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The multi-kernel support vector regression (MK—SVR) was used to construct remote sensing monitoring algorithmic models for estimating leaf area index (LAI) in wheat. The experiment was carried out during 2010—2013 in Jiangsu Province, China. Based on LAI in wheat and synchronous China’s domestic HJ—CCD multi-spectral data at jointing stage, booting stage and anthesis stage respectively, the relationships between LAI and eight vegetation indices were analyzed at corresponding period. Taking these vegetation indices which were significantly related to LNC at 0.01 level as input parameters, the remotely estimating model was established based on MK—SVR to invert LAI, named the MK—SVR—LAI model. Meanwhile, in order to evaluate the MK—SVR—LAI model, single kernel support vector regression (SK—SVR）and partial least squares (PLS) were employed to establish models at each period, named the SK—SVR—LAI and PLS—LAI models. Comparing predicted LAI by model with actual measured LAI, the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate models. The results showed that the lowest RMSE and the highest R2,were obtained by using MK—SVR—LAI model at each stage, of which the RMSE and the R2 were 0.2931 and 0.7624 at jointing stage, 0.4668 and 0.8018 at booting stage, 0.5486 and 0.6689 at anthesis stage, respectively.