Validation of MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared channel with field measurements

doi:10.1364/OE.20.017760

« Show journal navigation

Validation of MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared channel with field measurements

Bo-Hui Tang, Hua- Wu, Zhao-Liang Li, and Françoise Nerry »View Author Affiliations

Optics Express, Vol. 20, Issue 16, pp. 17760-17766 (2012)
http://dx.doi.org/10.1364/OE.20.017760

View Full Text Article

Acrobat PDF (885 KB)

Journal Search
Article Lookup

Article Tools

Citations

Alert me when this article is cited
Export Citation/Save

Article Navigation

▸ Article Outline
– Abstract
1. Introduction
2. Algorithm
3. Description of ground measurements
4. Results and analysis
5. Conclusions
– References and links

Article Tools
Full Text
Article Info
References
Cited By
Figures
Metrics
Download PDF

Viewing Equations in the
HTML Article

Optimal Viewing Recommendations

Full Text
Article Info
References (11)
Cited By
Figures (3)
Metrics

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.

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 [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]

–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]

]. 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 [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]

–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]

Tang and Li [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]

] 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

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]

], 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 (ρ_b) in the MIR channel from MODIS channels 22 and 23 with

ρ_{b} = \frac{B (T_{g_22}) - B (T_{g}^{0})}{R_{22}^{s}},

(1)

where B is the Planck function,

T_{g_22}

is the daytime ground brightness temperature of MODIS channel 22,

R_{22}^{s}

is the solar irradiance at ground level in MODIS channel 22,

T_{g}^{0}

is the MIR ground brightness temperature without the contribution of the solar direct beam and can be estimated from the ground brightness temperatures

T_{g_22}

and

T_{g_23}

in the channels 22 and 23.

A split-window-like algorithm has been developed to determine the MIR ground brightness temperature

T_{g}^{0}

with Eq. (2)

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}

(2)

in which the coefficients

a_{1} - a_{3}

are dependent only on the Solar Zenith Angle (SZA) and can be estimated using Eq. (3)

a_{i} = b_{1 i} + b_{2 i} \cos (S Z A) + b_{3 i} \cos^{2} (S Z A),

(3)

where

b_{1 i} - b_{3 i}

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

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]

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.

Table 1 Nine surface materials used as the validation data

Name	Description	Name	Description
slate	Homogeneous and flat piece of slate. Composition: SiO₂ (60%), Al₂O₃ (17%), Fe₂O₃ (7.6%), K₂O (3.9%), MgO (2.5%), ...	stone#2	Flat rough and homogeneous rock. Composition: SiO₂ (77%), Al₂O₃ (12.6%), K₂O (4.6%), Na₂O (3.1%), Fe₂O₃ (1.2%), ...
sand#1	Morroco sand. Red color. Various grain size < 750 μm. Composition: SiO₂ (96.3%), Al₂O₃ (1%), ...	stone#3	Flat rough and homogeneous rock. Composition: SiO₂ (97.6%), Al₂O₃ (0.8%), ...
sand#2	Fontainebleau type sand. Various grain size < 750 μm. Composition: SiO₂ (98.4%), Al₂O₃ (0.6%), ...	wood	plywood
negev	Soil from the Negev desert. Various grain size < 2 mm. Composition: SiO₂ (42.2%), CaO (22.8%), Al₂O₃ (5.5%), Fe₂O₃ (2.7%), ...	water	water
Stone#1	Flat rough and homogeneous rock. Composition: SiO₂ (97.4%), Al₂O₃ (0.7%), ...

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 (ρ_b) 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

T_{g}^{0}

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 (ρ_b) in the MIR channel can be determined accurately, assuming that the ground brightness temperature

T_{g}^{0}

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

T_{g}^{0}

determined with Eq. (2) can indirectly validate the accuracy of the bidirectional reflectivity (ρ_b) 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

T_{g_22}

and

T_{g_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

T_{g}^{0}

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

T_{g}^{0}

using Eq. (4), combined with the inversion of the Planck function and emissivity

ε_{i}

measured in the laboratory.

B_{i} (T_{g_i}^{0}) = ε_{i} B_{i} (T_{s}) + (1 - ε_{i}) (R_{a t m_i} ↓ + R_{a t m_i}^{s} ↓)

(4)

where

R_{a t m_i}^{} ↓

is the channel downward atmospheric radiance, defined as 1/π times the total downward atmospheric irradiance,

R_{a t m_i}^{s} ↓

is the channel downward solar diffusion radiation over a hemisphere divided by π.

Then, we compared these two kinds of brightness temperature

T_{g}^{0}

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

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]

] 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

T_{g}^{0}

estimated using Tang and Li’s algorithm and the atmospheric radiative transfer equation, respectively.

In addition, Table 2 gives the statistical variation range of calculated brightness temperature

T_{g}^{0}

, the RMSE, maximum absolute error and minimum absolute error between the modeled and calculated

T_{g}^{0}

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

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]

] analysis, this error affects the retrieval accuracy of the land surface bidirectional reflectivity in the MIR less than 0.006.

Table 2 Statistical error of

T_{g}^{0}

between the modeled and calculated brightness temperature and the variation range of calculated

T_{g}^{0}

Name	RMSE (K)	Maximum absolute error (K)	Minimum absolute error (K)	Rang of $T_{g}^{0}$ (K)
slate	0.59	0.85	0.29	312.95-333.62
sand#1	0.78	1.84	0.09	305.39-321.14
sand#2	1.31	2.01	0.14	304.27-318.84
negev	1.45	2.63	0.05	304.84-318.56
stone#1	4.81	6.75	1.05	303.31-315.51
stone#2	1.45	1.81	0.03	303.20-317.94
stone#3	1.62	2.47	0.27	299.48-314.07
wood	2.92	4.41	0.15	299.68-313.18
water	1.38	3.29	0.06	290.47-329.87
Total	1.94	6.75	0.03	290.47-333.62

After determining of brightness temperature

T_{g}^{0}

, the bidirectional reflectivity ρ_b 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.

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

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]

], 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.

It should be pointed out that the validation of Tang and Li’s algorithm [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]

] 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

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]

] 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

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]

], 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

T_{g}^{0}

with Tang and Li’s (2008) algorithm and the atmospheric radiative transfer equation respectively, the accuracy of the MODIS-derived bidirectional reflectivity (ρ_b) 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(S1 Supp), 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]

OCIS Codes
(070.4790) Fourier optics and signal processing : Spectrum analysis
(120.0120) Instrumentation, measurement, and metrology : Instrumentation, measurement, and metrology
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(350.6980) Other areas of optics : Transforms
(280.4991) Remote sensing and sensors : Passive remote sensing
(290.6815) Scattering : Thermal emission

ToC Category:
Remote Sensing

History
Original Manuscript: June 18, 2012
Revised Manuscript: July 13, 2012
Manuscript Accepted: July 15, 2012
Published: July 19, 2012

Citation
Bo-Hui Tang, Hua- Wu, Zhao-Liang Li, and Françoise Nerry, "Validation of MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared channel with field measurements," Opt. Express 20, 17760-17766 (2012)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-20-16-17760

Sort: Author | Year | Journal | Reset

References

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]
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]
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]
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]
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]
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]
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]
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]
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]
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]

IEEE Trans. Geosci. Rem. Sens.

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.

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

Remote Sens. Environ.

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]
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]
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]
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]

Sci. China Ser. E: Technol. Sci.

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]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

Click here to see a list of articles that cite this paper