Land surface emissivity retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surfaces of Delhi city

Emissivity and surface temperature enables better understanding of the overall urban land use/land cover classes and in turn helps in understanding the energy budget issues. In the present study it has been demonstrated that the notion of the assumed spectral emissivity (i.e. 1) induces errors in modeling the surface energy budget and urban climatology (micro-climate), especially over heterogeneous surface areas (urban) where emissivity is far smaller than unity.An attempt has been made to derive emissivity by using normalized difference moisture index (NDMI). The emissivity per pixel has been retrieved directly from satellite data and has been estimated as narrow band emissivity at the satellite sensor channel in order to have least error in the surface temperature estimation. The estimated emissivity values over few land use/land cover (LULC) classes of LANDSAT TM have been compared with the literature values and field measurement emissivity data using infrared thermometer.A strong correlation is observed between surface temperatures with NDMI over different LULC classes. A regression relation between these parameters has been estimated (Pearson's correlation of 0.938), indicating that surface temperatures can be predicted if NDMI values are known. The error in field data (in situ) and satellite derived surface temperature is within the range of 2–3 °C. The correlation coefficient between the satellite derived and field observed surface temperature is very high ≈0.942 (significant at p value = 0.01). The results suggest that the methodology is feasible to estimate NDMI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban areas.

► Reveals that constant emissivity notion induces errors in modeling energy budget.
► Derivation of emissivity by using normalized difference moisture index.
► Emissivity and temperature derivation over heterogeneous surfaces.
► Robust methodology to estimate NDMI, surface emissivity and surface temperature.