Estimating Evapotranspiration from Satellites: The Invisible Water Flux

Satellites estimate evapotranspiration — the combined water loss from soil and plants — by reading land surface temperature and solving the surface energy balance, turning an invisible flux into field-scale maps of actual water use. If you could see water leaving the landscape in real-time, you'd see an invisible river flowing upward from every field, forest, and wetland — water molecules departing plant stomata, evaporating from soil surfaces, and rising into the atmosphere. This process — evapotranspiration (ET) — is the largest consumptive use of water on Earth, yet it's invisible to the eye and nearly impossible to measure directly over large areas.

Satellite-based ET estimation makes this invisible flux visible, transforming water management from guesswork to data-driven decision-making.

What is evapotranspiration?

Evapotranspiration (ET) is the combined loss of water from a landscape through two paths: evaporation from soil and wet surfaces, and transpiration through plant stomata. It is the largest consumptive use of water on Earth, returning roughly 60% of land precipitation to the atmosphere. Its two components behave differently:

Evaporation: Direct conversion of liquid water to vapor from soil surfaces, water bodies, and wet vegetation surfaces. Driven by available energy and vapor pressure deficit.

Transpiration: Water absorbed by plant roots, transported through stems, and released through leaf stomata during photosynthesis. Plants "spend" water to acquire CO₂ — typically 200-500 grams of water per gram of CO₂ fixed.

Together, ET returns approximately 60% of global terrestrial precipitation to the atmosphere. In irrigated agriculture, ET is the primary consumptive water use — the water that doesn't return to rivers or aquifers.

How do satellites estimate ET?

Satellites estimate ET by solving the surface energy balance rather than measuring water directly. Evaporating water consumes energy (latent heat of vaporization: ~2.45 MJ/kg), so whatever energy is left after net radiation heats the ground and the air must have gone into ET. Land surface temperature from a thermal sensor supplies the missing term. The balance at the surface:

Rn = G + H + λET

Where:

• Rn = Net radiation (incoming solar and longwave minus reflected and emitted)
• G = Ground heat flux (energy stored in the soil)
• H = Sensible heat flux (energy warming the air)
• λET = Latent heat flux (energy used for evapotranspiration)

Rearranging: λET = Rn − G − H

Satellites estimate each component:

• Rn: From incoming solar radiation (weather data) and surface albedo (satellite)
• G: Estimated as a fraction of Rn based on land cover type
• H: Proportional to the temperature difference between the surface and the air. This is where satellite thermal data becomes essential — land surface temperature (LST) from Landsat or ECOSTRESS provides the surface temperature; air temperature comes from weather stations or reanalysis data.

The key insight: surfaces that are actively transpiring (well-watered vegetation) are cooler than surfaces that aren't (dry soil, stressed crops). Temperature is the proxy for ET.

Major Satellite ET Algorithms

METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration)

Developed at the University of Idaho:

• Uses Landsat thermal data (100m resolution)
• Self-calibrating: selects "hot" (dry, no ET) and "cold" (well-watered, maximum ET) reference pixels within each image to anchor the energy balance
• Produces field-scale ET maps
• Widely used in western US water management

SEBAL (Surface Energy Balance Algorithm for Land)

Similar to METRIC but with some methodological differences:

• Also uses the hot/cold pixel calibration approach
• Applied globally in numerous countries
• Available as commercial software (eLEAF)

SSEBop (Simplified Surface Energy Balance)

Developed by USGS:

• Simplified version using pre-defined reference ET and thermal anomaly fraction
• Produces the operational FEWS NET ET product for drought monitoring
• Available as gridded ET data through USGS Earth Explorer

PT-JPL (Priestley-Taylor Jet Propulsion Laboratory)

Uses satellite NDVI and meteorological data without thermal imagery:

• Estimates potential ET from radiation and temperature
• Scales by vegetation index and moisture availability
• Produces the ECOSTRESS ET product

ECOSTRESS: Dedicated ET Mission

NASA's ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) is specifically designed for ET estimation:

• Resolution: 70m thermal
• Platform: International Space Station (variable overpass time — captures different times of day)
• Products: LST, ET, Evaporative Stress Index (ESI), Water Use Efficiency
• Key advantage: Multiple daily overpasses capture the diurnal ET cycle, unlike Landsat's fixed overpass time

Applications

Irrigation Management

The most direct application: identifying fields that are using too much or too little water, the core of satellite irrigation management:

Over-irrigated fields: Lower LST than neighboring fields of the same crop → higher ET → wasting water

Under-irrigated fields: Higher LST than neighbors → lower ET → crop stress, yield loss risk

Irrigation scheduling: ET maps tell farmers how much water their crop actually consumed since the last irrigation, directly informing the next application amount.

Water Rights and Allocation

In water-scarce regions, water rights define how much water each user may consume. Satellite ET provides:

• Independent measurement of actual water consumption per parcel
• Verification that users aren't exceeding their allocation
• Basin-wide water balance accounting

Several western US states (Idaho, Nevada, Oregon) use satellite ET operationally for water administration.

Water Budget Accounting

At basin scale: Precipitation = ET + Runoff + Storage change

Satellite ET provides the largest and hardest-to-measure term in this equation. Combined with satellite precipitation (GPM) and storage change (GRACE gravity data), satellite-based water budgets enable:

• Assessment of sustainable water use
• Detection of groundwater depletion (when ET exceeds precipitation + runoff for extended periods)
• Climate change impact on water resources

Drought Early Warning

The Evaporative Stress Index (ESI) — actual ET divided by potential ET — provides early drought indication:

• ESI near 1.0: Vegetation is transpiring at potential rate (no water stress)
• ESI declining: Vegetation is water-stressed (ET decreasing despite energy availability)
• ESI << 1.0: Severe water stress

ESI responds to developing drought 2-4 weeks before NDVI shows visible crop stress, because transpiration decreases (stomata close) before canopy greenness changes.

How accurate is satellite ET?

Satellite ET is accurate to roughly ±10–15% for seasonal totals from energy-balance models like METRIC and SEBAL, loosening to ±15–25% for daily estimates, when validated against eddy-covariance flux towers — ground instruments that directly measure the turbulent exchange of water vapor, heat, and CO₂. Longer accumulations are more accurate because random errors average out.

Method	Typical Accuracy (field scale)
METRIC/SEBAL	±10-15% for seasonal totals
SSEBop	±15-20% for monthly totals
PT-JPL / ECOSTRESS	±15-25% for daily estimates

Seasonal or monthly accumulations are more accurate than daily estimates because random errors average out. For water management applications, ±10-15% seasonal accuracy is sufficient for most decisions.

Crop ET Reference Values: Calibrating Satellite Estimates

Comparing satellite-derived ET against expected values by crop type and climate helps identify estimation errors and over/under-irrigation:

Crop	Peak Daily ET (mm/day)	Seasonal ET Total	Irrigation Efficiency Target	Typical Over-Irrigation Indicator
Irrigated alfalfa	8–12	900–1,400 mm/season	85–90%	Satellite ET > 10 mm/day consistently
Corn (rainfed, humid)	6–9	400–600 mm/season	N/A	Satellite ET anomaly vs. neighbors
Corn (irrigated, arid)	7–10	600–800 mm/season	80–85%	ET > regional potential ET
Winter wheat	3–7	350–550 mm/season	75–80%	Excess ET during grain fill
Cotton (irrigated)	6–9	700–900 mm/season	80–85%	High late-season ET (poor cutoff)
Rice (flooded)	7–12	900–1,300 mm/season	50–60% (high loss)	Consistently near upper bound
Vineyard	3–6	300–500 mm/season	85–90%	ET significantly above neighbors
Forest (temperate)	2–5	400–700 mm/year	N/A (natural)	Seasonal anomaly vs. 5-year mean

ET deficit = potential ET minus actual ET: A field showing actual ET of 5 mm/day when crop potential ET is 8 mm/day has a 3 mm/day deficit — indicating water stress. Summed over a growing season, this deficit translates directly to yield loss risk. The Colorado State University ET algorithm METRIC was explicitly validated for use in crop insurance loss assessment; insurers in Idaho and Nevada use satellite ET to verify crop loss claims without field visits.

Basin-scale validation: In California's San Joaquin Valley, satellite ET totals from METRIC have been compared against agricultural water district delivery records. The agreement is typically ±8–12% at district scale (50,000–200,000 ha), confirming that satellite ET is reliable enough for basin-scale water accounting and detection of unauthorized extraction.

Limitations

Temporal resolution: Landsat provides thermal data every 16 days — many days are cloudy. Actual cloud-free ET observations may occur only a few times per month. Gap-filling models interpolate between observations.

Instantaneous to daily scaling: A satellite captures one moment in the day. Scaling this instantaneous ET to daily total requires assumptions about the diurnal ET curve (typically using the evaporative fraction method, which assumes the ratio of ET to available energy remains approximately constant throughout the day).

Spatial resolution vs. frequency trade-off: Landsat (100m thermal, 16-day) provides field-scale detail; MODIS (1km thermal, daily) provides temporal frequency. Neither provides both. ECOSTRESS partially bridges this gap.

Wind and advection: In arid irrigated areas, hot dry air blowing over cool irrigated fields enhances ET beyond what the surface energy balance predicts without accounting for this "oasis effect." Advanced algorithms account for this, but it remains a source of error.

Satellite-based ET estimation transforms an invisible process into a mappable, quantifiable water resource variable. In water-scarce regions — which include much of the world's irrigated agriculture — this capability is not academic. It's the foundation for fair water allocation, efficient irrigation, and sustainable water management at scales that no network of ground instruments could achieve. Track ET for a defined field or basin over time — the same principle behind any satellite area monitoring workflow — and these snapshots become an operational record of water use.