Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe

Abstract

ElsevierCaselles, E.; Valor, E.; Abad Cerdá, FJ.; Caselles, V. (2012). Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe. Remote Sensing of Environment. 124:321-333. doi:10.1016Environment. 124:321-333. doi:10. /j.rse.2012 Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe ABSTRACTThe remote sensing measurement of land surface temperature from satellites provides a monitoring of this magnitude on a continuous and regular basis, which is a critical factor in many research fields such as weather forecasting, detection of forest fires or climate change studies, for instance. The main problem of measuring temperature from space is the need to correct for the effects of the atmosphere and the surface emissivity. In this work an automatic procedure based on the Vegetation Cover Method, combined with the GLOBCOVER land surface type classification, is proposed. The algorithm combines this land cover classification with remote sensing information on the vegetation cover fraction to obtain land surface emissivity maps for AATSR split-window bands. The emissivity estimates have been compared with ground measurements in two validation cases in the area of rice fields of Valencia, Spain, and they have also been compared to the classification-based emissivity product provided by MODIS (MOD11_L2). The results show that the error in emissivity of the proposed methodology is of the order of ±0.01 for 2 most of the land surface classes considered, which will contribute to improve the operational land surface temperature measurements provided by the AATSR instrument.

Assessment of Mono- and Split-Window Approaches for Time Series Processing of LST from AVHRR—A TIMELINE Round Robin

“…A first comparison with MODIS data using the Becker and Li 1990 approach combined with the Vegetation Cover Method (VCM) from [30] showed despite of some cloud contaminated pixels a good agreement with an overall MAD between AVHRR and MODIS of 1.8 K [K], and an overall standard deviation of 1.4 [K]. This showed a clear improvement over the original AVHRR LST approach assessed in [40], which had an overall MAD of 3.3 K.…”

“…Further, a land use classification [29] was used to retrieve emissivity values for the application of the algorithm on real AVHRR data. The emissivity estimation was done using the Vegetation Cover Method (VCM) from [30].…”

“…For the comparison with MODIS data, the emissivity was estimated using the Vegetation cover method (VCM) from [30]. The VCM method was proposed for AATSR data.…”

“…The coefficients ε kg , ε kv and dε k are dependent on land surface cover and the spectral band. Additionally, to the estimation of band emissivity, the method of [30] offers the possibility to estimate the related uncertainty of the emissivity. It should be noted that this expression does not account for the anisotropy of emissivity.…”

“…To check the validity of the approach, AVHRR LST calculated with the Becker and Li 1990 algorithm combined with the Vegetation Cover Method (VCM) from [30] was compared with the MODIS V005 LST product (MOD11A1/MYD11A1) and the results of using the original Becker and Li 1990 coefficients and the NDVI approach of [40]. The comparison was done for the year 2001 and NOAA-16.…”