This book focuses solely on the issues of agricultural productivity analysis with advanced modeling approaches bringing solutions to food-insecure regions of the world, especially in south and southeast Asia and in Africa. Advanced modeling tools and their use in regional planning provide an outstanding opportunity to help face the challenges of climate change. The sudden effect of flash floods, drought, salinity, and sea water rises causing saltwater intrusions and its impact on agricultural production are some of the disastrous results of climate change. In this edited volume, information on climate-induced impacts for flooding, flash floods, and drought impact on agricultural crops is provided to address possible solutions for food security in south Asia, southeast Asia, and some regions of Africa. Leading-edge research methodology is presented as it relates to remote sensing applications for regional science and allied fields. In regional policy planning, agriculture andforestry play key roles in food security along with environmental conservation and depend on geo-spatial variability. Satellite remote sensing and geographical information systems have an immense potential to encompass all these factors and to catalogue the regional variability of climate change and climate economics. In the satellite remote sensing domain, advanced modeling tools, deep learning applications, and cloud-based earth engines significantly increase the flexibility of decision making and its application for regional perspectives. The result can increase agricultural and forest productivity and ensure its resilience and sustainability. The books chapters introduce modeling techniques such as machine learning and fuzzy expert system using satellite remote sensing datasets based on cloud application. These methods assist regional planners to increase crop production, land use, and detection of changes in land cover in order to better understand their vulnerability toclimate-related disaster. Furthermore, remote sensing and in-depth GIS analysis are integrated with machine learning to address natural uncertainties such as flash floods, droughts, and cyclones so that emergency responses for agricultural production management can be adopted more effectively.