3824 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 10, OCTOBER 2011 Improved Corrections of Forest Effects on Passive Microwave Satellite Remote Sensing of Snow Over Boreal and Subarctic Regions Alexandre Langlois, Alain Royer, Florent Dupont, Alexandre Roy, Kalifa Goïta, and G. Picard Abstract—Microwave radiometry has been extensively used in order to estimate snow water equivalent in northern regions. However, for boreal and taiga environments, the presence of forest causes important uncertainties in the estimates. Variations in snow cover and vegetation in northeastern Canada (north of the Québec province) were characterized in a transect from 50 ◦ N to 60 ◦ N during the International Polar Year field campaign of February 2008. Forest properties show a strong latitudinal gra- dient in fraction and stem volume. A large database (> 2000 points with a stem volume ranging between 0 and 700 m 3 · ha −1 ) showed that brightness temperatures (T b ) decrease as forest cover fraction decreases until a cover fraction of about 25% is reached. Furthermore, T b values saturate at high stem volume, particularly at 37 GHz. We defined new relationships for the forest transmissivity as a function of stem volume and depending on the frequency/polarization. The proposed relationships give asymptotic transmissivity saturation levels of 0.51, 0.55, 0.53, and 0.53 for 19 GHz [vertical (V) polarization], 19 GHz [horizontal (H) polarization], 37 GHz (V polarization), and 37 GHz (H polariza- tion), respectively. These relationships were used to estimate snow T b from the Advanced Microwave Scanning Radiometer-Earth Observing System brightness temperatures at 18.7 and 36.5 GHz, and results show an estimated snow brightness temperature well correlated to the airborne snow brightness temperatures over vegetation-free areas. Index Terms—Advanced Microwave Scanning Radiometer- Earth Observing System (AMSR-E), airborne data, passive microwave, snow, stem volume, transmissivity, vegetation fraction. Manuscript received August 17, 2010; revised October 8, 2010 and November 30, 2010; accepted December 15, 2010. Date of publication May 18, 2011; date of current version September 28, 2011. This work was supported in part by Environment Canada through the Canadian International Polar Year Project, by the Natural Sciences and Engineering Research Council of Canada, and by the Collaboration Québec–France, Le Program International de Collaboration Scientifique du Centre National de la Recherche Scientifique. A. Langlois, A. Royer, A. Roy, and K. Goïta are with the Centre d’Applications et de Recherches en Télédétection, Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada (e-mail: A.Langlois2@USherbrooke.ca; Alain.Royer@USherbrooke.ca; Alexandre.R.Roy@USherbrooke.ca; kalifa.goita@usherbrooke.ca). F. Dupont is with the Centre d’Applications et de Recherches en Télédé- tection, Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada, and also with the Laboratoire de Glaciologie et Géophysique de l’Environnement, Université Joseph Fourier (Université Grenoble I)/Centre National de la Recherche Scientifique, 38402 Saint-Martin- d’Héres, France (e-mail: Florent.Dupont@bvra.e.ujf-grenoble.fr). G. Picard is with the Laboratoire de Glaciologie et Géophysique de l’Environnement, Université Joseph Fourier (Université Grenoble I)/Centre National de la Recherche Scientifique, 38402 Saint-Martin-d’Héres, France (e-mail: ghislain.picard@lgge.obs.ujf-grenoble.fr). 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.2011.2138145 I. I NTRODUCTION T HE SEASONAL snow cover represents a very important element of the cryosphere. It affects various aspects of the surface energy balance through its control on radiative transfers and turbulent fluxes [1], [2]. Of particular relevance, snow is a sensitive indicator of climate change via various feedback processes (such as snow-albedo feedback) that may affect both direction and amplitude of current climatic trends [3]. Furthermore, snow is an important freshwater reservoir [4], [5] necessary for the survival of ecosystems and is also an important source of energy. Given the recent evidence of depleting snow cover in the northern hemisphere [6]–[8], our ability to estimate snow water equivalent (SWE) in boreal, taiga, and tundra environments has to be further improved. Over the last three decades, passive microwave remote sensing has been widely used to estimate snow properties, such as SWE, from space [9]–[12]. Reasonable estimates of regional SWE were found over flat and vegetation-free areas [13]–[15]; however, uncertainties remain with regard to veg- etation contributions to the signal in boreal and taiga envi- ronments [16]. The ability of microwave radiometry to sense SWE through dense forest canopies is questionable as forest attenuates snow emission and adds its own contribution to the measured emitted radiation [17], [18]. Several field cam- paigns have been conducted in order to quantify the effects of forest on microwave signals using ground-based measure- ments [19], [20], airborne measurements [21]–[26], and satel- lite data [15], [27]. Previous authors have suggested models of forest transmissivity using an exponential function of the stem volume [20], [22], but large differences remain (up to 30%–40%). The objective of this paper is to improve the empirical relationship between forest stem volume and transmissivity for 19 and 37 GHz, respectively. We first review the background of the microwave forest transmissivity model with regard to mixed pixels with dense forests and open areas. We then describe the data sets used for the observed land cover types in different ecological environments (boreal, taiga, and tundra environments). The derived empirical model for forest trans- missivity is then presented and compared to existing trans- missivity models. The model is then used to estimate snow brightness temperature from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data. These estimates were validated using the airborne snow brightness temperature measurements. 0196-2892/$26.00 © 2011 IEEE