#CERT #STATEOFAI #WRC23 #AGENDAITEM10

1. FORM 2
THE PATENTS ACT, 1970
(39 OF 1970)
AND
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“METHOD AND SYSTEM FOR AUTOMATICALLY DETERMINING
EVAPOTRANSPIRATION OF CROPS”
We, ARMS 4 AI Private Limited, an Indian National, of C - 49, Om Vihar, Uttam
Nagar, New Delhi – 110059, India.
The following specification particularly describes the invention and the manner in
which it is to be performed.

2. 2
METHOD AND SYSTEM FOR AUTOMATICALLY DETERMINING
EVAPOTRANSPIRATION OF CROPS
FIELD OF THE INVENTION:
The present invention generally relates to evapotranspiration of crops and more
5
particularly to methods and systems for automatically determining
evapotranspiration of crops.
BACKGROUND OF THE DISCLOSURE:
The following description of the related art is intended to provide background
information pertaining to the field of the disclosure. This section may include
10
certain aspects of the art that may be related to various features of the present
disclosure. However, it should be appreciated that this section is used only to
enhance the understanding of the reader with respect to the present disclosure,
and not as admissions of the prior art.
Water stress affects crop yields. Water stressed situations originate from a
15
plant's roots receiving insufficient water, which directly affects transpiration.
Less water is available, which results in physical limitations in plants. The stomata
control how much water, oxygen, and carbon dioxide enter and leave the plant.
Stomata close in response to water stress in an effort to conserve water,
blocking the channel for the exchange of oxygen, water, and carbon dioxide and
20
reducing photosynthesis. As a result, water stress has a greater impact on leaf
growth than root growth because roots are better able to adapt to moisture
stress. Reduced photosynthesis due to water stress ultimately causes a reduction
in crop growth and development. Evapotranspiration (ET) is one of the key
factors that affect crop water stress.
25

3. 3
The negative effects on productivity of crops can be reduced by taking
appropriate action if water stress is identified timely and accurately. Traditional
methods for estimating and measuring evapotranspiration are based on human
intervention and are limited to local or regional scales. At the local or regional
level, any ground-based measurement can be used to quantify plant water stress
5
successfully, but broad spatial scale, other techniques such as those based on
remote sensing techniques.
Thus, time and again, solutions have been developed to determine
evapotranspiration of crops using remote sensing techniques. These models are
based on the theory of surface energy balance and turbulent fluxes, which
10
estimates physical characteristics of the land surface like evapotranspiration (ET)
and evaporative fraction (EF) through processes related to surface radiation and
energy balance. In these solutions, information produced from remote sensing is
used to assess spatial and temporal differences in crop growth, crop stress, and
supports for agricultural development decision-making.
15
One of the known solutions includes using surface energy balance algorithm for
land (SEBAL) that determines ET using net surface radiation, soil heat flux, and
sensible heat flux to the atmosphere. In this, a "residual" energy flow is used for
determining evapotranspiration (i.e. energy that is used to convert the liquid
water into water vapour) after subtracting the soil heat flux and sensible heat
20
flux from the net radiation at the surface.
However, one of the major drawbacks of using this method is the unavailability
of satellite data under certain weather conditions, for example, during cloudy
conditions, as ET cannot be determined for the cloud covered land surfaces
because there can be a significant decrease in readings of thermal band due to
25
even a thin layer of cloud causing large anomalies in sensible heat flux
calculation. Also, the systems using SEBAL implement the concept of hot and

4. 4
cold pixels for calculating the temperature differences between pixel wise air
temperature, which involves manual intervention. This may lead to observational
errors which may in-turn lead to wrongful calculations of ET.
In order to solve the above problems, it is an imperative need to develop a
solution for automatically determining evapotranspiration of crops that is more
5
accurate way less time consuming while calculating ET.
SUMMARY OF THE DISCLOSURE
This section is provided to introduce certain objects and aspects of the present
invention in a simplified form that are further described below in the detailed
description. This summary is not intended to identify the key features or the
10
scope of the claimed subject matter.
Thus, a first object of the present disclosure is to obtain a method and system for
automatically determining evapotranspiration of crops that overcomes the
limitations of the existing approaches. Another object of the present disclosure is
to obtain a method and system for automatically determining evapotranspiration
15
of crops that is more accurate way and less time consuming.
In order to achieve at least one of the objectives as mentioned above, one aspect
of the present invention relates to a method automatically determining
evapotranspiration value of a desired crop. The method comprises receiving, by a
processing unit from a database, a set of images comprising one or more images
20
and a set of data. The method further comprises pre-processing, by the
processing unit, the set of data. The method also encompasses generating, by
the processing unit, a surface energy balance algorithm for land (SEBAL) model
for the desired crop. Further, the method comprises determining, by the
processing unit, a net radiation value (Rn), a soil heat flux value (G), a sensible
25
heat flux value (H), and an instantaneous evaporation value (λETi) for one or

5. 5
more pixels of the one or more images of the set of images, wherein the net
radiation value (Rn), the soil heat flux value (G), and the sensible heat flux value
(H) are based on the SEBAL model for the desired crop. The method further
comprises determining, by the processing unit, an instantaneous evaporative
fraction value (Ʌ) based on the net radiation value (Rn), the soil heat flux value
5
(G), and the instantaneous evaporation value (λETi). Further, the method
comprises determining, by the processing unit, the evapotranspiration value of
crops for a pre-determined time period value, wherein the evapotranspiration
value of crops for the pre-determined time period value, is based on an averaged
net radiation value for the pre-determined time period value, and a latent heat
10
of vaporization value (λ).
Another aspect of the present invention relates to a system for automatically
determining evapotranspiration value of a desired crop. The system comprises a
processing unit configured to receive, from a database, a set of images
comprising one or more images. Further, the processing unit is configured to pre-
15
process the set of data. Further, the processing unit is configured to generate a
surface energy balance algorithm for land (SEBAL) model for the desired crop.
The processing unit is further configured to determine a net radiation value (Rn),
a soil heat flux value (G), a sensible heat flux value (H), and an instantaneous
evaporation value (λETi) for one or more pixels of the one or more images of the
20
set of images, wherein the net radiation value (Rn), the soil heat flux value (G),
and the sensible heat flux value (H) are based on the SEBAL model for the
desired crop. Further, the processing unit is configured to determine an
instantaneous evaporative fraction value (Ʌ) based on the net radiation value
(Rn), the soil heat flux value (G), and the instantaneous evaporation value (λETi).
25
Further, the processing unit is configured to determine the evapotranspiration
value of crops for a pre-determined time period value, wherein the
evapotranspiration value of crops for the pre-determined time period value, is
based on an averaged net radiation value for the pre-determined time period

6. 6
value, and a latent heat of vaporization value (λ).
BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a
part of this disclosure, illustrate exemplary embodiments of the disclosed
methods and systems in which like reference numerals refer to the same parts
5
throughout the different drawings. Components in the drawings are not
necessarily to scale, emphasis instead being placed upon clearly illustrating the
principles of the present disclosure. Some drawings may indicate the
components using block diagrams and may not represent the internal circuitry of
each component. It will be appreciated by those skilled in the art that disclosure
10
of such drawings includes disclosure of electrical components, electronic
components or circuitry commonly used to implement such components.
Figure 1 illustrates an exemplary overview of components of a system for
automatically determining evapotranspiration value of a desired crop, in
accordance with exemplary embodiments of the present invention.
15
Figure 2 illustrates exemplary flow chart of a method for automatically
determining evapotranspiration value of a desired crop, in accordance with
exemplary embodiments of the present invention.
The foregoing shall be more apparent from the following more detailed
description of the disclosure.
20
DESCRIPTION OF THE INVENTION
In the following description, for the purposes of explanation, various specific
details are set forth in order to provide a thorough understanding of
embodiments of the present disclosure. It will be apparent, however, that
embodiments of the present disclosure may be practiced without these specific
25

7. 7
details. Several features described hereafter can each be used independently of
one another or with any combination of other features. An individual feature
may not address any of the problems discussed above or might address only
some of the problems discussed above.
The ensuing description provides exemplary embodiments only, and is not
5
intended to limit the scope, applicability, or configuration of the disclosure.
Rather, the ensuing description of the exemplary embodiments will provide
those skilled in the art with an enabling description for implementing an
exemplary embodiment. It should be understood that various changes may be
made in the function and arrangement of elements without departing from the
10
spirit and scope of the disclosure as set forth.
Specific details are given in the following description to provide a thorough
understanding of the embodiments. However, it will be understood by one of
ordinary skill in the art that the embodiments may be practiced without these
specific details. For example, circuits, systems, processes, and other components
15
may be shown as components in block diagram form in order not to obscure the
embodiments in unnecessary detail.
Also, it is noted that individual embodiments may be described as a process
which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure
diagram, or a block diagram. Although a flowchart may describe the operations
20
as a sequential process, many of the operations can be performed in parallel or
concurrently. In addition, the order of the operations may be re-arranged. A
process is terminated when its operations are completed but could have
additional steps not included in a figure.
The word “exemplary” and/or “demonstrative” is used herein to mean serving as
25
an example, instance, or illustration. For the avoidance of doubt, the subject
matter disclosed herein is not limited by such examples. In addition, any aspect

8. 8
or design described herein as “exemplary” and/or “demonstrative” is not
necessarily to be construed as preferred or advantageous over other aspects or
designs, nor is it meant to preclude equivalent exemplary structures and
techniques known to those of ordinary skill in the art. Furthermore, to the extent
that the terms “includes,” “has,” “contains,” and other similar words are used in
5
either the detailed description or the claims, such terms are intended to be
inclusive—in a manner similar to the term “comprising” as an open transition
word—without precluding any additional or other elements.
As used herein, a “processor” or a “processing unit” may be a general-purpose or
a special-purpose processing unit. Also, as used herein, a “processing unit” or
10
“general-purpose processing unit” or “special-purpose processing unit” or
“processor” or “operating processor” includes one or more processors, wherein
processor refers to any logic circuitry for processing instructions. A processor
may be a general-purpose processor, a special purpose processor, a conventional
processor, a digital signal processor, a plurality of microprocessors, one or more
15
microprocessors in association with a DSP core, a controller, a microcontroller,
Application Specific Integrated Circuits, Field Programmable Gate Array circuits,
any other type of integrated circuits, etc. The processor may perform signal
coding data processing, input/output processing, and/or any other functionality
that enables the working of the system according to the present disclosure.
20
More specifically, the processor or processing unit is a hardware processor.
As used herein, “storage unit” or “memory unit” refers to a machine or
computer-readable medium including any mechanism for storing information in
a form readable by a computer or similar machine. For example, a computer-
readable medium includes read-only memory (“ROM”), random access memory
25
(“RAM”), magnetic disk storage media, optical storage media, flash memory
devices or other types of machine-accessible storage media. The storage unit
stores at least the data that may be required by one or more units of the system

9. 9
to perform their respective functions. The memory unit may be distributed in
various components of the system or may also form a part of a remote server
with which the components of the disclosed invention may be interacting.
As disclosed in the background section, the existing models to determine
evapotranspiration of crops using remote sensing techniques are based on the
5
theory of surface energy balance and turbulent fluxes, which estimates physical
characteristics of the land surface like evapotranspiration (ET) and evaporative
fraction (EF) through processes related to surface radiation and energy balance.
Further, the existing technologies have many limitations such as during the case
of unavailability of satellite data under certain weather conditions, or are not
10
accurate due to human interventions, and are time consuming. The present
invention solves these problems by pre-processing the available data to maintain
quality and data completeness. For this purpose, the invention comprises using
an already available data or using the statistical forecasting, which is a special
technique of making predictions for the future by using historical data as inputs
15
and analyzing trends, and the same can be applied to meteorological parameters
such as average air temperature (Ta), atmospheric emissivity value (𝜀a),
aerodynamic resistance value (Rah). Also, in order to find temperature difference
between the land surface temperature (Ts) and air temperature (Ta), the values
of average air temperature from the available monitoring locations is used. This
20
determination of the temperature difference (Ts - Ta) based on the average air
temperature (Ta) improves the accuracy of determination of the value of the
sensible heat flux (H) as it removes the observational errors incurred due to
human intervention, and also provides a less time consuming way of determining
ET as compared to the method of using hot and cold pixel, as generally known in
25
the art.

10. 10
Hereinafter, exemplary embodiments of the present disclosure will be described
in detail with reference to the accompanying drawings so that those skilled in the
art can easily carry out the present disclosure.
Referring to Figure 1, an exemplary block diagram of a system [100] for
automatically determining evapotranspiration value of a desired crop is shown.
5
The system [100] comprises a processing unit [104] and a memory unit [106], all
components presumed to be connected with each other unless otherwise
indicated herein. Although only one of such units are shown in the figure, but the
disclosure encompasses a plurality of such units.
The processing unit [104] first receives, from a database [102], a set of images
10
comprising one or more images, and a set of data. The set of images may
comprise one or images acquired by a satellite. The satellite images may be
captured using at least two sensors namely the Operational Land Imager (OLI)
and Thermal Infrared Sensor (TIRS). The OLI sensor has nine bands, i.e., 1st band,
2nd band, 3rd band, 4th band, 5th band, 6th band, 7th band, 8th band, and 9th band.
15
The TIRS has two bands, i.e., 10th band and 11th band. The 10th band and the 11th
band are thermal bands. At a spatial resolution of 30 meters, the satellite images
may be obtained at specific intervals of time. Further, the database [102] may be
a publicly available database. For example, the database can be a database of a
geological survey website, etc.
20
Further, the set of data comprises a data related to atmospheric parameters
involved SEBAL algorithm such as air temperature, relative humidity, wind speed,
precipitation, solar insolation, vapor pressure. Also, the data may not be
complete and may also be inaccurate, which can lead to wrong determination of
evapotranspiration. Therefore, the data is pre-processed by the processing unit
25
[104], in order to maintain quality and data completeness. This pre-processing
may involve statistical techniques such as statistical forecasting which is a special

11. 11
technique of making predictions for the future by using historical data as inputs
and analyzing trends, or using the already available data available at one or more
publicly available databases such as the National Solar Radiation Database, etc.
The pre-processed data is stored by the processing unit [104] in the memory unit
[106]. The meteorological parameters such as an average air temperature (Ta),
5
an atmospheric emissivity value (𝜀a), an aerodynamic resistance value (Rah) are
determined based on using the statistical techniques such as statistical
forecasting.
Further, the processing unit [104] also generates a surface energy balance
algorithm for land (SEBAL) model for the desired crop. For example, the desired
10
crop can be Sugarcane.
Further, the processing unit [104] determines a net radiation value (Rn), a soil
heat flux value (G), a sensible heat flux value (H), and an instantaneous
evaporation value (λETi) for one or more pixels of the one or more images of the
set of images. The net radiation value (Rn), the soil heat flux value (G), and the
15
sensible heat flux value (H) are based on the SEBAL model for the desired crop.
For example, the determination of the sensible heat flux value (H) involves an
aerodynamic resistance value (Rah) which can be corrected using the average
sugarcane height.
In an implementation, the satellite images with a resolution of 30 meters, which
20
have cloud cover of less than 10% are received from the database [102]. Further,
in an implementation, the processing unit [104] retrieves from the memory unit
[106], a surface albedo value (as), an incoming shortwave radiation value (Rs), a
land surface temperature value (Ts), and a surface emissivity value (𝜀s). Then Rn
for each pixel is determined using surface albedo value (as), an incoming
25
shortwave radiation value (Rs), a land surface temperature value (Ts), and the
surface emissivity value (𝜀s), an average air temperature (Ta), an atmospheric

12. 12
emissivity value (𝜀a), and a Stefan Boltzmann’s constant (𝜎). The atmospheric
emissivity value (𝜀a), and a Stefan Boltzmann’s constant (𝜎) are pre-stored in the
memory unit [106].
In an implementation, the incoming shortwave radiation value (Rs) is determined
using a metadata file associated with the satellite images, which contains Sun
5
elevation angle value (𝛽) and an inverse squared relative distance between Earth
and Sun (dr), and the atmospheric transmissivity value (τ
sw
), as given in the
equation 1 below.
𝑅𝑠 = 𝐺𝑠𝑐 × 𝑆𝑖𝑛 𝛽 × 𝑑𝑟 × τ𝑠𝑤 …. (eq. 1)
Here, Gsc is the solar constant, i.e., 1367 W/m2. Also, the atmospheric
10
transmissivity value (τ
sw
) is an atmospheric parameter calculated using elevation
of the area above the mean sea level (z) and stored in the memory unit [106].
Also, in an implementation, the surface albedo value (as) is determined based on
a weighted albedo value (atoa), an incoming shortwave radiation flux value
(α𝑝𝑎𝑡ℎ−𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒), and an atmospheric transmissivity value (τ
sw
), as given in the
15
equation 2 below. For determining the surface albedo value (as), a band-specific
reflectance using the reflectance rescaling factors from the metadata file
associated with the satellite images for specific OLI-bands, i.e., for Bands 2, 3, 4,
5, 6 and 7. Also, the weighted albedo value (atoa) is further based on a set of one
or more top of atmosphere reflectance values, as given in the equation 3 below,
20
and the atmospheric transmissivity value (τ
sw
) is further based on an elevation
above mean sea level value (z), as given in the equation 4 below.
a𝑠 = (
a𝑡𝑜𝑎−α𝑝𝑎𝑡ℎ−𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒
τ 𝑠𝑤
2 ) …. (eq. 2)

13. 13
where, a𝑠 is surface albedo, α𝑝𝑎𝑡ℎ−𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒 is the incoming shortwave radiation
flux reflected back to the sensor (range from 0.025 – 0.04). In an exemplary
implementation, the value of α𝑝𝑎𝑡ℎ−𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒 is 0.03.
a𝑡𝑜𝑎 = (0.356 × ρ2
) + (0.326 × ρ3
) + (0.138 × ρ4
) + (0.084 × ρ5
) + (0.056 ×
ρ6
) + (0.041 × ρ7
) …. (eq. 3)
5
where, a
toa
is weighted surface albedo and ρ2
, ρ3
, ρ4
, ρ5
, ρ6
,and ρ7
are top of
atmosphere reflectance values for band 2, 3, 4, 5, 6, and 7 respectively, of the
satellite.
τ𝑠𝑤 = 0.75 + (2 × 10−5
× 𝑧) …. (eq. 4)
10
where, τ
sw
is atmospheric transmissivity and z is the elevation above mean sea
level (in meters).
Further, in an implementation, a normalized difference vegetation index (NDVI) is
determined using band 4 and band 5 of the satellite sensors. The NDVI is further
15
used to determine the surface emissivity value (𝜀s). Further, a data of band 10 of
the satellite sensors is used to determine a brightness temperature (BT) which is
used with εs to determine the land surface temperature value (Ts).
First step involved in determining Ts is the radiometric correction, in which Top-
of-Atmosphere (ToA) spectral radiance is calculated using band specific
20
multiplicative and additive rescaling factors. The ToA spectral radiance is the
energy or radiation which has reflected from an object or a surface in addition to
radiation that has bounced back from clouds and neighbouring pixels as well. The
ToA spectral radiance can be determined by the equation 5 below:
(𝐿𝜆) = 𝑀𝐿 ∗ 𝑄𝑐𝑎𝑙 + 𝐴𝐿 …. (eq. 5)
25

14. 14
where, Lλ is the spectral radiance measured in units Wm-2sr-1µm-1, Qcal is the
pixel value and ML and AL are the multiplicative and additive radiance rescaling
factor.
Also, ToA spectral reflectance, which is reflectance of whole Atmosphere-Earth
interaction can be determined based on equation 6 below:
5
𝜌 = (𝑀𝑝 × 𝑄𝑐𝑎𝑙) + 𝐴𝑝 …. (eq. 6)
where, ρ is a unitless entity of spectral reflectance and Mp and Ap are
multiplicative reflectance rescaling factor and additive reflectance rescaling
factor respectively. Values of ML, AL, Mp and Ap are available in metadata file of
satellite data.
10
Further, the brightness temperature is determined. The brightness temperature
can be defined as the temperature of a blackbody in a given spectral band, that
would produce same amount of radiation as a target object surface. For thermal
bands of the satellite, it can be determined based on the equation 7 below:
𝐵𝑇 =
𝐾2
ln[(
𝐾1
Lλ
)+1]
− 273.15 …. (eq. 7)
15
where, K1 and K2 are the thermal conversion constants. Their values are
available in metadata files of satellite images.
The surface emissivity value (εs) is the ability of the natural material to emit in
comparison to a blackbody at the equivalent thermodynamic temperature. εs
from the satellite can be retrieved using a semi-empirical, normalized difference
20
vegetation index (NDVI) based emissivity method (NBEM), as generally known in
the art, as given in equation 8 below. εs indicates the emissivity emanating from
different land surfaces which might be composed of heterogenous materials.
Therefore, it depends upon the structure of soil, its composition, amount of soil
moisture and organic content as well as green-cover characteristics. NDVI is a
25
standardized biophysical parameter which is used to quantify the health and

15. 15
amount of vegetation growth in an area. NDVI is estimated using the near
infrared and visible red wavelengths that are reflected by the biomass or
vegetation. Value of NDVI lies between -1 to +1, with low NDVI values assigned
to water, barren land and built up and high positive values attributed to healthy
green vegetation.
5
𝑁𝐷𝑉𝐼 =
(𝜌𝑁𝐼𝑅−𝜌𝑟𝑒𝑑)
(𝜌𝑁𝐼𝑅+𝜌𝑟𝑒𝑑)
…. (eq. 8)
Further, a fraction of vegetation cover (FVC or Pv) is determined based on
equation 9 below:
𝑃𝑣 = 𝑠𝑞𝑢𝑎𝑟𝑒 (
𝑁𝐷𝑉𝐼− 𝑁𝐷𝑉𝐼𝑚𝑖𝑛
𝑁𝐷𝑉𝐼𝑚𝑎𝑥− 𝑁𝐷𝑉𝐼𝑚𝑖𝑛
) …. (eq. 9)
Also, due to heterogeneity of the land surface, different types of land surface
10
materials have varying values of NDVI. Therefore, the NDVI threshold values are
based on the LSE model, provided in equations 10, 11, 12, and 13 below:
𝜀𝑖 = 0.979 − 0.046ρR NDVI<0.2 … …. (eq. 10)
𝜀𝑖 = ε𝑣Pv + ε𝑆(1 − Pv) + C′ 0.2≤NDVI≤0.5 …. (eq. 11)
15
𝜀𝑖 = ε𝑣 + C′ NDVI>0.2 …. (eq. 12)
C′ = (1 − ε𝑆). (1 − Pv). F. ε𝑣 …. (eq. 13)
where εv and εs are vegetation soil and emissivities, ρR is the reflectance of red
band of the satellite, Pv is the vegetation fraction and C’ accounts for the
modification in emissivity due to cavity effect and mixed-pixel scattering in
20
heterogenous surfaces, F corresponds to geometric form factor which has a
mean value of 0.55. In an implementation, the values of εv and εs considered for
band 10 are 0.989 and 0.977.
Further, using the above determined 𝜀𝑖 values, a combined raster image is
created using conditional formatting in which for NDVI<0.2, values of equation
25

16. 16
𝜀𝑖 = 0.979 − 0.046ρR will be assigned, while for 0.2≤NDVI≤0.5, equation 𝜀𝑖 =
ε𝑣Pv + ε𝑆(1 − Pv) + C′ and similarly the third equation and one raster image
having final surface emissivity values εs.
Further, the land surface temperature is based on the equation below:
𝑇𝑠 =
𝐵𝑇
[1+ {(
λ∗BT
ϼ
)∗ ln ε𝑠}]
…. (eq. 13)
5
where, Ts is the land surface temperature.
Further, in an implementation, a pixel wise soil heat flux (G), which is the rate of
heat storage into the soil and vegetation due to conduction, is determined based
on the surface albedo value (as), the land surface temperature value (Ts),
normalized difference vegetation index (NDVI), and the net radiation value (Rn)
10
values as given in the equation 14 below.
𝐺
𝑅𝑛
=
𝑇𝑠
𝛼(0.0038𝛼+0.074𝛼2)(1−0.98𝑁𝐷𝑉𝐼4)
…. (eq. 14)
where 𝛼 is equal to the surface albedo value (as).
In an implementation, the sensible heat flux (H) is determined using the air
density value (𝜌), a specific heat of air value (Cp), the average air temperature
15
value (Ta), the land surface temperature value (Ts), and an aerodynamic
resistance value (Rah) as given in the equation 15 below. The sensible heat flux
(H) is the heat transferred to the air by the molecular transfer of heat as a result
of the temperature difference, i.e., (Ts – Ta), between air and surface.
𝐻 = 𝜌𝐶𝑝
(𝑇𝑠−𝑇𝑎)
𝑟𝑎ℎ
…. (eq. 15)
20
Here, the determination of the temperature difference (Ts - Ta) is based on the
average air temperature (Ta), assuming that the air temperature for the pixel-
wise calculation remains constant. Pertinently, the air temperature at each pixel
is unknown but is an important parameter in determination of H. The air

17. 17
temperature (Ta) at each pixel of image, i.e. at each geographical location might
vary. This may lead to varying temperature difference between the land surface
temperature Ts) and air temperature (Ta). Thus, in order to find this temperature
difference, the value of average air temperature from the available monitoring
locations is used. This determination of the temperature difference (Ts - Ta)
5
based on the average air temperature (Ta) improves the accuracy of
determination of the value of the sensible heat flux (H) and also provides a less
time consuming way of determining ET as compared to the method of using hot
and cold pixel, as generally known in the art.
Moreover, aerodynamic resistance (Rah) calculated for the SEBAL algorithm uses
10
iterative process according to the Monin-Obukhov Stability theory as generally
known in the art. This also contributes in making overall determination of ET
simple and less time taking and also in providing accurate results. The
aerodynamic resistance (Rah) is determined as given in the equation 16 below:
Rah = (ln((z2 – 0.7 zc)/0.026zc))2/(u2k2) …. (eq. 16)
15
where Rah is aerodynamic resistance, z2 is height of 10 metre above the
ground, zc is canopy height of desired crop, u2 is the wind speed at 10 metre
height above the ground, and ‘k’ is von Karmans's constant (i.e., 0.41)
In an implementation, the processing unit [104] receive, from the memory unit
[106], the average air temperature value (Ta), the atmospheric emissivity value
20
(𝜀a), the air density value (𝜌), and the specific heat of air value (Cp).
Also, the atmospheric emissivity value (𝜀a) is determined using an actual vapor
pressure value (ea), as given in the equation 17 below.
ε𝑎 = 0.52 + 0.065 × √𝑒𝑎 …. (eq. 17)

18. 18
The actual vapor pressure value (ea) can be determined using a saturated vapor
pressure value and a relative humidity value.
Also, the net radiation value (Rn), the sensible heat flux value (H), and the pixel
wise soil heat flux (G) are instantaneous values at the satellite’s transit point-in-
time. Therefore, the latent heat flux values are also instantaneous, and referred
5
to as the instantaneous evaporation value (λETi). The subsequent latent heat flux
values are acquired from satellite images. The instantaneous evaporation values
at the moment of satellite transit are obtained (in millimeter per day) as given in
the equation 18 below.
𝑅𝑛 − 𝐺 − 𝐻 = 𝝀𝐸𝑇𝑖 …. (eq. 18)
10
Further, the processing unit [104] determines an instantaneous evaporative
fraction value (Ʌ) based on the net radiation value (Rn), the soil heat flux value
(G), and the instantaneous evaporation value (λET
i
), as given in the equation 19
below.
Ʌ =
𝜆𝐸𝑇𝑖
𝑅𝑛−𝐺
…. (eq. 19)
15
Further, the processing unit [104] determines the evapotranspiration value of
crops for a pre-determined time period value. For example, the pre-determined
time period value can be 24 hours. For the pre-determined time period value of
24 hours, the evapotranspiration value of crops (ET24) is based on an averaged
net radiation value (Rn24) for the same pre-determined time period value, i.e., 24
20
hours, and a latent heat of vaporization value (λ). For a pre-determined time
period value of 24 hours, i.e., at daily time scales, ET24 (in millimeter per day) can
be determined as given in the equation 20 below.
𝐸𝑇24 =
86400
𝜆
Ʌ𝑅𝑛24 …. (eq. 20)

19. 19
Here, the Rn24 is the 24 hour averaged net radiation, and λ (in Joules per
kilogram) is the latent heat of vaporization value.
Referring to Figure 2, which illustrates exemplary flow chart of a method for
automatically determining evapotranspiration value of a desired crop, in
accordance with exemplary embodiments of the present invention. As shown,
5
the method starts at step 402 upon and goes to step 404. At step 204, the
method comprises receiving, by a processing unit [104] from a database [102], a
set of images comprising one or more images, and a set of data. The set of
images may comprise one or images acquired by a satellite. The satellite images
may be captured using at least two sensors namely the Operational Land Imager
10
(OLI) and Thermal Infrared Sensor (TIRS). The OLI sensor has nine bands, i.e., 1st
band, 2nd band, 3rd band, 4th band, 5th band, 6th band, 7th band, 8th band, and 9th
band. The TIRS has two bands, i.e., 10th band and 11th band. The 10th band and
the 11th band are thermal bands. At a spatial resolution of 30 meters, the
satellite images may be obtained at specific intervals of time. Further, the
15
database [102] may be a publicly available database. For example, the database
can be a database of a geological survey website, etc.
Further, the set of data comprises a data related to atmospheric parameters
involved SEBAL algorithm such as air temperature, relative humidity, wind speed,
precipitation, solar insolation, vapor pressure. Also, the data may not be
20
complete and may also be inaccurate, which can lead to wrong determination of
evapotranspiration. Therefore, at step 206, the method comprises pre-
processing, by the processing unit [104], the set of data, in order to maintain
quality and data completeness. This pre-processing may involve statistical
techniques such as statistical forecasting which is a special technique of making
25
predictions for the future by using historical data as inputs and analyzing trends,
or using the already available data available at one or more publicly available
databases such as the National Solar Radiation Database, etc. The pre-processed

20. 20
data is stored by the processing unit [104] in the memory unit [106]. The
meteorological parameters such as an average air temperature (Ta), an
atmospheric emissivity value (𝜀a), an aerodynamic resistance value (Rah) are
determined based on using the statistical techniques such as statistical
forecasting.
5
Further, at step 208, the method comprises generating, by the processing unit
[104], a surface energy balance algorithm for land (SEBAL) model for the desired
crop. For example, the desired crop can be Sugarcane.
Further, at step 210, the method comprises determining, by the processing unit
[104], a net radiation value (Rn), a soil heat flux value (G), a sensible heat flux
10
value (H), and an instantaneous evaporation value (λETi) for one or more pixels of
the one or more images of the set of images. The net radiation value (Rn), the soil
heat flux value (G), and the sensible heat flux value (H) are based on the SEBAL
model for the desired crop. For example, the determination of the sensible heat
flux value (H) involves an aerodynamic resistance value which can be corrected
15
using the average sugarcane height.
In an implementation, the satellite images with a resolution of 30 meters, which
have cloud cover of less than 10% are received from the database [102]. Further,
in an implementation, the processing unit [104] retrieves from the memory unit
[106], a surface albedo value (as), an incoming shortwave radiation value (Rs), a
20
land surface temperature value (Ts), and a surface emissivity value (𝜀s). Then Rn
for each pixel is determined using surface albedo value (as), an incoming
shortwave radiation value (Rs), a land surface temperature value (Ts), and a
surface emissivity value (𝜀s), an average air temperature (Ta), an atmospheric
emissivity value (𝜀a), and a Stefan Boltzmann’s constant (𝜎). The atmospheric
25
emissivity value (𝜀a), and a Stefan Boltzmann’s constant (𝜎) are pre-stored in the
memory unit [106].

21. 21
In an implementation, the incoming shortwave radiation value (Rs) is determined
using a metadata file associated with the satellite images, which contains Sun
elevation angle value (𝛽) and an inverse squared relative distance between Earth
and Sun (dr), and the atmospheric transmissivity value (τ
sw
), as given in the
equation 1 above in this disclosure.
5
Also, in an implementation, the surface albedo value (as) is determined based on
a weighted albedo value (atoa), an incoming shortwave radiation flux value
(α𝑝𝑎𝑡ℎ−𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒), and an atmospheric transmissivity value (τ
sw
), as given in the
equation 2 above in this disclosure. For determining the surface albedo value
(as), a band-specific reflectance using the reflectance rescaling factors from the
10
metadata file associated with the satellite images for specific OLI-bands, i.e., for
Bands 2, 3, 4, 5, 6 and 7. Also, the weighted albedo value (atoa) is further based
on a set of one or more top of atmosphere reflectance values, as given in the
equation 3 above in this disclosure, and the atmospheric transmissivity value
(τ
sw
) is further based on an elevation above mean sea level value (z), as given in
15
the equation 4 above in this disclosure.
Further, in an implementation, a normalized difference vegetation index (NDVI) is
determined using band 4 and band 5 of the satellite sensors. The NDVI is further
used to determine the surface emissivity value (𝜀s). Further, a data of band 10 of
the satellite sensors is used to determine a brightness temperature (BT) which is
20
used with εs to determine the land surface temperature value (Ts).
First step involved in determining Ts is the radiometric correction, in which Top-
of-Atmosphere (ToA) spectral radiance is calculated using band specific
multiplicative and additive rescaling factors. The ToA spectral radiance is the
energy or radiation which has reflected from an object or a surface in addition to
25
radiation that has bounced back from clouds and neighbouring pixels as well. The
ToA spectral radiance can be determined by the equation 5 above.

22. 22
Also, ToA spectral reflectance, which is reflectance of whole Atmosphere-Earth
interaction can be determined based on equation 6 above.
Further, the brightness temperature is determined. The brightness temperature
can be defined as the temperature of a blackbody in a given spectral band, that
would produce same amount of radiation as a target object surface. For thermal
5
bands of the satellite, it can be determined based on the equation 7 above.
The surface emissivity value (εs) is the ability of the natural material to emit in
comparison to a blackbody at the equivalent thermodynamic temperature. εs
from the satellite can be retrieved using a semi-empirical, normalized difference
vegetation index (NDVI) based emissivity method (NBEM), as generally known in
10
the art, as given in equation 8 above. εs indicates the emissivity emanating from
different land surfaces which might be composed of heterogenous materials.
Therefore, it depends upon the structure of soil, its composition, amount of soil
moisture and organic content as well as green-cover characteristics. NDVI is a
standardized biophysical parameter which is used to quantify the health and
15
amount of vegetation growth in an area. NDVI is estimated using the near
infrared and visible red wavelengths that are reflected by the biomass or
vegetation. Value of NDVI lies between -1 to +1, with low NDVI values assigned
to water, barren land and built up and high positive values attributed to healthy
green vegetation.
20
Further, a fraction of vegetation cover (FVC or Pv) is determined based on
equation 9 above.
Also, due to heterogeneity of the land surface, different types of land surface
materials have varying values of NDVI. Therefore, the NDVI threshold values are
based on the LSE model, provided in equations 10, 11, 12, and 13 above.
25

23. 23
Further, using the determined 𝜀𝑖 values, a combined raster image is created
using conditional formatting, and one raster image having final surface emissivity
values εs as explained above in this disclosure.
Further, the land surface temperature is based on the equation 13 above.
Further, in an implementation, a pixel wise soil heat flux (G), which is the rate of
5
heat storage into the soil and vegetation due to conduction, is determined based
on the surface albedo value (as), the land surface temperature value (Ts),
normalized difference vegetation index (NDVI), and the net radiation value (Rn)
values as given in the equation 14 above in this disclosure.
In an implementation, the sensible heat flux (H) is determined using the air
10
density value (𝜌), a specific heat of air value (Cp), the average air temperature
value (Ta), the land surface temperature value (Ts), and an aerodynamic
resistance value (Rah) as given in the equation 6 above in this disclosure.
Pertinently, in equation 15, the determination of the temperature difference (Ts -
Ta) is based on the average air temperature (Ta), assuming that the air
15
temperature for the pixel-wise calculation remains constant. Pertinently, the air
temperature at each pixel is unknown but is an important parameter in
determination of H. The air temperature (Ta) at each pixel of image, i.e. at each
geographical location might vary. This may lead to varying temperature
difference between the land surface temperature Ts) and air temperature (Ta).
20
Thus, in order to find this temperature difference, the values of average air
temperature from the available monitoring locations is used. This determination
of the temperature difference (Ts - Ta) based on the average air temperature
(Ta) improves the accuracy of determination of the value of the sensible heat flux
(H) and also provides a less time consuming way of determining ET as compared
25
to the method of using hot and cold pixel, as generally known in the art.

24. 24
Moreover, aerodynamic resistance calculated for the SEBAL algorithm uses
iterative process according to the Monin-Obukhov Stability theory as generally
known in the art. This also contributes in making overall determination of ET
simple and less time taking and also in providing accurate results. The
aerodynamic resistance is determined as given in the equation 16 above.
5
In an implementation, the processing unit [104] receive, from the memory unit
[106], the average air temperature value (Ta), the atmospheric emissivity value
(𝜀a), the air density value (𝜌), and the specific heat of air value (Cp).
Also, the atmospheric emissivity value (𝜀a) is determined using an actual vapor
pressure value (ea), as given in the equation 17 above in this disclosure.
10
Also, the net radiation value (Rn), the sensible heat flux value (H), and the pixel
wise soil heat flux (G) are instantaneous values at the satellite’s transit point-in-
time. Therefore, the latent heat flux values are also instantaneous, and referred
to as the instantaneous evaporation value (λETi). The subsequent latent heat flux
values are acquired from satellite images. The instantaneous evaporation values
15
at the moment of satellite transit are obtained (in millimeter per day) as given in
the equation 18 above in this disclosure.
At step 212, the method comprises determining, by the processing unit [104], an
instantaneous evaporative fraction value (Ʌ) based on the net radiation value
(Rn), the soil heat flux value (G), and the instantaneous evaporation value (λETi),
20
as given in the equation 19 above in this disclosure.
Further, at step 214, the method comprises determining, by the processing unit
[104], the evapotranspiration value of crops for a pre-determined time period
value. For example, the pre-determined time period value can be 24 hours. For
the pre-determined time period value of 24 hours, the evapotranspiration value
25
of crops (ET24) is based on an averaged net radiation value (Rn24) for the same

25. 25
pre-determined time period value, i.e., 24 hours, and a latent heat of
vaporization value (λ). For a pre-determined time period value of 24 hours, i.e.,
at daily time scales, ET24 (in millimetre per day) can be determined as given in the
equation 20 above in this disclosure.
Thus, the present invention provides a novel solution for automatically
5
determining evapotranspiration of crops that is technically advanced over the
currently known solutions. By implementing the features as disclosed herein, one
can accurately determine ET during unavailability of satellite data under certain
weather conditions. Further, the features of the present disclosure also enable
one to obtain a method for automatically determining evapotranspiration of
10
crops that is more accurate and less time consuming.
While considerable emphasis has been placed herein on the preferred
embodiments, it will be appreciated that many embodiments can be made and
that many changes can be made in the preferred embodiments without
departing from the principles of the invention. These and other changes in the
15
preferred embodiments of the invention will be apparent to those skilled in the
art from the disclosure herein, whereby it is to be distinctly understood that the
foregoing descriptive matter to be implemented merely as illustrative of the
invention and not as limitation.
20

26. 26
WE CLAIM:
1. A method for automatically determining evapotranspiration value of a
desired crop, the method comprising:
receiving, by a processing unit [104] from a database [102], a set of
images comprising one or more images, and a set of data;
5
pre-processing, by the processing unit [104], the set of data;
generating, by the processing unit [104], a surface energy balance
algorithm for land (SEBAL) model for the desired crop;
determining, by the processing unit [104], a net radiation value (Rn), a soil
heat flux value (G), a sensible heat flux value (H), and an instantaneous
10
evaporation value (λET
i
) for one or more pixels of the one or more images
of the set of images,
wherein the net radiation value (Rn), the soil heat flux value (G),
and the sensible heat flux value (H) are based on the SEBAL model for
the desired crop;
15
determining, by the processing unit [104], an instantaneous evaporative
fraction value (Ʌ) based on the net radiation value (Rn), the soil heat flux
value (G), and the instantaneous evaporation value (λET
i
);
determining, by the processing unit [104], the evapotranspiration value of
crops for a pre-determined time period value, wherein the
20
evapotranspiration value of crops for the pre-determined time period
value, is based on an averaged net radiation value for the pre-determined
time period value, and a latent heat of vaporization value (λ).
25

27. 27
2. The method as claimed in claim 1, the method comprising:
receiving, by the processing unit [104] from a memory unit [106], a
surface albedo value (as), an incoming shortwave radiation value (Rs), a
land surface temperature value (Ts), a surface emissivity value (𝜀s), an
average air temperature value (Ta), an atmospheric emissivity value (𝜀a),
5
an air density value (𝜌), and a specific heat of air value (Cp).
3. The method as claimed in claim 2, wherein the surface albedo value is
further based on a weighted albedo value (α toa), an incoming shortwave
radiation flux value (α𝑝𝑎𝑡ℎ−𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒), and an atmospheric transmissivity
10
value (τ
sw
).
4. The method as claimed in claim 3, wherein the weighted albedo value
(αtoa) is further based on a set of one or more top of atmosphere
reflectance values.
15
5. The method as claimed in claim 3, wherein the atmospheric transmissivity
value (τ
sw
) is further based on an elevation above mean sea level value (z).
6. The method as claimed in claim 3, wherein the incoming shortwave
20
radiation value (Rs) is further based on a sun elevation angle value (𝛽), an
inverse squared relative earth-sun distance value (dr), and the
atmospheric transmissivity value (τ
sw
).

28. 28
7. The method as claimed in claim 2, wherein the atmospheric emissivity
value (𝜀a) is further based on an actual vapor pressure value (ea).
8. The method as claimed in claim 2, wherein the sensible heat flux value
(H) is based on the air density value (𝜌), a specific heat of air value (Cp),
5
the average air temperature value (Ta), the land surface temperature
value (Ts), and an aerodynamic resistance value (Rah).
9. The method as claimed in claim 1, wherein the soil heat flux value (G) is
based on the surface albedo value (α s), the land surface temperature
10
value (Ts), a normalized difference vegetation index (NDVI) and the net
radiation value (Rn).
10. A system for automatically determining evapotranspiration value of a
desired crop, the system comprising:
15
a processing unit [104] configured to:
receive, from a database [102], a set of images comprising one or
more images, and a set of data;
pre-process the set of data;
generate a surface energy balance algorithm for land (SEBAL)
20
model for the desired crop;
determine a net radiation value (Rn), a soil heat flux value (G), a
sensible heat flux value (H), and an instantaneous evaporation
value (λET
i
) for one or more pixels of the one or more images of
the set of images,
25

29. 29
wherein the net radiation value (Rn), the soil heat flux
value (G), and the sensible heat flux value (H) are based on the
SEBAL model for the desired crop;
determine an instantaneous evaporative fraction value (Ʌ) based
on the net radiation value (Rn), the soil heat flux value (G), and the
5
instantaneous evaporation value (λET
i
);
determine the evapotranspiration value of crops for a pre-
determined time period value, wherein the evapotranspiration
value of crops for the pre-determined time period value, is based
on an averaged net radiation value for the pre-determined time
10
period value, and a latent heat of vaporization value (λ).
11. The system as claimed in claim 10, wherein the processing unit [104] is
configured to:
15
receive, from a memory unit [106], a surface albedo value (as), an
incoming shortwave radiation value (Rs), a land surface
temperature value (Ts), a surface emissivity value (𝜀s), an average
air temperature value (Ta), an atmospheric emissivity value (𝜀a), an
air density value (𝜌), and a specific heat of air value (Cp).
20
12. The system as claimed in claim 11, wherein the surface albedo value (α s)
is further based on a weighted albedo value (α toa), an incoming
shortwave radiation flux value (α𝑝𝑎𝑡ℎ−𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒), and an atmospheric
transmissivity value (τ
sw
).
25

30. 30
13. The system as claimed in claim 12, wherein the weighted albedo value
(atoa) is further based on a set of one or more top of atmosphere
reflectance values.
5
14. The system as claimed in claim 12, wherein the atmospheric
transmissivity value (τ
sw
) is further based on an elevation above mean sea
level value (z).
15. The system as claimed in claim 12, wherein the incoming shortwave
10
radiation value (Rs) is further based on a sun elevation angle value (𝛽), an
inverse squared relative earth-sun distance value (dr), and the
atmospheric transmissivity value (τ
sw
).
16. The system as claimed in claim 11, wherein the atmospheric emissivity
15
value (𝜀a) is further based on an actual vapor pressure value (ea).
17. The system as claimed in claim 11, wherein the sensible heat flux value
(H) is based on the air density value (𝜌), a specific heat of air value (Cp),
the average air temperature value (Ta), the land surface temperature
20
value (Ts), and an aerodynamic resistance value (Rah).
18. The system as claimed in claim 10, wherein the soil heat flux value (G) is
based on the surface albedo value (as), the land surface temperature

31. 31
value (Ts), a normalized difference vegetation index (NDVI) and the net
radiation value (Rn).
Dated this 24th day of April, 2023
(GARIMA SAHNEY)
IN/PA-1826
AGENT FOR THE APPLICANT[S]

32. 32
ABSTRACT
METHOD AND SYSTEM FOR AUTOMATICALLY DETERMINING
EVAPOTRANSPIRATION OF CROPS
The present disclosure relates to a method and system for automatically
determining evapotranspiration value of a desired crop. The method comprises:
5
receiving, by a processing unit [104] from a database [102], a set of images and a
set of data; pre-processing, by the processing unit, the set of data; analyzing, by
the processing unit [104], one or more pixels in images of the set of images;
generating, by the processing unit, surface energy balance algorithm for land
(SEBAL) model for desired crop; determining, by the processing unit, a net
10
radiation value (Rn), soil heat flux value (G), sensible heat flux value (H), and
instantaneous evaporation value (λETi); determining, by the processing unit,
instantaneous evaporative fraction value (Ʌ) based on net radiation value (Rn),
soil heat flux value (G), and instantaneous evaporation value (λETi); determining,
by the processing unit, evapotranspiration value of crops for a pre-determined
15
time period value.
[FIG. 2]

33. APPLICANT: ARMS 4 AI Private Limited NO. OF SHEETS: 2
INDIAN PATENT APPLICATION NO.: _____________ SHEET NO. 1/2
(GARIMA SAHNEY)
IN/PA-1826
AGENT FOR THE APPLICANT(S)
33

34. APPLICANT: ARMS 4 AI Private Limited NO. OF SHEETS: 2
INDIAN PATENT APPLICATION NO.: _____________ SHEET NO. 2/2
(GARIMA SAHNEY)
IN/PA-1826
AGENT FOR THE APPLICANT(S)
34