Review

Geospatial Technology for Sustainable Agricultural Water

Management in India—A Systematic Review
Suryakant Bajirao Tarate 1 , N. R. Patel 2 , Abhishek Danodia 2 , Shweta Pokhariyal 2,3
and Bikash Ranjan Parida 4, *

1 ICAR—National Research Centre for Makhana, Darbhanga 846005, Bihar, India; suryakant.tarate@icar.gov.in
2 Indian Institute of Remote Sensing, Indian Space Research Organization, Government of India, 4, Kalidas
Road, Dehradun 248001, Uttarakhand, India; nrpatel@iirs.gov.in (N.R.P.); abhidanodia@iirs.gov.in (A.D.);
shwetap@iirs.gov.in (S.P.)
3 Department of Agrometeorology, Govind Ballabh Pant University of Agriculture & Technology,
Pantnagar 263145, Uttarakhand, India
4 Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand,
Ranchi 835222, Jharkhand, India
* Correspondence: bikash.parida@cuj.ac.in

Abstract: Effective management of water resources is crucial for sustainable development in any
region. When considering computer-aided analysis for resource management, geospatial technology,
i.e., the use of remote sensing (RS) combined with Geographic Information Systems (GIS) proves to
be highly valuable. Geospatial technology is more cost-effective and requires less labor compared
to ground-based surveys, making it highly suitable for a wide range of agricultural applications.
Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a
crucial challenge in the field of water resource management. Satellite-based RS measurements offer
consistent information on agricultural and hydrological conditions across extensive land areas. In this
study, we carried out a detailed analysis focused on addressing agricultural water management issues
in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting
Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed
published research articles, providing a comprehensive and detailed analysis. This study aims to
Citation: Tarate, S.B.; Patel, N.R.; explore the use of RS and GIS technologies in crucial agricultural water management practices
Danodia, A.; Pokhariyal, S.; Parida,
with the goal of enhancing their effectiveness and efficiency. This study primarily examines the
B.R. Geospatial Technology for
current use of geospatial technology in Indian agricultural water management and sustainability. We
Sustainable Agricultural Water
revealed that considerable research has primarily used multispectral Landsat series data. Cutting-
Management in India—A Systematic
edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology
Review. Geomatics 2024, 4, 91–123.
https://doi.org/10.3390/
have not been fully investigated for the assessment and monitoring of water resources. Integrating
geomatics4020006 RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for
effective management.
Academic Editor: Frédéric Frappart

Received: 29 January 2024 Keywords: agricultural water management; remote sensing; Geographic Information Systems; India;
Revised: 17 March 2024 climate change
Accepted: 20 March 2024
Published: 22 March 2024

1. Introduction
Copyright: © 2024 by the authors.
Water, a crucial natural resource, is facing growing strain globally. This is attributed
Licensee MDPI, Basel, Switzerland. to various factors, such as rising populations; excessive agricultural irrigation leading to
This article is an open access article overuse and salinization; population growth in arid regions with limited water supply;
distributed under the terms and very high pollution from urban areas, agriculture, and industry; increased human and
conditions of the Creative Commons industrial demand; and impacts of climate change. Moreover, surface water resources
Attribution (CC BY) license (https:// suffer from irregularity, scarcity, and unequal distribution [1]. In order to tackle the
creativecommons.org/licenses/by/ expected impacts of climate change on water and agriculture, especially while fulfilling
4.0/). diverse and competitive water needs, it is essential to implement smart water management

Geomatics 2024, 4, 91–123. https://doi.org/10.3390/geomatics4020006 https://www.mdpi.com/journal/geomatics

Geomatics 2024, 4 92

approaches [2]. By relying on scientific evidence, agricultural water management can

play a vital role in minimizing unsustainable water usage. It aids in improving water
resilience and facilitates adaptation to climate change challenges [3]. Crop water stress
disrupts essential physiological processes, emphasizing the importance of efficient water
management to maintain equilibrium in agronomy, hydrology, and climatology. This is
especially critical in areas where irrigation is indispensable for achieving the desired crop
quality and yield. To enhance irrigation management and scheduling, it is essential to
have a precise understanding of the quantity and timing of the water supply, which can
be assessed through an accurate spatial evaluation of plant water stress [4,5]. Efficient
agricultural water management practices are essential to expand irrigation coverage in
India. Effectively managing water resources poses a significant challenge for countries
like India. Developing water resources necessitates tackling vital aspects such as storage,
conservation, and subsequent utilization [6].
Mismanagement of the water supply and other natural resources makes drought, a
serious natural calamity, much worse [7]. However, rainfall patterns vary significantly in
terms of both location and timing across the country. Intense, concentrated rainfall in a short
period leads to devastating floods, while delayed and sparse rainfall results in drought
conditions [8]. Considering the potential adverse effects of global climate change on water
resources, the risks to food security, human resource employment, and power security are
increasingly pronounced [9,10]. Therefore, it is imperative to assess future water availability
at various spatial and temporal scales to effectively address these challenges. Agricultural
drought significantly impacts the economies of agrarian nations such as India, where
over 68% of the population relies on agriculture [11]. Approximately 16% of India’s total
land area is prone to drought, affecting around 50 million people annually [12,13]. An
effectively developed mitigation and preparedness strategy is essential for decision makers
to mitigate the impact of drought. Therefore, monitoring the onset, duration, intensity,
and extent of drought has become crucial in managing its adverse effects on agricultural
production [14,15]. India experienced devastating famines due to droughts in the last
century [7]. Additionally, the unsustainable depletion of groundwater, crucial for irrigation,
is anticipated to worsen agricultural challenges amid climate change, severely disrupting
routine farming activities [16].
The crop evapotranspiration (ET) phenomenon is pivotal in the exchange of energy
among crops, soil, water, and the atmosphere, and is crucial in studies related to energy
exchange and water resource management [17,18]. Traditional methods have limitations
in offering a wide-ranging spatial distribution of ET across large areas. However, ad-
vancements in satellite remote sensing techniques based on energy balance algorithms
have enhanced the ability to map ET at a finer scale [19]. Efficient irrigation scheduling
is crucial in the cultivation of different crops to prevent water wastage. In the past, dif-
ferent empirical equations and lysimeters were used to estimate crop ET. However, these
methods were limited as they were point-based and could not be applied at a regional
scale. To conserve water, it is essential to adopt new technologies for accurate monitoring
of irrigation needs across large areas. Advanced geospatial techniques offer a solution by
enabling regional estimation of crop water requirements in a shorter time, overcoming the
limitations of traditional methods [9]. This helps farmers optimize irrigation schedules,
preventing both overwatering and water scarcity, which directly impacts crop yield and
quality. Runoff estimation in agricultural water management is vital for efficient water
use. Understanding runoff patterns aids in erosion control, preserving soil fertility and
structure. Proper estimation supports strategic crop selection, ensuring that farmers choose
crops suited to local water availability, leading to sustainable agricultural practices [20].
Additionally, runoff estimation assists in managing water resources effectively, enabling
the construction of reservoirs and facilitating the overall planning of irrigation systems.
The use of satellite imageries to map natural resources, such as water bodies, has become
increasingly significant. Water bodies are subject to intense utilization, necessitating reg-
ular monitoring for sustainable management. Identifying and mapping water bodies is
Geomatics 2024, 4 93

essential for various purposes, including accurate surface water estimation and ensuring
their sustainable use [21].
Water scarcity and climate change intensify the vulnerability of rainfed agriculture,
impacting food production [22]. Soil moisture deficit significantly impacts agricultural pro-
ductivity and hydrological processes [23]. To tackle these challenges, rainwater harvesting
emerges as a vital solution. Food security requires a consistent supply of water, especially
in areas with high population density. This can be achieved by capturing rainwater to
combine surface and groundwater [24,25]. Rainwater harvesting not only stabilizes agri-
cultural output but also enhances productivity and aids in restoring degraded lands. In
India, both agricultural and domestic sectors are increasingly dependent on groundwater,
leading to the depletion of this vital resource [26–28]. Rainwater harvesting stands out
as a premier solution for enhancing both surface and groundwater resources [29–31]. In
addition to the different traditional methods, geospatial technologies like RS and GIS have
recently become important resources for acquiring spatio-temporal meteorological and
crop status information [4,32]. RS data significantly enhance monitoring efforts by offering
timely, comprehensive, cost-effective, and repetitive insights into the Earth’s surface. The
acquisition of precise spatio-temporal meteorological and crop data is indispensable for
precise analysis, forecasting, and agricultural planning. It plays a vital role in making
informed decisions concerning irrigation scheduling, crop stress management, disaster
readiness, and the preservation of natural resources and ecosystems in diverse regions [5].
The overarching objective of sustainable agriculture is to achieve a harmonious balance
between available land resources and crop requirements, with a strong emphasis on opti-
mizing resource usage to ensure sustained productivity over an extended period. Although
traditional methods of gathering weather and crop growth data are reliable, they come with
the drawback of being labor-intensive and time-consuming [23]. In such circumstances,
geospatial technology, specifically RS and GIS, proves highly effective for gathering and
managing extensive spatio-temporal data through satellite data, digital maps, and sim-
ulation models [9]. Because of its ability to provide data quickly and repeatedly, this
technology has many benefits. It facilitates speedy analysis and the creation of useful
information for planners and decision makers [33].
This study investigates innovative methods for identifying agricultural water manage-
ment challenges to provide a precise assessment of the research background. It explores
existing remote sensing datasets, methodological approaches, and GIS applications. The
systematic literature review, conducted following Preferred Reporting Items for Systematic
Reviews and Meta-Analysis (PRISMA) guidelines, ensures a thorough overview of the
subject matter.

2. Research Method and Literature Search

Systematic Literature Review
In order to comply with the PRISMA criteria, we used a rigorous systematic literature
review technique in this review paper. Our primary objective was to explore research
studies pertaining to the integration of RS and GIS technologies in the context of agricultural
water management. The PRISMA framework consists of four distinct stages—identification,
screening, eligibility, and inclusion [5]. Three research questions are the primary source
of this review article: (1) What specific agricultural water management challenges in
India are addressed through geospatial technology? (2) Which primary RS datasets and
tools are utilized to analyze issues related to agricultural water management? (3) What
is the progress and future scope of geospatial technology to manage agricultural water
management in India? We conducted a comprehensive literature search by exploring
different databases, like Web of Science, Science Direct, MDPI, Springer, Scopus, and Google
Scholar. Our search focused on specific keywords like “evapotranspiration estimation”,
“water productivity estimation”, “drought management”, “runoff estimation”, “water
resource mapping”, “waterlogged areas mapping”, “rainwater harvesting”, “soil moisture
estimation”. We specifically identified these keywords in the titles, abstracts, and keywords
cs 2024, 4 Geomatics 2024, 4 94 94

Geomatics 2024, 4 “water productivity

“waterestimation”,
productivity“drought
estimation”,management”, “runoff estimation”,
“drought management”, “runoff“water re-
estimation”, “water re-94
source mapping”, “waterlogged areas mapping”, “rainwater harvesting”,
source mapping”, “waterlogged areas mapping”, “rainwater harvesting”, “soil moisture“soil moisture
estimation”. We specificallyWe
estimation”. identified
specificallythese keywords
identified in the
these titles, abstracts,
keywords andabstracts,
in the titles, key- and key-
words of the articles.
words ofFurthermore,
the articles. our focus
Furthermore, was specifically
our focus wason research
specifically articles
on pub-
research articles pub-
of the articles. Furthermore, our focus was specifically on research articles published
lished in the English
lished language concerning India. As a result, we excluded papers pub-
in the in the English
English language language
concerningconcerning
India. India. As a result,
As a result, we excluded
we excluded paperspapers pub-
published
lished in languages
lished other
in languages than
in languages
otherEnglish,
than review
otherEnglish, articles,
than English,
review preprints,
review chapters,
articles,
articles, and chapters,
preprints,
preprints, master’s
chapters, and master’s
and master’s and
and doctorate and dissertations/theses
doctorate
doctorate from our analysis.
dissertations/theses
dissertations/theses ourIn
fromfrom adherence
our analysis.
analysis. to
Inthe PRISMA
adherence
In adherence toguide-
to the the PRISMA
PRISMA guide-
guidelines,
lines, a total oflines,
a 60 research
totalaoftotal articles
of were identified
60 research
60 research articles articles andidentified
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[5,34–36].
review studies [5,34–36].

Figure 1. Flowchart of methodologyof

Figure adopted for selection of articles for review considering PRISMA
guidelines. Figure 1.1.Flowchart
Flowchart methodology
of methodologyadopted for selection
adopted of articles
for selection for review
of articles forconsidering PRISMA
review considering
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PRISMA guidelines.
The notable increase
The in published research concerning theconcerning
utilization of geospatial
The notable
notable increase
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research concerning the the utilization
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agricultural water in India
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tinct level of expertise
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distinct level of expertise in this domain. As depicted in Figure 2, this trend emphasizes
nificant advancements
nificant in geospatial technology,
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significant advancements in geospatial technology, including the accessibility of high-
lution satellitelution
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depth of knowledge and technological progress within the country. country.

Figure 2. Number of publications with keywords “Geospatial technology”, “Agricultural water

management” and “India” from the year 2010 to 2022 as per Google Scholar search results. The red
dots are the data points over the years.
Geomatics 2024, 4 95

Different remote sensing-based indices used based on stress in plants, soil moisture,
evaporation, precipitation, temperature, and water bodies are presented in Table 1. The
data and products identified in this review useful for agricultural water management are
presented in Table 2.

Table 1. List of the different remote sensing-based indices used in this review based on stress in
plants, soil moisture, evaporation, precipitation, temperature, and water bodies.

Abbreviation Meaning
Plants
NDVI Normalized Difference Vegetation Index
VCI Vegetation Condition Index
VHI Vegetation Health Index
CDI Composite Drought Index
NDMI Normalized Difference Moisture Index
Soil moisture
SMI Soil Moisture Index
SWDI Soil Water Deficit Index
SMDI Soil Moisture Deficit Index
LSWI Land Surface Water Index
SASI Shortwave Angle Slope Index
VSWI Vegetation Supply Water Index
NVSWI Normalized Vegetation Supply Water Index
Evaporation
ET Evapotranspiration
ESI Evaporative Stress Index
Precipitation
SPI Standardized Precipitation Index
Temperature
LST Land Surface Temperature
TCI Temperature Condition Index
Water bodies
WRI Water Ratio Index
NDWI Normalized Water Difference Index
MNDWI Modified Normalized Water Difference Index
SDI Streamflow Drought Index
NDDI Normalized Difference Drought Index

Table 2. List of the different remote sensing-based sensors and products identified in this review for
agricultural water management.

Abbreviation Meaning
MODIS Moderate Resolution Imaging Spectroradiometer
PCA Principal component analysis
GRACE Gravity Recovery and Climate Experiment
GLDAS Global Land Data Assimilation System
SMAP Soil Moisture Active Passive
INSAT Indian National Satellite
IRS Indian Remote Sensing
TRMM Tropical Rainfall Measuring Mission
TIRS Thermal Infrared Sensor
OLI Operational Land Imager
TM Thematic Mapper
MSS Multi-Spectral Sensor
ETM Enhanced Thematic Mapper
AWiFS Advanced Wide Field Sensor
SAR synthetic aperture radar
Geomatics 2024, 4 96

Table 2. Cont.

Abbreviation Meaning
GLEAM Global Land Evaporation Amsterdam Model
IMD India Meteorological Department
LISS Linear Imaging and Self Scanning sensors
ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer
SRTM Shuttle Radar Topography Mission
DEM Digital Elevation Model
AVHRR Advanced Very High-Resolution Radiometer
NOAA National Oceanic and Atmospheric Administration
USGS United States Geological Survey
SOI Survey of India

3. Results
The selected studies were classified into different categories, namely, evapotranspi-
ration (ET), irrigation water requirement, and water productivity estimation; drought
assessment and monitoring; runoff estimation from agriculture watersheds; water body
and waterlogged area mapping; identification of suitable sites for groundwater recharge
and rainwater harvesting; and soil moisture estimation.

3.1. Evapotranspiration (ET), Irrigation Water Requirement, and Water Productivity Estimation
In both irrigated and rain-fed agriculture, determining when and how much water to
supply, as well as finding the optimal sowing time based on soil moisture and precipitation,
is crucial. Estimating irrigation water demand primarily relies on ET procedures. Besides
precipitation, ET is a vital component of the hydrological budget. Ground-based methods
like lysimeters, eddy covariance, and the Bowen ratio are employed to measure actual
ET (AET) with high temporal resolution at specific points. However, extending these
methods to obtain spatial AET distribution at a basin scale is challenging and costly in
terms of installation and maintenance. Satellite imagery, on the other hand, provides
essential data for estimating spatial AET distribution at fine resolution. This is achieved
through satellite-based physical, empirical, and semi-empirical models, spanning from
basin to global scales [37]. To address the worldwide issue of water scarcity, the crop
water footprint (WF) has become a crucial tool. It enables policymakers to analyze water
usage effectively, encouraging justified and sustainable water use. Policymakers can more
effectively plan, manage, and conserve water resources by having a better understanding
of how surface and groundwater resources are used throughout the industrial process [38].
Different geospatial technology-based studies identified in this review for ET, irrigation
water requirement, and water productivity estimation in India are presented in Table 3.

Table 3. Geospatial technology-based studies for estimation of ET, irrigation water requirement and
water productivity.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
SEBAL-based actual ET
RS-based surface
Landsat-7, -8 data, can serve as a valuable
energy balance
Climatic data from tool for irrigation
Junagadh, algorithm for land
1 Junagadh 2014 scheduling within canal [39]
Gujarat State (SEBAL) algorithm
Agricultural irrigation commands,
was used to
University enhancing water
estimate crop ET
use efficiency
Geomatics 2024, 4 97

Table 3. Cont.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
The crop
coefficient-based
Evaluated the
approach proves
applicability of the
beneficial for specific
Kangsabati simplified surface
points when sufficient
reservoir SRTM DEM, energy balance
data is accessible.
2 command in Landsat-8, IMD 2015 index [40]
Conversely, the S-SEBI
West Bengal weather data (S-SEBI) method for
method is applicable in
State determining
regions with limited
spatially distributed
data, enabling the
daily ET
estimation of spatially
distributed ET
Utilized satellite Crop water requirement
Sentinel-2
RS-based maps generated through
multispectral data,
vegetation index to multispectral vegetation
Panchmahal Climate data from
assess the crop indices from RS are
3 district of main maize 2020–2021 [18]
acreage and crop valuable tools for
Gujarat research station of
water requirements evaluating crop water
Anand Agricultural
of the predominant usage at both regional
University, Gujarat
maize crop and field levels
This approach enables
Tarafeni South Crop ET was precise irrigation by
Main Canal estimated on the matching water supply
(TSMC) Landsat-5 TM data, basis of NDVI. Kc with crop demand,
4 2011 [41]
irrigation SOI toposheets maps were conserving water in late
command area prepared by using growth stages and
of West Bengal NDVI enhancing canal
system efficiency
The study incorporates
Estimated the an innovative approach
spatial distribution to validate the
of daily ET effectiveness of this
Indian Landsat-8 by using the method in water
Sundarban OLI, SRTM (Mapping Evapo conservation. It also
5 2020 [42]
Biosphere DEM, MODIS Transpiration at utilizes satellite-based
Reserve ET data high resolution technology, providing
with internalized efficient tools for
Calibration integrated
(METRIC) model evapotranspiration
estimation
Kondamallepally Estimation of ET The obtained ET data
Mandal, using simplified have value for diverse
6 Nalgonda Landsat-8 data 2020 surface energy applications, including [43]
district of balance the evaluation of
Telangana State (SSEB) model water productivity
Geomatics 2024, 4 98

Table 3. Cont.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
ET was estimated
using the
The S-SEBI model
simplified SEBI
accurately maps ET with
Agricultural model and then
high precision across
7 farm, Landsat-8,9 data 2021–2023 then compared to [19]
pixels, making it perfect
New Delhi eddy covariance
for integrating into
measurements over
irrigation scheduling
a semi-arid
agricultural farm
SRTM DEM, FAO Assessed water Awareness about the
soil map, rainfall footprints like blue water footprint provides
Upper Baitarani and weather data water flow, green a clear and
River Basin, from IMD, water flow, and multidisciplinary
8 1991–2011 [6]
Odisha State streamflow data green water storage framework for
of India from Central Water spatio-temporally evaluating and
Commission using the enhancing water
(CWC) of India SWAT model policy decisions
Quantified the
This comparative analysis
green, blue, and
would assist
Banjar River grey water
policymakers and
watershed, footprints of crops
relevant government
9 Mandla district Global weather data 2000–2013 cultivated in the [44]
agencies in maximizing
of Madhya study area for
crop yields by effectively
Pradesh comparative
utilizing both surface and
analysis with
groundwater resources
other studies
The paddy yield
and water footprint
The AquaCrop model
were quantified
Soil, irrigation, and precisely forecasted rice
Manipur State under varying
10 weather data from 2011–2020 yield and water footprint [45]
of India rainfall conditions
different sources under different
utilizing the
rainfall conditions
AquaCrop
GIS software
The crop water
requirement
assessment The CropWRA model
(CropWRA) model proves a valuable tool for
was developed as a promoting sustainable
Bansloi River
Landsat-8 OLI, valuable tool for water resource
basin on
Weather data from evaluating the management, facilitating
11 eastern edge of 2018–2019 [46]
IMD and World satisfied degree of the development of
the Chota
weather online data crop water irrigation infrastructure
Nagpur Plateau
requirements and integrating various
considering crop, modern technologies for
hydrological, agricultural advancement
climate, and
DEM data
Geomatics 2024, 4 99

Table 3. Cont.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
This research serves as a
Evaluation of the
foundation for
water and carbon
Konkan region optimizing water usage
Weather and footprint of onion
12 of Maharashtra 2015–2016 efficiency and reducing [47]
crop data crops cultivated
State of India the carbon and water
under varied
footprint linked to
irrigation conditions
onion cultivation
Satellite data was
used to classify
major crops using a
Narayanpur This finding suggests
supervised
command area that, during the Kharif
Sentinel-2 MSI data, algorithm. SEBAL
of Gulbarga season, crops receive
13 climate data 2018–2019 was used to [17]
and Raichur sufficient irrigation
from IMD determine crop ET.
districts of compared to the Rabi
Assessed the
Karnataka season in the study area
irrigation
performance in
canal command area

3.2. Drought Assessment and Monitoring

Drought is a natural, recurring aspect of the climate, exhibiting varying characteristics
and impacts across regions. It is a climatic anomaly marked by insufficient moisture due to
factors like low or erratic rainfall and increased water demand. When insufficient rainfall
and soil moisture impede timely cultural practices and healthy crop growth during the
growing season, an agricultural drought occurs. Drought directly affects the crop area,
production, and farm jobs. Insufficient sowing, delayed planting, and poor crop growth due
to lack of soil moisture result in decreased yields, significantly impacting livelihoods [48].
Around 53% of India’s agriculture depends on rainfall, making droughts a major issue
for the country’s rain-reliant farmers and causing severe water crises [49]. Drought is
anticipated to worsen due to predicted climate change, leading to an expansion in drought-
affected areas. This escalation could significantly and adversely impact agriculture [50].
The most dependable method for addressing drought-related issues at the local and global
levels is clearly monitoring the drought. By ensuring long-term gains in agricultural output,
this approach improves livelihoods [51]. Reducing the global risk of drought, particularly in
arid and semi-arid areas, requires assessment and monitoring. These are vital for effective
management of natural resources and agriculture [52]. The constraints of conventional
drought monitoring indices complicate the assessment and monitoring of agricultural
drought. RS-based indices have given rise to a novel method for assessing and keeping
track of agricultural droughts [53]. To effectively execute methods for managing water
resources, it is imperative that scientific research should be conducted to determine the
severity of the drought [54]. Different geospatial technology-based studies identified in
this review for drought assessment and monitoring in India are presented in Table 4.
Geomatics 2024, 4 100

Table 4. Geospatial technology-based studies for drought assessment and monitoring in India.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
A comprehensive
SPI, SDI, and VCI
Terra MODIS (500-m approach, integrating
were combined to
Marathwada region resolution) data, multiple indicators, is
1 1980–2014 prepare the [55]
of Maharashtra State precipitation, and essential for a more
composite drought
streamflow data precise assessment of
index using PCA
drought conditions
Terrestrial water Constructed an
storage and integrated drought
groundwater storage index that
anomalies from amalgamates the Integrated drought
GRACE satellite, indicators of indices can be utilized
daily gridded meteorological, effectively to monitor
Sabarmati and
2 precipitation, 1951–2017 hydrological, and and assess droughts in
Brahmani River basin [11]
maximum and agricultural India, both in the present
minimum droughts, and future
temperatures, incorporating climate scenarios
MODIS considerations for
evapotranspiration, groundwater
and NDVI storage
Compared the
SMAP and GLDAS Both SWDI and SMDI
GLDAS and SMAP
soil moisture time demonstrate proficiency
enhanced Level-3
3 Godavari River basin 2015–2020 series with the in discerning the spatial [56]
surface and ERA5
ERA5 soil moisture distributions of dry and
soil moisture product
product for the wet conditions
study period
A fractional wetness
MODIS data were
approach developed
SASI images derived used for the
using MODIS data is
using MODIS determination of
4 Entire India 2001–2012 capable of forewarning [57]
surface- fractional wetness
of early season
reflectance data using NDVI
agricultural
and SASI
drought condition
A combined deficit index
serves as a valuable
A combined deficit
NDVI from INSAT indicator for evaluating
index developed
3A, rainfall product late-season regional
Gujarat, Maharashtra, from antecedent
5 from KALPANA-1, 2009–2013 agricultural drought by [58]
and Karnataka rainfall deficit and
MODIS LST, and capturing the lagged
deficit in monthly
ET data relationship between
vegetation vigor
water supply and
crop vigor
Terra-derived
Compared
MODIS-based A combined approach
satellite-derived
surface reflectance using multiple indices
indices like NDWI,
6 Tamil Nadu and LST data, 2000–2013 can effectively serve as a [59]
NMDI, and NDDI
GLDAS–NOAH land proxy for identifying
with in-situ rainfall
surface data, and vegetation stress
and SPI data
TRMM rainfall data
Geomatics 2024, 4 101

Table 4. Cont.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
Necessity of
VCI, TCI, and VHI
implementing real-time
utilized for
Bikaner city Landsat 5 TM and 8 drought monitoring
7 1990–2020 monitoring [60]
of Rajasthan OLI/TIRS data systems based on VCI for
drought-
effective drought
prone areas
management
SPI is considered SPI proves more adept at
Actual ET and ESI for characterizing detecting drought
data collected from drought occurrences when
8 Marathwada Region 1980–2020 [61]
GLEAM, rainfall occurrences at observed over longer
extracted from IMD multiple time frames compared to
time frames shorter durations
RS data can help to
assess drought frequency
and intensity, guiding
NDVI and LSWI for
TRMM rainfall data, the strategic deployment
9 Entire India 1998–2010 mapping drought- [62]
MODIS NDVI data of technologies to
induced changes
enhance productivity in
regions vulnerable
to drought
The integration of NDVI
and other indices like
Agricultural
LST offers valuable
drought
Raichur district MODIS LST and insights for agricultural
10 2002–2012 assessment using [63]
of Karnataka NDVI data drought monitoring,
combination of LST
serving as an effective
and NDVI data
early warning system
for farmers
Integrated use of SMI,
SPI, and NDVI anomaly
presents a near-real-time
Prakasam district of MODIS NDVI and SMI is calculated
11 2007–2020 indicator for identifying [64]
Andhra Pradesh LST data using LST data
water deficit conditions
in soils with both light
and heavy textures
Agricultural
VSWI obtained from
drought
satellite data is effective
monitoring using
Rayalaseema region CHIRPS rainfall and in mapping and keeping
12 2000–2018 indices like SPI, [65]
of Andhra Pradesh MODIS NDVI data track of agricultural
NDVI, LST, TCI,
drought in
VCI, VHI, VSWI,
semi-arid regions
and NVSWI

3.3. Runoff Estimation from Agriculture Watersheds

The Soil Conservation Service-Curve Number (SCS-CN) model, developed by the
U.S. Bureau of Agriculture, National Resources Conversion Service (NRCS), stands as
the predominant and widely adopted method for estimating direct runoff. This model is
extensively employed to assess direct runoff in small agricultural watersheds for specific
rainfall events [66]. It needs less input data; hence, different models like the Soil and
Water Assessment Tool (SWAT), Environmental Policy Integrated Climate (EPIC), Agri-
cultural Non-Point Source Pollution (AGNPS), and Chemicals, Runoff and Erosion from
Agricultural Management Systems (CREAMS) models use the SCS-CN method for runoff
estimation [67,68]. This method has gained widespread acceptance and is extensively
Geomatics 2024, 4 102

utilized in numerous hydrologic studies. It is employed for estimating surface runoff, par-
ticularly in ungauged agricultural watersheds, determining soil erosion vulnerability, and
studying the spatio-temporal variations in land use/land cover (LULC) patterns [69]. Dif-
ferent geospatial technology-based studies identified in this review for drought assessment
and monitoring for India are presented in Table 5.

Table 5. Geospatial technology-based studies for runoff estimation from agricultural watersheds in India.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
The effectiveness of
three
slope-adjusted
RS and GIS techniques
curve number
Kalu watershed, enhance the accuracy of
IRS (LISS-III) and models and the
1 Ulhas River basin, 1999–2002 SCS-CN model inputs, [70]
ASTER DEM original SCS-CN
Maharashtra enabling more precise
method was
runoff predictions
assessed using
LISS-III and ASTER
DEM data
Emphasized the
SWAT-CUP importance of choosing
(SWAT-Calibration suitable climate models
Uncertainty in regional investigations
Weather data from Programme) was to analyze the
Krishna River basin
2 IMD, 1970–2005 designed lengthening of monsoon [71]
of Peninsular India
SRTM-DEM specifically for rainfall and variations in
calibrating and the maximum long-term
validating the mean Indian Summer
SWAT model Monsoon rainfall and
surface runoff
These models are most
valuable in ungauged
Runoff simulation
watersheds and
TRMM and is carried out using
water-scarce regions
Doddahalla IMD rainfall, the HEC-HMS
where limited monitored
3 watershed of Krishna Cartosat-1 2008–2012 hydrological [72]
data exist. They are
basin, Karnataka CartoDEM, IRS LISS simulation model,
crucial for accurate
III data integrating RS and
runoff estimation, which
GIS techniques
is vital for sustaining
water resources
Geospatial
technology is
employed to The study concludes that
analyze land use alterations in LULC
Landsat TM, ETM+, and land cover result in increased runoff
OLI/TIRS, rainfall (LULC) changes volume within the basin,
Koraiyar Basin, Tamil
4 data from water 1986–2016 and their effects on even when the extreme [73]
Nadu, India
resource surface runoff by rainfall remains constant,
department, Chennai utilizing indicating the significant
multi-dated impact of changing
Landsat satellite LULC conditions
images spanning
from 1986 to 2016
Geomatics 2024, 4 103

Table 5. Cont.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
The SCS-CN method has
demonstrated
remarkable efficiency,
SCS-CN and GIS
LANDSAT-8 requiring minimal time
techniques are
for LULC, Survey of and expertise to manage
Sind River basin, employed for
5 India (SOI) 2005–2014 extensive datasets. This [74]
Madhya Pradesh estimating
toposheets, approach proves
rainfall–runoff
global weather data superior in identifying
relationship
potential sites for
artificial
recharge structures
The SCS-CN method was
validated as a superior
Estimation of method, demanding
IRS -LISS III for
rainfall–runoff minimal time and
LULC, toposheet
Pappiredipatti relationship by resources to manage
(SOI), rainfall data
6 watershed, 2000–2014 integrating the extensive datasets and [75]
from public works
Tamil Nadu SCS-CN method assess larger
department,
and remote sensing environmental areas for
Dharmapuri
and GIS techniques selecting sites for
artificial recharge
structures
This method is valuable
for identifying changes
LANDSAT-7 for
Estimation of in land use/land cover
LULC, Survey of
LULC change over time and
India (SOI)
impact on runoff understanding their
Koyna River basin in toposheets, ASTER
generation and impact on runoff
7 Satara district, DEM, FAO global 1999–2011 [53]
study of generation. It
Maharashtra soil data, rainfall
applicability of emphasizes the
from Maharashtra
SCS-CN method for significance of making
Agriculture
runoff estimation rainwater harvesting
department
structures to facilitate
groundwater recharge

3.4. Water Body and Waterlogged Area Mapping

India relies heavily on agriculture, making efficient water usage vital. However, a
significant portion of water is wasted due to insufficient understanding of crop water
requirements and the absence of effective water management. Monitoring water resources
and surface water availability is crucial to understanding temporal water storage. Mapping
surface water bodies using satellite imagery has become essential, providing rapid and
timely information about surface water bodies. RS offers a valuable tool, offering quick in-
sights into water resources, aiding in efficient management and conservation efforts [76,77].
When there is an excessive amount of moisture or water content, the crop root zone is
deprived of adequate aeration, resulting in waterlogging. This causes high water tables and
surface ponding, which makes the land useless. Plant growth is directly hampered by this
circumstance, which lowers agricultural yield. The degree and length of waterlogging in
agricultural fields can significantly reduce the yield of crops. Salinization and alkalinization
are intimately related to waterlogging, which makes it extremely dangerous for irrigated
agriculture to continue. It impacts roughly 6 million hectares of India’s arable land [78].
Before determining the best course of action, it is imperative that these waterlogged areas
must be carefully investigated. Traditionally, ground surveys are used to map waterlogged
areas, but they are neither cost-effective nor timely for large regions. Combining GIS with
Geomatics 2024, 4 104

satellite RS offers a productive real-time substitute for tracking and determining the size of
areas that are inundated. Different geospatial technology-based studies identified in this
review for mapping surface water bodies and waterlogged areas in India are presented
in Table 6.

Table 6. Geospatial technology-based studies for water body and waterlogged area mapping in India.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
NDWI and MNDWI are
highly efficient
indicators for monitoring
and mapping surface
NDWI and
water bodies. They not
MNDWI were used
Godavari Delta, only help identify
1 Landsat-5 TM 2005–2019 for mapping and [79]
Andhra Pradesh changes but also serve as
change detection of
a warning against
water bodies
relying on moisture
content to extract soil
moisture from
water bodies
The use of indices like
WRI, NDWI, and
MNDWI in conjunction
WRI, NDWI, and
with satellite images
MNDWI were used
offers reliable
for the assessment
Landsat-4 MSS, 5 spatio-temporal
2 Chennai, Tamil Nadu 1977–2016 of the [80]
TM, 8 OLI data information when
spatio-temporal
applied to RS data,
variations of surface
allowing for precise
water bodies
analysis and monitoring
of water resources
over time
RS and GIS techniques
Water bodies were
prove to be valuable
Parts of Krishna extracted using an
Resourcesat-2 alternatives to traditional
3 and Godavari 2004–2014 automated [81]
AWiFS data methods for monitoring
River basins extraction
and characterizing
algorithm
surface water bodies
Temporal changes
The machine learning
in waterbody
algorithm is vital for
surface areas were
planning crops,
identified using
evaluating restoration
indices such as
4 Telangana State Landsat-8 data 2013–2019 efforts, monitoring [82]
NDVI, NDWI, and
floods, and
MNDWI, and a
understanding land use
random forest
impact on
machine learning
water resources
algorithm
Geomatics 2024, 4 105

Table 6. Cont.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
Dynamic change in These indices rapidly
the water spread indicate extraction of
area is investigated water bodies, while
using NDWI, Mann–Kendall and Sen’s
Nainital Lake of
5 Landsat-7, -8 data 2001–2018 MNDWI and WRI. slope estimator prove [83]
Uttarakhand State
The non-parametric efficient in determining
Mann–Kendall trends and their
trend test was magnitudes in
also applied hydrological data
Extracted surface
water body area The accuracy assessment
Nagarjuna Sagar using NDVI, revealed that MNDWI
6 reservoir, Landsat-5, -8 data 1989–2017 NDMI, NDWI, outperforms other index [84]
Andhra Pradesh MNDWI, and methods, providing
unsupervised superior results
classification
Supervised Immediate action is
classification is advised to transform
used to classify waterlogged areas into
water bodies from permanent water bodies
other land use with reduced surface area
Lower Gandak Landsat-5, -7, and
7 2000–2020 classes. NDVI, while maintaining their [78]
command of Bihar -8, IRS-1D, IRS-P6
NWDI, and maximum volume. These
MNWDI were also bodies can be utilized for
used to enhance irrigation, ecological
water features from purposes, and various
collected data economic activities
Mapped
Satellite images can
waterlogged area
identify and map
on the basis of
Moyna basin, Purba waterlogged areas
Landsat-5 TM, supervised
8 Medinipur district, 2009 through supervised [85]
ASTER data classification and
West Bengal classification, NDVI,
NDVI, NDWI, and
NDWI, and modified
modified NDWI
NDWI or NDMI
or NDMI
The surface extent
of salt-affected and
RS and GIS provide an
waterlogged areas
efficient platform for
was identified and
comprehending intricate
delineated through
IRS P6, LlSS-III relationships among
Muzaffarpur district the analysis of
9 data, TRMM 3B43 1998–2009 hydro-geological factors [86]
of Bihar multi-temporal
rainfall data that influence the
satellite images
severity of waterlogging
during both
and salt-affected areas in
pre-monsoon and
the region
post-monsoon
seasons
Geomatics 2024, 4 106

Table 6. Cont.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
RS and GIS
techniques were
used for identifying
spatio-temporal
changes in This analysis can
drainage networks enhance farmers’
Gosaba Island, Landsat-1 MS, and congestion livelihoods by
10 Sundarban, Landsat-8 2017 patterns by harnessing waterlogging [87]
West Bengal OLI data overlaying as an opportunity for
multi-temporal integrated rice and
vector layers. fish farming
Drainage induced
waterlogging
problems
were assessed
The spatio-temporal
analysis of waterlogging
The areas affected
dynamics conducted in
by surface
Vaishali district of Landsat-5 this study can provide
11 1998, 2006 waterlogging were [88]
North Bihar TM data valuable insights for
identified using the
protective measures
NDWI technique
against waterlogging
problems

3.5. Identification of Suitable Sites for Groundwater Recharge and Rainwater Harvesting
Water, an indispensable resource in our daily lives, is becoming increasingly scarce in
both rural and urban areas. This scarcity is primarily due to reduced infiltration rates caused
by deforestation and extensive surface paving. Despite India having a substantial amount
of surface water, limitations in topography and other factors restrict its storage [89]. In areas
where surface water is scarce, groundwater becomes a crucial alternative for water supply.
However, excessive groundwater extraction has led to declining water levels in many areas,
escalating both investment and operational costs. Addressing this issue involves artificially
recharging potential aquifers, which can alleviate the problem to some extent. Rainwater
harvesting and artificial groundwater recharge have become cornerstone tactics for the
long-term viability of freshwater resources, which include surface and groundwater. The
best sites for artificial recharge have been identified through a number of studies [90,91].
Different geospatial technology-based studies identified in this review for mapping and
identification of suitable sites for groundwater recharge and rainwater harvesting in India
are presented in Table 7.

Table 7. Geospatial technology-based studies for identification of suitable sites for groundwater
recharge and rainwater harvesting.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
Identified artificial
recharge sites using
Semi-arid region of Artificial recharge sites
different thematic
Anantapur Landsat-8 data, SOI can be successfully
1 2012 layers as good, [92]
district, Andhra toposheets identified using
moderate to good,
Pradesh geospatial technology
moderate, and poor
for artificial recharge
Geomatics 2024, 4 107

Table 7. Cont.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
The results will be useful
Assessed
for decision makers and
groundwater
local communities for
potential zones
SRTM DEM, responsible use of
(GWPZs) and
Environmental groundwater resources.
identified suitable
Peddavagu River Systems Research This knowledge enables
2 2018 areas for artificial [93]
basin, Telangana State Institute (ESRI) sustainable planning and
recharge using a
land cover, management, ensuring
combination of GIS,
IMD rainfall the availability and
analytic hierarchy
viability of these
process (AHP), and
resources for
fuzzy AHP
future generations
The zoning maps
GWPZs were created
depicting groundwater
using different
potential and artificial
thematic layers.
Mahesh River basin recharge hold
Different thematic
comes under Akola IRS-P6 LISS-III significance for
layers were
3 and Buldhana satellite data, 2010–2015 initiatives related to soil [94]
combined for
districts in SOI toposheets and water conservation
groundwater
Maharashtra projects, watershed
exploration and
development programs,
watershed
and the management of
management
groundwater resources
GWPZs were The integration of GIS
identified with nine provides an efficient
SOI toposheet,
Mand thematic layers using platform for the
Sentinal-2,
catchment of the Multi-Criteria comprehensive analysis
4 SRTM-DEM, 2021 [95]
Mahanadi basin Decision Analysis of diverse datasets in the
rainfall, soil map,
in Chhattisgarh (MCDA) method realm of groundwater
runoff data
with geospatial management
technology and planning
Weighted index The findings from
overlay analysis groundwater level
(WIOA) was applied observations in
Namakkal district of SOI toposheet,
5 2005 by integrating the designated GWPZs [96]
Tamil Nadu soil map
thematic layers for reveal the effectiveness
delineation of RS and GIS in
of GWPZs identifying recharge sites
RS and GIS techniques
The groundwater
serve as efficient tools for
recharge potential
appraising groundwater
Upper Betwa map was created by
SRTM-DEM, potential, aiding in the
Watershed, Raisen overlaying thematic
6 Landsat-8 OLI data, 2016 identification of optimal [97]
district of maps using the
soil map locations for
Madhya Pradesh weighted index
groundwater withdrawal
overlay
wells to meet
(WIO) method
water demands
Geomatics 2024, 4 108

Table 7. Cont.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
An integrated The conclusive findings
approach using RS indicate favorable
Landsat 5-TM and GIS methods is groundwater zones in
Bokaro district satellite data, SRTM employed to map the study area, holding
7 2003–2013 [98]
of Jharkhand DEM, rainfall data, groundwater significant implications
soil map potential zones and for improved planning
identify suitable sites and management of local
for artificial recharge groundwater resources
This approach saves
Rainwater harvesting time, significantly
sites were identified reduces costs by
IRS Resourcesat-2
using DEM, LULC, minimizing earthwork
Alwar district LISS III data,
8 2016 soil map, drainage expenses, and can be [99]
of Rajasthan ASTER-DEM,
map, and depression applied in the planning
soil data
map with the of efficient water
SCS-CN method resource
management strategies
Estimation of surface Geospatial technology
SRTM-DEM, IRS runoff using SCS-CN can effectively support
Upper Kangsabati
LISS-III, SOI analysis and sustainable watershed
9 Watershed, 2004–2017 [100]
toposheet, identification of development and water
West Bengal
IMD rainfall suitable locations for resource
rainwater collection management efforts
Determined optimal
Strategic water
SRTM-DEM, IMD zones for surface
management planning
Mirzapur, rainfall, soil data water storage and
through MCDA and GIS
Chandauli, and from National groundwater
improves surface and
10 Sonbhadra districts Bureau of Soil 1980–2020 recharge to boost [101]
groundwater resources.
of Uttar Survey and Land irrigation water
This approach enhances
Pradesh State Use Planning supply, employing
agricultural land
(NBSS-LUP) geospatial tools
use possibilities
and AHP
Utilizing multi-criteria
Implemented fuzzy
analysis with fuzzy logic
AHP to assign
Kandi subdivision of IMD rainfall, provides a
weights to different
Murshidabad Resoursesat-2 comprehensive
11 2015–2016 criteria essential for [102]
district, West satellite data, evaluation for both
selecting appropriate
Bengal State SOI toposheets rainwater harvesting
sites for
structures and
rainwater harvesting
site selections
Landsat-7 satellite Weights were allocated
This study will prove
data, ASTER DEM, to thematic layers,
West Midnapur, valuable for policymakers,
soil data from specifically those
Purulia and aiding them in allocating
12 NBSS-LUP, 2011 related to slope and [103]
Bankura regions of government funds
rainfall data from runoff coefficient, and
West Bengal according to
Agriculture these features were
administrative boundaries
department of state ranked accordingly

3.6. Soil Moisture Estimation

At both micro and mega scales, soil moisture is essential for maintaining life-sustaining
processes in ecosystems [104]. Its levels exhibit significant variability across both space
and time, contingent upon factors such as topography, soil composition, land cover, and
climate. Monitoring the moisture content of the soil in the root zone, which controls crop
growth, provides important information about possible moisture shortages. Therefore,
Geomatics 2024, 4 109

regular monitoring of soil moisture levels is essential, enabling efficient irrigation practices
that enhance crop productivity and facilitate accurate yield forecasts. It is essential to
have precise and accurate knowledge of soil moisture at different scales for agricultural
purposes, flood monitoring, and soil health understanding. Measuring this parameter is
imperative in agriculture, particularly for early detection of drought conditions, enabling
timely interventions and warnings [105]. Different geospatial technology-based studies
identified in this review for soil moisture estimation are presented in Table 8.

Table 8. Different geospatial technology-based studies identified in this review for soil moisture estimation.

Sr. Data/ Time/

Location Approach Outcome References
No. Products Used Period
SAR Sentinel-1A This model suggests
Maiyur and
data were used to ideal crops for vast and
Sampathinallur Sentinel-1A
1 2022 determine soil intricate areas by [23]
villages, Tamil SAR data
moisture using the analyzing projected
Nadu
Water Cloud Model moisture content
Assessed the The findings from this
capability of C-band study have practical
Sentinel-1 SAR data applications in
in estimating soil monitoring soil surface
Kosi River basin, Sentinel-1A, 1B
2 2020 surface moisture moisture, crop water [106]
North Bihar SAR data
during the dry utilization, irrigation
season in both bare planning, water
soil and vegetated management, droughts,
agricultural fields floods, and soil erosion
MODIS LST and Employed a Mapping soil moisture
Rewari district, NDVI data, triangular network levels within crop fields
3 2013 [107]
Haryana Landsat-7 method for soil is achievable using
ETM + data moisture estimation satellite data inputs
The results demonstrate
SAR data with V and
that dual-polarized SAR
VH polarization
data can effectively
channels were used
Sentinel 1 A, model soil moisture
4 Rupnagar, Punjab 2017–2019 for surface soil [2]
C-band SAR data estimation, particularly
moisture estimation
when fields are fallow or
and validated
crops are in their early
with NDMI
growth stage
Satellite-based
National
Hydrological
Model-India The NHM-I will offer a
Damodar River MODIS NDVI and
(NHM-I) water platform for evaluating
basin (boundary of LAI data, IMD
5 2009–2018 demand module was irrigation demands and [108]
West Bengal rainfall and
developed to soil moisture levels over
and Jharkhand) temperature data
determine irrigation both space and time
water needs on the
basis of soil
moisture deficit

This review article encompassed a total of 60 studies. The selected articles showed
significant diversity in their content and scope. The research covered various parts of
India, spanning almost all of the country’s land area, as shown in Figure 3. Most of the
studies were conducted in West Bengal, Maharashtra, Andhra Pradesh, Karnataka, Tamil
Nadu, Rajasthan, Gujrat, Madhya Pradesh, and Bihar, and a few were conducted in Orissa.
Additionally, single studies were conducted in other states of the country.
 significant diversity in their content and scope. The research covered various parts of In-
dia, spanning almost all of the country’s land area, as shown in Figure 3. Most of the stud-
ies were conducted in West Bengal, Maharashtra, Andhra Pradesh, Karnataka, Tamil
Nadu, Rajasthan, Gujrat, Madhya Pradesh, and Bihar, and a few were conducted in
Geomatics 2024, 4 110
Orissa. Additionally, single studies were conducted in other states of the country.

3. Selected
Figure 3.
Figure Selectedgeospatial
geospatialtechnology-based articles
technology-based related
articles to agricultural
related water water
to agricultural management
management
conducted in different parts of India.
conducted in different parts of India.
The distribution of the selected studies was examined annually, as shown in Figure 4.
The distribution
The trajectory of the
of research selected
in India studiesthe
regarding was examinedofannually,
integration geospatialastechnologies
shown in Figure
4. The trajectory of research in India regarding the integration of geospatial
shows significant fluctuations. As depicted in Figure 4, there was no consistent increase technologies
in
shows
studiessignificant
between 2014fluctuations.
and 2020. As depicted
However, in Figure
in 2021, there4,was
there was nosurge
a notable consistent increase in
in selected
studies
studies, between
marking a2014 and 2020.
significant 18.33%However,
increase. in 2021,
This trendthere wasina2022,
peaked notable
withsurge in selected
an increase
of 23.33%.
studies, The rising
marking number of
a significant published
18.33% studies
increase. Thisreflects a notable
trend peaked inlevel
2022,ofwith
expertise
an increase
and
of proficiency
23.33%. in geospatial
The rising numbertechnology
of publishedin India. Thereflects
studies widespread use of
a notable these
level of modern
expertise and
methods bodes well for the nation’s long-term objectives pertaining to sustainable and
profitable agriculture activities.
Over the years, data and products from numerous satellites and sensors have been
employed for agricultural water management in India, as shown in Figure 5. In more
than half of the selected studies, Landsat MSS/TM/ETM+/OLI satellite data were utilized.
The 30 m spatial resolution and 16-day revisit cycle provided invaluable data for water
resource management. Terra and Aqua (MODIS) are also widely used because they cover
a larger area per scene, have satellite images available for the entire study period, and
provide frequent data. Similarly, IRS satellites, particularly LISS III (23.5 m) and LISS IV
(5.8 m), have been extensively employed in water management studies, specifically for
monitoring water resources. These selected articles address different issues of agricultural
water management, as shown in Figure 6. The percentage of studies selected to address
different areas of agricultural water management in India is shown in Figure 6. The
 Geomatics 2024, 4 111

temporal scale of selected studies for data analysis concerning water management is shown
in Figure 7. About 62% of the selected studies considered multi-year data and 38% of
studies considered single-year data for addressing different water management issues. A
Geomatics 2024, 4 variety of indices were utilized in the selected articles to target various facets of agricultural
109
water management in India, and the distribution of selected articles among different remote
sensing categories is depicted in Figure 8. Among the individual indices documented,
NDVI emerged as the predominant choice for water management in the region, followed
proficiency in geospatial technology in India. The widespread use of these modern meth-
by NDWI as the second most frequently employed index. The most commonly utilized
ods bodes
data wellisformulti-sensor
source the nation’s type
long-term
remoteobjectives
sensing pertaining
followed by to optical
sustainable andsensing
remote profita-for
blewater
agriculture activities.
management.

Geomatics 2024, 4 110

Figure
Figure BarBar
4. 4. chart
chart representingthe
representing thepercentage
percentageofofrecently
recently published
published selected
selected articles
articles considered
consideredfor
review (* as of September 2023).
for review (* as of September 2023).

Over the years, data and products from numerous satellites and sensors have been
employed for agricultural water management in India, as shown in Figure 5. In more than
half of the selected studies, Landsat MSS/TM/ETM+/OLI satellite data were utilized. The
30 m spatial resolution and 16-day revisit cycle provided invaluable data for water re-
source management. Terra and Aqua (MODIS) are also widely used because they cover a
larger area per scene, have satellite images available for the entire study period, and pro-
vide frequent data. Similarly, IRS satellites, particularly LISS III (23.5 m) and LISS IV (5.8
m), have been extensively employed in water management studies, specifically for moni-
toring water resources. These selected articles address different issues of agricultural wa-
ter management, as shown in Figure 6. The percentage of studies selected to address dif-
ferent areas of agricultural water management in India is shown in Figure 6. The temporal
scale of selected studies for data analysis concerning water management is shown in Fig-
ure 7. About 62% of the selected studies considered multi-year data and 38% of studies
considered single-year data for addressing different water management issues. A variety
of indices were utilized in the selected articles to target various facets of agricultural water
management in India, and the distribution of selected articles among different remote
sensing categories is depicted in Figure 8. Among the individual indices documented,
NDVI emerged as the predominant choice for water management in the region, followed
by NDWI as the second most frequently employed index. The most commonly utilized
data source is multi-sensor type remote sensing followed by optical remote sensing for
Figure 5. Satellite data/products used in selected articles for agricultural water management in
water management.
different
Figure parts ofdata/products
5. Satellite India. used in selected articles for agricultural water management in dif-
ferent parts of India.
 Geomatics 2024, 4
Figure 5. Satellite data/products used in selected articles for agricultural water management
112
ferent parts of India.

atics 2024, 4

Figure 6.
Figure Percentage of of
6. Percentage studies selected
studies to address
selected differentdifferent
to address areas of agricultural water management
areas of agricultural water manag
in India.
in India.

Figure
Figure Percentage of of
7.7.Percentage single- and multi-year
single- studies considered
and multi-year studies in this review.
considered in this review.
Geomatics 2024, 4 113
Figure 7. Percentage of single- and multi-year studies considered in this review.

Figure 8. The bar chart represents different indices used in the selected articles for addressing
Figure 8. The bar chart represents different indices used in the selected articles for addr
different areas of water management in India while the pie chart illustrates the percentage of selected
ferent areas of water management in India while the pie chart illustrates the percentage
articles across various remote sensing categories.
articles across various remote sensing categories.
4. Discussion
India encounters water stress in many areas due to the limited utilization of its accessi-
4. Discussion
ble water resources, with only a small fraction being effectively utilized [109]. Accurate,
India encounters
comprehensive, and timely water stress information
agricultural in many areas duefor
is crucial to decision
the limited
making utilization
in a o
sible water
country resources,
like India, involvingwith only a small
all stakeholders fraction
[110]. being effectively
The long-term utilized [109].
viability of agriculture
comprehensive, and timely agricultural information is crucial forisdecision
depends on the sustainable management of available water resources. Therefore, it essen- ma
tial to conduct a thorough and realistic assessment of water usage within the constraints
country like available
of the limited India, involving all stakeholders
resources, coupled with careful [110]. The long-term
future planning viability of a
[111]. Improving
depends
water use on the sustainable
efficiency in agriculturemanagement of available
is crucial for sustainable water water
resource resources.
management. Therefor
Implementing
sential to conducteffective
a agricultural
thorough water management
and realistic techniques on
assessment of awater
regional scale within
usage
is challenging due to the absence of real-time data on soil moisture and evapotranspi-
straints of the limited available resources, coupled with careful future planning
ration. However, this challenge can be addressed through the utilization of geospatial
proving
technology water use efficiency
[112,113]. Soil moisture inisagriculture
vital for plant isgrowth,
crucialplaying
for sustainable
a key role inwater
the resou
agement.
hydrological Implementing effective agricultural
cycle. Accurate measurement of this factorwater management
is crucial for agriculture,techniques
enabling on
early detection of drought warnings [105]. Assessing soil moisture
scale is challenging due to the absence of real-time data on soil moisture and e changes over time and
space is vital for pinpointing regions and periods facing significant water stress [114]. Soil
spiration. However, this challenge can be addressed through the utilization of g
moisture and vegetation water content are fundamental elements in studies concerning
technology [112,113].
vegetation, drought, Soil moisture
and climate change. Theiris vital for plant
significance growth,
is paramount playingwithin
in research a key role
drological cycle.The
these fields [115]. Accurate measurement
rising demand for irrigationofhasthis factor
elevated the is crucial of
importance forestimat-
agriculture
ing consumptive water use through geospatial techniques in the field of irrigation water
early detection of drought warnings [105]. Assessing soil moisture changes over
management. Over the years, irrigation and agricultural applications have successfully
space is vital for
used geospatial datapinpointing
[116]. Real-time regions and
irrigation periodscanfacing
scheduling significant
be achieved with thewater
use of stress
moisture
geospatial and vegetation
technology [117]. water content are fundamental elements in studies co
Due to significant fluctuations in climatic conditions, crops often experience various
stresses, resulting in decreased productivity and yearly fluctuations. In such circumstances,
the swift advancements in geospatial technology play a vital role in monitoring crop growth,
identifying and managing different stress factors, and estimating regional yields. These
technologies are essential for sustaining natural resources and agricultural productivity [33].
The growth of water-intensive crops, excessive irrigation, poor maintenance of drainage
Geomatics 2024, 4 114

systems, and inadequate surface and subsurface drainage are the main causes of the water-
logging in the area [118]. For mapping areas that are flooded, conventional techniques such
as ground surveys are employed; however, they are not economical or timely for studies
conducted on a regional scale. A better real-time option for monitoring and determining
the size of flooded areas is to integrate satellite remote sensing with GIS [119]. Analyz-
ing surface runoff based on rainfall is a significant challenge in hydrological modeling,
and is essential for water resource development, planning, and management. The sur-
face runoff model dependent on rainfall is crucial for planning the development of water
resources [120]. Precisely assessing surface runoff in watersheds, whether gauged or un-
gauged, is imperative for strategic planning and the implementation of water conservation
structures [121]. Rapid urbanization has significantly reduced rainwater infiltration into
the sub-soil, leading to a drastic decline in groundwater recharge. Consequently, rainwater
harvesting has become essential due to the inadequacy of surface water to meet our needs,
forcing us to rely heavily on groundwater. Rainwater harvesting refers to the purposeful
collection and storage of rainwater, essentially augmenting groundwater reservoirs through
human-made structures designed to capture and utilize rainwater effectively [122].
Traditional methods for gathering weather and crop growth data are reliable but
laborious and time-consuming. In recent times, the integration of RS and GIS technologies
has emerged as indispensable for obtaining spatio-temporal meteorological and crop
status data, thereby augmenting conventional methodologies. RS data significantly aids
monitoring by offering timely, comprehensive, cost-effective, and repetitive Earth surface
information [33]. RS has substantiated its significant utility in the mapping and surveillance
of agricultural land utilization, outperforming conventional methodologies in terms of cost-
effectiveness and the expeditious provision of data across expansive territories. Satellite-
based remote sensing, with its repetitive and multispectral nature, stands out as an ideal
option for monitoring dynamic agricultural resources. For planners and policymakers,
timely and reliable data on agricultural water management are essential for efficient and
timely agricultural development, as well as for making critical decisions [110]. Satellite
remote sensing provides significant prospects for the observation of land surface conditions
and the monitoring of water resource status across diverse spatial and temporal scales.
There is a growing necessity to harness RS technology for accurately estimating crop water
requirements in irrigation areas [109]. RS stands out as one of the few techniques capable of
offering representative measurements of numerous essential physical parameters, ranging
from a specific point to an entire continent [123,124].
Advancements have been observed in the identification, mapping, and monitoring
of water resources through the use of remotely sensed data over the years, as depicted in
Figure 5. In our analysis, we found that a significant portion of research has predominantly
utilized multispectral sensors. These sensors include a range of instruments like Landsat,
MODIS, and IRS, with only a few studies opting for the use of Sentinel, TRMM, etc. The
significant development of Earth-monitoring technologies is responsible for this research
achievement. In terms of management, these technologies are economical and time-efficient.
The abundance of data sources is a result of the availability of various instruments and
missions. The dominance of the multispectral-based approach can be attributed to (a) the
availability of extensive data from various satellite missions since the first Landsat mission
in the 1970s, (b) a substantial number of free-of-cost optical sensors with improved resolu-
tion, particularly in recent years, and (c) the straightforward interpretation of data. Landsat
sensors have proven particularly valuable for evaluating and monitoring water resources.
It is important to acknowledge that the effectiveness of medium- and low-spatial-resolution
sensors may be limited in detecting and mapping water resources, especially when dealing
with areas smaller than the size of a pixel. The Landsat and Sentinel datasets, known for
their improved spatial resolutions, have been traditionally employed for water resource
management. While their images are freely available, handling them can be challeng-
ing due to their large file sizes. In the past, platforms like Earth Explorer from USGS or
Sentinel–Copernicus Open Access Hub were used, but with the rise of cloud platforms
Geomatics 2024, 4 115

like Google Earth Engine, managing and processing the entire dataset has become sim-
pler. High-resolution images are now easily accessible, providing extensive coverage for
large areas worldwide. MODIS is crucial for mapping changes in water resources at a
broad spatial resolution. Its accessibility and extended operational period make it valuable
for large-scale, long-term, and seasonal monitoring. Despite these advantages, using the
MODIS sensor comes with challenges. One challenge is linking the coarse spatial resolution
it offers with on-the-ground field data. Moreover, the sensor encounters difficulties when
trying to effectively monitor small areas. The IRS satellite series provides high-resolution
data useful for many applications, including natural resource management.
Cloud cover and limited temporal and spatial resolution are common challenges for
multispectral sensors like Landsat sensors. Drones, on the other hand, operate at a lower
altitude and can collect data over remote areas, meaning they are unaffected by cloud cover.
Studies employing drones to manage water resources are currently lacking. Therefore,
using drones to monitor water resources and comparing their performance with satellite
sensors is necessary. In future studies, combining remotely sensed data with physical-based
hydrologic models can lead to more effective water resource management and decision
making. Further research is crucial to develop advanced models that use multi-source data
and improved algorithms. Progress in Earth-observation technology, marked by enhanced
image acquisition features, has steadily broadened our capability to identify the Earth’s
features. Instruments like Sentinel-1 SAR offer a chance to combine optical and radar data,
enhancing mapping capabilities even on cloudy days. It is crucial to monitor regions like
semi-arid environments where significant rainfall happens during particular wet seasons.
The integration of Sentinel-1 and -2 (SAR and MSI) data proves beneficial at a local to
regional scale, yielding enhanced outcomes when compared to relying solely on the optical
sensor. Sentinel-1 and -2 sensors are available for free and have successfully monitored
water resources separately [125]. However, it was found that their complete potential has
not been fully utilized in the evaluation and monitoring of water resources. As per the
findings of this research, there is a growing interest in evaluating, mapping, and monitoring
water resources using Landsat and MODIS image platforms. However, advanced Earth-
observation technologies like Sentinel data, UAVs, and hyperspectral technology have not
been fully investigated for assessing and monitoring water resources. The application of
these advanced technologies could be effective in keeping track of water resources.

5. Progress and Future Scope

Undoubtedly, the 2030 Agenda for Sustainable Development establishes ambitious ob-
jectives, encompassing targets such as Sustainable Development Goal (SDG)-1 (Eradication
of poverty) and SDG-2 (Zero hunger), which strives to enhance agricultural productivity
and implement sustainable, resilient agricultural practices. Furthermore, SDG-12 (Respon-
sible consumption and production) directs attention towards the promotion of sustainable
patterns in production and consumption. Simultaneously, SDG-13 (Climate action) and
SDG-15 (Life on land) underscore the imperative for sustainable management of natural
resources. To align with these objectives, it is imperative to systematically and scientifi-
cally redesign agricultural policies and programs. This redesign should aim to incentivize
sustainable agricultural practices and enhance food security, ensuring the harmonious
integration of ecological and economic principles for long-term agricultural viability [34].
Finding appropriate solutions for agricultural water management is crucial, particularly
in countries anticipating population growth and increased production and consumption.
These solutions need to focus on monitoring and addressing various aspects of agricultural
water management while evaluating the effects of threats such as climate change, food se-
curity, spatial planning, and land management on the system. The diversity of data sources
emphasizes the availability of instruments and missions at our disposal. The extensive
adoption of the multispectral-based approach can be attributed to various factors. Firstly,
there is an availability of extensive data from various satellite missions, dating back to the
1970s with the launch of the first Landsat mission. Secondly, the availability of numerous
Geomatics 2024, 4 116

free optical sensors, with significantly improved resolutions in recent years, has bolstered
this trend. Lastly, the straightforward and uncomplicated interpretation of the data has also
played a significant role in the widespread adoption of this approach [126]. Optical sensors
are frequently used because of their simple processing and the ease of interpreting their
images. These sensors closely match human visual perception, making them a preferred
option for researchers.
Due to the better spatial resolutions offered, Landsat-(5, 6, 7, 8) TM, ETM+, OLI/TIRS
(30 m), IRS–LISS III (24 m), IRS–LISS IV (5.8 m), and Sentinel-1, -2 (10 m) are mostly used to
address different issues of agricultural water management in India. These finer resolutions
are particularly valuable for addressing water management concerns in relatively small
areas. Nevertheless, the launch of Landsat-9 on 27 September 2021 has halved the revisit
period, sparking expectations of increased utilization of these data in the upcoming years.
Data from Terra and Aqua MODIS, offering resolutions ranging from 250 to 1000 m, are
predominantly utilized owing to the wider coverage per scene, the availability of satellite
imagery throughout the entire study period, and, primarily, their temporal resolution. The
Terra and Aqua MODIS instruments systematically acquire imagery of the Earth’s entire
surface at intervals of one to two days, establishing them as indispensable tools in scientific
research. Within the domain of water resource management, researchers have commonly
utilized a multispectral or radar-based approach.
Even though we have made progress in using geospatial technology for long-term
monitoring, there is a delay in quickly adopting modern Earth-observation methods,
including Sentinel data, that are easy to access. Sentinels, with better spectral resolution
and a 5-day revisit time, open up new possibilities for checking water resources every
two weeks or each season. Multispectral sensors like those in Sentinel 2 and Landsat face
challenges with cloudy weather and coarser spatial and temporal resolution. The future
upcoming sensors, such as the NASA-ISRO SAR Mission (NISAR), can be a game-changing
satellite, which will allow detailed scientific insights into sustainability for agricultural
water management under climate change scenarios.
Different emerging technologies like cloud computing, augmented reality, Internet of
Things (IoT), 3D GIS, mobile GIS, machine learning, artificial intelligence, hyperspectral
drones and balloons, blockchain, digital twins, and robotics, as depicted in Figure 9, are
poised to enhance the future of water management [127]. The evaluation and monitoring
of water resources using these advanced technologies have not received considerable at-
tention until now. Advanced algorithms and software in cloud computing can be used to
comprehensively analyze field conditions for water management. Integrating GIS with
the IoT is crucial for automating irrigation systems for water management. This combi-
nation promises transformative effects not only in the irrigation sector, but also across
various industries. Augmented reality, a research domain merging actual surroundings
with computer-generated data, enhances real-time human perception in water manage-
ment. In water management, 3D GIS enhances object details and visibility by introducing
an additional dimension (z-axis). The inclusion of an elevation component that is lacking
in 2D maps can provide a comprehensive representation and 3D GIS technologies offer
illustrative scale representations for real-world objects. Mobile GIS streamlines feasibility
studies for water management, empowering field personnel to collect, store, update, edit,
analyze, and present geospatial data. The integration of mobile devices, GIS software,
GPS, and wireless connectivity enables internet-based GIS access. The amalgamation of RS,
GIS, and artificial intelligence promotes the automation of data collection, analysis, and
decision making in water management [126]. Drones, which fly lower, are not affected by
clouds and can reach remote, hard-to-access areas to collect data. With the advancement
of technology (the fourth industrial revolution), using drones in future studies is recom-
mended. They offer a new and innovative way to gather real-time spatial data, especially
for mapping and monitoring water resources. Balloons, when filled with helium gas, can
operate at lower altitudes compared to airplanes, making them valuable for detecting small
objects [36]. Digital twins can be used for the prediction of crop irrigation requirements and
 tecting small objects [36]. Digital twins can be used for the prediction of crop irrigation
requirements and irrigation water management [128]. Robotics and blockchain are also
useful for irrigation water management.
In achieving sustainable water management amidst a growing population and its in-
Geomatics 2024, 4
creasing needs, utilizing multispectral sensor data is crucial. This information is117 highly
valuable for professionals in water management, catchment management, and land plan-
ning. It empowers them to customize their strategies for managing land and water by
irrigation water management [128]. Robotics and blockchain are also useful for irrigation
taking into account spatial differences and seasonal variations in water resources.
water management.

Figure 9. Technological
Figure 9. Technologicaladvancements forprogress
advancements for progressand
and futuristic
futuristic agricultural
agricultural water
water management.
management.

In achieving sustainable water management amidst a growing population and its

6. Conclusions
increasing needs, utilizing multispectral sensor data is crucial. This information is highly
This review
valuable encompassed
for professionals 60 recently
in water published
management, research
catchment articles in
management, the
and field
land of agri-
plan-
cultural
ning. water resource
It empowers them management,
to customize specifically focusing
their strategies on geospatial
for managing land andtechnology
water by ap-
plications. The
taking into ever-expanding
account globaland
spatial differences population, coupledinwith
seasonal variations waterpressing issues such as
resources.
the consequences of ongoing climate change, exerts significant pressure on agricultural
6. Conclusions
This review encompassed 60 recently published research articles in the field of agri-
cultural water resource management, specifically focusing on geospatial technology appli-
cations. The ever-expanding global population, coupled with pressing issues such as the
consequences of ongoing climate change, exerts significant pressure on agricultural systems.
Consequently, there is a growing need for advanced monitoring systems and models that
can provide comprehensive insights into how countries are strategically managing spatial
planning, land and water resources, and food security. In response to this need, this paper
systematically evaluated cutting-edge geospatial technology solutions in this field, adher-
ing to the rigorous guidelines outlined in the PRISMA statement. This methodological
approach proved highly effective in quantifying essential information regarding various
parameters of interest within the subject. The resulting information holds considerable
value for forthcoming studies in the field. As the world is changing very rapidly and factors
like climate change, seasonal patterns, and water scarcity are projected to become more
noticeable, these factors will play a significant role in enhancing the development of precise
monitoring tools.
The literature shows that geospatial technology is receiving a lot of attention for
agricultural water management. We observed that different remote sensing spectral indices
Geomatics 2024, 4 118

are used for agricultural water management in the study area. Here, we found that most
of the studies focus on Landsat images, overlooking the potential of other sensors like
Sentinel-1 and -2. These sensors have better revisit times and improved spatial resolution
and radiometric capabilities, but they have not been explored as much. Seasonal monitoring
is rendered possible by these sensors. According to the results of this study, there is an
increasing interest in assessing, mapping, and monitoring water resources using Landsat
and MODIS image platforms. However, more advanced Earth-observation technologies
such as Sentinel data, UAVs, and hyperspectral technology have not been thoroughly
explored for the assessment and monitoring of water resources in the study area. Using
data from different sensors helps us learn more about water resources. Platforms like
drones and helium-filled balloons enable the acquisition of high-resolution data in near real
time, substantially enhancing the precision of mapping and monitoring water resources.
Advancements in machine learning algorithms that reduce processing time for data will
significantly improve the application of machine learning in remote sensing. Machine
learning can effectively organize data obtained from systematic ground observations,
sensors, meteorological instruments, and various remote sensing sources like satellites,
airborne platforms, and drones. It is concluded that the fusion of cloud computing, IoT,
artificial intelligence, 3D GIS, mobile GIS, augmented reality, hyperspectral drones and
balloons, robotics, digital twins, and blockchain with GIS and remote sensing technologies
can revolutionize agriculture, maintaining a crucial role in agricultural water management.
The Google Earth Engine (GEE) platform, renowned for its ability to manage large-scale
remote sensing data, stands as a valuable and time-efficient tool for water management.
The effective management of water resources at both local and regional scales requires
the full integration of innovative technologies and methodologies. These advancements
have markedly enhanced our capability to assess and monitor water resources, thereby
enabling the implementation of more efficient planning and management strategies. The
advancements in geospatial technology applications have furthered our comprehension
of water resources, thereby promoting sustainable water management practices. Hence,
this research significantly contributes to the existing literature by offering a comprehensive
analysis of the advancements in geospatial technology within the realm of agricultural
water management in India, providing a holistic perspective. The advancement of new
sensors, both passive and active, offering superior spatial, spectral, radiometric, and
temporal resolutions, along with enhanced data integration techniques and the availability
of sophisticated algorithms/software and platforms, enables the efficient utilization of
geospatial technology to tackle the aforementioned challenges.

Author Contributions: Conceptualization, S.B.T., N.R.P., A.D. and S.P.; methodology, S.B.T., N.R.P.,
A.D., S.P. and B.R.P.; resources, N.R.P. and A.D.; data curation, S.B.T., N.R.P., A.D. and S.P.;
writing—original draft preparation, S.B.T.; writing—review and editing, S.B.T., N.R.P. and B.R.P.;
visualization, S.B.T., N.R.P. and B.R.P.; supervision, N.R.P. and B.R.P. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The authors confirm that the data supporting the findings of this study
are available within the article.
Conflicts of Interest: The authors declare no conflicts of interest.

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