Abstract: 【Objective】Application of satellite remote sensing data can timely get field planting crops ‘planar’ growth information, accurately reflect the situation and trend of crop seedling condition, serve the yield forecast and actual production. The purpose of this research was to deepen the mechanism and methods of remote sensing inversion of winter wheat seedling condition in the key period, and this research will provide timely support information and technology for farm production management. 【Method】Based on experimental data obtained from 2011-2013 in the fixed-point observation experiment, and using HJ-CCD satellite images, the quantitative correlations between key seedling condition parameters of winter wheat at booting stage in sampling regions and the grain quality parameters, production, and remote sensing variables were emphatically analyzed. In order to further enhance the mechanism and reproducibility of remote sensing inversion models, which were built and analyzed with ground measuring results, the quantitative level and reliability of remote sensing inversion models were raised. Models for monitoring the leaf area index, biomass, SPAD value, and leaf nitrogen content of winter wheat at booting stage using remote sensing variables extracted from the HJ-CCD images were built and assessed, respectively. 【Result】It is possible to invert leaf area index, SPAD value and leaf nitrogen content of winter wheat at booting stage by plant senescence reflectance index (PSRI), and invert biomass by ratio vegetation index (RVI), respectively. The remote sensing inversion models of the leaf area index, SPAD value, leaf nitrogen content and biomass of winter wheat were credible, and higher precision was obtained with determination coefficient (R²) of 0.651, 0.585, 0.630 and 0.675, respectively, and with root mean square error (RMSE) of 1.344, 4.62, 0.618% and 2 804.3 kg·hm^-2, respectively. It was especially reliable to inverse leaf nitrogen content by PSRI. According to the above results, the spatial distribution of the seedling condition parameters of winter wheat could be implemented with agricultural thematic maps of monitoring the key seedling condition parameters at different classes with remote sensing method, thus achieved quantitative expression of regional spatial distribution of the seedling condition parameters. It not only contributes to drawing up the plan of winter wheat field remedial measures and the water and nutrient resources scheduling, but also offers the decision basis for determination of agricultural policy and food trade. 【Conclusion】 The remote sensing inversion models of winter wheat key growth of seedling parameters at booting stage is feasible, and provide a quick, convenient and affordable method to extract the parameters seedling growth of large area for field production. The results of this research can provide timely valuable agricultural information for agronomists, agricultural departments, and farm managers.

Key words: remote sensing, HJ-CCD images, booting stage, key seedling condition parameters, inversion models