GEOSPATIAL ANALYTICS FOR AGRICULTURE REMOTE SENSING, UAV IMAGING AND MODEL-ASSISTED SAMPLING

Agriculture is undergoing a profound transformation driven by advances in geospatial technologies, artificial intelligence and data-driven decision-making. The convergence of satellite remote sensing, Unmanned Aerial Vehicle (UAV) imaging, Geographic Information Systems (GIS), cloud computing and statistical methodologies has revolutionized the way agricultural resources are monitored, assessed and managed. These technologies now provide unprecedented opportunities to generate timely, accurate and scalable information for crop monitoring, acreage estimation, yield forecasting, pest surveillance, drought assessment and sustainable agricultural planning. The geospatial analytics has emerged as a cornerstone of digital agriculture, enabling the integration of diverse datasets collected from satellites, drones, field surveys and environmental sensors. However, the growing availability of high-resolution spatial data also necessitates robust statistical frameworks to ensure reliable estimation and scientific inference. In particular, model-assisted sampling, spatial sampling and geostatistical approaches have become increasingly important for integrating earth observation data with survey-based agricultural statistics. Despite rapid progress in individual domains, comprehensive resources that jointly address remote sensing, UAV imaging and advanced sampling methodologies remain limited. This edited book entitled "Geospatial Analytics for Agriculture, Remote Sensing, UAV Imaging and Model-Assisted Sampling," has been conceived to bridge this gap. The book provides an interdisciplinary platform that integrates geospatial technologies with statistical science to address contemporary challenges in agricultural monitoring and resource management. It presents both theoretical foundations and practical applications, covering satellite data analysis, vegetation indices, crop classification, UAV-based monitoring, spatial sampling designs, model-assisted estimation, machine learning, climate risk assessment and decision support systems. The chapters included in this volume have been contributed by experts from diverse disciplines, including agricultural sciences, remote sensing, statistics, geoinformatics, computer science, environmental sciences and precision agriculture. Together, these contributions reflect current research trends, innovative methodologies and practical case studies that demonstrate the transformative role of geospatial analytics in modern agriculture. This book has been organized into four thematic parts. The first part introduces the fundamentals of remote sensing and geospatial data for agricultural monitoring. The second part focuses on UAV imaging and precision agriculture applications. The third part presents spatial sampling methodologies, model-assisted estimation and integration of survey and geospatial information. The final part explores advanced analytical approaches, including crop acreage estimation, yield prediction, climate risk assessment and decision support systems. The primary objective of this volume is to serve as a comprehensive reference for researchers, academicians, students, policymakers, agricultural statisticians, geospatial professionals and practitioners working in digital agriculture. We hope that the concepts, methodologies and applications presented in this book will inspire interdisciplinary research, encourage innovation and support the development of sustainable, resilient and data-driven agricultural systems. We sincerely believe that this book will contribute to the growing body of knowledge in geospatial analytics and become a valuable resource for the scientific community, helping bridge the gap between technological innovation and practical agricultural applications. We welcome constructive feedback from readers and look forward to future developments that continue to advance the field of geospatial agriculture.