The accuracy of satellite LAI inversions is better than SVI-LAI empirical relationships, with R\({}^{2}\) values from 0.54 to 0.82 and RMSE values from 0.17 m\({}^{2}\)/m\({}^{2}\) to 0.71 m\({}^{2}\)/m\({}^{2}\) for crops (e.g., maize and soybean), shrubs, and planted forests [30; 31; 33; 34]. LAI field measurements were acquired via direct and indirect methods [40]. Direct LAI measurements, including leaf collection from deciduous forests and the destructive sampling of crops or low shrubs, are time consuming and difficult to collect at larger areas [40]. Indirect LAI measurement, including optical sensor-based method (such as those using the Licor LAI-2200 Plant Canopy Analyzer (LI-COR, Lincoln, NE, USA) [41], Tracing Radiation and Architecture of Canopies (TRAC) (3 rd Wave, Nepean, ON, Canada) [42], AccuPar (Decagon Devices, Inc., Pullman, WA, USA) [43]), digital hemispherical photography (DHP), and new smartphone camera sensor technology (such as LAISmart [44; 45] and PocketLAI [46]). These indirect methods are generally convenient, especially those that allow for LAI estimation using a smartphone, and generally efficient over larger spatial scales. In recent years, China has developed and launched several satellites, such as the HJ-1A/B with a CCD sensor, the GaoFen No. 1 satellite (GF-1) with a wide field of view (WFV) camera and the resources satellite No. 3 (ZY-3) with a multi-spectral camera (MUX). Currently, the satellite data of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD have been applied for vegetation monitoring. HJ-1 CCD data have been widely used for LAI extraction at the regional scale based on SVI-LAI relationships or physical models [21; 30; 33; 34; 47]. In addition, GF-1 WFV data have been used to extract the fractional vegetation cover (FVC) [48; 49] and estimate LAI from the NDVI-LAI empirical relationship [21; 50; 51] at the regional scale. However, there have not been any studies that have reported the extraction of LAI from ZY-3 MUX data. One of the objectives of this study is to analyze the potential use of three Chinese satellites, especially ZY-3 MUX, to extract LAI at the regional scale. The study area was selected in the Huailai experiment station, Hebei Province, China. The satellite data from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD for the study area were collected close to the date of LAI field measurements. The LAI extraction method was a physical model-based SVI- LAI relationship that was generated from the LUT based on the PROSPECT-5B + SAILH (PROSAIL) model with specific input parameters for each sensor (Section 2). In order to assess the surface reflectance quality of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD after data preprocessing, the well- processed surface reflectance products of Landsat-8 OLI with a convincing data quality are used to compare the performances of these sensors, both in theory and reality. Furthermore, the accuracy of the LAI estimation results from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD was validated based on LAI field measurements from the Huailai experiment station (Section 3). The up-scaled LAI inversions for ZY-3 MUX and GF-1 WFV were also compared with the HJ-1 CCD data at the same spatial resolutions for pure and mixed pixels (Section 3). The discussion and conclusions are presented in Sections 4 and 5, respectively.