Exploring the Role of AI in Enhancing Remote Sensing Technologies for Climate Change Studies

The current study focuses on the application of Intelligent Systems in the improvement of data analysis for climate changevariables using remote sensing systems. The idea is to enhance greatly interpretability of sophisticated remote sensing data, which is essential for analyzing the effects of the climate change. The use of the AI means that the climate ascertaining process of big data patterns can be expedited and consequently augment the knowledge in climate science. This research will further explore on the approaches and uses of these technologies so as to demonstrate how they can be used to enhance knowledge on climate change. the study will use advanced models from the AI classification, including CNNs and RNNs. They were used where the climate work is most sensitive: for land cover classification, vegetation health assessment, and the identification of climatic abuses such as glaciers” melting and heat island effect. CNN models gave much better accuracy in classification with accuracies as high as 92. 7 percent for actual experiment results compared to conventionally developed methods. The research also managed to assess the vegetation health using the NDVI analysis and identify severe climatic fluctuations. They stress the positive impact of the key ideas of artificial intelligence on enhancing the accuracy and frequency of the identification of environmental changes for improving climate change mitigation and adaptation. Future work will concern the further development of the AI models, the comprehensive integration of multi-source data as well as the further improvement of the models explainability to increase the AI solution's contribution to the actual scientific analysis of the global climate.