Enhancing High-Temperature Prediction via Sixfold Strategy Consensus-Reaching Processes: A Case Study Using FY-3E Spatiotemporal Remote Sensing Satellite Data - Chao Zhang; Haonan Hou; Arun Kumar Sangaiah; Deyu Li; Wentao Li (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Spatiotemporal (ST) remote sensing satellites (RSSs) play essential roles in numerous fields including environmental monitoring, agriculture, urban planning, disaster management, and scientific research. The Fengyun III E satellite, also known as FY-3E, is dedicated to advancing numerical weather prediction and offers distinctive capabilities in weather forecasting, tropical cyclone, and other extreme meteorological event warnings, climate monitoring, as well as solar and space weather observation. Given the escalating concerns surrounding global warming, high-temperature predictions (HTPs) have emerged as a pressing issue. Nonetheless, ongoing challenges persist, encompassing inaccurate identification of satellite images, sluggish HTP speeds, heightened risks linked to meteorological information fusion, and disparities in interpreting conflicting ST RSS data from various satellites. To address these challenges, an FY-3E RSS data-based HTP model grounded in multigranularity (MG) picture fuzzy (PF) probabilistic rough sets (PRSs) is proposed, complemented by an improved sixfold strategy consensus-reaching process (6FS-CRP) to facilitate meteorologists in consensus reaching. Initially, the concept of adjustable MG PF PRSs is introduced by integrating picture fuzzy sets with PRSs. Subsequently, a clustering algorithm based on trust similarity analysis is employed as the clustering method within the proposed framework, alongside the incorporation of 6FS-CRP. Following this, the 6FS-CRP algorithm is seamlessly integrated into the adjustable MG PF PRS model, thereby introducing the PF 6FS-CRP application on HTPs. Finally, leveraging satellite data from the China Meteorological Data Service Centre, the proposed model is applied to satellite image analysis, weather forecasting, and HTPs, thereby validating its feasibility and effectiveness.