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

Bo-Hui Tang; Hua- Wu; Zhao-Liang Li; Françoise Nerry

doi:10.1364/OE.20.017760

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

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

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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)

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Table of Contents Category
- Remote Sensing
Optics & Photonics Topics
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The topics in this list come from the OSA Optics and Photonics Topics applied to this article.
Previously assigned OCIS codes
- Spectrum analysis (070.4790)
- Instrumentation, measurement, and metrology (120.0120)
- Transforms (350.6980)
- Passive remote sensing (280.4991)
- Remote sensing and sensors (280.0280)
- Thermal emission (290.6815)

About this Article
History
- Original Manuscript: June 18, 2012
- Revised Manuscript: July 13, 2012
- Manuscript Accepted: July 15, 2012
- Published: July 19, 2012

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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.

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References

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Year
|
Author
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Publication

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. Express 15(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. Express 17(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)

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]

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)

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]

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]

Curran, P. J.

DaCamara, C. C.

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.

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.

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 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.

Nerry, F.

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.

Peres, L. F.

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]

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.

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.

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 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]

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. (1)

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Fig. 1 Comparison of brightness temperature

T_{g}^{0}

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

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

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

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Tables (2)

Table 1 Nine surface materials used as the validation data

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Table 2 Statistical error of $T_{g}^{0}$ between the modeled and calculated brightness temperature and the variation range of calculated $T_{g}^{0}$

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Equations (4)

Equations on this page are rendered with MathJax. Learn more.

ρ_{b} = \frac{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_{1 i} + b_{2 i} \cos (S Z A) + b_{3 i} \cos^{2} (S Z A),

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} ↓)

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%), ...

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

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%), ...

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

Abstract

References

2010 (1)

2009 (1)

2008 (1)

2007 (1)

2004 (1)

2000 (1)

1999 (1)

1998 (1)

1997 (1)

1994 (1)

1993 (1)

Becker, F.

Bi, Y.

Boyd, D. S.

Curran, P. J.

DaCamara, C. C.

Feng, Y.

Foody, G. M.

Kanani, K.

Kaufman, Y. J.

Li, Z.-L.

Libonati, R.

Nerry, F.

Pereira, J. M. C.

Peres, L. F.

Petitcolin, F.

Poutier, L.

Remer, L. A.

Snyder, W. C.

Stoll, M. P.

Tang, B.

Tang, B.-H.

Wan, Z.

Zhang, R. H.

Zhang, Y.

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

Int. J. Remote Sens. (3)

Opt. Express (2)

Remote Sens. Environ. (4)

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

Cited By

Figures (3)

Tables (2)

Equations (4)

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