Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA Hao Tang a, ⁎, Matthew Brolly a , Feng Zhao a,b , Alan H. Strahler b , Crystal L. Schaaf b,c , Sangram Ganguly d,e , Gong Zhang d,e , Ralph Dubayah a a Department of Geographical Sciences, University of Maryland, MD, United States b Department of Earth and Environment, Boston University, MA, United States c School for the Environment, University of Massachusetts Boston, MA, United States d Bay Area Environmental Research Institute, West Sonoma, CA, United States e NASA Ames Research Center, Moffett Field, CA, United States abstract article info Article history: Received 21 August 2013 Received in revised form 4 December 2013 Accepted 17 December 2013 Available online 21 January 2014 Keywords: Lidar LAI Sierra National Forest LVIS GLAS Echidna Increasing the accuracy and spatial coverage of Leaf Area Index (LAI) values is an important part of any attempt to successfully model global atmosphere/biosphere interactions. It is further a fundamental parameter in land sur- face processes and Earth system climate models. Remote sensing methods offer an opportunity to improve on each of these requirements but are typically limited by the necessity for validation using labor intensive and sparsely collected in situ measurements. In this paper we present the results of an intercomparative study of ground-based, airborne and spaceborne re- trievals of total LAI over the conifer-dominated forests of Sierra Nevada in California. The efficacy of LVIS (Laser Vegetation Imaging Sensor) airborne waveform lidar LAI measurements (total and vertical profile) has previous- ly been validated at the site specific level using destructive sampling. We also explore the efficacy of ground based measurements obtained from hemispherical photography, LAI-2000, and ground based lidar, acknowledg- ing discrepancies existing between the systems and collected data. We highlight their use and role in validating the relationship between ground and airborne estimates of total LAI (LVIS LAI correlation with i) hemispherical photographs, r 2 = 0.80, ii) LAI-2000, r 2 = 0.85, and iii) terrestrial lidar, r 2 = 0.76. The existence of such rela- tionships offers immediate implications for LAI estimation where LVIS data is available, creating the potential to obtain, not only total LAI values but also corresponding vertical LAI distributions from a ground validated source previously unobtainable at this spatial scale. The ability to validate airborne lidar LAI data collected at different spatial scales to the available ground measure- ments allows further upscaled validation using global lidar datasets provided by spaceborne lidar, such as the Geo- science Laser Altimeter System (GLAS). In the absence of adequate ground validation plots coincident with GLAS footprints, GLAS LAI validation is examined using geographically limited but spatially continuous LVIS data. Under favorable conditions, significantly the absence of slopes greater than ~20°, the comparison between LVIS and GLAS LAI values obtained using a recursive algorithm constrained by independently validated LAI limits exposes the capability of GLAS as an accurate standalone LAI sensor (r 2 = 0.69, bias = -0.05 and RMSE = 0.33). The cor- relation comparison between LVIS and GLAS LAI estimates not only significantly exceed those associated with equiv- alent space borne passive remote sensing datasets, such as MODIS (r 2 = 0.20, bias = -0.16 and RMSE = 0.67) but also offers significant advantages to future research including the prospective validation of regional and global LAI products and data comparison with ecosystem model inputs. The encountered effectiveness of these relation- ships allows the implementation of a scaling-up strategy where ground-based LAI observations are related to aircraft observations of LAI, which in turn are used to validate GLAS LAI derived from coincident data. Successful implemen- tation of this strategy paves the way for the future recovery of vertical LAI profiles on a global scale and opens up the potential for fusion studies to incorporate widely available and spatially abundant passive optical datasets. © 2014 Elsevier Inc. All rights reserved. 1. Introduction 1.1. LAI using remote sensing Leaf Area Index (LAI), defined as one half of the total leaf area projected per unit horizontal ground area, is an important biophysical Remote Sensing of Environment 143 (2014) 131–141 ⁎ Corresponding author at. Department of Geographical Sciences, University of Maryland, 2120 Lefrak Hall, College Park, MD 20742. 0034-4257/$ – see front matter © 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.12.007 Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse