Review article
Land surface temperature and emissivity estimation
from passive sensor data: theory and practice; current trends
Prasanjit Dash1,
Frank-M. Göttsche, Folke-S. Olesen, and Herbert Fischer
Institute for Meteorology and Climate ResearchForschungszentrum Karlsruhe/University of Karlsruhe
Postfach 3640, D-76021 Karlsruhe, Germany
1Corresponding
author: E-Mail: prasanjit.dash@imk.fzk.de; Tel.: +49-7247-82-3822;
Fax:
+49-7247-82-4742; WWW: http://www.fzk.de/imk/imk2/isys
International Journal of Remote Sensing, vol. 23, issue 13, pp. 2563-2594
|
Abstract
Land Surface Temperature (LST) and emissivity for
large areas can only be derived from
surface-leaving radiation measured by satellite sensors. These measurements
represent the integrated effect of the surface and are, thus, for many
applications, superior to point measurements on the ground, e.g. in Earth’s
radiation budget and climate change detection. Over the years, a substantial
amount of research was dedicated to the estimation of LST and emissivity from
passive sensor data. This paper provides the theoretical basis and gives an
overview of the current status of this research. Sensors operating in the
visible, infrared, and microwave range onboard various meteorological
satellites are considered, e.g. Meteosat-MVIRI, NOAA-AVHRR, ERS-ATSR,
Terra-MODIS, Terra-ASTER, and DMSP-SSM/I. Atmospheric effects on measured
brightness temperatures are described and atmospheric corrections using
Radiative Transfer Models (RTM) are explained. The substitution of RTM with
Neural Networks (NN) for faster forward calculations is also discussed. The
reviewed methods for LST estimation are the single-channel method, the
Split-Window Techniques (SWT), and the multi-angle method, and, for emissivity
estimation, the Normalized Emissivity Method (NEM), the Thermal Infrared
Spectral Indices (TISI) method, the spectral ratio method, alpha residuals,
Normalized Difference Vegetation Index (NDVI)-based methods,
classification-based emissivity, and the Temperature Emissivity Separation
(TES) algorithm.
