivity (ρb) retrieval algorithm in the MIR channel has been validated. The result showed that the Root Mean Square Error (RMSE) between the brightness temperatures estimated respectively using two different methods is 1.94 K, and the RMSE between the retrieved and the measured hemispherical-directional reflectances in the MIR is 0.011, which indicates that Tang and Li’s algorithm is feasible and acceptable for retrieving land surface bidirectional reflectivity in MIR spectral region.

Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China under Grant Nos. 40801140 and 41171287, the National High Technology Research and Development Program of China under grants 2012AA12A302 and 2009AA122102, and the Special Foundation of Excellent Doctoral Dissertations of the Chinese Academy of Sciences. The authors would like to thank the MODTRAN development team for making their code available to us. Special thanks are also given to the reviewers for their valuable comments.

References and links

1. Y. J. Kaufman and L. A. Remer, “Detection of forests using MID-IR reflectance: an application for aerosol studies,” IEEE Trans. Geosci. Rem. Sens. 32(3), 672–683 (1994). [CrossRef]  

2. W. C. Snyder, Z. Wan, Y. Zhang, and Y. Feng, “Thermal infrared (3-14 μm) bi-directional reflectance measurement of sands and soils,” Remote Sens. Environ. 60(1), 101–109 (1997). [CrossRef]  

3. D. S. Boyd, G. M. Foody, and P. J. Curran, “The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0 μm),” Int. J. Remote Sens. 20(5), 1017–1023 (1999). [CrossRef]  

4. D. S. Boyd and F. Petitcolin, “Remote sensing of the terrestrial environment using middle infrared radiation (3.0-5.0 μm),” Int. J. Remote Sens. 25(17), 3343–3368 (2004). [CrossRef]  

5. R. Libonati, C. C. DaCamara, J. M. C. Pereira, and L. F. Peres, “Retrieving middle-infrared reflectance for burned area mapping in tropical environments using MODIS,” Remote Sens. Environ. 114(4), 831–843 (2010). [CrossRef]  

6. Z.-L. Li and F. Becker, “Feasibility of land surface temperature and emissivity determination from NOAA/AVHRR data,” Remote Sens. Environ. 43(1), 67–86 (1993). [CrossRef]  

7. F. Nerry, F. Petitcolin, and M. P. Stoll, “Bidirectional reflectivity in AVHRR channel 3: application to a region in northern Africa,” Remote Sens. Environ. 66(3), 298–316 (1998). [CrossRef]  

8. Z.-L. Li, F. Petitcolin, and R. H. Zhang, “A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data,” Sci. China Ser. E: Technol. Sci. 43(S1Supp), 23–33 (2000). [CrossRef]  

9. B.-H. Tang and Z.-L. Li, “Retrieval of land surface bidirectional reflectivity in the mid-infrared from MODIS channel 22 and 23,” Int. J. Remote Sens. 29(17-18), 4907–4925 (2008). [CrossRef]  

10. K. Kanani, L. Poutier, F. Nerry, and M. P. Stoll, “Directional effects consideration to improve out-doors emissivity retrieval in the 3-13 mum domain,” Opt. Express 15(19), 12464–12482 (2007). [CrossRef]   [PubMed]  

11. Z.-L. Li, B. Tang, and Y. Bi, “Estimation of land surface directional emissivity in mid-infrared channel around 4.0 µm from MODIS data,” Opt. Express 17(5), 3173–3182 (2009). [CrossRef]   [PubMed]  

References

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  1. Y. J. Kaufman and L. A. Remer, “Detection of forests using MID-IR reflectance: an application for aerosol studies,” IEEE Trans. Geosci. Rem. Sens.32(3), 672–683 (1994).
    [CrossRef]
  2. W. C. Snyder, Z. Wan, Y. Zhang, and Y. Feng, “Thermal infrared (3-14 μm) bi-directional reflectance measurement of sands and soils,” Remote Sens. Environ.60(1), 101–109 (1997).
    [CrossRef]
  3. D. S. Boyd, G. M. Foody, and P. J. Curran, “The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0 μm),” Int. J. Remote Sens.20(5), 1017–1023 (1999).
    [CrossRef]
  4. D. S. Boyd and F. Petitcolin, “Remote sensing of the terrestrial environment using middle infrared radiation (3.0-5.0 μm),” Int. J. Remote Sens.25(17), 3343–3368 (2004).
    [CrossRef]
  5. R. Libonati, C. C. DaCamara, J. M. C. Pereira, and L. F. Peres, “Retrieving middle-infrared reflectance for burned area mapping in tropical environments using MODIS,” Remote Sens. Environ.114(4), 831–843 (2010).
    [CrossRef]
  6. Z.-L. Li and F. Becker, “Feasibility of land surface temperature and emissivity determination from NOAA/AVHRR data,” Remote Sens. Environ.43(1), 67–86 (1993).
    [CrossRef]
  7. F. Nerry, F. Petitcolin, and M. P. Stoll, “Bidirectional reflectivity in AVHRR channel 3: application to a region in northern Africa,” Remote Sens. Environ.66(3), 298–316 (1998).
    [CrossRef]
  8. Z.-L. Li, F. Petitcolin, and R. H. Zhang, “A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data,” Sci. China Ser. E: Technol. Sci.43(S1Supp), 23–33 (2000).
    [CrossRef]
  9. B.-H. Tang and Z.-L. Li, “Retrieval of land surface bidirectional reflectivity in the mid-infrared from MODIS channel 22 and 23,” Int. J. Remote Sens.29(17-18), 4907–4925 (2008).
    [CrossRef]
  10. K. Kanani, L. Poutier, F. Nerry, and M. P. Stoll, “Directional effects consideration to improve out-doors emissivity retrieval in the 3-13 mum domain,” Opt. Express15(19), 12464–12482 (2007).
    [CrossRef] [PubMed]
  11. Z.-L. Li, B. Tang, and Y. Bi, “Estimation of land surface directional emissivity in mid-infrared channel around 4.0 µm from MODIS data,” Opt. Express17(5), 3173–3182 (2009).
    [CrossRef] [PubMed]

2010 (1)

R. Libonati, C. C. DaCamara, J. M. C. Pereira, and L. F. Peres, “Retrieving middle-infrared reflectance for burned area mapping in tropical environments using MODIS,” Remote Sens. Environ.114(4), 831–843 (2010).
[CrossRef]

2009 (1)

2008 (1)

B.-H. Tang and Z.-L. Li, “Retrieval of land surface bidirectional reflectivity in the mid-infrared from MODIS channel 22 and 23,” Int. J. Remote Sens.29(17-18), 4907–4925 (2008).
[CrossRef]

2007 (1)

2004 (1)

D. S. Boyd and F. Petitcolin, “Remote sensing of the terrestrial environment using middle infrared radiation (3.0-5.0 μm),” Int. J. Remote Sens.25(17), 3343–3368 (2004).
[CrossRef]

2000 (1)

Z.-L. Li, F. Petitcolin, and R. H. Zhang, “A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data,” Sci. China Ser. E: Technol. Sci.43(S1Supp), 23–33 (2000).
[CrossRef]

1999 (1)

D. S. Boyd, G. M. Foody, and P. J. Curran, “The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0 μm),” Int. J. Remote Sens.20(5), 1017–1023 (1999).
[CrossRef]

1998 (1)

F. Nerry, F. Petitcolin, and M. P. Stoll, “Bidirectional reflectivity in AVHRR channel 3: application to a region in northern Africa,” Remote Sens. Environ.66(3), 298–316 (1998).
[CrossRef]

1997 (1)

W. C. Snyder, Z. Wan, Y. Zhang, and Y. Feng, “Thermal infrared (3-14 μm) bi-directional reflectance measurement of sands and soils,” Remote Sens. Environ.60(1), 101–109 (1997).
[CrossRef]

1994 (1)

Y. J. Kaufman and L. A. Remer, “Detection of forests using MID-IR reflectance: an application for aerosol studies,” IEEE Trans. Geosci. Rem. Sens.32(3), 672–683 (1994).
[CrossRef]

1993 (1)

Z.-L. Li and F. Becker, “Feasibility of land surface temperature and emissivity determination from NOAA/AVHRR data,” Remote Sens. Environ.43(1), 67–86 (1993).
[CrossRef]

Becker, F.

Z.-L. Li and F. Becker, “Feasibility of land surface temperature and emissivity determination from NOAA/AVHRR data,” Remote Sens. Environ.43(1), 67–86 (1993).
[CrossRef]

Bi, Y.

Boyd, D. S.

D. S. Boyd and F. Petitcolin, “Remote sensing of the terrestrial environment using middle infrared radiation (3.0-5.0 μm),” Int. J. Remote Sens.25(17), 3343–3368 (2004).
[CrossRef]

D. S. Boyd, G. M. Foody, and P. J. Curran, “The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0 μm),” Int. J. Remote Sens.20(5), 1017–1023 (1999).
[CrossRef]

Curran, P. J.

D. S. Boyd, G. M. Foody, and P. J. Curran, “The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0 μm),” Int. J. Remote Sens.20(5), 1017–1023 (1999).
[CrossRef]

DaCamara, C. C.

R. Libonati, C. C. DaCamara, J. M. C. Pereira, and L. F. Peres, “Retrieving middle-infrared reflectance for burned area mapping in tropical environments using MODIS,” Remote Sens. Environ.114(4), 831–843 (2010).
[CrossRef]

Feng, Y.

W. C. Snyder, Z. Wan, Y. Zhang, and Y. Feng, “Thermal infrared (3-14 μm) bi-directional reflectance measurement of sands and soils,” Remote Sens. Environ.60(1), 101–109 (1997).
[CrossRef]

Foody, G. M.

D. S. Boyd, G. M. Foody, and P. J. Curran, “The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0 μm),” Int. J. Remote Sens.20(5), 1017–1023 (1999).
[CrossRef]

Kanani, K.

Kaufman, Y. J.

Y. J. Kaufman and L. A. Remer, “Detection of forests using MID-IR reflectance: an application for aerosol studies,” IEEE Trans. Geosci. Rem. Sens.32(3), 672–683 (1994).
[CrossRef]

Li, Z.-L.

Z.-L. Li, B. Tang, and Y. Bi, “Estimation of land surface directional emissivity in mid-infrared channel around 4.0 µm from MODIS data,” Opt. Express17(5), 3173–3182 (2009).
[CrossRef] [PubMed]

B.-H. Tang and Z.-L. Li, “Retrieval of land surface bidirectional reflectivity in the mid-infrared from MODIS channel 22 and 23,” Int. J. Remote Sens.29(17-18), 4907–4925 (2008).
[CrossRef]

Z.-L. Li, F. Petitcolin, and R. H. Zhang, “A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data,” Sci. China Ser. E: Technol. Sci.43(S1Supp), 23–33 (2000).
[CrossRef]

Z.-L. Li and F. Becker, “Feasibility of land surface temperature and emissivity determination from NOAA/AVHRR data,” Remote Sens. Environ.43(1), 67–86 (1993).
[CrossRef]

Libonati, R.

R. Libonati, C. C. DaCamara, J. M. C. Pereira, and L. F. Peres, “Retrieving middle-infrared reflectance for burned area mapping in tropical environments using MODIS,” Remote Sens. Environ.114(4), 831–843 (2010).
[CrossRef]

Nerry, F.

K. Kanani, L. Poutier, F. Nerry, and M. P. Stoll, “Directional effects consideration to improve out-doors emissivity retrieval in the 3-13 mum domain,” Opt. Express15(19), 12464–12482 (2007).
[CrossRef] [PubMed]

F. Nerry, F. Petitcolin, and M. P. Stoll, “Bidirectional reflectivity in AVHRR channel 3: application to a region in northern Africa,” Remote Sens. Environ.66(3), 298–316 (1998).
[CrossRef]

Pereira, J. M. C.

R. Libonati, C. C. DaCamara, J. M. C. Pereira, and L. F. Peres, “Retrieving middle-infrared reflectance for burned area mapping in tropical environments using MODIS,” Remote Sens. Environ.114(4), 831–843 (2010).
[CrossRef]

Peres, L. F.

R. Libonati, C. C. DaCamara, J. M. C. Pereira, and L. F. Peres, “Retrieving middle-infrared reflectance for burned area mapping in tropical environments using MODIS,” Remote Sens. Environ.114(4), 831–843 (2010).
[CrossRef]

Petitcolin, F.

D. S. Boyd and F. Petitcolin, “Remote sensing of the terrestrial environment using middle infrared radiation (3.0-5.0 μm),” Int. J. Remote Sens.25(17), 3343–3368 (2004).
[CrossRef]

Z.-L. Li, F. Petitcolin, and R. H. Zhang, “A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data,” Sci. China Ser. E: Technol. Sci.43(S1Supp), 23–33 (2000).
[CrossRef]

F. Nerry, F. Petitcolin, and M. P. Stoll, “Bidirectional reflectivity in AVHRR channel 3: application to a region in northern Africa,” Remote Sens. Environ.66(3), 298–316 (1998).
[CrossRef]

Poutier, L.

Remer, L. A.

Y. J. Kaufman and L. A. Remer, “Detection of forests using MID-IR reflectance: an application for aerosol studies,” IEEE Trans. Geosci. Rem. Sens.32(3), 672–683 (1994).
[CrossRef]

Snyder, W. C.

W. C. Snyder, Z. Wan, Y. Zhang, and Y. Feng, “Thermal infrared (3-14 μm) bi-directional reflectance measurement of sands and soils,” Remote Sens. Environ.60(1), 101–109 (1997).
[CrossRef]

Stoll, M. P.

K. Kanani, L. Poutier, F. Nerry, and M. P. Stoll, “Directional effects consideration to improve out-doors emissivity retrieval in the 3-13 mum domain,” Opt. Express15(19), 12464–12482 (2007).
[CrossRef] [PubMed]

F. Nerry, F. Petitcolin, and M. P. Stoll, “Bidirectional reflectivity in AVHRR channel 3: application to a region in northern Africa,” Remote Sens. Environ.66(3), 298–316 (1998).
[CrossRef]

Tang, B.

Tang, B.-H.

B.-H. Tang and Z.-L. Li, “Retrieval of land surface bidirectional reflectivity in the mid-infrared from MODIS channel 22 and 23,” Int. J. Remote Sens.29(17-18), 4907–4925 (2008).
[CrossRef]

Wan, Z.

W. C. Snyder, Z. Wan, Y. Zhang, and Y. Feng, “Thermal infrared (3-14 μm) bi-directional reflectance measurement of sands and soils,” Remote Sens. Environ.60(1), 101–109 (1997).
[CrossRef]

Zhang, R. H.

Z.-L. Li, F. Petitcolin, and R. H. Zhang, “A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data,” Sci. China Ser. E: Technol. Sci.43(S1Supp), 23–33 (2000).
[CrossRef]

Zhang, Y.

W. C. Snyder, Z. Wan, Y. Zhang, and Y. Feng, “Thermal infrared (3-14 μm) bi-directional reflectance measurement of sands and soils,” Remote Sens. Environ.60(1), 101–109 (1997).
[CrossRef]

IEEE Trans. Geosci. Rem. Sens. (1)

Y. J. Kaufman and L. A. Remer, “Detection of forests using MID-IR reflectance: an application for aerosol studies,” IEEE Trans. Geosci. Rem. Sens.32(3), 672–683 (1994).
[CrossRef]

Int. J. Remote Sens. (3)

B.-H. Tang and Z.-L. Li, “Retrieval of land surface bidirectional reflectivity in the mid-infrared from MODIS channel 22 and 23,” Int. J. Remote Sens.29(17-18), 4907–4925 (2008).
[CrossRef]

D. S. Boyd, G. M. Foody, and P. J. Curran, “The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0 μm),” Int. J. Remote Sens.20(5), 1017–1023 (1999).
[CrossRef]

D. S. Boyd and F. Petitcolin, “Remote sensing of the terrestrial environment using middle infrared radiation (3.0-5.0 μm),” Int. J. Remote Sens.25(17), 3343–3368 (2004).
[CrossRef]

Opt. Express (2)

Remote Sens. Environ. (4)

W. C. Snyder, Z. Wan, Y. Zhang, and Y. Feng, “Thermal infrared (3-14 μm) bi-directional reflectance measurement of sands and soils,” Remote Sens. Environ.60(1), 101–109 (1997).
[CrossRef]

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[CrossRef]

Sci. China Ser. E: Technol. Sci. (1)

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[CrossRef]

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Figures (3)

Fig. 1
Fig. 1

Comparison of brightness temperature T g 0 estimated using Tang and Li’s algorithm and the atmospheric radiative transfer equation, respectively.

Fig. 2
Fig. 2

Angular variation of the MIR bidirectional reflectivity with the solar zenith angle.

Fig. 3
Fig. 3

Comparison of the calculated and measured hemispherical-directional reflectance for the nine samples.

Tables (2)

Tables Icon

Table 1 Nine surface materials used as the validation data

Tables Icon

Table 2 Statistical error of T g 0 between the modeled and calculated brightness temperature and the variation range of calculated T g 0

Equations (4)

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ρ b = B( T g_22 )B( T g 0 ) R 22 s ,
T g 0 = T g_22 + a 1 + a 2 ( T g_22 T g_23 )+ a 3 ( T g_22 T g_23 ) 2
a i = b 1i + b 2i cos(SZA)+ b 3i cos 2 (SZA),
B i ( T g_i 0 )= ε i B i ( T s )+(1 ε i )( R atm_i + R atm_i s )

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