Spectral Adjustment Model's Analysis and Application to Remote Sensing Data

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2019-03, Vol.12 (3), p.961-972
• Main Authors: Villaescusa-Nadal, Jose Luis, Franch, Belen, Roger, Jean-Claude, Vermote, Eric F., Skakun, Sergii, Justice, Chris
• Format: Article
• Language: English
• Subjects: Accuracy; Advanced very high resolution radiometer (AVHRR); Artificial satellites; calibration; Computer simulation; Data; Data processing; Dependence; Earth; Environmental Sciences; Exploitation; Imaging techniques; Infrared imaging; Infrared radiometers; Land cover; Landsat; Landsat satellites; Meteorological satellites; Methods; moderate resolution imaging spectroradiometer (MODIS); normalized difference vegetation index (NDVI); Normalized difference vegetative index; Optical measuring instruments; Physics; Radiometers; Radiometry; Regression analysis; relative spectral response; Remote sensing; Remote sensors; Resolution; Response functions; Satellite broadcasting; Satellites; Sensors; Sentinel 2; Spatial discrimination; Spatial resolution; Spectra; spectral adjustment; spectral band adjustment factor (SBAF); Spectral sensitivity; Spectroradiometers; surface reflectance; visible infrared imaging radiometer suite (VIIRS)
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2018.2890068

Differences in the relative spectral response functions of sensors lead to data inconsistencies that should be harmonized before multisensor exploitation. In this paper, we use spectral libraries to simulate satellite data and build models to correct them. We then explore and compare different models for coarse and medium spatial resolution optical sensors, including moderate resolution imaging spectroradiometer, advanced very high resolution radiometer (AVHRR), visible infrared imaging radiometer suite, multispectral instrument aboard Sentinel-2, and Operational Land Imager aboard Landsat 8. We found that optimal correction of different bands depends on the model used. For the green and near infrared bands, a multilinear land cover dependent regression improves the accuracy by up to 80.9%. For the red band, a novel exponential dependence of the spectral band adjustment factor with the normalized difference vegetation index (NDVI) provides an accuracy improvement of up to 72.8%. The best way to correct the NDVI value is to use the corrected NIR and red bands using these models. We apply the proposed methods to 445 BELMANIP2 sites using AVHRR data from the long-term data record from 1982-2017. High NDVI pixels result in 30-year trends varying up to 0.06 when comparing uncorrected to spectrally adjusted NDVI. Further application of these methods to NASA's Harmonized Landsat and Sentinel 2 product shows that for the red band and NDVI, our proposed method provides improved accuracy (54.6% and 62.5%) over the linear spectral adjustment currently used.