International Journal of Applied Earth Observation and Geoinformation 50 (2016) 26–38 Contents lists available at ScienceDirect International Journal of Applied Earth Observation and Geoinformation journal homepage: www.elsevier.com/locate/jag Linking in situ LAI and fine resolution remote sensing data to map reference LAI over cropland and grassland using geostatistical regression method Yaqian He a,b,c , Yanchen Bo a,b,∗ , Leilei Chai a,b , Xiaolong Liu d , Aihua Li e a State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, School of Geography, Beijing Normal University, Beijing 100875, China b Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China c Department of Geology and Geography, West Virginia University, Morgantown, WV 26506, USA d College of Tourism & Geography Science, Yunnan Normal University, Kunming, Yunnan Province 650500, China e Department of Geoscience, Boise State University, Boise, ID 83725, USA a r t i c l e i n f o Article history: Received 8 December 2015 Received in revised form 22 February 2016 Accepted 26 February 2016 Keywords: Leaf area index Up-scaling Geostatistical regression Reduced major axis Vegetation index a b s t r a c t Leaf Area Index (LAI) is an important parameter of vegetation structure. A number of moderate resolution LAI products have been produced in urgent need of large scale vegetation monitoring. High resolution LAI reference maps are necessary to validate these LAI products. This study used a geostatistical regression (GR) method to estimate LAI reference maps by linking in situ LAI and Landsat TM/ETM+ and SPOT-HRV data over two cropland and two grassland sites. To explore the discrepancies of employing different vegetation indices (VIs) on estimating LAI reference maps, this study established the GR models for different VIs, including difference vegetation index (DVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI). To further assess the performance of the GR model, the results from the GR and Reduced Major Axis (RMA) models were compared. The results show that the performance of the GR model varies between the cropland and grassland sites. At the cropland sites, the GR model based on DVI provides the best estimation, while at the grassland sites, the GR model based on DVI performs poorly. Compared to the RMA model, the GR model improves the accuracy of reference LAI maps in terms of root mean square errors (RMSE) and bias. © 2016 Elsevier B.V. All rights reserved. 1. Introduction Leaf Area Index (LAI), defined as half the total leaf area per unit ground surface areas (Chen and Black, 1992), is an impor- tant parameter of vegetation structure and function (Abuelgasim et al., 2006). LAI provides substantial information on the exchange of energy, mass, and momentum flux between the Earth’s surface and its atmosphere (Morisette et al., 2006; Myneni et al., 1997). LAI has been widely used as an input in climate, hydrology, and biogeo- chemistry models (Berterretche et al., 2005; Knyazikhin et al., 1998; Morisette et al., 2006). To date, a number of global and regional moderate-resolution LAI products have been produced, including ∗ Corresponding author at: State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, School of Geography, Beijing Normal University, Beijing 100875, China. E-mail address: boyc@bnu.edu.cn (Y. Bo). Moderate Resolution Imaging Spectroradiometer (MODIS), Carbon Cycle and Change in Land Observational Products from and Ensem- ble of Satellites (CYCLOPES), Canada Centre for Remote Sensing (CCRS), and Global Land Surface Satellite (GLASS) (Chen et al., 2002; Tian et al., 2000; Weiss et al., 2007; Xiao et al., 2014). Owing to the influence of model algorithms, vegetation heterogeneity, and observation conditions, these LAI products inevitably have inher- ent uncertainties (Chen et al., 2002), which subsequently may impact the accuracy of any resulting modeling activities. Specify- ing the uncertainties of these coarse spatial resolution LAI products is essential for users to determine the most appropriate dataset for their applications, and for producers to improve methodologi- cal algorithms. However, a direct comparison between in situ LAI measurements and these corresponding moderate resolution LAI products is not recommended because of scale-mismatch, geoloca- tion errors, and land surface heterogeneity (Huang et al., 2006; Yang et al., 2006). The proposed way to validate coarse resolution remote http://dx.doi.org/10.1016/j.jag.2016.02.010 0303-2434/© 2016 Elsevier B.V. All rights reserved.