Development of compositing algorithms for MODIS data
This paper focuses on the evaluation of compositing algorithms of MODerate resolution Imaging Spectroradiometer (MODIS) . The atmospheric effects of channel 1-7 have been investigated by radiative transfer simulation using 6S code over variety of aerosol and climatic conditions. As a result, blue wa...
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Published in | Journal of the Japan society of photogrammetry and remote sensing Vol. 43; no. 5; pp. 36 - 48 |
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Format | Journal Article |
Language | English |
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Japan Society of Photogrammetry and Remote Sensing
2004
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Abstract | This paper focuses on the evaluation of compositing algorithms of MODerate resolution Imaging Spectroradiometer (MODIS) . The atmospheric effects of channel 1-7 have been investigated by radiative transfer simulation using 6S code over variety of aerosol and climatic conditions. As a result, blue wavelength (channel 3) was found to be the most subject to atmospheric effects and its apparent reflectance values were higher than other channels. However, this method is subject to select cloud shadows as a optimum values. To avoid this problem, the combination of minimum blue constrained with thermal channel criteria is newly developed based on the assumption that the cloud shadow is cooler than normal pixels. These two approaches, to say, the minimum blue criterion (MinB) and the minimum blue and maximum thermal criteria (TMinB) have been tested along with five approaches including the maximum NDVI criterion (MaxN), the maximum temperature criterion (MaxT), the NDVI criteria followed by minimum scan angle (NMinS), the thermal criteria followed by minimum scan angle (TMinS) and the NDVI and thermal criteria followed by minimum scan angle method (NTMinS), to improve on the heritage of Advanced Very High Resolution Radiometer (AVHRR) . Then the 10 day composite images over Japan main islands have been generated using the above mentioned seven approaches. The comparative performances of these seven methods are judged from five criterion including cloud screening, satellite zenith angle, smoothness of the image, snow/ice identification and cloud shadow. It is concluded that the TMinB method produces fairly consistent results with possible refinements and more sophisticated methods to escape from cloud shadows. |
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AbstractList | This paper focuses on the evaluation of compositing algorithms of MODerate resolution Imaging Spectroradiometer (MODIS) . The atmospheric effects of channel 1-7 have been investigated by radiative transfer simulation using 6S code over variety of aerosol and climatic conditions. As a result, blue wavelength (channel 3) was found to be the most subject to atmospheric effects and its apparent reflectance values were higher than other channels. However, this method is subject to select cloud shadows as a optimum values. To avoid this problem, the combination of minimum blue constrained with thermal channel criteria is newly developed based on the assumption that the cloud shadow is cooler than normal pixels. These two approaches, to say, the minimum blue criterion (MinB) and the minimum blue and maximum thermal criteria (TMinB) have been tested along with five approaches including the maximum NDVI criterion (MaxN), the maximum temperature criterion (MaxT), the NDVI criteria followed by minimum scan angle (NMinS), the thermal criteria followed by minimum scan angle (TMinS) and the NDVI and thermal criteria followed by minimum scan angle method (NTMinS), to improve on the heritage of Advanced Very High Resolution Radiometer (AVHRR) . Then the 10 day composite images over Japan main islands have been generated using the above mentioned seven approaches. The comparative performances of these seven methods are judged from five criterion including cloud screening, satellite zenith angle, smoothness of the image, snow/ice identification and cloud shadow. It is concluded that the TMinB method produces fairly consistent results with possible refinements and more sophisticated methods to escape from cloud shadows. |
Author | TAKEUCHI, Wataru YASUOKA, Yoshifumi |
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References | Holben, B.N., 1986. Characteristics of maximumvalue composite images from temporal AVHRR data. Int. J. Remote Sens., 15 (1) , 145-161. Hall, D.K., Riggs, G.A., and Salmonson, V.V., 2001. Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms, 15-24. 竹内渉, 安岡善文, 2003b. MODISデータにおける大気補正効果の検討. 日本写真測量学会平成15年春期学術講演会論文集, 45-48. 雷莉萍, 横山隆三, 2001. 全アジア地域NOAA/AVHRR 10日間モザイク画像の合成法 (II) . 日本リモートセンシング学会誌, 21 (2) , 168-178. Vermote, E.F., and Vermeulen, A., 1999. Algorithm Theoretical Basis Document (ATBD) for atmospheric correction algorithm: spectral reflectances (MOD09) , 32-34. Simpson, J.J., McIntire, T., Jin, Z., and Stitt, J.R., 2000. Improved Cloud Top Height Retrieval under Arbitrary Viewing and Illumination Conditions Using AVHRR Data. Remote Sens. Environ, 72 (1) , 95-110. Stoms, D.M., Bueno, D.M., and Davis, F.W., 1997. Viewing geometry of AVHRR image composites derived using multiple criteria. Photogram. Eng. and Remote Sens., 63 (6) , 681-689. Moody, A., and Strahler, A.H., 1994. Characteristics of composited AVHRR data and problems in their classification. Int. J. Remote Sens., 23 (15) , 3473-3491. Sandmeier, S., and Itten, K.I., 1997. A Physicallybased model to correct atmospheric and illumination effects in optical satellite data of rugged terrain. IEEE. Geosci. and Remote Sens., 35 (3) , 708-717. Cihlar, J., Ly, H., Li, Z., Chen, J., Pokrant, H., and Huang, F., 1997. Multitemporal, multichannel AVHRR data sets for land biosphere studiesartifacts and corrections. Remote Sens. Environ., 60, 35-57. 橋本俊昭, 2002. MODISの幾何補正について. 写真測量とリモートセンシング, 41 (3) , 23-27. 竹内渉, 根本利弘, Baruah, PJ. , 越智士郎, 安岡善文, 2003a. WWWを利用したTerra MODISデータ処理システムの構築. 写真測量とリモートセンシング, 42 (2) , 21-27. Cihlar, J., Manak, D., and D'lorio, M., 1994. Evaluation of compositing algorithms for AVHRR data over land. IEEE Trans. on GeoSci. and Remote Sens., 32 (2) , 427-437. Justice, C., et al., 1998. The Moderate Resolution Imaging Spectroradiometer (MODIS) : Land remote sensing for global change research. IEEE Trans. on Geosci. and Remote Sens., 36 (4) , 1228-1249. Van Leeuwen, W.J.D., Huete, A.R., and Laing, T.W., 1999. MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data. Remote Sens. Environ., 69 (3) , 264-280. |
References_xml | – reference: Vermote, E.F., and Vermeulen, A., 1999. Algorithm Theoretical Basis Document (ATBD) for atmospheric correction algorithm: spectral reflectances (MOD09) , 32-34. – reference: Cihlar, J., Manak, D., and D'lorio, M., 1994. Evaluation of compositing algorithms for AVHRR data over land. IEEE Trans. on GeoSci. and Remote Sens., 32 (2) , 427-437. – reference: 雷莉萍, 横山隆三, 2001. 全アジア地域NOAA/AVHRR 10日間モザイク画像の合成法 (II) . 日本リモートセンシング学会誌, 21 (2) , 168-178. – reference: Simpson, J.J., McIntire, T., Jin, Z., and Stitt, J.R., 2000. Improved Cloud Top Height Retrieval under Arbitrary Viewing and Illumination Conditions Using AVHRR Data. Remote Sens. Environ, 72 (1) , 95-110. – reference: 竹内渉, 根本利弘, Baruah, PJ. , 越智士郎, 安岡善文, 2003a. WWWを利用したTerra MODISデータ処理システムの構築. 写真測量とリモートセンシング, 42 (2) , 21-27. – reference: Hall, D.K., Riggs, G.A., and Salmonson, V.V., 2001. Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms, 15-24. – reference: Sandmeier, S., and Itten, K.I., 1997. A Physicallybased model to correct atmospheric and illumination effects in optical satellite data of rugged terrain. IEEE. Geosci. and Remote Sens., 35 (3) , 708-717. – reference: Stoms, D.M., Bueno, D.M., and Davis, F.W., 1997. Viewing geometry of AVHRR image composites derived using multiple criteria. Photogram. Eng. and Remote Sens., 63 (6) , 681-689. – reference: 竹内渉, 安岡善文, 2003b. MODISデータにおける大気補正効果の検討. 日本写真測量学会平成15年春期学術講演会論文集, 45-48. – reference: Holben, B.N., 1986. Characteristics of maximumvalue composite images from temporal AVHRR data. Int. J. Remote Sens., 15 (1) , 145-161. – reference: Justice, C., et al., 1998. The Moderate Resolution Imaging Spectroradiometer (MODIS) : Land remote sensing for global change research. IEEE Trans. on Geosci. and Remote Sens., 36 (4) , 1228-1249. – reference: 橋本俊昭, 2002. MODISの幾何補正について. 写真測量とリモートセンシング, 41 (3) , 23-27. – reference: Cihlar, J., Ly, H., Li, Z., Chen, J., Pokrant, H., and Huang, F., 1997. Multitemporal, multichannel AVHRR data sets for land biosphere studiesartifacts and corrections. Remote Sens. Environ., 60, 35-57. – reference: Van Leeuwen, W.J.D., Huete, A.R., and Laing, T.W., 1999. MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data. Remote Sens. Environ., 69 (3) , 264-280. – reference: Moody, A., and Strahler, A.H., 1994. Characteristics of composited AVHRR data and problems in their classification. Int. J. Remote Sens., 23 (15) , 3473-3491. |
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