**1. Introduction**

Leaf area index (LAI), defined as one-half of the total green leaf area per unit of ground horizontal surface area [1], is a key biophysical parameter in land surface processes and Earth system models [2,3]. Global LAI products have been derived from satellites, which have the advantage of large spatial coverage and serve as inputs for many numerical models. For example, LAI is used in the European Centre for Medium-Range Weather Forecasts land surface model and has obvious impacts on simulation of carbon and water fluxes [4]. LAI is used to estimate the vegetation water content and then the contribution of vegetation layer to the microwave signals that could influence the performance of a land data assimilation system [5]. LAI is also the input of one-dimensional hydrology (1 dH) model for radiation flux estimation, particularly for estimation of transmissivity of shortwave radiation for canopy [6]. In addition, LAI is used as a parameter for estimating evapotranspiration based on some energy balance algorithms such as Two-Source Models (TSM) [7]. LAI can also be used in crop yield estimation system, and accurately assessing LAI is proven to be the key to improving estimation [8]. In order to effectively use LAI derived from remote sensing in various disciplines, it is critical to understand the characteristics and uncertainties of these products [9], because the quality, accuracy, and spatial–temporal coverage of these products still requires significant improvements [10].

With the development of remote sensing technology in the last few decades, remote sensors on board various satellite platforms have provided many LAI products of different spatial and temporal resolution at global or regional scales. For instance, GEOLAND (European FP6 project aiming at building up a European capacity for Global Monitoring of Environment and Security) LAI [11] is derived from SPOT/VEGETATION (The SPOT satellites are operated by the French Space Agency and Centre National d'Etudes Spatiales, and the VEGETATION instrument aims to provide accurate measurements of the main characteristics of the Earth's plant cover) with a 10-day time step and 1/112◦ (1 km at the equator) spatial resolution. The Moderate Resolution Imaging Spectroradiometer (MODIS) [12] on board the TERRA and AQUA satellites can provide global LAI in 1 km spatial resolution on four-day and eight-day time step LAI. GLASS LAI (Global Land Surface Satellites) is an improved LAI dataset based on MODIS reflectance data with eight-day temporal resolution and 0.05◦ (5 km at the equator) spatial resolution from 1981 to the present and 1 km spatial resolution from 2001 to the present [10]. GLOBALBNU LAI (GLOBAL LAI generated by Beijing Normal University) is a dataset that improved from MODIS LAI, with 1 km and eight-day resolution from 2000–2016 [13]. GLOBMAP LAI (GLOBal LAI MAP generated by Chinese Academy of Science) is another LAI dataset based on MODIS reflectance data, with 8 km and 16-day resolution from 1981–2000 and 500 m and eight-day resolution from 2001–2011 [14]. CYCLOPES (European Union FP5 project) LAI [15] is generated from the SPOT/VEGETATION sensor, with a 1/112◦ (1 km at the equator) spatial resolution and 10-day temporal resolution. GLOBCARBON (Europe Space Agency project intends to hone the accuracy of climate change forecasting) LAI [16] provides a monthly period and 1/11.2◦ (10 km at the equator) spatial resolution generated from a combination of SPOT/VEGETATION and ENVISAT/AATSR(Advanced Along Track Scanning Radiometer on board the European Space Agency's Envisat satellite) observations. ECOCLIMAP (National Center for Scientific Research program that provides a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modeling) LAI [17] obtained from NOAA/AVHRR (Advanced Very High Resolution Radiometer of National Oceanic and atmosphere administration) provides a one month and 1 km product. And CCRS (Canada Centre for Remote Sensing) LAI [18] is a regional LAI product that covers Canada based on the SPOT/VEGETATION sensor with 1 km and 10-day resolution. Land-SAF (Land Surface Analysis Satellite Applications Facility) LAI [19] is derived from the MSG/SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager) instrument over four specific regions (Europe, North Africa, South Africa and South America), with 3 km and daily resolution.

The uncertainty of LAI retrieval is easily influenced by atmosphere, sensor status and other factors [20]. In order to apply these products in various applications effectively, there is a great demand to validate their accuracy. Validation is the process of assessing by independent means the accuracy of data products [21]. At present, the method of validating LAI products can be divided into direct validation and comparison [22]. Direct validation involves directly assessing the uncertainty of products through in situ measurements. For better direct validation, there is a need to consider the problem of spatial scale and to choose sites with homogeneous land cover. To achieve this goal, the committee on Earth Observation System-Land Product Validation (CEOS-LPV) organization generated an On Line Interactive Validation Exercise (OLIVE) platform [23], and some researches utilized this dataset to direct validate remote sensing land products including LAI [22,24]. Although they have made some progress, the existing validation datasets for direct validation are not representative of the global and seasonal variability of vegetation [24]. However, product comparison can achieve a spatial and temporal evaluation over a global and complete vegetation cycle, and can also provide the relative performance of each LAI retrieval algorithm [22]. Fang et al. [19] compared five major global moderate LAI products and analyzed the climatological and theoretical uncertainties. Zhu et al. [25] compared the FY-3A/MERSI (Medium Resolution Spectral Imager) LAI and MODIS LAI products over mainland China, and the results showed that both products could follow the growing season, but there are some disagreements due to different land cover types and terrain. Martin et al. [26] compared and evaluated the GIMMS LAI3g (Global Inventory Modeling and Mapping Studies three generation) and GGRS (Goettingen GIS & Remote Sensing) LAI products over Kazakhstan, and found pronounced LAI differences at both the start (spring) and end (fall) of the growing season.

The objective of this study was to compare and evaluate four global remotely sensed LAI products over China, namely GLASS (Global Land Surface Satellites) [10], GLOBALBNU (Global LAI Product of Beijing Normal University) [13], GLOBMAP (Global LAI Map of Chinese Academy of Sciences) [14], and MODIS LAI [27]. The first three are newly-released LAI products developed by Chinese groups and their performance has not been comprehensively evaluated. We used measured LAI from OLIVE and existing literature for direct validation [14]. The differences among the four products were also analyzed according to the Koppen–Geiger climate classification map and a land cover map. Such comparison and evaluation will help researchers with selecting LAI products, and in turn will help the producer to further improve the quality of their products. In the next section, we introduce the data and method used in this study. Section 3 presents the comparison results and evaluates performances; a discussion is presented in Section 3, and conclusions are given in Section 4.
