How much water are crops in my area using, and are they water-stressed?

What you can answer

How much water a field consumes as evapotranspiration (mm/day or mm over a season) from ECOSTRESS L3T ET at 70 m — fine enough to resolve individual fields.
Seasonal crop water-use totals by accumulating MODIS MOD16A2GF 8-day ET across a growing season (coarser 500 m, but continuous and gap-filled).
Whether a crop is water-stressed right now by comparing actual ET against potential ET (the PT-JPL product carries both), or by reading the evaporative stress index — low actual/potential ratios flag stress before visible wilting.
Irrigation efficiency signals: pair high ET with SMAP soil moisture — fields drawing down ET while soil moisture stays high suggest healthy supply; ET dropping with falling soil moisture suggests under-irrigation or onset of agricultural drought.

What you can NOT answer with these datasets alone

The volume of water actually applied (irrigation diversions or pumping) — ET is consumptive use, not applied water; deep percolation and runoff are not measured here.
Which crop is planted — ET alone does not classify crop type; you need a crop layer (e.g., USDA CDL) to attribute water use per crop.
Sub-field or canopy-level stress finer than ~70 m (ECOSTRESS floor) — individual plant stress is below resolution.
Continuous daily ET from ECOSTRESS alone — ECOSTRESS observes from the ISS at irregular, non-sun-synchronous times, so daily coverage has gaps; MOD16 fills the temporal record at coarser resolution.

Code template (Python, cloud-direct, ~30 lines)

import earthaccess
import xarray as xr

earthaccess.login(strategy="netrc")

# 1. Define AOI + growing-season window
aoi = (-121.5, 35.5, -119.0, 37.5)  # W, S, E, N — California Central Valley
season = ("2023-04-01", "2023-09-30")

# 2. ECOSTRESS L3T ET (PT-JPL) — field-scale 70m evapotranspiration
eco_et = earthaccess.search_data(
    short_name="ECO_L3T_ET_PT-JPL",
    bounding_box=aoi,
    temporal=season,
)
# ...open the tiled COGs into xarray; band ETdaily = mm/day, ETinst, ETinstUncertainty
# stress proxy: actual ET vs potential ET (PET) carried in the PT-JPL bundle

# 3. MODIS MOD16A2GF — gap-filled 8-day ET (500m) for the seasonal total
mod16 = earthaccess.search_data(
    short_name="MOD16A2GF",
    bounding_box=aoi,
    temporal=season,
)
# ...sum the 8-day ET composites → cumulative season ET (mm); units are 0.1 mm/8-day, scale 0.1

# 4. SMAP enhanced soil moisture (9km) as a supply-side complement
smap = earthaccess.search_data(
    short_name="SPL3SMP_E",
    bounding_box=aoi,
    temporal=season,
)
# ...read soil_moisture from the 9km grid; align to AOI for ET-vs-moisture comparison

# 5. Plot: ECOSTRESS ET map (mm/day) + MOD16 seasonal-total map + ET/soil-moisture time-series

Expected output

Map: mean daily ET (mm/day) from ECOSTRESS L3T at 70 m, showing field-to-field contrast across the AOI
Map: cumulative season ET (mm) from MOD16A2GF, the coarse but complete water-use total
Time-series: AOI-mean actual ET vs potential ET (stress gap widening = water stress), overlaid with SMAP soil moisture
Optional: per-field histogram of seasonal ET once masked to cropland

Caveats

ECOSTRESS overpass times are irregular (ISS orbit, not sun-synchronous), so cloud-free clear-sky scenes over any given field may be sparse in a season — expect uneven temporal sampling.
MOD16A2GF “GF” means gap-filled with climatology where MODIS observations are missing; the fill reduces gaps but smooths real anomalies, so trust ECOSTRESS over MOD16 for event-scale stress.
ECOSTRESS L3T ET is derived from L2 LST; thermal retrievals degrade under thin cloud and high aerosol, and emissivity assumptions affect bare-soil vs full-canopy pixels differently.
OpenET (US-only) is an ensemble that already blends several of these models and is often the easier starting point inside the United States; outside the US, ECOSTRESS + MOD16 are the primary path.

Cross-DAAC composition

This is a 2-DAAC join plus an optional external source: LP DAAC (ECOSTRESS, MOD16) + NSIDC DAAC (SMAP). Auth is uniform (Earthdata Login via earthaccess) but the products differ in geometry — ECOSTRESS and MOD16 are gridded tiles/COGs while SMAP SPL3SMP_E is an HDF5 EASE-Grid 2.0 product, so reprojection/alignment is needed before the ET-vs-moisture comparison. See recipes/r01-three-daac-composition.mdx for the general join pattern.