Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for [PERSON]  
1 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (B.X. ); [EMAIL_ADDRESS] (S.W. )1 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; zhaojing1@radi. estimations [7; 8], and ecological and environmental assessments [9]. High-spatial-resolution LAI products play important roles in monitoring regional vegetation changes and evaluating the accuracy of low- resolution LAI products [10; 11; 12]. From the Landsat-5 satellite launched in 1984 to the present, there are many moderate- to high-resolution satellite sensors that are available in the world, such as the Landsat thematic mapper (TM)/enhanced thematic mapper plus (ETM+)/operational land imager (OLI), Terra advanced spaceborne thermal emission and reflection radiometer (ASTER), SPOT high resolution geometrical (HRG), IKONOS multi-spectral (MS), Sentinel-2 multispectral imager (MSI), and the Chinese environment satellite (HJ-1) charge coupled device (CCD), etc. Generally, LAI extracted from this moderate- to high-resolution imagery largely depends on the empirical relationships. Empirical expressions were established between LAI field measurements and the spectral vegetation indices (SVIs) from isolated dates [13; 14; 15; 16; 17; 18; 19]. The most commonly used SVIs include the normalized difference vegetation index (NDVI), the simple ratio index (SR), and the enhanced vegetation index (EVI) [14; 15; 20; 21; 22; 23]. In addition, the reduced simple ratio (RSR), the soil-adjusted vegetation index (SAVI), and the perpendicular vegetation index (PVI) are widely used for LAI extraction [24; 21; 25; 26]. When compared with LAI field measurements, the accuracies of satellite LAI estimates based on linear and non-linear regressions of the SVI-LAI relationships have coefficient of determination (R\({}^{2}\)) values from approximately 0.37 to 0.98 and root mean square error (RMSE) values from approximately 0.17 m\({}^{2}\)/m\({}^{2}\) to 1.14 m\({}^{2}\)/m\({}^{2}\) for both crops (e.g., winter wheat, maize, and soybean) and forests (e.g., coniferous and deciduous) [16; 21; 23; 27; 28]. In addition, the SVI-LAI relationships are stronger for crop canopies than for coniferous forests, and are weakest for deciduous forests [15; 22; 29]. Empirical methods are computationally efficient when using remote sensing datasets at regional or large scales. However, empirical relationships that typically depend on unique vegetation types and regions are often constructed and used locally. The physical model method, which is suitable for a variety of vegetation types, is also used to extract LAI from moderate- to high-resolution imagery [30; 31; 32; 33; 34; 35; 36; 37]. Canopy reflectance models simulate the physical relationship between the canopy reflectance and the LAI in the forward direction. The scattering by arbitrarily inclined leaves with the hot-spot effect (SAILH) model [30; 32; 38], the Markov chain reflectance model (MCRM) [31], and the Li-Strahler geometric-optical model [34] have been used to extract LAI from moderate- to high-resolution imagery. LAI has also been estimated by indirect methods based on the inversion of canopy reflectance models, such as look-up tables (LUTs) and hybrid methods. The hybrid methods include decision tree learning, artificial neural networks, kernel methods, and Bayesian networks [39]. Additionally, the currently used indirect methods of the radiative transfer model (RTM) for Landsat ETM+ and Sentinel-2 MSI data are the LUT and neural networks [31; 32; 35; 36].