Advances in remote sensing and artificial intelligence (AI) have facilitated efficient management of environment resources. The use of machine learning and data modeling enabled with high computing power can aid in modeling, integrating, assessing environmental and social systems - providing the opportunity to enhance their resilience. While, the coupling, and simulation of multi-spatial and multi-temporal remote sensing and discrete social data (behavioral, economic, and policies) provided opportunities, it also highlighted relevant challenges of integrating such data with deep machine learning algorithms to make better informed decisions. Topics considered in this section can be related to those addressing resilience and sustainability of food and water security, agricultural water management, drought monitoring and impact prediction, adaptive capacity of agriculture systems, and impacts of socio-economic changes on agriculture-water systems utilizing remote sensing data and products along with the use of AI tools such as machine learning and multi agent systems.