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Research on fusion modeling of high spatial and temporal resolution images typically uses MODIS products at 500 m and 250 m resolution with Landsat images at 30 m, but the effect on results of the date of reference images and the 'mixed pixels' nature of moderate-resolution imaging spectroradiometer (MODIS) images are not often considered. In this study, we evaluated those effects using the flexible spatiotemporal data fusion model (FSDAF) to generate fusion images with both high spatial resolution and frequent coverage over three cotton field plots in the San Joaquin Valley of California, USA. Landsat images of different dates (day-of-year (DOY) 174, 206, and 254, representing early, middle, and end stages of the growing season, respectively) were used as reference images in fusion with two MODIS products (MOD09GA and MOD13Q1) to produce new time-series fusion images with improved temporal sampling over that provided by Landsat alone. The impact on the accuracy of yield estimation of the different Landsat reference dates, as well as the degree of mixing of the two MODIS products, were evaluated. A mixed degree index (MDI) was constructed to evaluate the accuracy and time-series fusion results of the different cotton plots, after which the different yield estimation models were compared. The results show the following: (1) there is a strong correlation (above 0.6) between cotton yield and both the Normalized Difference Vegetation Index (NDVI) from Landsat (NDVIL30) and NDVI from the fusion of Landsat with MOD13Q1 (NDVIF250). (2) Use of a mid-season Landsat image as reference for the fusion of MODIS imagery provides a better yield estimation, 14.73% and 17.26% higher than reference images from early or late in the season, respectively. (3) The accuracy of the yield estimation model of the three plots is different and relates to the MDI of the plots and the types of surrounding crops. These results can be used as a reference for data fusion for vegetation monitoring using remote sensing at the field scale. [ABSTRACT FROM AUTHOR]

• Proposed yield estimation models at field scale with fusion result from Landsat and MODIS. • Considered the importance of specific date information of MODIS composite products. • Reconstructed the MODIS NDVI product by a data fitting method (Extreme Model). • Proposed and applied a different accuracy evaluation method for the results of image fusion. Deficiencies in the spatiotemporal resolution of remote sensing (RS) images limit crop yield estimation at the farm and field scale. These deficiencies may be alleviated by fusion of high spatial and temporal resolution images such as MODIS and Landsat. In this study, a new daily MODIS NDVI product (reconstructed MODIS) was generated from 16-day composite images using the Extreme Model, which integrates the NDVI value with the corresponding specific date information at each pixel. The Flexible Spatiotemporal Data Fusion (FSDAF) model was then used to create two fused, high-resolution time-series products (fused MODIS and fused reconstructed MODIS) in order to enhance the spatial and temporal effectiveness of satellite images for field-scale applications. Three yield estimation models were then built using time-series data of Landsat NDVI, predicted NDVI from fused MODIS, and predicted NDVI from fused reconstructed MODIS. The methodology was tested on a farm field over the cotton growing season in the San Joaquin Valley of California. Results showed that: (1) the time trend of NDVI over the growing season for the fused reconstructed MODIS was more similar to that of Landsat than were either of MODIS or fused MODIS, indicating that the specific date of MODIS pixels is important for time-series analysis; (2) the NDVI from fused reconstructed MODIS provided the best correlation with Landsat NDVI, with R 2 and RMSE values 15% higher than for fused MODIS; (3) correlation between cotton yield and all three datasets at the pixel level was statistically significant for all image dates, and (4) the accuracy of the cotton yield estimation model using predicted NDVI from fused reconstructed MODIS (R2 = 0.79; RMSE = 488.01) was higher than with fused MODIS (R2 = 0.77; RMSE = 513.96) and only slightly lower than with Landsat (R2 = 0.84, RMSE = 463.12). This study improved the accuracy of MODIS-based yield estimation using fusion images, and the results can be applied to improve vegetation monitoring and quantitative modeling using MODIS NDVI at the field scale. [ABSTRACT FROM AUTHOR]