Kriging Interpolator

Upload sparse point samples (soil moisture, pH, pollution readings, anything measured at scattered locations) and predict a full continuous surface across the whole area — fitting a variogram model first, not just averaging nearby points. Runs entirely in your browser, powered by R.

Upload a point file with a numeric column — soil moisture, pH, pollutant concentration, anything measured at scattered sample locations — pick that column, and this tool fits a variogram model to how similar nearby points really are, then predicts a value everywhere across the area, not just at the points you measured.

Tip

Private by design. Your file is processed entirely in your browser (R via WebAssembly) — it’s never uploaded to any server, including ours. Nothing to configure, nothing to trust: the code never gets the chance to see your data.

The code itself isn’t shown here — it’s a fixed, non-editable computation using gstat::variogram(), fit.variogram() and krige() underneath. Want to see the same pipeline built up one step at a time, and why this tool runs on R, not Python? Read Interpolating Soil Moisture with Kriging.

First run loads the R engine — expect 1-3 minutes (R’s WebAssembly build is noticeably slower to start than this site’s Python tools). Instant after that, and again each time you come back to this page.

First tool in the GeoStatistics family. The green dots are your own sample points (brown = low, teal = high, on the same scale as the surface underneath); the colored surface is the prediction everywhere else. Want the code, or why this runs on R, not Python? Read the full article.