## 6 Summary and Future Work  
This paper indicated that different sources of remote sensing data, such as satellites, manned aircraft, or UAVs, are not consistent and performed differently, although they are somewhat correlated. Spatial resolution decides the scope of the application of the data from each source. Cloud cover is an issue, especially for the satellites with long revisit time. UAVs have great potential for high-performance monitoring of crop growth. However, they have limits in varied sensor quality and image mosaicking for unignorable geometric and radiometric mismatches. So too do the manned aircraft-based sensors and images. Satellite systems are typically thoroughly calibrated geometrically and radiometrically. Satellite and manned aircraft images can cover large areas at one time and can be integrated to monitor the field area. Although the spatial resolution of Landsat is not good enough for the fields in this study, if the larger areas surrounding the fields are considered, the products of Landsat (monitoring and classification) are good sources to show general vegetation profiles of the area, and then the data can be scaled down to the specific area and fields with Sentinel-2, PlanetScope, manned aircraft, and/or UAVs if necessary. This is advantageous in that the study of a field can be opened to associate with the surrounding environment and ecology and properly uses the data sources. Remote sensing crop growth monitoring is a critical component of precision agriculture technology and has a potential to enhance crop growth management in nutrient status determination, irrigation schedule, weed/pest control, irrigation scheduling, and harvesting planning to boost crop yield, increase resource use efficiency, and minimize negative environmental impact. Various remote sensing systems with corresponding decision support models are continuously attracting more attention locally and areawide with climate change. Remote sensing research has a need to develop integrated platforms and corresponding data pipelines for crop production management decision support. The integrated systems will enable more accurate characterization of crop growth status at optimal spatiotemporal resolution. Therefore, to improve remote sensing crop growth monitoring, different sources of remote sensing data should be coordinated and integrated or fused to build a paradigm of multisource remote sensing. This new paradigm should consider the spatial, spectral, and temporal factors in the sense of optimization for a specific focus of concerned field areas. To develop multisource remote sensing for crop monitoring in the area and other similar areas, remotely sensed images from different sources for the same area can be processed to fully extract features and information from the images from different sources, and the extracted features and information can be further integrated into a resulting image that contains rich spectral information and spatial details. This process involves multisource remote sensing data fusion, which can be conducted at different levels:  
* Pixel level, which is conducted to merge images from different sources pixel by pixel by a certain scheme for a new image as an independent product or input of the model;* Feature level, which is conducted to merge features extracted from images from different sources by a certain scheme, such as fused NDVI, for a new feature image as an independent product or input of the model;
* Decision level, which is conducted to combine the outputs of different image analytic models. This development will provide a more accurate data basis for advanced remote sensing image classification, target detection, and spectral analysis for improved crop growth remote sensing monitoring for precision crop production management in the area of lower northern Mississippi and hopefully other similar areas. Conceptualization, Y.H. ; methodology, Y.H. ; software, Y.H. ; formal analysis, Y.H. ; investigation, Y.H. ; data curation, Y.H. ; writing--original draft preparation, Y.H.