This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 A Detailed Study of Land Surface Microwave Emissivity Over the Indian Subcontinent Tinu Antony, C. Suresh Raju, Nizy Mathew, Korak Saha, and K. Krishna Moorthy Abstract—Microwave emissivities of land surfaces on global basis have been derived using Special Sensor Microwave/Imager brightness temperature data. These derived emissivities are com- pared with other reported emissivity values to demonstrate the accuracy of the retrievals. Following these results, detailed anal- yses on the microwave emissivities of the Indian subcontinent are carried out using the monthly mean emissivity estimate for two years. The Indian subcontinent has a wide variety of geo- graphic and biospheric classes with distinctly different emissivity characteristics. The spectral and monthly variations of microwave emissivity for different tropical land surface classes are examined. This study is significant for microwave radiance assimilation in weather forecast models and also for the utilization of the data from passive microwave sensors onboard the Indo-French satellite “Megha-Tropiques,” which is dedicated to tropical atmospheric studies. Index Terms—Indian subcontinent, land surface emissiv- ity, Megha-Tropiques, microwave radiometry, Special Sensor Microwave/Imager (SSM/I). I. I NTRODUCTION S ATELLITE microwave radiometry has been utilized to re- trieve geophysical parameters for more than three decades. The relatively high land emissivity, the large spatiotemporal variability of surface features, and the coarse spatial resolution of the spaceborne microwave passive sensors are the primary factors that limit the utility of satellite microwave radiometry over continents [1]. The signal received by the satellite ra- diometer is an ensemble of contributions from different surface classes, with distinctly different emission/scattering charac- teristics. Separating and characterizing these contributions of each class is a challenging research problem [2]. Reasonably accurate characterization of land emissivity is possible over extended areas dominated by homogeneous classes, such as forest, grassland, barren/semiarid region, desert, and snow-/ ice-covered area [3]. It is feasible to extend the applications of existing satellite microwave radiometers for cloud and at- mospheric studies over the continental region, provided that ac- curate surface emissivity measurements are made available [3], Manuscript received January 17, 2012; revised January 25, 2013, May 22, 2013, and July 11, 2013; accepted July 12, 2013. The work of T. Antony was supported by an Indian Space Research Organisation Research Fellowship for the Ph.D. program. T. Antony, C. Suresh Raju, N. Mathew, and K. Krishna Moorthy are with the Space Physics Laboratory, Vikram Sarabhai Space Centre, Indian Space Research Organisation, Thiruvananthapuram 695 022, India (e-mail: c_sureshraju@vssc.gov.in). K. Saha is with the Center for Satellite Application and Research (STAR), NOAA NESDIS, College Park, MD 20740 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2013.2274010 [4]. Apart from that, microwave emissivity estimate is useful in defining the characteristics of different land surface classes and the spatiotemporal variations of land surface properties at both regional and global scales. The land surface emissivities are also used for delineating wetlands, waterlogged areas, and flood-affected regions [5]. The microwave emission received by a satellite radiome- ter has contributions from both the Earth’s surface and the intervening atmosphere. The emission from the land surface depends on the surface composition (soil types and veg- etation canopy), topography, and physical properties such as soil moisture and temperature [6]. The Special Sensor Microwave/Imager (SSM/I) data have been extensively used by Prigent et al. [7] to estimate the land surface emissivity globally under clear-sky condition. Karbou et al. [8] have retrieved land surface emissivities from Advanced Microwave Sounding Unit (AMSU-A and AMSU-B) and have used it for the 4-D-Var assimilation system in numerical weather prediction. A wide variety of geographical features and climatic con- ditions prevails over the Indian subcontinent. The northern part of India has a temperate climate with large seasonal and diurnal variability in temperature, while the southern part has humid tropical climate with less variability. Apart from that, the Indian monsoon also influences the land use/land cover and the climate over this region. The Indian subcontinent is also a region of diversity with a wide range of landforms including mountains, deserts, elevated plateaus, low delta regions, and tropical and temperate forests. A detailed classification in terms of microwave emissivity for this region has not been attempted so far. In this paper, we monitor seasonal and geographical vari- ations of microwave land surface emissivity. The investigation is significant for the data utilization of the microwave payloads MADRAS (imager) and SAPHIR (humidity sounder) onboard the Indo-French “Megha-Tropiques” satellite [9]. In this paper, microwave land emissivity is retrieved on a global scale using the SSM/I data, and detailed analyses of emissivity variations over the Indian subcontinent (0 ◦ –40 ◦ N and 60 ◦ E–100 ◦ E) are carried out. The atmospheric contri- bution is accounted in the retrieval scheme using an in-house- developed line-by-line microwave radiative transfer (RT) code under clear-sky conditions. The emissivity retrieval scheme and the data used are described in Section II. Retrieval of global emissivity and comparison with existing models are described in Section III. Monthly mean land surface emissivity maps over the Indian subcontinent for different frequencies along with their seasonal variations are reported in Section IV. A sensitiv- ity analysis on emissivity variation due to input ancillary data is discussed in Section V. 0196-2892 © 2013 IEEE