The early and accurate acquisition of crop yields is of great significance for maintaining food market stability and ensuring global food security. Unmanned aerial vehicle (UAV) remote sensing offers the possibility of predicting crop yields with its advantages of flexibility and high resolution. However, most of the existing remote sensing yield estimation studies focused solely on crops but did not fully consider the influence of soil on yield formation. As an integrated system, the status of crop and soil together determines the final yield. Compared to crop-only yield prediction, the approach that additionally considers soil background information will effectively improve the accuracy and reduce bias in the results. In this study, a novel method for segmenting crop and soil spectral images based on different vegetation coverage is first proposed, in which pixels of crop and soil can be accurately identified by determining the discriminant value Q. On the basis of extracting crop and soil waveband’s information by individual pixel, an innovative approach, projected non-negative matrix factorization based on good point set and matrix cross fusion (PNMF-MCF), was developed to effectively extract and fuse the yield-related features of crop and soil. The experimental results on winter wheat show that the proposed segmentation method can accurately distinguish crop and soil pixels under complex soil background of four different growth periods. Compared with the single reflectance of crop or soil and the simple combination of crop and soil reflectance, the fused yield features spectral matrix FP obtained with PNMF−MCF achieved the best performance in yield prediction at the flowering, flag leaf and pustulation stages, with R2 higher than 0.7 in these three stages. Especially at the flowering stage, the yield prediction model based on PNMF-MCF had the highest R2 with 0.8516 and the lowest RMSE with 0.0744 kg/m2. Correlation analysis with key biochemical parameters (nitrogen and carbon, pigments and biomass) of yield formation showed that the flowering stage was the most vigorous season for photosynthesis and the most critical stage for yield prediction. This study provides a new perspective and complete framework for high-precision crop yield forecasting using UAV remote sensing technology.

Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, up until now, there has been no proposed remote sensing fusion product that offers high spatial-temporal resolution specifically for farmland monitoring. Therefore, focusing on the demands of farmland remote sensing monitoring, identifying quantitative conversion relationships between multiple sensors, and providing high spatial-temporal resolution products is the first step that needs to be addressed. In this study, a fused Landsat 8 (L8) Operational Land Imager (OLI) and Sentinel-2 (S-2) multi-spectral instruments (MSI) data product for regional monitoring of farmland at high, mid, and low latitudes in China is proposed. Two image pairs for each study area covering different years were acquired from simultaneous transits of L8 OLI and S-2 MSI sensors. Then, the isolation forest (iForest) algorithm was employed to remove the anomalous pixels of image pairs and eliminate the influence of anomalous data on the conversion relationships. Subsequently, the adjustment coefficients for multi-source sensors at mixed latitudes with high spatial resolution were obtained using an ordinary least squares regression method. Finally, the L8-S-2 fused dataset based on the adjustment coefficients is proposed, which is suitable for different latitude farming areas in China. The results showed that the iForest algorithm could effectively improve the correlation between the corresponding spectral bands of the two sensors at a spatial resolution of 10 m. After the removal of anomalous pixels, excellent correlation and consistency were obtained in three study areas, and the Pearson correlation coefficients between the corresponding spectral bands almost all exceeded 0.88. Furthermore, we mixed the six image pairs of the three latitudes to obtain the adjustment coefficients derived for integrated L8 and S-2 data with high-spatial-resolution. The significance and accuracy quantification of the adjustment coefficients were thoroughly examined from three dimensions: qualitative and quantitative analyses, and spatial heterogeneity assessment. The obtained results were highly satisfactory, affirming the validity and precision of the adjustment coefficients. Finally, we applied the adjustment coefficients to crop monitoring in three latitudes. The normalized difference vegetation index (NDVI) time-series curves drawn by the integrated dataset could accurately describe the cropping system and capture the intensity changes of crop growth within the high, middle, and low latitudes of China. This study provides valuable insights into enhancing the application of multi-source remote sensing satellite data for long-term, continuous quantitative inversion of surface parameters and is of great significance for crop remote sensing monitoring.