Practical explanations of the methods behind land cover classification, trend detection, and spectral indices — written for people who'll actually use the numbers.
How planetary-scale cloud platforms let anyone analyse decades of satellite imagery without downloading files or writing code.
Sea-surface temperature anomalies and multi-date reflectance change give reef conservation an early, wide-area warning system.
How supervised machine-learning classification turns satellite imagery into thematic maps — and why every map needs an accuracy assessment.
How NDVI is calculated from red and near-infrared reflectance, what the values actually mean, and the three places it commonly breaks down.
Why a slope alone can't tell you if a trend is real, and how a non-parametric significance test separates genuine change from year-to-year noise.
A practical comparison of the three most common classifiers, and why your validation method matters more than which one you pick.
Land-cover classification, NDVI trends with Mann-Kendall significance, spectral indices and more — explained in depth across our guides and interactive tools.
Explore the interactive tools →Resolution, revisit time, and historical depth compared — and how to choose between them for a given analysis.
Why Kappa exists alongside overall accuracy, and the legitimate criticisms of relying on it too heavily.
Why a relative comparison, not absolute temperature, is what actually defines an urban heat island.
How zonal statistics summarize raster data within boundaries, and why the mean alone can mislead.
Deep-dive explainers for every index and analysis — formula, bands, interpretation, limitations and citation.