Atmospheric CH4 Dynamics of South-West Bangladesh: A Google Earth Engine-Based Remote Sensing Approach


Atmospheric CH4 Dynamics of South-West Bangladesh: A Google Earth Engine-Based Remote Sensing Approach

Authors: Tahmid Anam Chowdhury, Nusrat Jahan Ety, Warefta E Murshed, Abed Chaudhury

Author for correspondence: Abed CHAUDHURY

Project: Krishan Foundation, Kanihati, Hafiza Khatun Memorial Trust.
Village: Hazipur, Upazila: Kulaura, District: Moulvibazar, Bangladesh


A severe contributor to global warming, Methane (CH4) is a significant element of Green House Gases  (GHGs). Both anthropogenic and natural sources are responsible for its emission. The development of  satellites for remote sensing has made it convenient to study the spatiotemporal distribution of any gaseous  component. Tropospheric Monitoring Instrument (TROPOMI) integrated with Sentinel-5 Precursor  (Sentinel-5P) satellite is proven efficient in studying CH4. Sentinel-5P TROPOMI CH4 data from February  2019 to September 2022 has been used to research southwest districts with the Sundarbans mangrove forest.  Besides, satellite data from MODIS (Moderate Resolution Imaging Spectroradiometer), ERA5 and Climate  Hazards Group Infrared Precipitation with Station data (CHIRPS) are used to analyse methane changes  from 2019 to 2022. All these diverse datasets have been retrieved by using Google

Earth Engine (GEE)  platform. CH4 emission shows an increasing trend over the study area. The emission rate is higher (more  than 1900 to 1950 ppb) in all districts during the dry winter season, especially from January to March.  Particularly, cropland and water have regular and higher emissions. On the contrary, bare ground and  rangeland have irregular and higher emissions. Built area exerts higher emission trend (more than 1900  ppb) in Satkhira district. All districts, including the Sundarbans, have shown positive relation with  Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), 

indicating single cropland and shrimp aquaculture as significant emitters. The presence of massive  vegetation and waterbodies in the Sundarbans is responsible for low emissions (below 1900 ppb).  Sundarbans have been found with an anomalous correlation with meteorological variables. Apart from the  anthropogenic perspective, there could also be potential environmental and geological sources of CH4  emissions.


Methane (CH4) is one of the important greenhouse gas in the atmosphere, after Carbon Dioxide (CO2)  (Kozicka et al., 2021; Naik et al., 2021). Its warming potential is 25 times greater than CO2 (Boucher et al.,  2009). CH4 can be emitted due to geological and anthropogenic causes (Judd 2000; Etiope & Klusman  2002; Jacob et al., 2016; Jacob et al., 2021). Geological sources include degradation by bacteria, breakdown  of complex organic molecules and inorganic processes deep within the earth’s crust (Judd 2000).  Anthropogenic sources include livestock, oil–gas, landfills, coal mines, wastewater management, and rice  cultivation (Jacob et al., 2016). CH4 in the atmosphere is increased three times since the pre-industrial  period. Slow CH4 rise between 1984 and 1999 (8.42 ± 0.62 ppb/year), lower CH4 rise between 2000 and  2010 (2.5 ± 0.52 ppb/year), and resumed CH4 surge between 2011 and 2021 (9.46 ± 0.57 ppb/year)  (Dlugokencky, 2021). In this industrial era, prolonged emissions of CH4 are caused by anthropogenic or  natural/geologic sources, and reduction in the atmospheric oxidative capacity or combined action of all  three factors remains unresolved (Nisbet et al., 2019; Turner et al., 2019). 

According to Tareq et al. 2022, The Ganges River discharges to mangrove forests which is a hotspot for  the study of biogenic gases (CO2, CH4 & N2O) which are radioactively active gases. The Acharya et al.  study showed that estuaries are a minor source of CH4 but oscillate between sources and sinks for CO2 and  N2O gases through water-air CO2, CH4, and N2O fluxes. It was also found that the CO2 fluxes were ~10  times higher during the monsoon seasons. In an important river in South-West Bangladesh, The Rupsha  River metal concentration of As, Pb, Cd and Cr were found in sediment sample which attributed

to natural  sources and anthropogenic industrial activities (Tareq et al., 2022). Due to river erosion and new alluvial  deposition the estuarine islands constantly change shape and position (Ashrafuzzaman et al., 2022a). 

The phenomenon as climate change and sea level rise is worsened by the emission of Green House Gases  (GHG’s). About half of the population lives 5m below sea level, it is estimated that 17% of the territory of  Bangladesh will disappear and create 20 million refugees. It has been projected by scientists that in the year  2100 the sea level rise will range from 0.53 to 0.97 m in 37 coastal stations at the Bay of Bengal, where the  predicted global sea level rise is 0.09–0.88 m (Haque et al., 2019). The impacts of SLR are worsened by 

floods, eroding coastlines, saltwater intrusion, increased storm frequency and ecosystem changes  (Ashrafuzzaman et al., 2022b). The residents of the southwestern coastal region of Bangladesh (SWCRB)  are trying to adapt to the effects of climate change and sea level rise (CCSLR) as saltwater intrusion,  agricultural loss, and high-intensity cyclones (Ashrafuzzaman et al., 2022a)

Eutrophication is the process of deposition of nutrients and minerals such as phosphate and nitrates. This  phenomenon hinders aquaculture farmers by changing the biochemical oxygen demand (BOD) and  transparency. The agricultural sector is supposed to be the largest emitter of CH4 gas. All the same, CH4  emission varies seasonally depending on agricultural activities and meteorology which is intensive during  June-September from agriculture. The CH4 production and oxidation influence fertilization (Schimel  2000). The human impact of CH4 emission induced by agricultural activities can be therefore reduced by  biochar–based fertilizers modulating soil biogeochemistry and microbial activity. Spaceborne measurements as AIRS (Atmospheric Infrared Sounder), OCO (Orbiting Carbon Observatory),  SCIAMACHY (Scanning Absorption Spectrometer for Atmospheric Cartography), and GOSAT  (Greenhouse Gases Observing Satellite) can be used for the study of atmospheric trace gases (Schaefer et  al., 2016; Singh et al., 2021).

Geographic Information System (GIS) is an effective tool to represent CH4 emission spatiotemporally.  Satellite sensors help to define CH4 content at the global scale (Jacob et al., 2016). CH4 observation from  space on a worldwide scale includes various satellite sensors. It started SCIAMACHY instrument  (Frankenberg et al., 2005), GOSAT and the TROPOMI (Tropospheric Monitoring Instrument) instrument  (Lorente et al., 2021). Satellite-based methane observations have been used to study emission globally, on  a continental scale and even on a local/small scale (Alexe et al., 2015; Bergamasch et al., 2013; Wang et  al., 2013; Qu et al., 2021; Wecht et al., 2014; Maasakkers et al., 2021; Lu et al., 2022; Miller et al., 2019;  Zhang et al., 2020; Shen et al., 2021; Pandey et al., 2019; Sadavarte et al., 2021; Lauvaux et al., 2022;  Maasakkers, et al., 2022a,b; Feng et al., 2022). Javadinejad et al. (2019) examined GOSAT-based CH4 emissions with different climatic parameters and observed a significant relationship between them. Among  all satellites, TROPOMI is one of the most efficient sensors to monitor CH4, because of high spatial  resolution, frequent revisit, time and free availability. It is integrated with Sentinel-5 Precursor (Sentinel 5P), and its data is available from 2019. CH4 content is evaluated using two spectral ranges, i.e., near infrared (NIR: 675–775 nm) and short-wave infrared (SWIR: 2305–2385 nm) (Hu et al., 2016).

Study Area

The specific area of this study is located southwest region of Bangladesh, covering Satkhira, Khulna, and Bagerhat districts with the UNESCO world heritage site- Sundarbans mangrove forest. It has an Indian  province in the west, Barishal district in the east, Jessore district in the south and Bay of Bengal in the north.

The extent of the study area is from 88°54’46.849 “East to 89°50’3.643 “East, from 22°52’53.362 “North to  21°57’47.624″North (Figure 1). Khulna, Satkhira and Bagerhat districts have a total population of 5,780,576  (BBS, 2011). Khulna and Bagerhat districts have a decreasing trend in population from 2001 to 2011 (Table  1). A maximum decrease has occurred in the Bagerhat district, and only the Satkhira district has an increase  in population. Elevation ranges from 16 meters below Mean Sea Level (MSL) to 29 meters. The average  maximum height (11.45 meters MSL) is in Sarankhola upazila (Bagerhat district), and the minimum  elevation (1.89 meters MSL) is in Shyamnagar upazila (Satkhira district) (Figure 1). This area receives a  total rainfall of 1750-2000 millimetres annually, and the annual average temperature is from 26° to more  than 26.5° Celsius (Barry and Chorley, 2009). A geologically significant portion of this study area  comprises Tidal deltaic deposits and Mangrove swamp deposits (Alam et al., 1990; Figure 2a). General soil  types are majorly Calcareous Dark Grey Floodplain and Non-calcareous Grey Floodplain (non-saline)  (Brammer, 1996; Figure 2b). Apart from the Sundarbans mangrove forest, major land cover classes are  cropland (2962.90 sq. km) and water (1630.32 sq. km) until 2021 (Figure 2(d)(e)(f); Table 5). The  Sundarbans have the highest amount of vegetation and water bodies (Table 5). Salinity level appears slight  to very strong across the middle portion covering Shyamnagar, Kaliganj, Koyra, Dacope, Paikgacha,  Rampal, Morelganj upazilas. The southern part of the Satkhira district is non-saline and the southern part  of the Bagerhat district is intensely saline (SRDI, 2010; Figure 2c). Single crop (Boro rice) is cultivated in  the majority area of agricultural land of Satkhira, Khulna and Bagerhat districts (Table 2, 3). The principal  crop after Boro rice is Vegetable, cultivated in cropland and floating gardens (Nowreen et al., 2013; Table  3). Primary fish production comes from Shrimp aquaculture and ponds. Bagerhat district has a considerable  fish supply from the Sundarbans area (Table 4). From the physiographic view, the concerned study area  holds a unique location, environment and ecosystem in Bangladesh.

Figure 2: Different Environmental Parameters and Land Cover of the Study Area

Ma et al. (2018) observed that CH4 emissions from inland aquaculture ponds contribute to regional and  national GHG (greenhouse gas) budgets. Rosentreter et al. (2021) found that aquatic ecosystem emits half  of global CH4. Only coastal aquaculture ponds emit almost 5.9 ± 15.1 Tg (Teragram) CH4/year. Commercial shrimp culture is also included in the aquatic ecosystem (Rosentreter et al., 2021). The  conversion of brackish marsh to coastal shrimp pond causes increased emission of CH4, even 10-fold higher

than brackish marsh emission (Yang et al., 2017). To measure CH4 emissions from the shrimp aquaculture  area, (Yang et al., 2019) collected water and sediment samples. The middle stage of culture emits a  maximum CH4 than the initial and final stages (Yang et al., 2018; Yang et al., 2019). Integrated activities  of organic and inorganic processes in shrimp aquaculture are responsible for significant CH4 emissions.  The South-west region of Bangladesh is located along a coastal belt with unique land cover and  environmental characteristics. Here, coastal aquaculture, especially shrimp in brackish water, is a  significant fish production sector (DOF, 2021). On July 2022, fuel gas emission from a shrimp enclosure  at Mongla (Bagerhat district) was reported, which was also being used for cooking activity (The Daily Star,  2022). As far as it is known, no study on CH4 emission from this major coastal and aquaculture region of  Bangladesh has been conducted yet. So, this study aims to study atmospheric CH4 dynamics of southwest  Bangladesh using Sentinel-5 TROPOMI satellite. The specific objectives of this study are to (i) study CH4  emissions from spatial and different land cover dimensions, (ii) find out potential land cover behind CH4  emission, and (iii) relate between CH4 and different meteorological and surface variables. We hypothesise  that (i) specific anthropogenic activities emit a significant level of CH4, (ii) as the region is in a unique  physiographic division, it may have both geological and climatic factors of CH4 emission. This study’s  findings would illustrate the scientific basis for refining climate action plans to reduce GHGs (greenhouse  gases) on the national, and in turn, international levels. 

Table 1: Population Change, 2001-2011 (BBS, 2011) 

2001  2011
Khulna Urban  1284208  777588
Rural  1094763  1540939
Total  2378971  2318527
Bagerhat Urban  206554  195331
Rural  1342477  1280759
Total  1549031  1476090
Satkhira Urban  171614  197616
Rural  1693090  1788343
Total  1864704  1985959


Table 2: Land Utilization Statistics (sq. km) 2019-20 (BBS, 2021)

Satkhira  Khulna  Bagerhat
Total area  3816.19  4394.89  3957.83
Forest area  1497.34  2209.59  2294.57
Not available for cultivation area  546.33  789.14  348.03
Culturable waste area  68.8  44.52  48.56
Current fallow area  263.05  141.64  169.97


Single cropped area 890.31 752.72 704.15 

Double cropped area  428.97  339.94  263.05
Triple cropped area  121.41  117.36  121.41
Quadruple Cropped area  0.00  0.00  8.09
Net cropped area  1440.68  1210.01  1096.70
Gross cropped area  2112.46  1784.67  1626.84


Table 3: Area (sq. km) Irrigated under Different Crops 2019-20 (BBS, 2021) 

Region  Aman  Boro  Wheat  Potato  Vegetable  Other 





Satkhira  36.42  712.25  8.09  24.28  174.01  24.28  979.34
Khulna  24.28  598.94  –  8.09  72.84  28.33  732.48
Bagerhat  4.05  424.92  –  4.05  60.70  12.14  505.86


Table 4: District-wise Annual Fish Production (Metric Tons) of Inland Water bodies in 2019-20  (DOF, 2021) 

Satkhira  Khulna  Bagerhat
River  1380  3666  5318
Sundarbans  716  762  19529
Beel  33  228  32
Flood Plain  12674  19754  5093
Pond  39599  16040  17603
Seasonal waterbody  1761  1022  1779
Baor  201  10
Shrimp/Prawn Production Bagda  24088  12549  17488
Galda  8631  13325  16337
other shrimp/prawn  4383  1733  2117


Table 5: Different Land Cover Area (sq. km) of 2019, 2020, and 2021 (Adapted from Karra et al.,  2021)

Year  Bagerhat  Khulna  Satkhira  Sundarban  Total
Water 2019  320.73  583.82  800.45  1557.41  3262.41
2020  331.30  607.49  820.22  1551.49  3310.50
2021  312.53  552.65  765.13  1555.51  3185.82


on2019  397.86  131.56  216.06  3791.00  4536.48
2020  389.68  112.65  134.67  3801.10  4438.10


2021  385.78  115.46  161.46  3798.97  4461.67
Wetland 2019  57.32  66.52  18.05  10.80  152.69
2020  78.85  79.56  13.04  3.42  174.87
2021  81.42  82.56  8.84  6.76  179.58
Cropland 2019  1051.58  1034.53  924.81  7.06  3017.98
2020  994.51  960.53  859.93  7.20  2822.17
2021  1010.40  1007.02  945.48  6.54  2969.44
Built Area 2019  164.59  318.13  332.98  3.04  818.74
2020  197.83  361.64  463.15  3.26  1025.88
2021  199.63  351.58  407.88  3.22  962.31
Bare Ground 2019  3.17  2.06  0.53  0.56  6.32
2020  2.28  3.03  1.40  0.62  7.33
2021  4.03  6.17  1.97  0.62  12.79
Rangeland 2019  6.99  14.27  1.05  8.08  30.39
2020  7.79  25.98  1.53  10.86  46.16
2021  8.45  35.44  3.18  6.34  53.41


Methods and Data 

The study area has four parts: Satkhira, Khulna, Bagerhat district and Mangrove Forest Sundarban of  Bangladesh. Sentinel-5 TROPOMI has been used in this study. The used band for CH4 is the column averaged dry-air mixing ratio of CH4 (in ppb—parts per billion). Data were retrieved from 8 February 2019  to 22 September 2022 using Google Earth Engine (GEE). CH4 from Sentinel-5 TROPOMI has been  validated by GOSAT satellite and ground-based TCCON instruments. CH4 images have a spatial resolution  of 1 × 1 KM (ESA, 2021). A total of 767 images have been analysed for this study. Due to cloud coverage, 

there is also some missing CH4 data on specific dates/months (ESA, 2021). Raster datasets of the column averaged dry-air mixing ratio of CH4 have been used as the primary component. CH4 raster datasets have  been averaged for 2019, 2020, 2021, and 2022. The only exception marked was 2022, which had datasets  from January 2022 to September 2022. For the land cover of 2019, 2020 and 2021, raster datasets have

been adapted from Sentinel-2 land cover (Karra et al., 2021; Figure 2(d,e,f). Moreover, MODIS (Moderate  Resolution Imaging Spectroradiometer) sensor is an essential tool that covers data from both land and ocean  over a broad scale. It has 36 different bands, which collect data on the earth after 1 to 2 days. Its image  pixel has a resolution from 500 metres to 1 KM for other band datasets. Normalised difference vegetation  index (NDVI) from MOD13Q1 band, Normalised Difference Water Index (NDWI) from MOD09GA band  and land surface temperature (LST) from MOD11C3 band by MODIS satellite have also been used. NDVI  represents vegetation, cropland, and grassland, whereas NDWI correspondence to water, wetland, and  flooded surface. Time series of mean air temperature and precipitation for each district/area has been  retrieved from ERA5 Copernicus Climate Change Service and Climate Hazards Group InfraRed  Precipitation with Station data (CHIRPS) respectively (C3S, 2017; Funk et al., 2014)

Figure 3: Average CH4 Emission Map by Year

In the study, CH4 time series data for a particular district/boundary has been retrieved to assess temporal  change. Zonal statistics have been done using QGIS 3.10 software. First zonal statistics have been  calculated to get CH4 distribution statistics within a particular area/boundary. After that, zonal statistics  were done between CH4 and land cover raster datasets, revealing CH4 variations over different land covers. 

Javadinejad et al. (2019) stated that CH4 emission dynamics impact meteorological/climatic variables.  Correlation between the monthly average of CH4 and air temperature, precipitation, LST, NDVI, and  NDWI have been done using R (v. 4.1.3). Mean CH4 and other parameter values of each district/boundary  over a time series have been used to do Pearson’s correlation. The correlation formula is mentioned below:

In this formula, each variable has been taken as x and y representing CH4 count. Pearson’s correlation  coefficient represents a degree of changes and inter-relational strength between CH4 and each climatic  variable.  

Result & Discussion 

Figure 3 shows Spatio-temporal changes of CH4 over different districts. Among all districts, CH4 emissions  increased over Satkhira from 2019 to 2021. The interior of the Sundarban boundary has lower emissions in  all years. The statistical variables of the CH4 of different districts are shown in Figure 4a, including average,  minimum and maximum values. CH4 raster data is plotted over value and pixel frequency. Khulna district  has a full range of CH4 emissions, particularly Paikgacha upazila has the peak emission. Debhata upazila  of Satkhira district has remarkable CH4 emission on average. In comparison to the three districts,  Sundarban has low CH4 emission and its frequency is also down.

Figure 4: (a) Averaged CH4 Distribution by District, (b) Correlation between Monthly CH4 and  Other Parameters

The monthly emission from 2019-2022 of three districts and Sundarban is plotted in Figure 5. Some  dates/months do not have CH4 data due to cloudiness in the middle of the year, especially during monsoon  season (ESA, 2021). CH4 emission is increasing at particular months (especially the dry winter season)  than the previous year. Both Khulna and Bagerhat had continuous CH4 increases from January to March.  Besides, Satkhira and Sundarbans have similar trends from January to March and December to March,  respectively (Figure 5(c)(d)).

Figure 5: Monthly CH4 Change of Different Areas (2019-2022)

Figure 6(a)(b)(c) show comparatively irregular and higher CH4 emissions from bare ground and rangeland.  The bare ground represents brick kilns and heavy industries, potential CH4 emitters (Khan and Paul, 2021).  Most brick kilns are situated in Koyra, Dumuria, Rupsa (Khulna district), Satkhira sadar, Kaliganj, Assasuni  (Satkhira district). As Mongla seaport is located in this region, many industries in Rampal upazila  (Bagerhat) are built on the bank of Mongla river. The geographic location of navigation opportunities in  this region is a significant cause behind the development of seaports and industries. Rangeland is fallow  land seasonally used for crop cultivation and aquaculture. Cropland and water show an increasing trend of  CH4 in three districts. CH4 emissions from cropland can be interpreted as follows, single crop practice,

majorly boro rice cultivation (Table 2, 3). Aquaculture, especially shrimp culture, is being done over a  massive area of water and wetland. Seasonally it is also cultured in cropland with rice (Barmon et al., 2004).  As coastal aquaculture is regarded as a source of CH4, this major shrimp aquaculture also causes emissions  from the cultured area (Ma et al., 2018; Rosentreter et al., 2021). CH4 emission from built areas seems to  have an irregular trend in all districts except Satkhira. By 2021 in Sundarban, CH4 from cropland and built  area emits CH4 more than 1900 ppb (Figure 6d). Vegetation from everywhere shows an emission level  below 1900 ppb. Apart from anthropogenic factors, natural biogeochemical and geological processes in the  coastal region could release CH4 into the atmosphere (Arai et al., 2021; The Daily Star, 2022).

Figure 6: Average CH4 Distribution over Different Land Cover (2019-2021)

After preliminary statistical analysis, as shown in Figure 4a, 5, and 6, it is essential to understand a more  precise CH4 relationship. Pearson’s correlation was done to interpret the relationship between CH4 and the  meteorological-surface parameter. Each variable has been averaged by month from February 2019 to  August 2022. Meteorological parameters include temperature (C), precipitation (millimetre) and surface  parameters include LST (C), NDWI, and NDVI (Figure 4b).  

As shown in Figure 4b (i), CH4 has weak negative relation with LST in all areas except in Sundarbans. All  areas exhibit positive relations with NDWI (Figure 4b (ii)), where Satkhira has strong, Sundarbans has moderate, Bagerhat and Khulna have weak relations. CH4 emission from coastal aquaculture is verified  from this positive relation with NDWI. Bagerhat and Khulna exhibit middle positive relations with NDVI  (Figure 4b (iii)), whereas Satkhira and Sundarbans have weak positive relations. Cultivation of rice in most  cropland is a possible reason for such emission relation with NDVI (Table 2, 3, 5). In three districts, CH4  has a weak negative association with temperature (Figure 4b (iv)). Except for Sundarbans, other areas tend  to be negatively related to precipitation, which is lower than those found in similar studies (Figure 4b (v);  Javadinejad et al., 2019). Only in the Sundarbans, CH4 has a weak positive relation with LST, temperature  and a fragile positive association with precipitation (Figure 4b (i)(iv)(v)).  


The aforementioned study shows that each area has an increasing trend of CH4, especially in the dry winter  season. Significant CH4 emissions are observed from cropland and water, as these are dominant land cover  in all three districts. Integrated single-crop rice cultivation and shrimp aquaculture are essential factors.  Moreover, excessive brick kilns and industries near cities and seaports also trigger emissions. A weak  association has been found with CH4 regarding surface feature parameters like NDVI and NDWI, which  validates emission from shrimp enclosure and rice field hypothesis. Due to extensive coastal aquaculture,  CH4 has a strong relation (0.7) with NDWI in Satkhira (Table 4). Positive relation with NDVI in the coastal  region is also reported by Javadinejad et al. (2019). CH4 emission from crops, especially rice fields, is a  proven fact. But in recent decades, several studies have found CH4 emission from aquaculture in many  coastal regions of the world (Ma et al., 2018; Rosentreter et al., 2021); Yang et al., 2017; Yang et al., 2019).  LST, temperature and precipitation have behaved weakly negative for all districts. Only in the Sundarbans,  CH4 has a weak to moderate positive relationship with all parameters. There are some anomalies have been  observed for LST and temperature. Similarly, LST showed an anomalous trend in a study on Khulna  (Dewan et al., 2021). Apart from human-induced activities, there can be possible geological emission  causes, as reported in a report before (The Daily Star, 2022). 

In the near future, the findings of this study are expected to prove efficient for further work given climate

change. The study illustrates the scenario of CH4 emission on a regional scale to progress the UN’s 

sustainable development goals (SDGs) achievement. It will be a prospective field to study this region’s  biometeorological and microclimatic dynamics in the coming days. 


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