Abstract
To understand the absolute radiometric calibration accuracy of the HJ-A CCD-1 sensors, image from these sensors were compared to nearly simultaneously image from Landsat-7 ETM+ sensors. Although the HJ-A CCD-1 sensor has almost the same wavelength of each central band and band width as Landsat-7 ETM+ sensor, there is slightly difference in spectral response function (SRF). The impacts of SRF difference effects would produce ~2 % uncertainty in predicting reflectance of HJ-A CCD-1 sensor using Landsat-7 ETM+ sensor. The reflectance observed by satellite at top-of-atmosphere generally depends on its’ geometric conditions. The results reveal that the impacts of geometrical conditions would impact on the vicarious cross-calibration accuracy, which should be removed. The performances of cross-calibration are calibrated and validated by four image pairs collected from Yellow River Delta, China, and Qingdao City, China, at four independent times. The results indicate that the HJ-A CCD-1 sensors can be cross calibrated to the Landsat-7 ETM+ sensors to within an accuracy of 3.99 % (denoted by Relative Root Mean Square Error) of each other in all bands except band 4, which has a 6.33 % difference.
Similar content being viewed by others
References
Arai, K. (2007). Vicarious calibration of the solar reflection channels of radiometers onboard satellites through the field campaigns with measurements of refractive index and size distribution of aerosols. Advances in Space Research, 39, 13–19.
Chander, G., Makham, B. L., & Barsi, J. A. (2007). Revised Landsat-5 Thematic Mapper radiometric calibration. IEEE Transactions on Geoscience and Remote Sensing, 4, 490–494.
Chander, G., Coan, M. J., & Scaramuzza, P. L. (2008). Evaluation and comparison of the IRS-P6 and Landsat sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(1), 209–220.
Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113, 893–903.
Chander, G., Xiong, X., Choi, T., & Angal, A. (2010). Monitoring on-orbit calibration stability of the Terra MODIS and Landsat 7 ETM+ sensors using pseudo-invariant test sites. Remote Sensing of Environment, 114, 925–939.
Chelton, D. B., Esbensen, S. K., Schlax, M. G., Thum, N., Freilich, M. H., Wentz, F. J., et al. (2001). Observations of coupling between surface wind stress and SST in the eastern tropical Pacific. Journal of Climate, 14, 1479–1498.
Chen, J., & Zhao, Q. S. (2013). An approach for estimating MODIS reflectance at top-of-atmosphere at 667 and 678 nm from reflectance at 645nm in turbid waters. Earth Environmental Sciences. doi:10.1007/s12665-013-2514-9.
Chen, J., Fu, J., & Zhang, M. W. (2011a). An atmospheric correction algorithm for Landsat/TM imagery basing on inverse distance spatial interpolation algorithm: a case study in Taihu Lake. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(4), 882–889.
Chen, J., Lu, K., & Fu, J. (2011b). Theoretical model for estimating the scaling error of the two-band ratio of red to near-infrared in inhomogeneous pixels: simulation using a moving window. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(4), 877–881.
Chen, J., Fu, J., & Sun, J. H. (2012). Using Landsat/TM imagery to estimating nitrogen and phosphorus concentration in Taihu Lake, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(1), 273–280.
Chen, J., D’Sa, E., Cui, T. W., & Zhang, X. H. (2013a). A semi-analytical total suspended retrieval model in turbid coastal waters: a case study in Changjiang River Estuary. Optics Express, 21(11), 13018–13031.
Chen, J., Sheng, H., & Sun, J. H. (2013b). An empirical algorithm for hyperspectral remote sensing of chlorophyll-a in turbid waters: a case study on Hyperion sensor. Sensor Letters, 11, 623–631.
Chen, J., Yi, C. L., & Wen, Z. H. (2013c). Systematic underestimation of MODIS global chlorophyll-a concentration estimation algorithm associating with scale effect. IEEE Sensor Journal, 13(5), 1656–1661.
Chen, J., Zhang, M.W., Cui, T.W., & Quan, W.T. (2013d). An improved SWIR atmospheric correction model: a direction-based model. IEEE Transactions on Geoscience and Remote Sensing, Accepted.
Chen, J., Zhang, M. W., Cui, T. W., & Wen, Z. H. (2013e). A review of some important technical problems in respect of satellite remote sensing of chlorophyll-a concentration in coastal waters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi:10.1109/JSTARS.2013.2242845.
Gordon, H. R. (1993). Sensitivity of radiative transfer to small-angle scattering in the ocean: quantitative assessment. Applied Optics, 32(36), 7505–7511.
Gordon, H. R., & Franz, B. A. (2008). Remote sensing fo ocean color: assessment of the water-leaving radiance bidirectional effects on the atmospheric diffuse transmittance for SeaWiFS and MODIS intercomparisons. Remote Sensing of Environment, 112, 2667–2685.
Gordon, H.R., & Voss, K.J., (1999). MODIS normalized water-leaving radiance algorithm theoretical basis document. NASA Technical Report Series, NAS5-31363.
Green, R. O., & Shimada, M. (1997). On-orbit calibration of a multi-spectral satellite sensor using a high altitude airborne imaging spectrometer. Advances in Space Research, 19, 1387–1398.
Makham, B. L., Thome, K., Barsi, J. A., Kaita, E., Helder, D. L., Baker, J., et al. (2004). Landsat-7 ETM+ on-orbit reflective-band radiometric stability and absolute calibration. IEEE Transactions on Geoscience and Remote Sensing, 42, 2810–2820.
Markham, B. L., & Helder, D. L. (2012). Forty-year calibrated record of earth-reflected radiance from Landsat: A review keywords: Landsat, Radiometric Calibration, History. MSS, TM, ETM+. Remote Sensing of Environment. doi:10.1016/j.rse.2011.1006.1026.
Roy, D. P., Borak, J. S., Devadiga, S., Wolfe, R. E., Zheng, M., & Descloitres, J. (2002). The MODIS Land product quality assessment approach. Remote Sensing of Environment, 83, 62–76.
Scaramuzza, P. L., Markham, B. L., Barsi, J. A., & Kaita, E. (2004). Landsat-7 ETM+ on-orbit reflective-band radiometric characterization. IEEE Transactions on Geoscience and Remote Sensing, 42, 2810–2820.
Shimoda, H., & Xiong, X. X. (2012). Earth observing missions and sensors; development, implementation, and characterization II. Proceedings of SPIE, 8528, 41.
Teillet, P. M., Markham, B. L., & Irish, R. R. (2006). Landsat cross-calibration based on near simultaneous imaging of common ground targets. Remote Sensing of Environment, 102, 264–270.
Teillet, P. M., Fedosejevs, G., Thome, K. J., & Barker, J. L. (2007). Impacts of spectral band difference effects on radiometric cross-calibration between satellite sensors in the solar-reflective spectral domain. Remote Sensing of Environment, 110, 393–409.
Thome, K. J. (2001). Absolute radiometric calibration of Landsat 7 ETM+ using the reflectance-based method. Remote Sensing of Environment, 78, 27–38.
Thome, K. J., Biggar, S. F., & Wisniewski, W. (2003). Cross comparison of EO-1 sensors and other earth resources sensors to Landsat-7 ETM+ using railroad valley playa. IEEE Transactions on Geoscience and Remote Sensing, 41, 1180–1188.
Van-Laake, P. E., & Sanchez-Azofeifa, G. A. (2004). Simplified atmospheric radiative transfer modelling for estimating incident PAR using MODIS atmosphere products. Remote Sensing of Environment, 91, 98–113.
Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M., & Morcrette, J. J. (1997). Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35, 675–686.
von Engeln, A., Accadia, C., Ackermann, J., Marquardt, C., Andres, Y., Lazaro, D., et al. (2011). Potentials for radio occultation applications during inter-satellite calibration periods. Advances in Space Research, 47, 1731–1742.
Wang, S. D., Miao, L. L., & Peng, G. X. (2012). An improved algorithm for forest fire detection using HJ data. Procedia Environmental Sciences, 13, 140–150.
Zhai, P.-W., Hu, Y., Trepte, C. R., Lucker, P. L., & Josset, D. B. (2010). Decoupling error for the atmospheric correction in ocean color remote sensing algorithms. Journal of Quantitative Spectroscopy and Radiative Transfer, 111, 1958–1963.
Zhao, W. J., Tamura, M., & Takahashi, H. (2000). Atmospheric and spectral corrections for estimating surface albedo from satellite data using 6S code. Remote Sensing of Environment, 76, 202–212.
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Quan, W. Vicarious Cross-Calibration of the China Environment Satellite Using Nearly Simultaneously Observations of Landsat-7 ETM+ Sensor. J Indian Soc Remote Sens 42, 539–548 (2014). https://doi.org/10.1007/s12524-013-0322-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-013-0322-z