Abstract: Estimation of crop yield by remote sensing is a key research and application field in agriculture, and such research can provide timely and reliable yield information for regional food production. In order to further improve the accuracy of estimating wheat yield by remote sensing, and demonstrate the application of satellite imaging products in agricultural production, we used HJ-1A/1B images on April 26th 2010, April 28th 2011 and 2012, May 2nd 2013 at wheat anthesis stage as remote sensing data. 335 samples of wheat yield were collected from agriculture production field and divided into modeling dataset and validation dataset on a ratio of 3:2. Based on the minimum value of predictive residual error sum of square (PRESS), the number required for principal component model was determined. The yield estimation model was assessed through determination coefficient (R2), root mean square error (RMSE) and relative error (RE). This research was undertaken to make a systematic analysis on the quantitative relationship of satellite remote sensing variables to actual wheat yield. Depending on the partial least squares regression (PLS), the multivariable remote sensing estimation models and the space level distribution maps of actual wheat yield were constructed and verified by the modeling dataset and validation dataset, and the estimation effect of the PLS model was compared to linear regression (LR) and principal components analysis (PCA) algorithm models, respectively. The results of this research indicated that the majority of remote sensing variables were significantly (P< 0.05) related to practical yield, and there were significant (P < 0.05) multiple relationships among the majority of remote sensing variables. For the actual yield estimation model based on PLS, the number of the best principal components was 5. Plant senescence reflectance index (PSRI), green normalized difference vegetation index (GNDVI), optimal soil adjusted vegetation index (OSAVI), ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) were identified as the sensitive remote sensing variables for estimating wheat yield. Through testing the actual yield estimation model based on PLS algorithm with modeling dataset and validation dataset, the R2 were 0.74 and 0.71, respectively, and the RMSE were 754.05 kg/hm2 and 748.2 kg/hm2, respectively, the RE were 11.50% and 8.88%, respectively. The PLS model with selected sensitive variables performed better to estimate wheat yield. PLS algorithm models to estimate wheat yield obtained the higher accuracy by above 20% and above 40% than the LR algorithm models, by above 18% and above 30% than the PCA algorithm models for modeling dataset and validation dataset, respectively. Based on the above PLS model and HJ-1A/1B image on May 2nd, 2013, the wheat practical yield spatial distribution level was mapped in central Jiangsu region. The results of applying the PLS models were correspondent with the actual distribution of wheat yield. It was concluded that PLS algorithm can provide an effective way to improve the accuracy of estimating wheat yield on regional scale based on aerospace remote sensing, and can contribute to large-scale application of the research results.