# Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring
###### Abstract
Most satellites cannot take high temporal resolution and high spatial resolution imagery at the same time, e.g., MODIS (daily and 500 m) and Landsat-8 (16-day and 30 m); therefore, it is difficult to obtain complete high-quality time-series image datasets from a single sensor covering the whole growing season of summer corn in China. Spatiotemporal image fusion is one of the most effective ways to solve this problem [13; 14]. The spatiotemporal image fusion method combines the characteristics of high temporal resolution images with high spatial resolution images, which facilitates the characterization of the details of the land surface being surveyed. The method can also help to resolve apparent contradictions between high temporal resolution and high spatial resolution imagery datasets [15]. Spatiotemporal remote sensing image fusion is a practical and low-cost method. It not only utilizes remote sensing images with high temporal and spatial resolutions, but also promotes the utilization of current earth observation data [16]. Spatiotemporal data fusion based on machine learning is one notable algorithmic method that has shown potential in recent years [17]. Sparse representation is used to realize super-resolution reconstruction of remote sensing images and enhance the spatial detail expression ability of low spatial resolution images which mainly used to improve the serious problem of mixed pixels in complex terrain coverage areas [18]. [PERSON] et al. [19] improved the resolution of microwave images based on sparse optimization and compressive sensing, and the research results showed that the data fusion products were better than all the statistical indicators of the original AMSU-B and AVHRR. [PERSON] and [PERSON] [20] designed the Fit-FC method, including regression model (RM) fitting, spatial filtering, residual compensation and fusion of Sentinel-2 (MSI) and Sentinel-3 (OLCI) images, to create approximately daily Sentinel-2 images. Using these advanced methods, super-resolution reconstructions of remote sensing images can be achieved, which enhances the resolution of low quality images and increases the detail of spatial expression. This is mainly used to resolve the serious problems of mixed pixel types in areas with complex terrain [21]. [PERSON] et al. [22] introduced the spatial and temporal adaptive reflectance fusion Model (STARFM) method, which can blend MODIS and Landsat images for applications that require high resolution in both time and space. The STARFM model takes spatial variation and time variation information into account, and it is one of the most widely used space-time models. It is used in time series NDVI, LAI monitoring [23; 24], estimation of regional evapotranspiration [25], ground surface temperature monitoring [26; 27], and gross primary production (GPP) monitoring [28]. Other scholars [29; 30; 31; 32; 33; 34] have improved the STARFM model based on [PERSON]'s research. Among them, one of the most widely used is the ESTARFM algorithm, which is an enhanced spatial and temporal adaptive reflectance fusion model. The experimental results showed that ESTARFM [31] had a better prediction accuracy than STARFM. However, the ESTARFM model is sequentially input high time resolution data with high spatial resolution and high temporal resolution data and target during two periods, thereby generating high spatial resolution data of the target period. Thus, when the difference between the two moments is relatively large, the data generated during the target period will produce greater uncertainty. Kalman filtering is a linear simulation and prediction algorithm that can be used for image fusion and for predicting new images. It provides a mathematical framework for predicting unmeasured variables from indirectly noisy measurements. As a predictive tool, Kalman filter is mainly used to estimate the state of dynamic systems, such as process control [35; 36], flood forecasting [37], radar tracking [38], GNSS navigation [39; 40] and performance analysis of estimation systems. [PERSON] et al.