Advanced Artificial Intelligence and Multi-Source Remote Sensing for Agricultural Monitoring and Forecasting:

Agricultural systems are multifaceted adaptive systems that are constructed through dynamic processes of interaction between climate, soil, vegetation, and management activities, and which are progressively evidently, climate variability and extreme events. Conventional methods of monitoring have constraints of low spatial coverage, expensive nature and low sensitivity to the spatiotemporal heterogeneity. Current developments in satellite remote sensing have made available large amounts of multisensory data including optical, SAR, and thermal data, that when combined with the meteorological and edaphic data, can be used to fully monitor the agriculture. This chapter reviews the methods of artificial intelligence (AI) and machine learning, deep learning, and spatiotemporal models to monitor crops, predict yields and assess food security. The focus is put on the time-series analysis, multi-source data fusion, and early-warning applications, including the drought detection and yield-gap analysis.