Connect all four tables, aggregate soil data, and write your first real-world calculation: Available Water Storage from horizon data.
legend → mapunit → component → chorizon — all four tables
Now we go all the way down to horizon-level data. The key choice: INNER JOIN vs LEFT JOIN for chorizon. Use LEFT JOIN — not all components have horizon records, and an INNER JOIN would silently drop them.
Summarizing soil properties across map units
Aggregation lets you summarize data — average organic matter by drainage class, total acres by HSG, min/max texture by soil order. The pattern is always: SELECT group columns + aggregate functions, FROM + JOINs, GROUP BY the same group columns.
Categorizing soils by property — the SSURGO way
CASE WHEN is SQL's if/then — essential for translating coded values into readable labels, classifying soils into risk tiers, or building custom rating scales.
Breaking complex queries into readable, named steps
A CTE (Common Table Expression) names an intermediate result set. This turns a complex nested query into a series of readable steps — and in SSURGO, where queries often span 4+ tables with multiple conditions, CTEs are essential for maintainability.
WITH name AS (SELECT …) SELECT … FROM name. You can chain multiple CTEs: WITH a AS (…), b AS (SELECT … FROM a) SELECT … FROM b.A real-world multi-step soil calculation
Formula: AWS = Σ (min(hzdepb_r, 150) − max(hzdept_r, 0)) × awc_r for each horizon within 0–150 cm.
Soil organic carbon formula, NCCPI productivity index, interpretations, and spatial AOI queries.