Estimating Daily Actual Evotranspiration at Field scale Using Remote Sensing
- Arpit Shah
- Apr 29, 2023
- 11 min read
Updated: Dec 10, 2025
INTRODUCTION
When Jessey Dickson—a bright Ghanaian student pursuing his MSc in Environmental Quality Sciences on scholarship from the Hebrew University of Jerusalem—reached out to me in late January 2023 on LinkedIn, he had a very specific request: “Can you prepare a tutorial for deriving Evapotranspiration using SNAP software?” My initial surprise quickly gave way to a sense of satisfaction.
I had never even heard the term Evotranspiration before, let alone understood how to derive it. Yet the realization that my work on this professional website was being noticed and appreciated by student researchers across the world was reassuring. While I continue navigating the private sector in India attempting to carve out a niche in Mapping Solutions for Operations Improvement, moments like this bring genuine encouragement.
My early, somewhat lazy responses responses did not deter Jessey. Determined to complete his assignment on estimating Evapotranspiration using Remote Sensing, he shared several tutorial links. After reviewing them, I decided to support him.
While Jessey had chosen farmlands around Gadot, Israel as his study area, I replicated the workflow over an agricultural zone in Punjab, India. After hours of WhatsApp exchanges, video meetings, and hands-on work in ESA's Sentinels Application Platform (SNAP), we were finally able to meet our objectives—his assignment, and this post along with an accompanying video tutorial on estimating field-scale Daily Actual Evotranspiration using Remote Sensing.
SECTION HYPERLINKS
Process Flow for estimating Daily Actual Evotranspiration at Field scale using Remote Sensing
Estimating Daily Actual Evotranspiration at field scale over an agri-region in Punjab, India
Credits: ESA Sen-ET, DHI Gras, Sandholt ApS, SNAP | STEP Forum, Jessey Dickson
EVOTRANSPIRATION AND FACTORS AFFECTING IT
Evotranspiration (ET) represents the total loss of water—and energy—from the Earth’s surface to the atmosphere. It combines:
Evaporation: direct transfer of water from soil, canopies, groundwater capillaries, and inland water bodies.
Transpiration: indirect transfer through vegetation, where water moves from soil to roots, to leaves, and eventually to the atmosphere.
This release of water vapour forms one of the largest components of the global hydrologic cycle after precipitation.
Think for a moment about the benefits of quantifying water and energy transfer into the atmosphere. I’ll return to this question later.
A summary of the factors influencing evapotranspiration is shown below:
TYPES OF EVOTRANSPIRATION
Evapotranspiration is typically expressed:
in millimetres per unit time (water vapour released), or
in watts per unit area (energy released).
Actual Evotranspiration ETa represents the actual amount of water vapour (and energy) released from soil and vegetation over time in a defined area. It accounts for real meteorological conditions—precipitation, soil moisture, wind speed, and solar radiation—making it the most authentic measure. However, it is expensive, data-intensive, and technically demanding to derive.
Potential Evotranspiration ETp represents the maximum possible evapotranspiration assuming unlimited water availability. It depends only on meteorological conditions such as air temperature, solar radiation, and wind speed. ETp is often used in drought assessment, land–atmosphere studies, or when estimating ETa is not feasible (e.g. over very large areas or under budget constraints).
Reference Evotranspiration ETref is a standardized ETp measurement taken over a reference surface—typically well-watered, uniform grass. Weather stations commonly record ETref to quantify atmospheric water demand.
Evapotranspiration studies can also be classified by geographic scale—local, field, watershed, regional, and continental.
Local scale: Lysimeters are used to measure ETa.
Field scale: Drones can collect ET-related data.
Regional to continental scale: Satellite-based Remote Sensing is preferred for its cost-effectiveness and consistent temporal coverage.
Remote Sensing models frequently rely on the surface energy balance:
In this study, I used:
Sentinel-2 (multispectral imagery)
Sentinel-3 (thermal imagery)
Copernicus Climate Data Store (meteorological inputs)
to estimate Daily Actual Evapotranspiration (ETa) at field scale in Punjab, India.
For my study, I have utilized Remote Sensing too, obtaining Satellite Imagery from European Space Agency's' (Multispectral sensor) and (Thermal sensor) satellites and Field scale Meteorological data from to estimate Daily Actual Evotranspiration (ETa) at an agri-zone within the state of Punjab in India.
UTILITY OF EVOTRANSPIRATION
You can likely now appreciate the importance of monitoring ET—an essential indicator of the balance between water inflow and outflow. One way to assess the societal and environmental relevance of any global process is to examine its contribution to the 17 Sustainable Development Goals (SDGs).
Sentinels for Evotranspiration (SEN-ET), has established that ET monitoring directly supports:
and indirectly contributes to others, such as SDG 15: Life on Land.
More broadly, evapotranspiration studies support:
Irrigation planning & scheduling - supplying water to agri-zones that face moisture scarcity
Watershed and water-rights management - who should get to use the water and how much?
Crop yield forecasting - limited or excessive moisture impacts harvest, resulting in food shortage
Drought monitoring - by studying the interplay between precipitation and evotranspiration
Climate change impact assessment - global warming affects the hydrologic cycle (Evotranspiration being an integral component of it)
Drainage and runoff studies - as excess water transfers into the soil and water table, or flows as runoff
PROCESS FLOW FOR ESTIMATING DAILY ACTUAL EVOTRANSPIRATION AT FIELD SCALE USING REMOTE SENSING
For this daily actual evotranspiration estimation study at field-scale over an agricultural zone in Punjab, India, I used the methodology developed under the ESA-funded Sentinels for Evapotranspiration (Sen-ET) project. Chapters 1 and 2 of the SEN-ET user manual detail-
the theoretical background of ET estimation through Remote Sensing,
how combining Sentinel-2 and Sentinel-3 enables field-scale ET estimation at regular intervals (not possible earlier), and
the meteorological datasets required for daily ETa computation.
I used SNAP Version 9.0.0 to carry out the processing. The overall workflow is shown below:
clicking on the infographic will open an enlarged view. You can download it as well
To replicate this study, you should be familiar with:
working with SNAP,
installing the Sen-ET plugin and its updated scripts,
consulting the user manual and technical review documents,
browsing the Sen-ET community forum for common issues and solutions, and
understanding the basics of evotranspiration
Because the workflow is complex and the required information is scattered across multiple sources, I created a single, consolidated reference—a detailed step-by-step video tutorial. Jessey and I spent several hours resolving errors during our analysis; this walkthrough is intended to spare others from similar hurdles.
Before I describe my results from Punjab, the following tutorial serves as the definitive guide to understanding and implementing the Daily ETa workflow:
TIMESTAMPS
00:05 - Case Details
00:20 - P1: Background & Setting up
00:24 - P1.1: Understanding the Area of Interest (AoI)
01:12 - P1.2: Downloading the Geographic Extent of the AoI
03:25 - P1.3: Downloading Sentinel-2 Satellite Imagery Dataset
06:35 - P1.4: Downloading Sentinel-3 Satellite Imagery Dataset
11:25 - P1.5: Set-up Intricacies
14:00 - P1.6: SNAP Software Set-up
18:27 - P2: Sentinel-2 Processing Workflow
18:30 - P2.1: Pre-Processing Graph
26:10 - P2.2: Add Elevation Graph
28:04 - P2.3: Add Landcover Graph
33:24 - P2.4: Estimating Leaf Reflectance & Transmittance
35:47 - P2.5: Estimating Fraction of Green Vegetation
38:34 - P2.6: Producing Maps of Vegetation Structural Parameters
42.47 - P2.7: Estimating Aerodynamic Roughness
44.37 - P3: Sentinel-3 Processing Workflow
45.12 - P3.1: Loading Sentinel-3 Dataset
46.02 - P3.2: Pre-Processing Graph
53:15 - P3.3: Warp to Template
55:55 - P3.4: Sharpen LST
59.51 - P4: ERA-5 Pre-Processing Workflow
59.54 - P4.1: Downloading ECMWF ERA-5 Reanalysis Data
01:07:04 - P4.2: Preparing ECMWF ERA-5 Reanalysis Data
01:10:00 - P5: Land-surface Energy Fluxes Modelling Workflow
01:10:04 - P5.1: Estimating Longwave Irradiance
01:12:07 - P5.2: Estimating Net Shortwave Radiation
01:14:32 - P5.3: Estimating Land-Surface Energy Fluxes
01:17:29 - P5.4: Estimating Daily (Actual) Evotranspiration
01:19:39 - Summary Note
ESTIMATING DAILY ACTUAL EVOTRANSPIRATION AT FIELD SCALE OVER AN AGRI-REGION IN PUNJAB, INDIA
I selected this agricultural region (Figure 8) for several reasons
I wanted to analyse an agricultural zone within my home country.
Punjab is a leading producer of staples, especially wheat and rice.
The state’s average landholding is relatively high—3.62 hectares, appropriate for field-scale analysis.
The Rice–Wheat (RW) belt in north-west India has been experiencing a severe long-term decline in groundwater levels.
The timeline of this study (Sentinel-3 acquisition on 4th June 2022) also coincides with an important national event: the initial imposition of India's wheat export ban. Unseasonal rains had damaged the wheat harvest, prompting concerns regarding domestic food security and inflation amidst the Ukraine war, which was disrupting global agricultural supply chains.
As shown earlier in the Process Flow diagram (Figure 7), estimating Daily Actual Evapotranspiration at field scale requires processing three types of Remote Sensing inputs:
Sentinel-2 Multispectral Imagery,
Sentinel-3 Thermal Imagery, and
ECMWF ERA5 Meteorological datasets
(You may refer back to Figure 3 —Factors Affecting Evapotranspiration—and compare it with Figure 7, the video tutorial, as well as the outputs shown below to reinforce your understanding)
The Sentinel-2 Multispectral imagery dataset utilized in the study was acquired on 27th May 2022 at 05:36 am. This dataset helps characterize the biophysical state of the land surface at 20 m resolution.
The following outputs are derived from the S-2 processing chain-
Biophysical parameters such as Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, Fraction of Vegetation Cover, Chlorophyll content and Canopy Water Content
Reflectance bands at multiple spectral resolutions
Land Cover data from ESA's Climate Change Initiative
Fraction of Vegetation Cover which is green
Structural parameters of vegetation such as height, height-width ratio, leaf width
Sharing sample outputs from the S-2 processing chain below-
The Sentinel-3 thermal imagery dataset establishes the bottom boundary condition of the land-surface energy model—in simpler terms, it enables estimation of Land Surface Temperature (LST), which is directly linked to evapotranspiration.
Because Sentinel-3 imagery has a coarse resolution (~ 1 km), I have sharpened it to 20 m (same as that of the processed Sentinel-2 dataset) using the Data Mining Sharpener (DMS) machine-learning model. This is a prerequisite to perform subsequent SNAP-based analysis utilizing both the datasets.
The following outputs are derived from the S-3 processing chain-
High Resolution (20 m) Observation Geometry
Sharpened Land Surface Temperature data
Sharing sample outputs from the S-3 processing chain below-
The downloaded ECMWF ERA5 meteorological data is interpolated both temporally and spatially so that:
its timestamp matches the Sentinel-3 acquisition time (4 June 2022, 05:26 AM), and
its spatial resolution matches the processed S-2 dataset (20 m).
Processing this compatible meteorological dataset would help define the conditions that drive (e.g., air temperature) and modulate (e.g., wind speed) the land-atmosphere energy exchange.
The following outputs are derived from the ECMWF ERA5 Meteorological data processing chain-
Conversion of 2 m meteorological variables (air temperature, vapour pressure, air pressure, wind speed, clear-sky radiation, daily solar irradiance) to 100 m height
Integration of meteorological parameters with a few S-2 and S-3 outputs to compute:
Net Shortwave Radiation (canopy and soil)
Pairing several outputs from the previous processing chains to estimate Land Surface Energy Fluxes using the Two-Source Energy Balance (TSEB) Model. The four instantaneous fluxes below are estimated at the time of Sentinel-3 dataset acquisition i.e. 4th June 2022. (Latent Heat Flux represents the energy released during Evotranspiration. Recall that Evotranspiration is measured in energy released terms besides water vapour terms)
Finally, the instantaneous Latent Heat Flux is converted into water vapour units (mm/day) to obtain the Daily ETa at Field scale
Sharing sample outputs from the ECMWF ERA5 Meteorological data processing chain below-
As depicted in Figure 20 above, Daily ETa at 20 m resolution over the Punjab study area on 4 June 2022 at 05:26 AM ranged from:
Minimum: 0.2 mm/day
Maximum: 10.0 mm/day
Mean: 3.8 mm/day (Most pixels lie between 1.3 and 6.1 mm/day)
CONCLUDING OBSERVATIONS
How should this ETa map be interpreted? Is the ETa high or low, good or bad, increasing or decreasing?
Unfortunately, I am not a hydrology expert, and the values result from a complex model with many interacting variables. That being said, I can definitely remark that a single ETa snapshot cannot reveal long-term behaviour. Instead, a time-series combined with on-ground weather data is necessary to meaningfully interpret trends. You may also explore how this result compares with ET studies conducted elsewhere in Indian or internationally—I would be happy to hear what you discover.
There are other factors to be taken into consideration as well - for example, the meteorological data corresponds to 05:26 AM, a cooler part of the day. Even though June is peak summer in India, ET rates rise sharply as temperatures climb toward midday. The algorithm’s daily ETa interpolation may therefore differ if the Sentinel-3 overpass occurred in the afternoon. However, because S-3 has fixed overpass schedules and revisit constraints, no such dataset exists for this date over the study area.
I would like to bring to your attention an interesting aspect that I observed when I repeated the same workflow using the same datasets and timestamp, but for a different agri-zone located north-west of the original region-
In the new study area, the south-east half shows higher ETa values (7–9 mm/day).
In the previous study area, ETa values were mostly in the 3–5 mm/day range.
This difference is striking given the proximity of both areas and the identical acquisition time. Why might this be?
My interpretation is that the two regions may be cultivating different crops (Recall that Punjab lies in the Rice-Wheat belt). As shown in the Factors Affecting Evapotranspiration diagram (Figure 3), ET is strongly influenced by:
crop type,
root characteristics,
agricultural practices, and
growth stage.
Different crops exhibit different moisture-retention capacities, canopy structures, and transpiration rates. This could explain the large variation in ETa between the two zones.
Do you agree with this reasoning?
I hope you found this post—and the accompanying video tutorial—useful and informative. Your feedback and suggestions are welcome.
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