Cropping intensity (CI) is an important parameter used for evaluating agricultural land use efficiency, with significant implications for global food security, sustainable land management and economic stability. As the world’s population continues to grow, effective monitoring of CI is vital for fulfilling the rising food demand and addressing challenges caused by climate change. This review article explains the current state of the field, discusses significant advances made possible by remote sensing and geospatial technologies. The paper explores the evolution of methodologies, from traditional time-series analysis to modern machine and deep learning algorithms and highlights regional applications across different continents. However, persistent and critical challenges were identified that limit the full potential of these tools. some of the Key issues and significant data gaps were included. A major research gap remains due to less integration of socio-economic and policy data into geospatial models, which limits our ability to understand the complex drivers behind the changes observed in CI. The future of this field requires a coordinated, interdisciplinary approach. Recommendations include promoting open-access platforms and harmonized standardized datasets, developing automated algorithms that leverage multi-source data fusion and using the ground truth data to fill the gaps. This comprehensive approach is needed to provide the reliable, predictive intelligence required for accurate policy decisions and sustainable agriculture worldwide.
Prathyusha et al. (Wed,) studied this question.