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To effectively disseminate location-linked information despite the existence of digital walls across institutions, this study developed a cross-institution mobile App, named GeoFairy2, to overcome the virtual gaps among multi-source datasets and aid the general users to make thorough accurate in-situ decisions. The app provides a one-stop service with relevant information to assist with instant decision making. It was tested and proven to be capable of on-demand coupling and delivering location-based information from multiple sources. The app can help general users to crack down the digital walls among information pools and serve as a one-stop retrieval place for all information. GeoFairy2 was experimented with to gather real-time and historical information about crops, soil, water, and climate. Instead of a one-way data portal, GeoFairy2 allows general users to submit photos and observations to support citizen science projects and derive new insights, and further refine the future service. The two-directional mechanism makes GeoFairy2 a useful mobile gateway to access and contribute to the rapidly growing, heterogeneous, multisource, and location-linked datasets, and pave a way to drive us into a new mobile web with more links and less digital walls across data providers and institutions. 

GIS data layer on crop field boundary has many applications in agricultural research, ecosystem study, crop monitoring, and land management. Crop field boundary mapping through field survey is not time and cost effective for vast agriculture areas. Onscreen digitization on fine-resolution satellite image is also labor-intensive and error-prone. The recent development in image segmentation based on their spectral characteristics is promising for cropland boundary detection. However, processing of large volume multi-band satellite images often required high-performance computation systems. This study utilized crop rotation information for the delineation of field boundaries. In this study, crop field boundaries of Iowa in the United States are extracted using multi-year (2007-2018) CDL data. The process is simple compared to boundary extraction from multi-date remote sensing data. Although this process was unable to distinguish some adjacent fields, the overall accuracy is promising. Utilization of advanced geoprocessing algorithms and tools on polygon correction may improve the result significantly. Extracted field boundaries are validated by superimposing on fine resolution Google Earth images. The result shows that crop field boundaries can easily be extracted with reasonable accuracy using crop rotation information. 

WaterSmart project is an NSF funded projected seeks water consumption reduction using satellite observations. In order to fit the fine temporal resolution requirement, satellites are required to have a high revisit cycle. MODIS is an ideal platform for monitoring the ground thanks to its daily coverage while the spatial resolution is too coarse. Research has demonstrated the possibility to improve the spatial resolution of MODIS using the Landsat 8 images. This research is aimed to establish a workflow to adapt the data fusion algorithm to achieve automatically processing at real-time in order to support short-term decision making.