Momeni, M, Saradjian, MR (2007). Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by Day/Night LST algorithm. REMOTE SENSING OF ENVIRONMENT, 106(2), 190-198.
Surface emissivity estimation is a significant factor for the land surface temperature estimation from remotely sensed data. For fully vegetated surfaces, the emissivity estimation is performed in a simple manner since the emissivity is relatively uniform. However, for and land with sparse vegetation, the estimation is more complicated since the emissivity of the exposed soil and rock is highly variable. In this study, mean and difference emissivity for bands 31 and 32 of MODIS sensor have been derived based on NDVI values. First, the NDVI thresholds have been determined to separate bare soil, partially vegetated soil and fully vegetated land. Then regression relations have been derived to estimate mean and difference emissivity of the bare soil samples and partially vegetated surfaces. A constant emissivity is also used for fully vegetated area. Along with the correlations, standard deviations of the regression relations have been examined for a set of representative soil types. Standard deviations smaller than 0.003 in mean emissivity and smaller than 0.004 in difference emissivity are resulted in regression linear relations. Evaluation of the NDVI derived regression relations has been performed using the results of MODIS Day/Night Land Surface Temperature (LST) algorithm on a pair of MODIS images. Using around 45,500 pixels with different soil and land cover types, emissivity of each pixel in bands 31 and 32 have been estimated. The calculated emissivities have been compared with emissivities calculated by MODIS Day/Night LST algorithm. Biases and standard deviations of NDVI-based relations show relatively high agreement for mean and difference emissivity relations with Day/Night method results. It may be concluded that the proposed algorithm can be used as a rather simple alternative to complex emissivity-estimation algorithms. (c) 2006 Elsevier Inc. All rights reserved.