Fill gaps in soil moisture records.

Soil moisture networks routinely run at well under full data completeness — probes drift, freeze, and drop out for weeks, and the gaps cluster in exactly the seasons you care about.

How it works

Neighbouring depths, adjacent stations, rainfall and temperature carry most of the missing signal. Gaplad reconstructs the holes from what survived and proves the quality on synthetic gaps of the same length carved from your own observed data.

• Quality scan first — error codes, spikes, frozen sensors and re-installed sensors are found before anything is modelled.
• Validated before filled — synthetic gaps are carved from your observed soil moisture record, filled blind, and scored against held-out truth. The winning method per gap length fills the real gaps.
• Honest by construction — short gaps use interpolation because it wins there; unfillable gaps stay unfilled with the reason stated; every filled value is flagged, with uncertainty bands from measured residuals.

Your data never leaves your browser

The trust report states plainly which gap lengths the model fills well and which it cannot — including the honest case where nothing can be filled because every sensor died together. The model trains in a background thread on your machine — there is no upload, no account, and nothing to get security clearance for.

Free tier runs the full pipeline on up to 20,000 time steps (about two years of hourly data) with a watermark line on exports. A one-time licence lifts the size limit and cleans the exports. Exported CSVs flag every filled value; the trust report is a single HTML file you can attach to a deliverable.