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 on LinkedIn with a particular request - Can you prepare a tutorial for deriving Evotranspiration using SNAP? - my initial feeling of surprise was overcome by a sense of satisfaction.
I had never even heard of 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 for Operations Improvement, moments like this would bring joy and encouragement on my journey.
My lazy and superficial responses did not deter the ever-so-persistent Jessey who was determined to find a way to complete his assignment on estimating Evotranspiration using Remote Sensing shared by his professor. I eventually acceded and decided to support him the best I could.
While Jessey had already chosen the farmlands around Gadot, Israel as his study area, I decided to try the Evotranspiration derivation over another agri-zone - near Jalandhar in Punjab, India. After hours of exchanges, video meetings & working on Sentinels Application Platform (SNAP) software, we were finally able to fulfill our objectives - mine being this elaborate, end-to-end video tutorial on estimating Field-scale Daily (Actual) Evotranspiration using Remote Sensing and this post.
HYPERLINKED 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:Â ESA Sen-ET, DHI Gras, Sandholt ApS, SNAP | STEP Forum, Jessey Dickson
EVOTRANSPIRATION & FACTORS AFFECTING IT
Evotranspiration (ET) represents the total loss of water (& 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 & roots of vegetation. This release of Water Vapour into the atmosphere forms a crucial component (largest after Precipitation) of our planet's Hydrologic Cycle.
What could be the possible benefits of computing Water (and Energy) transfer into the atmosphere? Think about it - I'll elaborate later.
Here are some of the contributing factors to 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 can be measured in the following ways-
Actual Evotranspiration (ETa) involves figuring out how much water vapour (and energy) was actually released from the soil and vegetation into the atmosphere over a period of time at 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 method is the most authentic representation of the phenomenon. However, as you would imagine, to derive it is costly, time-consuming, and requires significant scientific expertise.
Potential Evotranspiration (ETp) considers the water availability as plentiful, be it from precipitation, soil moisture, irrigation, and so on. Hence, ETp is a way of saying what is the maximum water (& energy) that the soil and vegetation can transmit into the atmosphere over a period of time at a given study area through the influence of meteorological variables such as air temperature, solar radiation & wind speed. Measuring ETp is preferred particularly in drought assessments 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 it is out of budgetary means.
Reference Evotranspiration (ETref) is ETp measured on a reference surface, usually well-watered and even short grass. ETref is utilized when there is a need to standardize the atmospheric demand of water vapor instead of measuring ETp for different types of agricultural surfaces - Weather Stations make use of this extensively and house the reference surface as well.
Evotranspiration measurements can be classified on the basis of geographic extent as well - Local scale, Field scale, Watershed scale, Regional scale and Continental scale.
At Local-scale, Lysimeter equipment is used to measure ETa and for larger Field-scale studies, Drones can also being used to obtain Evotranspiration-related data.
As the geographic extent increases - Regional-scale & beyond - Remote Sensing via Satellite Imagery is preferred to estimate Evotranspiration (ETa and 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 as well, obtaining Satellite Imagery from European Space Agency's' Sentinel-2 (Multispectral sensor) and Sentinel-3 (Thermal sensor) satellites and Meteorological data from Copernicus Climate Data Store, albeit at a more granular Field-scale, to estimate Daily Actual Evotranspiration (ETa) at an agri-zone within Punjab, India.
UTILITY OF EVOTRANSPIRATION
It wouldn't be difficult for you to gauge the importance of monitoring Evotranspiration - after all keeping a tab on the dynamic relationship between water inflow & water outflow is important. One way in which the social and environmental impact of any global-scale phenomenon 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 - set by the United Nations in 2015.
The applications of Evotranspiration are 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 (e.g. Goal 15 - Life on land) as established by Sentinels for Evotranspiration (SEN-ET).
Evotranspiration studies can be utilized for-
Irrigation planning & scheduling - supplying water to agri-zones that face moisture scarcity
Watershed & 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 inflow transfers into the soil and water table, or 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 & 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 wasn't possible before at consistent time intervals),
the Meteorological datasets utilized in this Remote Sensing-based methodology developed to estimate Daily ETa
I have used Version 9.0.0 of ESA's SNAP Software to perform this study. The process flow of the steps involved can be diagrammatically represented as below-
(clicking on the graphic will open an enlarged view)
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
knowing the fundamentals of ET helps in understanding the workflow and the generated outputs
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 text format, I have attempted to develop a singular resource which would serve as a ready reckoner - a step-by-step, video tutorial for students & practitioners alike. When Jessey and myself were stuck, it took us hours to figure out what went wrong and to find a working solution and I hope this walkthrough would spare you from the ordeal.
While I will elaborate the processing steps and the generated outputs pertaining to my study over a cross-section in Punjab, India in the next section, the video tutorial below would be the definitive guide for you to understand the process involved in a visual and an engaging manner-
VIDEO 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 in Punjab, India as my Study Area due to-
wanting to shortlist an agricultural zone within my home country
the state of Punjab ranking high in staples production in the country, particularly for wheat & rice
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 was facing excessive decline in Groundwater table
Even the time selected for analysis (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 base types of Remote Sensing data which 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 data points
P.S. - You may refer to Figure 3 which lists the factors affecting Evotranspiration and compare it with both - the processing chain (Figure 7) as well as the video tutorial and written content below to enhance your understanding
The Sentinel-2 Multispectral Imagery dataset utilized was acquired on 27th May 2022 at 05:36 am. The objective behind processing this dataset is to characterize the biophysical state of the land surface at 20 m resolution.
The following outputs are derived during the S-2 processing chain-
Biophysical parameters such as Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, Fraction of Vegetation Cover, Chlorophyll content & 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 visuals of the outputs over the study area in the Sentinel-2 processing chain-
The Sentinel-3 Thermal Imagery dataset utilized was acquired on 4th June 2022 at 05:26 am. The objective behind processing this dataset is to establish the bottom boundary condition of the Land Surface Energy Model. In simpler words, we seek to estimate the Land Surface Temperature over the study area - an important input directly relevant to measure the surface energy and water vapour released i.e. Evotranspiration.
Sentinel-3 Imagery datasets have a low spatial resolution (~ 1 km), which is why the processing chain entails enhancing the resolution so that it matches Sentinel-2's spatial resolution (20 m) - this is done using the Data Mining Sharpener Machine Learning model and it helps make this dataset consistent with the previously generated outputs - necessary for processing data on SNAP software as well as to perform Evotranspiration derivation at Field-scale.
The following outputs are derived during the S-3 processing chain-
High Resolution (20 m) Observation Geometry
Sharpened Land Surface Temperature data
Sharing some visuals of the outputs over the study area in the 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 resolution of the Sentinel-2's processed data (20 m). The objective of this processing chain is to establish conditions which drive (e.g. air temperature) and modulate (e.g. wind speed) the energy transfer between the surface and the atmosphere.
It entails deriving the following outputs-
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 & Sentinel-3 processing chain in order to estimate the Longwave Irradiance & Net Shortwave Radiation of canopy and soil respectively
Pairing several layers derived across 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 Sentinel-3 data acquisition time (4th June 2022) - Sensible Heat Flux, Latent Heat Flux, Ground Heat Flux & Net Surface Irradiation. (Recollect that Evotranspiration is measured in energy released terms besides in water vapour terms - the 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 Daily Actual Evotranspiration (ETa) at Field-scale over the study area
Sharing some visuals of the outputs over the study area in the ECMWF ERA5 processing chain-
As you can observe from Figure 20 above, the derived Daily Actual Evotranspiration at Field-scale (20 m) over the study area 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 are between 1.3 and 6.1 mm / day).
CONCLUDING OBSERVATIONS
So how is one supposed to interpret this derived 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 there are several linkages with variables. That being said, in order to pass a judgement on the trend of Evotranspiration, a single isolated output would not be helpful, rather, a time-series of observations and the evolution of underlying causes (data obtainable from local weather stations) would be necessary. That being said, feel free to draw your own conclusions based on your independent research - see how this output compares with research studies on Evotranspiration done within India or outside. I'll be happy to know your thoughts and opinions.
There are other factors aspects to be taken into consideration as well - for example, the Meteorological data which I prepared (of which the Air Temperature and Solar Radiance was a component of) was at the time of Sentinel-3 overpass at Dawn (05:26 am) - a cooler part of the day even if the month was peak summer in India (June). I am certain that the algorithm would interpolate the Daily ETa in a different way if I had selected another Sentinel-3 imagery which was captured on the same day, albeit after noon. Unfortunately, I can't test this assumption as Sentinel-3 data over the study area is not available at that time-range - the Earth Observation satellites' orbit path stipulates that the overpass is made at specified times of the day at fixed intervals (revisit time for SLSTR instrument is <2 days near the equator).
I was able to observe an interesting aspect when I used the same datasets to estimate Daily ETa, however, over a different study area within (also an agri-zone located north-west to the previous study area). Refer the output below-
As you'd 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 be because the crops being cultivated in both the agri-zones are different. Recollect that Punjab cultivates Rice as well as Wheat extensively. 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 contribute towards 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.
I hope you found this article and the video tutorial to be interesting. Your feedback and suggestions are welcome.
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