Time Series Analysis

Time series analysis in the context of remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi... examines sequences of satellite or aerial observations collected over the same geographic area at regular time intervals to identify temporal patterns, trends, and anomalies. With decades of archived imagery from missions like Landsat (since 1972) and MODIS (since 2000), time series analysis has become a powerful approach for understanding how Earth's surface changes. Key techniquesTrend analysis fits statistical models to long-term data sequences to detect gradual changes like deforestation, urbanization, or climate-driven greening. Phenological analysis tracks seasonal vegetation cycles including green-up, peak productivity, and senescence dates. Break-point detection algorithms such as BFAST (Breaks For Additive Season and Trend) identify abrupt changes caused by disturbances like fire, logging, or flood. Harmonic analysis decomposes time series into seasonal components and residuals for anomaly detection. ApplicationsTime series analysis is used for forest disturbance monitoring, agricultural crop phenology tracking, drought early warning, urban expansion mapping, glacier retreat measurement, and water body extent monitoring. Cloud platforms like Google Earth EngineGoogle Earth EngineGoogle Earth Engine is a cloud-based geospatial analysis platform that combines a multi-petabyte catalog of satellite... have made planetary-scale time series analysis accessible to researchers worldwide. Time series analysis transforms archives of satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... from static snapshots into dynamic records of environmental change, enabling monitoring of gradual trends and sudden disturbances that single-date analyses cannot capture.