Due to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) framework encompassing multiple machine learning models for predicting UHI with high-frequency urban sensor network data combined with remote sensing data focused on Chicago, Illinois, from 2018 to 2020. Enabled by rapid advances in urban sensor network technologies and high-performance computing, this framework is designed to predict UHI in Chicago with fine spatiotemporal granularity based on environmental data collected with the Array of Things (AoT) urban sensor network and Landsat-8 remote sensing imagery. Our computational experiments revealed that a random forest regression (RFR) model outperforms other models with the prediction accuracy of 0.45 degree Celsius in 2020 and 0.8 degree Celsius in 2018 and 2019 with mean absolute error as the evaluation metric. Humidity, distance to geographic center, and PM_2.5 concentration are identified as important factors contributing to the model performance. Furthermore, we estimate UHI in Chicago with 10-min temporal frequency and 1-km spatial resolution on the hottest day in 2018. It is demonstrated that the RFR model can accurately predict UHI at fine spatiotemporal scales with high-frequency urban sensor network data integrated with satellite remote sensing data.

It is well established that nighttime light brightness value, which is measured from satellites, correlates with economic prosperity across the globe. Researchers have diverged over whether economic factors cluster in coastal areas or move to interior areas. By using nighttime light data and applying the random forest algorithm to measure the proportion of global “near regions” GDP, it was seen that global GDP decreased from 67.25% in 2000 to 63.02% in 2018. This research reveals that under the continuous promotion of economic globalization, there is still a spatial imbalance of economic development between global “near regions” and “far regions”; however, economic factors are gradually shifting to interior areas and forming a “coastal remoteness” evolution pattern. Within the intercontinental range, there are obvious differences in the evolution patterns and spatial structure of economic development between the sub-regions. The reduction of overseas transportation costs and diseconomies of scale are the primary reasons for the evolution of “coastal remoteness” in global economic development. Our findings can facilitate future policymaking and the management of global coastal and interior areas, as well as establish new horizons for relevant research topics within the context of land and marine-coordinated development.

Spatiotemporal satellite image fusion aims to generate remote sensing images satisfying both high spatial and temporal resolution by integrating different satellite imagery datasets with distinct spatial and temporal resolutions. Such fusion technique is crucial for numerous applications that require frequent monitoring at fine spatial and temporal scales spanning agriculture, environment, natural resources, and disaster management. However, existing fusion models have difficulty accommodating abrupt spatial changes in land cover among images and dealing with temporally distant image data. This article proposes a novel multistream spatiotemporal fusion generative adversarial network (STGAN) model for spatiotemporal satellite image fusion that can produce accurate fused images and accommodate substantial temporal differences between the input images. The STGAN employs a conditional generative adversarial network architecture with a multistream input design to better learn temporal features. The generator of STGAN comprises convolutional blocks, a spatial transformer module, a channel attention network, and a U-net module designed to better capture spatial and temporal features from the multiresolution input images. Comprehensive evaluations of the proposed STGAN model have been performed on the Coleambally Irrigation Area and Lower Gwydir Catchment datasets, using both visual inspection and spatial and spectral metrics, including root mean square error, relative dimensionless global error synthesis, spectral angle mapping, structural similarity index measure, and local binary pattern. The experiments show that the proposed STGAN model consistently outperforms existing benchmark models and is capable of generating high-quality fused remote sensing data product of high spatial and temporal resolution.