Integrating AVHRR satellite data and NOAA ground observations to predict surface air temperature: a statistical approach

• Published in: International journal of remote sensing Vol. 25; no. 15; pp. 2979 - 2994
• Main Authors: Florio, E. N., Lele, S. R., Chi Chang, Y., Sterner, R., Glass, G. E.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 01.08.2004; Taylor and Francis
• Subjects: Earth, ocean, space; Exact sciences and technology; External geophysics; Geophysics. Techniques, methods, instrumentation and models; Meteorology; Other topics in atmospheric geophysics; United States; North America; America; NOAA satellite; High resolution; Statistical analysis; Image processing; Linear regression; Ground based measurement; Space remote sensing; Geostatistics; Satellite observation; Surface temperature; Cross validation; Earth surface; Algorithm; Kriging; Multiple regression; Atmospheric temperature; Radiometry
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160310001624593

Ground station temperature data are not commonly used simultaneously with the Advanced Very High Resolution Radiometer (AVHRR) to model and predict air temperature or land surface temperature. Technology was developed to acquire near-synchronous datasets over a 1 000 000 km 2 region with the goal of improving the measurement of air temperature at the surface. This study compares several statistical approaches that combine a simple AVHRR split window algorithm with ground meterological station observations in the prediction of air temperature. Three spatially dependent (kriging) models were examined, along with their non-spatial counterparts (multiple linear regressions). Cross-validation showed that the kriging models predicted temperature better (an average of 0.9°C error) than the multiple regression models (an average of 1.4°C error). The three different kriging strategies performed similarly when compared to each other. Errors from kriging models were unbiased while regression models tended to give biased predicted values. Modest improvements seen after combining the data sources suggest that, in addition to air temperature modelling, the approach may be useful in land surface temperature modelling.