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ISSN: 0852-0682 | E-ISSN: 2460-3945
Research article Seasonal Variability in Soil Salinity and its Climatic Drivers in Khulna.
Bangladesh Karno Kumar Mondal1*.
Md.
Abdullah Elias Akhter1.
Muhammad Abul Kalam Mallik2 Department of Physics.
Khulna University of Engineering & Technology.
Bangladesh.
2 Bangladesh Meteorological Department.
Agargaon-Dhaka.
Bangladesh Citation:
Mondal.
Akhter.
, & Malik.
Seasonal Variability of Soil Salinity and Its Climatic Drivers in Khulna.
Bangladesh.
Forum Geografi.
, 307-317.
Article history:
Received: 17 March 2025 Revised: 24 July 2025 Accepted: 4 September 2025 Published: 23 September 2025 Correspondence: karno@kuet.
Abstract Bangladesh is one of the countries in the world most severely affected by soil salinity issues.
This research focuses on the seasonal variation in soil salinity and the associated impact of climate change across different sites in the Batiaghata sub-district of Khulna, located in the southwestern coastal belt of Bangladesh.
The study encompasses four meteorological seasons: pre-monsoon (March-April-Ma.
, monsoon (June-JulyAugust-Septembe.
, post-monsoon (October-Novembe.
, and winter (December-January-Februar.
Maximum and minimum electrical conductivity values are employed, collected from the Soil Research Development Institute (SRDI) in Khulna, and which show variations in the pre-monsoon and monsoon seasons.
The Normalized Difference Salinity Index (NDSI) is used to detect soil salinity aspects using remote sensing Satellite-derived NDSI indices, visualised via the ArcGIS template, indicate that soil salinity peaks during the pre-monsoon season, which is consistent with the observed data.
The minimum values were recorded in the monsoon season.
The highest maximum value of the NDSI indices for the pre-monsoon season was 0.
11580, while the lowest maximum value for the monsoon season was 0.
Rainfall is the main reason for lower soil salinity in the monsoon season.
Conversely, soil salinity increases during the premonsoon season due to higher average air temperatures .
m above surfac.
The broader implication of the study is that it highlights how climate drivers influence soil salinity.
It also supports the formulation of targeted climate adaptation and coastal resilience policies.
The main focus of the study is on temperature, rainfall and cyclone data.
however, this could be considered a limitation, as other elements that also affect soil salinity, such as wind patterns, evapotranspiration and tidal effects are not fully examined.
Furthermore, understanding of long-term salinity trends and variability influenced by interannual climatic patterns may be limited due to the use of short-term data.
Keywords: Soil Salinity.
Climatic Impression.
Variability.
Seasonal.
Indices.
Introduction Bangladesh faces various natural calamities due to its geological location, with coastal areas being the worst affected by soil salinity, cyclones, storm surges and flooding.
In such areas, soil salinity poses major risks to agriculture, and livelihoods, and to ecosystem health, which is a crucial environmental concern.
Its low elevation, tidal effects and seasonal freshwater scarcity are characteristics of the Ganges Delta, which includes the coastal region in the southwest of the country.
Agricultural interventions related to climate change-related salinity adaptation are vital to guarantee food security in the context of environmental change (Lam et al.
, 2.
A recent study found that estimations of the soil salinity in the south-central coast of Bangladesh are far higher than those found in historical records (Bhuyan et al.
, 2.
, with a decrease of 96% in suitable land for crops in the coastal areas, and a significant rise in soil salinity between 1990 and 2016 (Morshed et al.
, 2.
Significant seasonal .
ry and we.
variation in soil salinity in the coastal regions of Bhola island.
Bangladesh, has been found (Jamil et al.
, 2.
Studies have also been conducted on seasonal variation in the many parts in our Earth which indicate that coastal zones are plagued by high saline levels during the dry season and severe drought and flooding during the wet season (Yadav et al.
, 2.
Soil salinity is positively connected with depth.
it is higher in the accreted land of the Noakhali district in Bangladesh than in the non-accreted areas (Das et al.
, 2.
Copyright: A 2025 by the authors.
Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license .
ttps://creativecommons.
org/licenses/by/4.
0/).
Mondal et al.
In recent decades, soil salinisation has increased due to a combination of natural and man-made processes, such as tidal inundation, rising sea levels, increased evapotranspiration, and reduced upstream freshwater flow.
The primary cause of soil salinity is natural events such as periodic flooding, but incorrect and unscientific irrigation practices have also contributed over time.
Inappropriate irrigation, soil erosion, dispersion, and engineering issues are some of the factors which contribute to soil salinity.
The southwestern coastal district of Satkhira in Bangladesh has particularly suffered from climate change-driven salinity intrusion (Fahim et al.
, 2.
Excessive rainfall is inversely correlated with soil salinity levels in the Noakhali coastal region of Bangladesh, while rising temperatures have shown positive interaction with the salinity (Kawser et al.
, 2.
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The soil salinity scenario of the southwest coastal region of Bangladesh offers invaluable insights for mitigation and policy-making in the context of climate-driven issues (Sarkar et al.
, 2.
Soil salinity is negatively affected by climate change, and significantly hinders crop productivity in coastal areas (Sarkar et al.
, 2.
Remote sensing and GIS tools are a precise approach to mapping coastal salinity and addressing climate drivers.
A study conducted in China coastal zone which covering 350,000 square kilometers of sea area, shows potential for coastal monitoring and provides a robust framework for sustainable development (Liu et al.
, 2.
Another study, likewise conducted in China, also provides invaluable insights for the spatial prediction of soil salinity (Zhou et al.
, 2.
Remote sensing and GIS techniques indicate a high potential for mapping saline areas, with a correlation coefficient (R.
73 and RMSE of 0.
68 respectively (Thiam et al.
, 2.
Monitoring soil salinity levels and mapping salinity-affected areas can be achieved using remote sensing, a non-invasive and time-saving technique (Tripathi et al.
, 2.
Research has been conducted in the Batiaghata sub-district in Khulna to evaluate variations in soil The district is facing acute salinity due to salinity intrusion by storm surges and irrigation during cultivation from surface water (Shaibur et al.
, 2.
There was an increasing tendency from 2008 to 2018 of electrical conductivity in areas where rice was cultivated.
Research has revealed that in Bangladesh the spatial distribution of salinity has tended to increase, with approximately 222,300 ha of land newly affected over the last decade (Faisal et al.
, 2.
Some studies have documented the spatial distribution of soil salinity with limited reference to the role of seasonal climatic variability, and have rarely integrated satellite validation (Faisal et , 2.
However, our study overcomes such limitations by enhancing the remote sensing linkage to seasonal variation, thus providing more comprehensive climatic integration and variation, and by examining the differences between the four meteorological seasons in terms of soil salinity.
The research also suggests policies for a sustainable ecological environment in relation to the crop production sector.
We consider temperature, rainfall and cyclone-associated storm surges for comprehensive climatic integration, together with pre-monsoon, monsoon, post-monsoon and winter seasonal variations and tendencies, contributing to the uniqueness of the study.
The research aims to assess recent seasonal variability in soil salinity.
to evaluate the influence of climatic conditions on such variation.
and to establish the relationship between observed soil salinity and satellite-derived indices using remote sensing and GIS technology.
Materials and Methods Study Area The research was conducted in Khulna District, located in the coastal region of Bangladesh.
The study focuses on six sites within Batiaghata, a sub-district of Khulna.
Batiaghata is divided into two parts, western and eastern, by the Kajibacha River.
It is bounded by Rupsa.
Rampal and Fakirhat upazilas to the east.
Dumuria and Paikgaccha upazilas to the west.
Kotowali and Sonadanga thanas and Dumuria upazila to the north.
and Dakope.
Rampal and Paikgachha upazilas to the The area of Batiaghata covers 235.
32 square kilometers and is situated between latitudes 22A 34' and 22A 46' north and longitudes 89A 24' and 89A 37' east.
According to the 2022 Bangladeshi census, the population of Batiaghata was 171,752, with a population density of 730 per square kilometre, comprising 86,685 males and 85,067 females.
The main rivers in Batiaghata upazila are the Kazibachha.
Shoilmari.
Pasur and Vadra.
The annual average maximum and minimum rainfall totals in the region are 172.
60 mm and 152.
40 mm respectively, with average maximum and minimum temperatures of 31.
7AC and 22.
3AC.
Figure 1 and Table 1 present the selected study sites.
Table 1.
Study Site Description.
Upazila Union Batiaghata Batiaghata Jalma Mondal et al.
Site Name Krishnanagar_1 Krishnanagar_2 Kismat Fultala_1 Kismat Fultala_2 Fultala_1 Fultala_2 Page 308 Forum Geografi, 39.
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Figure 1.
Study Area Map .
Bangladesh Divisional Boundary Map.
Khulna Division Map.
Khulna District Map.
Batiaghata Sub-District Ma.
Data Electrical conductivity (EC) data from six sites in Batiaghata were collected from the Soil Research Development Institute (SRDI), at Khulna in Bangladesh (Table .
Satellite Landsat 8Ae 9 OLI data for the period from January 2023 to December 2023 were downloaded from the USGS Earth Explorer data portal (Table .
In addition, severe cyclonic storm data were collected from the India Meteorological Department (IMD).
Mondal et al.
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Table 2.
Description of the Temporal Range and Resolution of the Data.
Data
Electrical Conductivity Rainfall & Temperature Cyclone Satellite data
Source SRDI
BMD
IMD
USGS
Earth Explorer Temporal Range 30 Nov to 6 Dec 2021 Temporal Resolution Monthly Daily Every 16 Days *The integrated effects of soil salinity on soil due to cyclone are only covered.
Methodology The methodology is subdivided into three parts:
Data collection.
Processing and mapping.
Statistical analysis Data collection : For the study, climatic driver data .
ainfall and temperatur.
were collected from BMD, cyclone data from IMD, and EC data from SRDI.
The data were then processed using Excel software to analyse seasonal variation and trends through regression equations.
Subsequently, freely accessible remote sensing data were collected from the Landsat program .
ttps://earthexplorer.
gov/), which involves a series of Earth-observing satellite missions jointly managed by NASA and the US Geological Survey (USGS).
Processing and mapping : Monthly climatic data were obtained from the daily data.
From the monthly data, seasonal mean values were computed for the four seasons, pre-monsoon (MarchAe Ma.
, monsoon (JuneAeSeptembe.
, post-monsoon (OctoberAeNovembe.
and winter (DecemberAe Februar.
, in an Excel template.
Additionally, the average EC value was evaluated for the Khulna district from the six sites.
The influence of climatic conditions on soil salinity was evaluated using a bar graph.
Satellite data extraction, raster calculation, and the creation of cartographic materials were then conducted in the geographic information system (GIS) for the mapping of indices.
The seasonal field data of soil salinity and satellite indices were compared to determine the relationship between them based on maximum and minimum values.
Statistical analysis : The coefficient of determination (R.
was obtained through diagrams, while the significance of the R2 was assessed using a t-test.
NDSI Indices Using remote sensing data, especially Landsat imagery.
NDSI demonstrated encouraging results for the assessment of soil salinity.
Additionally.
NDSI frequently shows a high positive association with electrical conductivity (EC) recorded in the field.
Since NDSI performs well in detecting soil salinity in areas with minimal vegetation, and as the study area is a coastal region with low vegetation, it is effective in representing soil salinity in the local context.
NDSI indices were derived by using the formula shown in Table 3 for Landsat 8-9 OLI.
Table 3.
Description of the Spectral Indices.
Spectral Indices Equation Reference Normalized difference salinity index (NDSI) ycI Oe ycAyaycI ycI ycAyaycI Khan et al.
R (Re.
= Band 4 .
64 Ae 0.
67 AA.
NIR (Near Infrare.
= Band 5 .
85 Ae 0.
88 AA.
Results All of the results are discussed in the following sub-section.
Seasonal Trend Variation Soil salinity across the six sites in the coastal district of Khulna exhibits both spatial and seasonal The highest salinity levels were recorded during the pre-monsoon season, while the lowest occurred in the post-monsoon season (Figure .
As elevated salinity during the pre-monsoon period adversely affects crop production, the use of rainwater irrigation and the adoption of salt-tolerant crop varieties are recommended as effective mitigation strategies to enhance agricultural productivity in the region.
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EC .
S/.
Monsoon Winter Pre- monsoon Post- monsoon Krishnanagar_1 Krishnanagar_2 Year EC .
S/.
Year Pre- monsoon Post-monsoon Monsoon Winter Kismat Fultala_1 (C) EC .
S/.
Pre-monsoon Post-monsoon Kismat Fultala_2 Year .
Monsoon Winter Fultala_1 EC .
S/.
EC .
S/.
Monsoon Winter .
Year Pre-monsoon Post-monsoon Monsoon Winter .
EC .
S/.
Pre-monsoon Post- monsoon Pre-monsoon Post-monsoon Monsoon Winter Fultala_2 Year Year Figure 2.
Seasonal Trend Variation in Soil Salinity at the Six Sites in Khulna, 2000Ae2023.
Table 4.
Seasonal Variation in Maximum and Minimum Soil Electrical Conductivity.
Site Name Krishnanagar_1 Krishnanagar_2 Kismat Fultala_1 Kismat Fultala_2 Fultala_1 Fultala_2 Maximum (EC dS/.
Minimum (EC dS/.
Season Max.
Value Pre-monsoon Pre-monsoon Pre-monsoon Pre-monsoon Pre-monsoon Pre-monsoon Min.
Value Post- monsoon Post- monsoon Monsoon Monsoon Monsoon Monsoon Year Max.
Value Min.
Value In 2021, the highest soil salinity during the pre-monsoon season was observed at Fultala_2 .
dS/.
, followed by Fultala_1 and Kismat Fultala_2, while the lowest maximum salinity was recorded at Krishnanagar_1 .
6 dS/.
These peak values reflect the seasonal salinity buildup prior to the monsoon.
In contrast, the lowest soil salinity values occurred during the monsoon and post-monsoon seasons, with the highest minimum level recorded at Krishnanagar_1 .
5 dS/m in 2.
, and the lowest at Fultala_1 .
1 dS/m in 2.
ee Table .
These findings highlight a Mondal et al.
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clear seasonal pattern: pre-monsoon salinity levels remain higher than those of any other season, emphasising the strong influence of climatic factors on soil salinity dynamics across all the study Pre- monsoon Monsoon Post-monsoon Winter EC .
S/.
Year Figure 3.
Seasonal Trend Variation of Soil Salinity at Khulna District During 2000Ae2023.
Figure 3 illustrates the seasonal trend of soil salinity for the Khulna district, aggregated from individual site measurements.
The data reveal a consistent pattern, with peak salinity observed during the pre-monsoon season and the lowest levels during the post-monsoon season, mirroring the trends identified at individual sites.
This consistency indicates a uniform seasonal salinity pattern across the study area.
Figure 4 demonstrates that electrical conductivity (EC) values consistently peak during the premonsoon season across all sites and years.
The levels decline from pre-monsoon to monsoon periods, followed by a gradual increase through the post-monsoon and winter seasons, forming a cyclic pattern influenced by climatic factors.
The maximum EC values generally demonstrated an upward trend, with the minimum values also indicating a gradual baseline increase in soil salinity, which may pose long-term risks to agricultural sustainability.
Increases in the maximum and minimum EC values influence the crop tolerance range, consequently hindering crop productivity.
Table 5.
Seasonal Slope .
S mAA/y.
of EC at the Different Sites in Khulna, 2000Ae2023.
Site/District Pre-monsoon Monsoon Post-monsoon Winter Khulna Krishnanagar_1 Krishnanagar_2 Kismat Fultala_1 Kismat Fultala_2 Fultala_1 Fultala_2 AlthThough the highest EC values were observed during the pre-monsoon season (Figure .
, a decreasing trend is evident across all sites during the study period (Table .
In contrast, increasing trends are observed during the monsoon and post-monsoon seasons.
The winter season exhibits both increasing and decreasing trends, depending on the site.
The highest rate of increase in soil salinity was recorded at Kismat Fultala_2 during the post-monsoon season .
966 dSm-1/yea.
, which is statistically significant at the 90% level of significance, while the lowest was also at Kismat Fultala_2 in the winter period .
09 dSm-1/yea.
Conversely, the most significant decrease occurred at Fultala_2 in the pre-monsoon season (-0.
6967 dSm-1/yea.
, while the smallest decrease was at Krishnanagar_2 during the winter season (-0.
0993 dSm-1/yea.
These trends highlight seasonal and spatial variability in soil salinity dynamics across the study area.
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Krishnagar_1 .
EC .
S/.
EC dS/m Krishnagar_2 .
EC .
S/.
EC .
S/.
Kismat Fultala_1 .
Kismat Fultala_2 .
2023_Winter 2023_Post 2023_Monsoon 2023_Pre 2022_Winter 2022_Post 2022_Monsoon 2022_Pre 2021_Winter Year & Season 2021_Post 2021_Monsoon 2021_Pre 2020_Winter 2020_Post 2020_Monsoon 2023_Winter 2023_Post 2023_Monsoon 2023_Pre 2022_Winter 2022_Post 2022_Monsoon 2022_Pre 2021_Winter 2021_Post 2021_Monsoon 2021_Pre 2020_Winter 2020_Post 2020_Monsoon 2020_Pre 2020_Pre Year & Season .
Fultala_1 EC .
S/.
EC .
S/.
2023_Winter 2023_Post 2023_Monsoon 2023_Pre 2022_Winter 2022_Post 2022_Monsoon 2022_Pre 2021_Winter Year & Season Year & Season 2021_Post 2021_Monsoon 2021_Pre 2020_Winter 2023_Winter 2023_Post 2023_Monsoon 2023_Pre 2022_Winter 2022_Post 2022_Monsoon 2022_Pre 2021_Winter 2021_Post 2021_Monsoon 2021_Pre 2020_Winter 2020_Post 2020_Monsoon 2020_Pre 2020_Post 2020_Monsoon 2020_Pre .
Fultala_2 2023_Winter 2023_Post 2023_Monsoon 2023_Pre 2022_Winter 2022_Post 2022_Monsoon 2022_Pre 2021_Winter 2021_Post 2021_Monsoon 2021_Pre Year & Season 2020_Winter 2020_Post 2020_Monsoon 2020_Pre 2023_Winter 2023_Post 2023_Monsoon 2023_Pre 2022_Winter 2022_Post 2022_Monsoon 2022_Pre 2021_Winter 2021_Post 2021_Monsoon 2021_Pre 2020_Winter 2020_Post 2020_Monsoon 2020_Pre Year & Season Figure 4.
Periodic EC Pattern at Different Sites in Batiaghata.
Khulna.
Impacts of Climatic Conditions Rainfall reduces soil salinity by diluting salt concentrations through leaching, while high evaporation increases it by removing soil moisture and leaving salts behind.
In Bangladesh, rainfall is highest during the monsoon season and lowest in winter.
Figure 5 .
Ae.
illustrates the relationship between soil salinity and climatic parameters, namely rainfall and temperature, for the Khulna Excessive rainfall contributes to reduced soil salinity in the monsoon season.
In contrast, the lower rainfall during the post-monsoon and winter seasons leads to increased salinity in winter, peaking in the pre-monsoon season, due to continued moisture deficit.
Elevated average temperatures during the pre-monsoon season further intensify evaporation, exacerbating salinity levels.
Conversely, lower average temperatures correlate with reduced salinity.
These findings suggest a strong inverse relationship between rainfall and soil salinity, and a positive correlation between temperature and salinity, indicating the significant influence of climatic factors on soil salinity dynamics in coastal Bangladesh.
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Pre- monsoon_EC Pre-monsoon_Temperature Pre-monsoon_Rainfall .
Monsoon_EC Monsoon_Temperature Monsoon_Rainfall .
11,4730,0 8,9328,5 9,5529,6 8,2329,4 4,0 30,1 Post-monsoon_EC 5,2 30,3 Winter_EC Winter_Temperature Winter_Rainfall Post-monsoon_Temperature Post-monsoon_Rainfall 5,2 30,3 1,3 29,5 .
Figure 5.
Relationship Between EC .
S mAA).
Temperature (AC) and Rainfall .
Khulna faced a tropical cyclone from 30 November to 6 December, 2021, namely JAWAD (Table Storm surges, commonly associated with such events, transport saline water inland, leading to increased soil salinity through salt deposition.
The impact of tropical cyclones on soil salinity is clearly illustrated in Figure 6.
Following the cyclone event referred to above, soil salinity exhibited an exceptional increase in December 2021 compared to the same month in previous and subsequent years.
Table 6.
Description of Tropical Cyclone JAWAD.
Jawad: 30 November to 6 December 6, 2021 Region:
Bay of Bengal Wind speed:
Saffir-Simpson scale:
Affected region :
Max.
65 km/h Tropical depression Khulna This indicates a sustained influence of storm surge events on long-term soil salinisation in coastal Specifically.
EC levels sharply increased in December 2021, suggesting that a notable event had occurred during this transition period compared to other years.
This event may have been associated with the storm surge of tropical cyclone JAWAD.
5,00
4,50
4,00
3,50
3,00
2,50
2,00
1,50
1,00
0,50
0,00
October November December Year Figure 6.
Monthly Variation (October to Decembe.
in Soil Salinity Between 2020 and 2022.
Mondal et al.
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Relationship between Observed Data and Satellite Output Standard deviation is a fundamental statistical metric that quantifies the degree of dispersion or variability of data points relative to the mean of a dataset.
A higher standard deviation indicates a broader spread of values, suggesting that the data points are more widely distributed.
In contrast, a lower standard deviation signifies that the data are more tightly clustered around the mean.
The values of NDSI, which are computed using spectral reflectance data, normally fall between -1 and Higher salinity is correlated with positive NDSI levels, and lower levels with negative values.
Positive and negative values are produced because they arehigher and lower than the reference value respectively.
In this study.
NDSI values were calibrated to match field-measured electrical conductivity (EC) data to enhance the accuracy of salinity detection.
Satellite-derived NDSI values were processed and visualised using ArcGIS, as shown in Figure 7 .
Ae.
The highest NDSI values were observed during the pre-monsoon season, aligning with EC data obtained from SRDI.
Khulna (Table 7 and Figure .
A clear seasonal cyclic variation is evident in Figures 7 .
Ae.
and 8, showing strong correlation with ground data, except for some inconsistency during the monsoon season.
This ambiguity may have occurred due to the comparisons being made between the direct field observation values and the maximum and minimum values of the satellite data, which incorporate high spatial resolution.
Post- monsoon Figure 7.
NDSI Indices of Soil Salinity Based on Season.
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EC .
S/.
Pre-monsoon Monsoon Post- monsoon Winter Season Figure 8.
Seasonal Soil Salinity from Observed Data.
Table 7.
Description of the NDSI Indices for the Study Area.
Year
Season
Max
Min Mean
STD
Pre -monsoon Monsoon Post -monsoon Winter Discussion The highest maximum NDSI value during the pre-monsoon season was 0.
11580, which corresponds to the observational data obtained from SRDI.
Khulna.
In contrast, the lowest minimum value, 0.
06533, was recorded during the monsoon season, while notably low NDSI values were observed in both the monsoon and post-monsoon periods.
Rainfall exhibited a negative correlation with NDSI, whereas temperature and cyclonic events showed positive correlations.
The fall in soil salinity during the post-monsoon and winter seasons can be primarily attributed to increased monsoonal precipitation and lower evapotranspiration rates.
In contrast, elevated soil salinity in the pre-monsoon season results from higher ambient temperatures and storm surges, which enhance salt accumulation within the soil profile.
High rainfall is inversely correlated, with inundation due to cyclones being positively correlated with soil salinity level in the Noakhali coastal area of Bangladesh (Kawser et al.
, 2.
This study also observed a strong inverse relationship between excessive rainfall and soil salinity throughout the seasons.
Climate change, or rising air temperatures, causes more water to evaporate and more salt to be present in the soil (Corwin et , 2.
Our study is also consistent with previous research on temperature, as well as other climate drivers that are used in the study.
According to the study findings, the south-western region of Bangladesh are more vulnerable to saline intrusion than other regions because of greater storm surge impacts, lower elevation, and different land use patterns (Akter et al.
, 2.
The cropland of the Indian Sundarbans is experiencing a significant change in soil salinity due to cyclone-induced flooding, with coastal areas tending to have very high soil salinity (Barui et al.
The findings align with this observation.
The study demonstrates that soil salinity in the region exhibits significant seasonal variability, largely influenced by climatic parameters.
Climatic factors, particularly rainfall and temperature, were found to contribute substantially to the observed fluctuations in soil salinity.
However, the study was conducted within a limited spatial scope, specifically in Batiaghata Upazila, a sub-district of Khulna.
Bangladesh, over the period 2020Ae2023.
Further research is recommended in other regions of Bangladesh to validate spatial variability and encourage policymakers to implement targeted measures to mitigate the impacts of soil salinity on agriculture, thereby supporting long-term food security.
Conclusion The electrical conductivity was highest during the pre-monsoon season and lowest during the post-monsoon season across all individual sites and in the aggregated data.
These results are consistent with satellite-derived indices.
The observed seasonal variability in soil salinity, derived Mondal et al.
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from both satellite data and field measurements, aligns with the study objectives.
Furthermore, the findings provide a clear interpretation of soil salinity dynamics in relation to climatic drivers.
Acknowledgements The authors deeply acknowledge the Soil Research Development Institute (SRDI).
Khulna for supplying Electrical Conductivity data.
Corresponding author humbly acknowledges Md Moniruzzaman.
Graduate Teaching Assistant of the Department of Geography & Environmental Studies for his kind cooperation.
Author Contributions Conceptualization: Mondal.
Akhter.
Mondal.
Akhter.
investigation: Mondal.
Akhter, writingAioriginal draft preparation: Mondal.
Akhter, writingAireview and editing: Mondal.
Md.
Akhter, , & Mallik.
visualization: Mondal.
Akhter.
All authors have read and agreed to the published version of the manuscript.
Conflict of interest The Authors declare no conflict of Data availability Data will be made available on request to the corresponding author.
Funding This research received no external Mondal et al.
References