Our health checks against ISMN's quality flags#

2026-10-08. The §9.2 "replay of real recorded field data" that validation-data.md recommended, run for real: layer zero's §4.2 checks over 2,792,596 hourly readings from the three Tennessee SCAN stations, compared with the quality flags ISMN assigned to the same readings.

This measures agreement, not accuracy. Where we and ISMN differ, either could be right — and in the cases below where we looked closely, the disagreement turned out to be about the soil rather than the sensor.

What was scored#

The flag vocabulary was verified against ISMN_qualityflags_description.txt inside the archive before the reader was written, as the earlier note required. It matched the documentation.

Results#

After Q23 (decided 2026-10-08: spikes by shape, a cap on falls, no cap on rises), the same Tennessee record scores: negative jumps (D07) 56% (29 of 52), spikes (D06) 8% (27 of 355 — D06 mostly marks dry-soil noise, see spikes-and-jumps.md), positive jumps (D08) 0% by design. The new checks raised 253 suspect and 49 rate_exceeded flags on the 2.24 million readings ISMN rates good — 0.013%.

What we raise on readings ISMN rates good, by depth:

What it means#

In the root zone, the checks hold up#

In the top 20 cm — where an irrigation decision is made — our checks disagree with ISMN on no more than 0.27% of the readings it rates good, and on 0.02% at 20 cm. That is the first evidence on real sensors, rather than on the simulator, that the §4.2 checks are quiet when nothing is wrong.

Below that, the flatline check misfires — Q24#

All the deep disagreement is stuck. The §4.2 amendment already says a flatline threshold must exceed the quantisation dwell, how long a healthy probe repeats one value. Our 18 hours was set from a shallow probe's overnight dwell. The archive shows the dwell grows with depth, because water moves more slowly further down. Longest runs of one identical value among readings ISMN rates good:

Roughly exponential in depth, with an e-folding of about 0.44 m. Recorded as open-questions Q24, recommending a depth-scaled default taken from the §4.1 installation record, refined per series once a season of its own history exists.

The rate check is blind at hourly sampling — Q23, confirmed#

Zero of 1,849 readings ISMN flags as spikes or jumps. Predicted before the data arrived: the 0.00015/s limit was set for 15-minute sampling and allows a single step of 0.54 m³/m³ over an hour. Real data now confirms it. ISMN's own D06 and D07/D08 readings are the material to set a max_step from (Q23).

Range disagreements: the soil map sides with ISMN#

ISMN's C03 — "above the saturation point" — uses saturation from the global HWSD soil database, about 1 km resolution. At McAllister Farm, 5 cm, that puts saturation at 0.427: every C03 reading is at or above it, and every good reading below. The flagged readings run from 0.427 to 0.492 and cluster in wet winters.

The USDA soil survey (SSURGO), mapped at field scale, settles it. McAllister Farm is Dickson silt loam, whose top horizon saturates at 0.42; Allen Farms is Hampshire silt loam, also 0.42 near the surface; Eastview Farm is Decatur silty clay loam. So readings to 0.49 are above what the soil can physically hold, ISMN's flag is probably right, and our generic 0.46 was too lenient. The likeliest cause is the probes' generic factory calibration reading high in this soil — probes of this kind ship with one calibration whose accuracy depends on the soil.

Corrected twice. A first draft called these soils silt loam, by assumption. A revision "corrected" that to loam on the strength of HWSD's 50% sand and 25% clay, and argued the disagreement was the database's porosity against ours. The field-scale survey says silt loam and silty clay loam, and puts saturation where ISMN does. HWSD was wrong about the texture, and the revision was wrong to trust it.

One sensor dominates the generic agreement figure: Allen Farms at 1.02 m reads above both limits for 60% of its record. The soil survey says it sits in a horizon (76–119 cm) that saturates at 0.30 — so whatever it is measuring, it is not reading that soil's water content correctly. Without it, generic range agreement is about 42%.

A starting range from the soil map#

Replacing the generic 0.46 with each probe's soil-survey saturation at its own depth, plus 0.02:

99% of that 1.30% is the Allen Farms 1.02 m probe, where the map and ISMN agree something is wrong and the map is simply stricter. Without it, the map-derived limit flags 0.02% of good readings. A limit read off the soil map is a better starting point than any generic number; tools/soil_survey.py produces one for any US location and depth.

"Saturated plateaus" are mostly wet soil#

ISMN's D10 runs at shallow depth last 40–69 hours and move through 7–12 distinct values: soil sitting near saturation through a wet winter, not a frozen sensor. Our flatline, which needs identical values, is right not to call them stuck. ISMN marks them questionable, not bad. At depth, where values really are identical for days, both systems flag — which is where the 31% plateau agreement comes from, and also where Q24's false alarms come from.

What we cannot see at all, and one thing we could#

D01–D05 use soil temperature, air temperature and precipitation, which a single moisture series does not carry. D01 — soil below 0 °C — is within reach: capacitance probes read only liquid water, so frozen soil reads falsely dry, and our soil probes now report temperature on the same device (§4.1). A frozen-soil check is a candidate for §4.2. D04, a rise with no rain, needs a rain gauge on the farm.

Beyond Tennessee: soil texture, and the size of a jump#

The dwell depends on soil as much as on depth. 99.9th-percentile run of one identical value on good readings, in hours, for every humid-subtropical (Köppen Cfa) SCAN station — 87 stations, 1,720 probes — grouped by the soil-survey texture at each probe's own depth (sandy ≥ 70% sand, clayey ≥ 35% clay, loamy otherwise; probe counts in brackets):

The dwell grows with depth and with clay, cleanly in both directions. 18 hours is right for sandy and loamy soil near the surface and wrong almost everywhere else. Q24 now recommends a threshold learned from each series' own history, starting from this table looked up by the soil survey; tools/soil_survey.py does the lookup.

And climate, for sand. The same measurement over every SCAN station with a climate class and a soil-survey horizon at the probe's depth — 210 stations, by Köppen main class:

63 dry, 113 temperate and 34 cold stations; probe counts in brackets; cold-climate clay had five probes or fewer per cell and is left out. Cells under ten probes take the temperate value in the tool. Temperate reproduces the Cfa table above and cold is close to it. Dry climates reverse the ordering for sand: dry sandy soil sits unchanged for days, so at 1 m it dwells longest of all. tools/soil_survey.py now takes the climate class (--climate B|C|D); without one it uses the longest dwell any climate showed, so an unknown climate errs towards quiet. 144 probes had no soil-survey horizon at their depth and are not counted.

An earlier version of this table was wrong twice. It grouped stations by HWSD texture, which misclassifies at field scale — it calls Tennessee's silt loams loam and Starkville, Mississippi, 55% clay where the survey finds a sandy loam over 28%-clay loam. And a first rerun had a bug in reading the climate class that dropped most stations, leaving 15. This table replaces both.

A spike is not a big step. One hourly step in the top 20 cm, 8 million good readings:

Healthy soil can rise 0.17 in an hour after heavy rain on sand, and ISMN's spike flags sit mostly on small steps, because its detector marks the region around an excursion rather than the jump. No step-size threshold separates these. Q23 was revised to recommend detecting the shape — an excursion that returns within a sample or two, which rain does not do. That has since been measured on every SCAN probe in the top 21 cm, in spikes-and-jumps.md: the shape check is quiet on healthy probes and finds broken sensors ISMN rates good, a cap on falls catches most of ISMN's negative jumps, and a cap on rises turned out not to be worth keeping.

A second network: NOAA's Climate Reference Network#

Scored 2026-10-11. The same archive carries NOAA's Climate Reference Network (USCRN). In the same box it has five stations' worth of probes: Crossville in Tennessee, and two at Asheville, on its North Carolina edge. That is 15 probes and 1.9 million readings.

USCRN is a different operator, with three probes at each depth and its own quality control before ISMN's. The probes are Stevens Hydra Probe II on SDI-12, the same make as SCAN's. The thresholds are the ones above, unchanged:

python3 -I ismn_score.py ~/Downloads/ISMN_archive_20261008.zip \
    --network USCRN --box 34.98,36.68,-90.31,-81.65

What we raise on readings ISMN rates good, by depth (the 1,009 suspect flags are not split by depth):

• The root zone holds up on a second operator's probes. At 0.09% to 0.29% of good readings in the top 20 cm, it is within SCAN's 0.02% to 0.27%.
• The deep disagreement is the same stuck, and the flatline limit learned per soil and depth (Q24) is its answer, as on SCAN.
• ISMN's spikes on USCRN are mostly real glitches. The shape check agrees on 52% of them, against 8% on SCAN, where D06 mostly marked dry-soil noise.
• No rise is capped, by design (Q23).

Caveats#

• One region, one sensor make, for the agreement figures. The scoring is three Tennessee stations, all Stevens Hydraprobes. The dwell tables reach further — 210 United States stations across three climate classes — but are still one network's sensors.
• ISMN's flags are not ground truth. They are another automated system's judgement, with its own thresholds and its own soil database.
• Our thresholds were Harrow Bend's, chosen for a fictional farm before any of this data existed. That they hold in the root zone is evidence for them; that they fail at depth is the point of running this.

Attribution#

ISMN data is used under CC-BY-4.0; this note publishes only derived statistics, not the data. The quality flags are described in:

Dorigo, W.A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiová, A., Sanchis-Dufau, A.D., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M. (2013). Global Automated Quality Control of In Situ Soil Moisture data from the International Soil Moisture Network. Vadose Zone Journal 12(3). doi:10.2136/vzj2012.0097

Dorigo, W. et al. (2021). The International Soil Moisture Network: serving Earth system science for over a decade. Hydrology and Earth System Sciences 25, 5749–5804.

The SCAN stations are operated by the USDA Natural Resources Conservation Service.

Reproducing#

cd /tmp
python3 -I ~/dev/fieldmesh/tools/ismn_score.py ~/Downloads/ISMN_archive_20261008.zip \
    --network SCAN --box 34.98,36.68,-90.31,-81.65

About 15 minutes for all 49 files. Run from a neutral directory with -I, because the archive is downloaded data.