Extractor Column Mapping Reference🔗

Each extractor inherits from BaseExtractor and uses column_mapping to resolve entity fields from your DataFrame. This document lists which internal fields each extractor actually uses at runtime.

How to read these tables:

Internal key — the canonical name used in get_entity_value(row, key)
Default column — the DataFrame column name looked up if no mapping is provided
Purpose — what the extractor uses it for
Fields marked (required) will cause errors if missing

Foundational🔗

CoverageExtractor🔗

Module: earthdaily.agriculture.extractors.coverage_function

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for coverage query
`crop`	`crop`	Crop type (used in `crop_coverage` filter mode)
`start_date`	`start_date`	Coverage period start
`end_date`	`end_date`	Coverage period end
`historical_seasons`	`historical_seasons`	List of years for `crop_coverage` filter mode

FLMExtractor🔗

Module: earthdaily.agriculture.extractors.FLM_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for field-level map generation
`crop`	`crop`	Crop type (optional, for metadata)

VegationTsExtractor🔗

Module: earthdaily.agriculture.extractors.VTS_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon (required)
`crop`	`crop`	Crop type (required)
`start_date`	`start_date`	Time series period start
`end_date`	`end_date`	Time series period end

MRTSExtractor🔗

Module: earthdaily.agriculture.extractors.VTS_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for medium-resolution query
`crop`	`crop`	Crop type
`start_date`	`start_date`	Time series period start
`end_date`	`end_date`	Time series period end

WeatherExtractor🔗

Module: earthdaily.agriculture.extractors.weather_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon (centroid used for weather location)
`start_date`	`start_date`	Weather data period start
`end_date`	`end_date`	Weather data period end

cropidExtractor🔗

Module: earthdaily.agriculture.extractors.cropid_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for crop classification
`crop`	`crop`	Crop type (for result enrichment)

ZoningExtractor🔗

Module: earthdaily.agriculture.extractors.zoning_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for zoning query

Regional🔗

RegionalExtractor🔗

Module: earthdaily.agriculture.extractors.regional_ts_extractor

Internal key	Default column	Purpose
`id`	`id`	Used for cache key columns
`amu_id`	`amu_id`	Regional AMU entity identifier (sent to API)
`start_date`	`start_date`	Regional time series period start
`end_date`	`end_date`	Regional time series period end

Crop Development & Stressors🔗

DiseaseExtractor🔗

Module: earthdaily.agriculture.processors.processor_disease_risk_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon (centroid for weather-based risk)
`start_date`	`start_date`	Disease risk period start
`end_date`	`end_date`	Disease risk period end

EmergenceExtractor🔗

Module: earthdaily.agriculture.processors.processor_emergence_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for emergence detection
`crop`	`crop`	Crop type for phenology model
`historical_seasons`	`historical_seasons`	(optional, `HISTORICAL` only) Calendar years this field actually grew the crop — `"2020,2022,2024"` or `[2020, 2022, 2024]`. Off-years are nulled out of `emergence_year_N`, and `avg_emergence_matching_years` averages only the years kept. Absent, nothing is filtered and no error is raised — all five years come back looking correct.

sowing_date is not read by this extractor. The season window comes from season_start_month / season_start_day / season_duration / year on setup_emergence_parameters(), and the request body carries only the geometry.

GreennessExtractor🔗

Module: earthdaily.agriculture.processors.processor_greenness_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for greenness detection
`crop`	`crop`	Crop type for greenness model
`sowing_date`	`sowing_date`	Sowing date for season window

HarvestExtractor🔗

Module: earthdaily.agriculture.processors.processor_harvest_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for harvest detection
`crop`	`crop`	Crop type for harvest model
`historical_seasons`	`historical_seasons`	(optional, `HISTORICAL_HARVEST` only) Same contract as EmergenceExtractor above — filters `harvest_year_N` to the matching years and drives `avg_harvest_matching_years`. Silently inert when absent.

sowing_date is not read by this extractor, for the same reason as EmergenceExtractor.

PlantedExtractor🔗

Module: earthdaily.agriculture.processors.processor_plantedarea_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for planted area estimation

InSeasonMonitoringExtractor🔗

Module: earthdaily.agriculture.processors.processor_inseason_monitoring_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for monitoring
`crop`	`crop`	Crop type for monitoring model

Risk Management🔗

HistoricalScoreExtractor🔗

Module: earthdaily.agriculture.processors.processor_score_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for score computation
`crop`	`crop`	Crop type for historical comparison
`historical_seasons`	`historical_seasons`	List of years to compare against

InseasonScoreExtractor🔗

Module: earthdaily.agriculture.processors.processor_score_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for score computation
`crop`	`crop`	Crop type for in-season scoring
`sowing_date`	`sowing_date`	Sowing date for season window
`end_date`	`end_date`	Season end date
`historical_seasons`	`historical_seasons`	List of years to compare against

ZARCExtractor🔗

Module: earthdaily.agriculture.processors.processor_zarc_functions

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for ZARC zone lookup
`crop`	`crop`	Crop type for ZARC compliance check

Sustainability🔗

BaresoilExtractor🔗

Module: earthdaily.agriculture.processors.processor_baresoil_function

Internal key	Default column	Purpose
`id`	`id`	Entity identifier
`geometry`	`geometry`	WKT polygon for bare soil analysis

Tips & Tricks🔗

Forcing setup-level values by neutralizing column_mapping🔗

When chaining extractors in a workflow, the workflow-level column_mapping (e.g. start_date: "sowingDate") is applied to all extractors. A downstream extractor will then pick up sowingDate from entity rows even when explicit values are set in the setup params — because get_entity_value checks the entity row first and only falls back to params when the mapped column is not found.

Workaround: Override the mapping at the step level to point at a column name that doesn't exist in the entity rows. The extractor will then fall back to the setup-level value.

# Example: entity rows carry "sowingDate" but we want the extractor to use setup dates
column_mapping:
  start_date: "start_date"   # no column named "start_date" in rows → falls back to setup value
  end_date: "end_date"       # no column named "end_date" in rows → falls back to setup value

Why it works: get_entity_value(row, "start_date", params.get("start_date")) resolves the mapped column name, finds no match in the row, and returns the params default.

This pattern applies to any internal key you want to force to the setup default: start_date, end_date, sowing_date, crop, etc.

Quick Reference — All Fields by Extractor🔗

x = read at runtime · o = read at runtime, optional and silently inert when absent — the run succeeds and the output looks complete, so a missing column is only visible in the values.

Extractor	`id`	`geometry`	`crop`	`start_date`	`end_date`	`sowing_date`	`historical_seasons`	`amu_id`
CoverageExtractor	x	x	x	x	x		x
FLMExtractor	x	x	x
VegationTsExtractor	x	x	x	x	x
MRTSExtractor	x	x	x	x	x
WeatherExtractor	x	x		x	x
cropidExtractor	x	x	x
ZoningExtractor	x	x
RegionalExtractor	x			x	x			x
DiseaseExtractor	x	x		x	x
EmergenceExtractor	x	x	x				o
GreennessExtractor	x	x	x			x
HarvestExtractor	x	x	x				o
PlantedExtractor	x	x
InSeasonMonitoringExtractor	x	x	x
HistoricalScoreExtractor	x	x	x				x
InseasonScoreExtractor	x	x	x		x	x	x
ZARCExtractor	x	x	x
BaresoilExtractor	x	x