Reflectance factor retrieval from Landsat TM and SPOT HRV data for bright and dark targets

Abstract In recent years, there have been many land-surface studies based on visible and near-infrared reflectance values retrieved from the Landsat Thematic Mapper (TM) and SPOT High Resolution Visible (HRV) sensors. Retrieval of reflectance from satellite sensor digital count requires knowledge of the atmospheric conditions and the sensor absolute calibration. In most cases, atmospheric conditions are simulated with a radiative transfer code and sensor calibration coefficients are obtained from preflight sensor calibrations or in-flight calibrations over bright surfaces (such as White Sands, New Mexico, USA, or La Crau, France). Though these procedures are well accepted, there have been few studies specifically designed to validate the accuracy of such reflectance factor retrievals (RFR) for both bright and dark targets. Data from two experiments conducted in an agricultural region in central Arizona were analyzed to quantify the accuracy of RFR from the Landsat TM and SPOT HRV sensors. These data included measurements made with groundbased and aircraft-based four-band radiometers and the NASA Advanced Solid-State Array Spectrometer (ASAS) aboard a C130 aircraft, and TM and HRV images acquired at nadir and off-nadir viewing angles. Results showed that the off-nadir reflectance factors measured using ground- and aircraft-based instruments, including ASAS, were comparable. The RFR from the satellite-based TM and HR V sensors generally resulted in an overestimation of dark target reflectance (up to 0.05 reflectance in the visible) and an underestimation of bright target reflectance (up to 0.1 reflectance in the near-infrared). Even greater error was possible when RFR was based on outdated sensor calibrations, particularly those conducted prelaunch. There was supporting evidence from studies at three sites (White Sands, New Mexico; Maricopa, Arizona; and Walnut Gulch, Arizona) that the Landsat-5 TM sensor sensitivity may have degraded by as much as 20% from the prelaunch calibration. Regarding the potential error in RFR related to recent changes in the processing of Landsat TM data (Level-0 and Level-1) by EOSAT Corporation, we found that the Level-0 data was slightly greater (∼2 digital counts) than the Level-1 data for all bands and all targets in our study.

[1]  B. Herman A NUMERICAL SOLUTION TO THE EQUATION OF RADIATIVE TRANSFER FOR PARTICLES IN THE MIE REGION , 1965 .

[2]  M. S. Moran,et al.  Obtaining Surface Reflectance Factors from Atmospheric and View Angle Corrected SPOT-1 HRV Data , 1990 .

[3]  J. Hill,et al.  Comparative analysis of landsat-5 TM and SPOT HRV-1 data for use in multiple sensor approaches , 1990 .

[4]  M. S. Moran,et al.  Bidirectional measurements of surface reflectance for view angle corrections of oblique imagery , 1990 .

[5]  Darrel L. Williams,et al.  An off-nadir-pointing imaging spectroradiometer for terrestrial ecosystem studies , 1991, IEEE Trans. Geosci. Remote. Sens..

[6]  Xingfa Gu,et al.  Effect of radiometric corrections on NDVI-determined from SPOT-HRV and Landsat-TM data , 1994 .

[7]  M. Spanner,et al.  Atmospheric correction of remotely sensed image data by a simplified model , 1992 .

[8]  Xingfa Gu,et al.  Evaluation of measurement errors in ground surface reflectance for satellite calibration , 1992 .

[9]  J. Hill,et al.  Radiometric correction of multitemporal Thematic Mapper data for use in agricultural land-cover classification and vegetation monitoring , 1991 .

[10]  J. C. Price,et al.  Combining panchromatic and multispectral imagery dual resolution satellite instruments , 1987 .

[11]  R. D. Jackson The MAC experiments , 1990 .

[12]  P. Deschamps,et al.  Description of a computer code to simulate the satellite signal in the solar spectrum : the 5S code , 1990 .

[13]  Philip N. Slater,et al.  Atmospheric effects on radiation reflected from soil and vegetation as measured by orbital sensors using various scanning directions. , 1982, Applied optics.

[14]  M. S. Moran,et al.  A window-based technique for combining landsat thematic mapper thermal data with higher-resolution multispectral data over agricultural lands , 1990 .

[15]  M. S. Moran,et al.  Reflectance- and radiance-based methods for the in-flight absolute calibration of multispectral sensors , 1987 .

[16]  P. Slater,et al.  Improved evaluation of optical depth components from langley plot data , 1990 .

[17]  Brian L. Markham,et al.  Surface reflectance retrieval from satellite and aircraft sensors: Results of sensor and algorithm comparisons during FIFE , 1992 .

[18]  Brian L. Markham,et al.  Aerosol optical depth retrievals over the Konza Prairie , 1992 .

[19]  M. S. Moran,et al.  Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output , 1992 .

[20]  M. S. Moran,et al.  Bidirectional reflectance factors of agricultural targets: A comparison of ground-, aircraft-, and satellite-based observations , 1990 .

[21]  Craig S. T. Daughtry,et al.  Differences in vegetation indices for simulated Landsat-5 MSS and TM, NOAA-9 AVHRR, and SPOT-1 sensor systems , 1987 .

[22]  G. Dedieu,et al.  SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum , 1994 .

[23]  M. S. Moran,et al.  Field calibration of reference reflectance panels , 1987 .

[24]  J. L. Barker,et al.  Landsat MSS and TM post-calibration dynamic ranges , 1986 .

[25]  M. S. Moran,et al.  Surface Reflectance Factor Retrieval from Thematic Mapper Data , 1989 .

[26]  M. S. Moran,et al.  Absolute Radiometric Calibration Of The Thematic Mapper , 1986, Other Conferences.

[27]  Stuart F. Biggar,et al.  Review of SPOT-1 and -2 calibrations at White Sands from launch to the present , 1993, Defense, Security, and Sensing.

[28]  M. S. Moran,et al.  Normalization of sun/view angle effects using spectral albedo-based vegetation indices , 1995 .

[29]  Stuart F. Biggar,et al.  In-flight radiometric calibration of Landsat-5 Thematic Mapper from 1984 to the present , 1993, Defense, Security, and Sensing.