Large-scale retrieval of leaf area index and vertical foliage proﬁle from the spaceborne waveform lidar (GLAS/ICESat) Hao Tang a, ⁎, Ralph Dubayah a , Matthew Brolly b , Sangram Ganguly c,d , Gong Zhang c,d a Department of Geographical Sciences, University of Maryland, College Park, MD, USA b School of Environment and Technology, University of Brighton, United Kingdom c Bay Area Environmental Research Institute (BAERI), West Sonoma, CA, USA d NASA Ames Research Center, Moffett Field, CA, USA abstract article info Article history: Received 27 February 2014 Received in revised form 3 August 2014 Accepted 6 August 2014 Available online xxxx Keywords: Lidar LAI Vertical foliage proﬁle GLAS Landsat Leaf area index (LAI) and canopy vertical proﬁles are important descriptors of ecosystem structure. The Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice, Cloud, and land Elevation Satellite) provided three-dimensional observations that can be used to derive these canopy structure parameters globally. While several canopy height products have been produced globally from GLAS, no comparable data sets for LAI and can- opy proﬁles exist across large areas. In this study we develop a physically based method of retrieving LAI and ver- tical foliage proﬁles (VFPs) from GLAS observations over the entire state of California, USA. This method reﬁnes lidar derived LAI and VFP through a recursive analysis of GLAS waveforms using ancillary data obtained from existing remote sensing products. Those supplemental inputs include canopy clumping index derived from POLDER, 500 m land cover type and 1 km LAI data derived from MODIS. Implementation of our method created state-level LAI and VFP data for the existing GLAS transects over California. We then analyzed the variability of LAI and VFP data sets across environmental gradients and as a function of land cover type and elevation. Both LAI and VFP showed strong variability across elevational gradients and among land cover types. We compared our results at the scale of GLAS footprints with an LAI map derived from Landsat (at 30 m) and found moderate agreement (r 2 = 0.34, bias = 0.26, RMSD (Root Mean Square Difference) = 1.85) between the two. In particular, Landsat LAI not only appeared to saturate relative to GLAS LAI at around LAI = 5, but also showed an overestimation for LAI less than about 2. Best agreement between the two LAI data sets was shown to occur in areas with slope less than 20°. Results from our study suggest the possibility of retrieving global LAI and VFP data from GLAS data and the potential for synergetic observation of lidar and passive optical remote sensing data such as Landsat. © 2014 Elsevier Inc. All rights reserved. 1. Introduction Leaf area index (LAI) and vertical foliage proﬁle (VFP, or foliage height proﬁle) are important biophysical variables in terrestrial ecosys- tems (Aber, 1979; Gower & Norman, 1991; Parker, Lefsky, & Harding, 2001; Stark et al., 2012). Recent studies have reviewed the importance and potential applications of LAI and VFP derived from large footprint waveform lidar (Tang et al., 2012, 2014) and have shown the efﬁcacy of a physical model to derive these proﬁles from waveform lidar data when compared to destructively sampled proﬁles in a tropical rain for- est (Tang et al., 2012). This model was then transferred to the Montane forests of the Sierra Nevada using GLAS sensor on board of ICESat. The comparison between GLAS-derived LAI data and airborne lidar data (r 2 = 0.69, bias = -0.05 and RMSE = 0.33) demonstrated the more general capability of our algorithm to provide total LAI and LAI proﬁles across biomes (Tang et al., 2014). The logical extension of these efforts is a further application of our methods over much larger areas, and is the goal of our work presented here. Large scale derivation of GLAS LAI and VFP products has the potential to serve as a source of validation data for passive optical data sets, as well as providing needed canopy information that may be used within ecosystem and other models. While observations from airborne lidar sensors have been used to derive both LAI and VFP, these data are lim- ited spatially. Demonstration of the viability of using space-based re- trievals of these from lidar over large areas opens the possibility of enhanced descriptions of the vertical organization of canopy elements that play large roles in the transfer of energy and mass between the surface and atmosphere in ecosystem models. For example, there cur- rently exists no regional data set of the LAI proﬁles, let alone for areas as large as states and beyond. Providing such data would improve our understanding of LAI structure and dynamics, its role in terrestrial gross primary production (GPP) (Kotchenova et al., 2004), and global carbon cycling (Houghton, 2007). Furthermore, foliar proﬁles have long been postulated to have an impact on habitat quality, species Remote Sensing of Environment 154 (2014) 8–18 ⁎ Corresponding author. http://dx.doi.org/10.1016/j.rse.2014.08.007 0034-4257/© 2014 Elsevier Inc. All rights reserved. Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse