Developing a spatiotemporal fusion framework for generating daily UAV images in agricultural areas using publicly available satellite data

Monitoring agricultural areas, given their rapid transformation and small-scale spatial changes, necessitates obtaining dense time series of high-resolution remote sensing data. In this manner, the unmanned aerial vehicle (UAV) that can provide high-resolution images is indispensable for monitoring and assessing agricultural areas, especially for rapidly changing crops like alfalfa. Considering the practical limitations of acquiring daily UAV images, the utilization of spatiotemporal fusion (STF) approaches to integrate publicly available satellite images with high temporal resolution and UAV images with high spatial resolution can be considered an effective alternative. This study proposed an effective STF algorithm that utilizes the Generalized Linear Model (GLM) as the mapping function and is called GLM-STF. The algorithm is designed to use coarse difference images to map fine difference images via the GLM algorithm. It then combines these fine difference images with the original fine images to synthesize daily UAV image at the prediction time. In this study, we deployed a two-step STF process: (1) MODIS MCD43A4 and Harmonized Landsat and Sentinel-2 (HLS) data were fused to produce daily HLS images; and (2) daily HLS data and UAV images were fused to produce daily UAV images. We evaluated the reliability of the deployed framework at three distinct experimental sites that were covered by alfalfa crops. The performance of the GLM-STF algorithm was compared with five benchmark STF algorithms: STARFM, ESTARFM, Fit-FC, FSDAF, and VSDF, by using three quantitative accuracy evaluation metrics, including root mean squared error (RMSE), correlation coefficient (CC), and structure similarity index (SSIM). The proposed STF algorithm yielded the most accurate synthesized UAV images, followed by VSDF, which proved to be the most accurate benchmark algorithm. Specifically, GML-STF achieved an average RMSE of 0.029 (compared to VSDF’s 0.043), an average CC of 0.725 (compared to VSDF’s 0.669), and an average SSIM of 0.840 (compared to VSDF’s 0.811). The superiority of GLM-STF was also observed with the visual comparisons as well. Additionally, GLM-STF was less sensitive to the increase in the acquisition time difference between the reference image pairs and prediction date, indicating its suitability for STF tasks with limited input reference pairs. The developed framework in this study is thus expected to provide high-quality UAV images with high spatial resolution and frequent observations for various applications.