Estimating Daily Actual Evotranspiration at Field scale using Remote Sensing
- Arpit Shah
- Apr 29, 2023
- 14 min read
Updated: Apr 23
INTRODUCTION
When Jessey Dickson, a bright Ghanaian pursuing his MSc. in Environmental Quality Sciences on scholarship from the Hebrew University of Jerusalem, reached out to me late-January this year (2023) on LinkedIn with a particular request - Can you prepare a tutorial for deriving Evotranspiration using SNAP software? - my initial feeling of surprise was soon overcome by a sense of satisfaction.
For starters, I had never even heard of the term Evotranspiration before, let alone know how to derive it. But to realize that my work posted on this professional site was increasingly being observed and appreciated by student researchers around the world signalled to me that while I grapple with the private sector in India trying to create a niche in Mapping Solutions for Operations Improvement, moments like this would bring joy and encouragement along the way.
My lazy and superficial responses did not deter Jessey who was determined to find a way to complete his assignment on estimating Evotranspiration using Remote Sensing shared by his professor. He shared hyperlinks to web tutorials on this topic and upon reviewing it, I decided to support him in his endeavour.
While Jessey had already chosen the farmlands around Gadot, Israel as his study area, I decided to replicate the Evotranspiration workflow over an agri-zone within the state of Punjab in my country India. After hours of whatsapp exchanges, video meetings and workings on Sentinels Application Platform (SNAP) software, we were finally able to fulfill our objectives - mine being this post and the elaborate, step-by-step video tutorial on estimating Field-scale Daily (Actual) Evotranspiration using Remote Sensing within it.
HYPERLINKS TO SECTIONS
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 into the atmosphere and is a combination of two terms - Evaporation, which is the direct movement of water from soil, canopies, capillary fringe of the groundwater table and water bodies on land into the atmosphere, and Transpiration, which is the indirect transfer of water from the soil surface into the atmosphere via the leaves and roots of vegetation. This release of water vapour into the atmosphere forms a crucial component (largest after precipitation) of our planet's Hydrologic Cycle.
Think about what could be the possible benefits of computing Water (and Energy) transfer into the atmosphere. I'll address it later in this post.
Here are some of the factors which contribute towards Evotranspiration-
TYPES OF EVOTRANSPIRATION
Evotranspiration is typically expressed in millimeters/unit of time (in water vapour released terms) or in watts/unit of distance (in energy released terms) and come in three variants-
Actual Evotranspiration (ETa) involves figuring how much water vapour (and energy) was actually released from the soil and vegetation into the atmosphere over a period of time in a given study area. The exact values of parameters such as precipitation, soil moisture, wind speed and solar radiation are taken into consideration during the computation of ETa, and thus, this is the most authentic representation of the phenomena. However, as you would imagine, to derive ETa is costly, time-consuming, and requires significant technical expertise.
Potential Evotranspiration (ETp) involves measuring the phenomena considering water supply as unlimited. Hence, ETp is a way of saying what is the maximum water (and energy) that the soil and vegetation can transmit into the atmosphere over a period of time in a given study area through the influence of meteorological variables such as air temperature, solar radiation and wind speed. Measuring ETp is preferred in Drought Assessment studies and other Land-Air interaction studies, or when the derivation of ETa is not feasible - for example, if the study area is very large or if it is beyond budgetary means.
Reference Evotranspiration (ETref) is ETp measured on a reference surface, typically well-watered and short, even grass. ETref is utilized when there is a need to standardize the measurement of the atmospheric demand of water vapor instead of measuring it on different types of surfaces. Weather Stations measure ETref extensively and even house the reference surface within its premises.
Evotranspiration studies can be classified on the basis of geographic scale as well - Local, Field, Watershed, Regional and Continental scale.
At Local scale, Lysimeter equipment is used to measure ETa and for larger Field-scale studies, Drones can be used to obtain Evotranspiration-related data.
As the geographic extent extends to Regional scale and beyond, Satellite Imagery-based Remote Sensing is preferred to estimate Evotranspiration (be it ETa or ETp). Besides being cost-effective, Remote Sensing offers a synoptic view of the study area at regular time intervals.
For my study, I have utilized Remote Sensing too, obtaining Satellite Imagery from European Space Agency's' Sentinel-2 (Multispectral sensor) and Sentinel-3 (Thermal sensor) satellites and Field scale Meteorological data from Copernicus Climate Data Store to estimate Daily Actual Evotranspiration (ETa) at an agri-zone within the state of Punjab in India.
UTILITY OF EVOTRANSPIRATION
By now I believe you can gauge the importance of monitoring Evotranspiration - the necessity to keep a tab on the dynamic relationship between water inflow and water outflow. One way in which the social and environmental impact of any global-scale phenomena can be assessed is by seeing if, and by how much, it contributes towards the 17 Sustainable Development Goals (SDGs) for peace and prosperity for people and the planet prescribed by the United Nations in 2015.
As established by Sentinels for Evotranspiration (SEN-ET), Evotranspiration monitoring is directly relevant to at least two of the SDGs: Zero hunger (Goal 2) and Clean water and sanitation (Goal 6) besides being potentially useful for others (eg. Goal 15 - Life on land).
In general, Evotranspiration studies can be utilized for-
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 forecast - limited or excessive moisture impacts harvest, resulting in food shortage
Drought monitoring - by studying the interplay between precipitation and evotranspiration
Climate Change impact - Global Warming affects the Hydrologic cycle (ET being integral to it)
Drainage 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 Field scale, Daily Actual Evotranspiration estimation study over an agricultural zone in Punjab (India), I have utilized the the methodology developed by the European Space Agency-funded Sentinels for Evapotranspiration (Sen-ET) project - you may refer to Chapter 1 and Chapter 2 in their user manual which outlines-
the literature pertaining to measuring Evotranspiration using Remote Sensing techniques,
how leveraging the synergies between Sentinel-2 & Sentinel-3 satellites allows for Field scale measurement of ET (something that was not possible to do before at consistent time intervals),
the Meteorological datasets utilized in this Daily ETa estimation methodology
I had used Version 9.0.0 of ESA's SNAP Software to perform my study. The process flow of the steps involved have been diagrammatically represented below-
clicking on the infographic will open an enlarged view and you can download it as well
Some of the vital aspects to be taken into consideration if you decide to replicate this study are-
knowing how to operate SNAP software,
installing the Sen-ET related plugin and its revised scripts carefully,
having access to the user manual + its technical review document,
referring to the Sen-ET community forum discussions, and
As the process flow is complicated, and also because the information and tweaks pertaining to performing this study is scattered across a few websites in written form, I have attempted to develop a singular resource which would serve as a ready reckoner - a step-by-step, video tutorial for students and practitioners alike. When Jessey and myself were stuck, it took us hours to figure out what went wrong and to find a working solution - I hope this walkthrough would spare you from a similar ordeal.
While I will elaborate the processing steps and the generated outputs pertaining to my study in the next section, the video tutorial below would be the definitive guide for you to understand the process involved in estimating Daily ETa at Field scale in a visual and engaging manner-
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 chose this agricultural region (Figure 8) in the state of Punjab, India as my study area because-
I wanted to shortlist an agricultural zone within my home country
the state of Punjab ranks high in staples production, particularly for Wheat and Rice
the average landholding in the state is high at 3.62 hectares, suitable for Field scale analysis
the Rice–Wheat (RW) belt in north-west India has faced excessive decline in Groundwater table
The timeline of the study (the Sentinel-3 dataset was acquired was on 4th June 2022) was when the still-ongoing export ban on Wheat was initially implemented due to unseasonal rains i.e. excessive precipitation which damaged the harvest - the government felt the need to protect India's food security and keep the food inflation in check amidst the Ukraine war which was adversely impacting agri-supply chains worldwide.
As depicted in the Process Flow diagram (Figure 7), there are three types of Remote Sensing data that need to be processed in order to estimate the Daily Actual Evotranspiration at Field scale-
Sentinel-2 Multispectral Imagery,
Sentinel-3 Thermal Imagery, and
ECMWF ERA5 Meteorological datasets
P.S. You may refer to Figure 3 which lists the factors affecting Evotranspiration and compare it with the processing chain (Figure 7), the video tutorial as well as the written content in the next section to enhance your understanding.
The Sentinel-2 Multispectral Imagery dataset utilized in the study was acquired on 27th May 2022 at 05:36 am. Processing this dataset would help 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 some output visuals over my study area extracted from the Sentinel-2 processing chain-
The Sentinel-3 Thermal Imagery dataset utilized in the study was acquired on 4th June 2022 at 05:26 am. Processing this dataset would help establish the bottom boundary condition of the Land Surface Energy Model. In simpler words, I seek to estimate the Land Surface Temperature over the study area which is an important input directly relevant to measure the water vapour and surface energy released i.e. Evotranspiration.
Sentinel-3 datasets have a low spatial resolution (~ 1 km), and the processing chain entails enhancing it using the Data Mining Sharpener Machine Learning model. I will enhance it to 20 m - same as that of the processed Sentinel-2 dataset as this is a necessary condition in SNAP in order to perform subsequent analysis involving 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 some output visuals over my study area extracted from Sentinel-3 processing chain-
The downloaded ECMWF ERA5 Meteorological data is interpolated in order to match the time of Sentinel-3 acquisition (4th June 2022) as well as the spatial resolution of the processed Sentinel-2 dataset (20 m). Processing this dataset would help establish conditions which drive (eg. air temperature) and modulate (eg. wind speed) the energy transfer between surface and the atmosphere.
The following outputs are derived from the ECMWF ERA5 Meteorological data processing chain-
Conversion of Meteorological data (Air Temperature, Vapour Pressure, Air Pressure, Wind Speed, Clear Sky Solar Radiation & Average Daily Solar Irradiance) from 2 m above ground to 100 m above ground
Pairing the modified and enhanced meteorological data with some of the outputs derived during the Sentinel-2 and Sentinel-3 processing chain in order to estimate the Longwave Irradiance and Net Shortwave Radiation of Canopy and Soil respectively
Pairing several output datasets derived from all the processing chains to estimate Land Surface Energy Fluxes using the Two Source Energy Balance Model. There are four instantaneous fluxes which are estimated at the time of Sentinel-3 dataset acquisition (4th June 2022) - Sensible Heat Flux, Latent Heat Flux, Ground Heat Flux and Net Surface Irradiation (recollect that Evotranspiration is measured in energy released terms besides in water vapour terms - Latent Heat Flux represents the energy released during Evotranspiration)
Finally, the instantaneous Latent Heat Flux is converted to Water Vapour terms (millimeters/unit of time) to obtain the estimate of the Daily Actual Evotranspiration (ETa) at Field scale over the study area
Sharing some output visuals over my study area extracted from the ECMWF ERA5 Meteorological data processing chain-
As indicated in Figure 20 above, the derived estimate of Daily Actual Evotranspiration at Field scale (20 m) over the selected agri-zone in Punjab, India (north of the Sutlej river) ranges from a minimum of 0.2 mm/day to a maximum of 10.0 mm/day with a mean of 3.8 mm/day (bulk of the pixels have values between 1.3 and 6.1 mm/day).
CONCLUDING OBSERVATIONS
So how is one supposed to interpret this estimated Daily Actual Evotranspiration at Field-scale output? Is it high or low, good or bad, improving or deteriorating?
Unfortunately, I am not a hydrology expert and am unable to assess the output which has been derived using complex manipulations and with several linkages between the variables. That being said, I can definitely remark that in order to pass a judgement on the trend of Evotranspiration, a single output such as this one would not be conclusive evidence, rather, a time-series of observations and the evolution of underlying causes (data can be obtained from local weather stations) would need to be studied. You can also conduct independent research to see how this output compares with other research studies on Evotranspiration performed on Indian territory or overseas. I'll be happy to know what you find.
There are other factors aspects to be taken into consideration as well - for example, the Meteorological data which I prepared (Air Temperature and Solar Radiance constituted a portion of it) was at the time of Sentinel-3 overpass (05:26 am i.e. Dawn). This is a cooler part of the day despite the month (June) being peak summer in India. I am certain that the algorithm would interpolate the Daily ETa in a different way had I selected another Sentinel-3 dataset that was acquired during the same season, albeit when the temperatures are higher (afternoon). Unfortunately, I cannot test this assumption as Sentinel-3 data over the study area is not available in this time window - the satellite's orbit path stipulates that the overpass is made only at specified times of the day at fixed intervals (revisit time for S-3 is <2 days near the equator).
I would like to highlight an interesting aspect that I observed when I used the same datasets and timeline to estimate the Daily ETa at Field scale, albeit for a different study area (an agri-zone located north-west to the previous study area). Refer the output below-
As you would observe, the south-east half of the new study area depicted in Figure 21 is red in shade signifying a higher rate of ETa - between 7-9 mm/day. In comparison, as evident in Figure 20, much of the previous study area is predominantly dark yellow - a lower rate of ETa - between 3-5 mm/day.
Doesn't this strike you as surprising given that both the study areas are so close to each other and the measurement was done during the exact same time? What could be the reason(s) behind it?
In my opinion, this could very well be attributed to both the agri-zones cultivating a different type of crop. Recollect that Punjab cultivates Rice as well as Wheat extensively and this zone lies within the Rice–Wheat (RW) belt. As indicated in the Factors affecting Evotranspiration infographic (Figure 3) - the nature of crop, its root system, agricultural practises used, and the growth stage of the crop - all affect the rate of Evotranspiration. Hence, I surmise that this large difference in ETa values could be particularly due to the different moisture retention properties of the crop being cultivated in both the study areas - the crop in the new study area retains much less moisture than the crop in the previous study area.
I hope you found this post and the accompanying video tutorial to be useful. Your feedback and suggestions are welcome.
ABOUT US
Intelloc Mapping Services, Kolkata | Mapmyops.com offers Mapping services that can be integrated with Operations Planning, Design and Audit workflows. These include but are not limited to Drone Services, Subsurface Mapping Services, Location Analytics & App Development, Supply Chain Services, Remote Sensing Services and Wastewater Treatment. The services can be rendered pan-India and will aid your organization to meet its stated objectives pertaining to Operational Excellence, Sustainability and Growth.
Broadly, the firm's area of expertise can be split into two categories - Geographic Mapping and Operations Mapping. The Infographic below highlights our capabilities-
Our Mapping for Operations-themed workflow demonstrations can be accessed from the firm's Website / YouTube Channel and an overview can be obtained from this brochure. Happy to address queries and respond to documented requirements. Custom Demonstration, Training & Trials are facilitated only on a paid-basis. Looking forward to being of service.
Regards,
