Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends

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 article 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 methods reviewed 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.