Abstract: 【Objective】In order to improve the inversion accuracy of chlorophyll content in cotton leaves and grasp its spatial distribution characteristics in Xiajin county, Shandong province. 【Method】Taking Xiajin county, Dezhou city, Shandong province as the study area and Dalizhuang cotton field in Xiajin county as the test area, the relative value of chlorophyll content (SPAD value) in cotton leaves in the experimental area was measured by SPAD (soil and plant analyzer development), and obtained the near earth multispectral image of unmanned aerial vehicle (UAV) and Sentinel-2A MSI (MSI) satellite image in the study area in the same period; Then, based on the spectral reflectance of UAV and MSI satellite images, the optimal spectral parameters were constructed and selected, and the inversion model of SPAD value was established by multiple linear regression (MLR); Finally, the quadratic polynomial fitting method was used to fuse the optimal spectral parameters corresponding to UAV and Sentinel-2A MSI. By compared and analyzed the model effects before and after fusion, the inversion model was optimized, and the SPAD value inversion of the study area was realized. 【Result】The results showed that (REG-R)/(REG+R), R/G, Cl(red edge) and NDVI could be the optimal spectral parameters of SPAD value. The precision of cotton leaf SPAD inversion model based on UAV near ground image was better than that based on satellite image; After quadratic polynomial fitting, calibration R² was increased by 0.015-0.057, RMSE was decreased by 0.457-0.638, and validation R² was increased by 0.040-0.085, RMSE was decreased by 0.387-0.397, and RPD was increased by 0.020-0.139. The fused spectral parameters based on Sentinel-2A MSI image were input to the inversion model based on UAV data (Fused MSI-Mod_UAV), the high inversion accuracy of SPAD value in cotton leaves could be obtained, with the model calibration R² up to 0.672, RMSE of 3.982, validation R² up to 0.713, RMSE of 3.859 and RPD of 1.685. Based on the above model, two inversion prediction maps of different scales were obtained. The SPAD value of cotton leaves in the test area showed the distribution trend of high in the south and low in the north, and the study area showed the distribution trend of low in the middle and high around, which were consistent with the field situation and showed the model had a good prediction effect. 【Conclusion】Therefore, the fusion of UAV and satellite image data by using quadratic polynomial fitting method can better realize the quantitative inversion of regional high-precision crop growth indicators. The research results can enrich the theory and technology of multi-source remote sensing fusion, and provide technical reference and data support for cotton growth monitoring and precision production.

Key words: SPAD value, UAV, sentinel-2A MSI, inversion model, quadratic polynomial fitting method