A Grid-Based Spatiotemporal Deviation Framework for Agricultural Landscape Monitoring Using Remote Sensing

Agricultural monitoring systems increasingly rely on satellite-derived indices to assess crop and field conditions; however, many existing approaches depend on absolute index values or static thresholds, limiting their ability to capture localized and temporal variability. This paper presents a grid-based spatiotemporal deviation framework for agricultural landscape monitoring that emphasizes relative performance assessment against historical baselines rather than absolute measurements. The proposed framework partitions agricultural regions into fine-resolution spatial grids and constructs multi-year temporal baselines for each grid using satellite-derived vegetation and environmental indicators. Current observations are evaluated using standardized deviation metrics to identify significant departures from historical norms while accounting for contextual interactions among multiple indices. The system incorporates strict data quality controls, including cloud-cover filtering and conditional temporal interpolation, to ensure analytical robustness under real-world data constraints. Outputs are designed to support interpretability through grid-level anomaly maps, temporal trend visualizations and aggregated field indicators. A representative demonstration is presented to illustrate how the framework enables early detection of spatial and temporal variability in agricultural conditions. The proposed approach offers a modular and extensible foundation for decision-support applications in precision agriculture, sustainability assessment and risk monitoring.