Abstract:
Soil salinity has been one of the long-term disasters in the coastal belt of Bangladesh for the last 20 years and does have the potential to increase in the next 60-70 years in allRepresentative Concentration Pathway (RCP) scenarios. Soil Salinityharms ecology, crop production, livelihood, drinking water,and public health. Coastal areas of Bangladesh are exposed to the regular cyclone, storm surges, and low flow of non-saline river water from upstream. Due to climate change cyclones, storm surge is more frequent and the magnitude of precipitation has also increased.This phenomenon increases soil salinity intrusion more than before through tidal floods. Low production of rice is encouraging coastal farmers to shift to saline-water-based shrimp aquaculture more than before. Due to improper zoning and mixing practices of shrimp aquaculture with farmland salt properties are spreading at an alarming rate.As the study area two upazilas of Khulna District: The Paikgacha and The Koyra Upazila have been chosen as this area is very exposed to soil salinity and this study area can representsthe total salinity scenario of other coastal areas of Bangladesh. Accurate Soil salinity data is necessary for policymakers to plan a wide range of adaptation and mitigation policies to counter the negative impact of soil salinity. This study assessed the present soil salinity status and temporal variation of soil salinity for the last decade through field survey data and satellite remote sensing data. This study conducted the field surveyon 9th December of 2023 just a day after the satellite image was captured by Landsat 8-9. For the Field survey, this study used the portable probe approach rather than a laboratory testing approach as it is cost-effective, convenient to use, and shows high accuracy (R2=0.997) with laboratory-based salinity results.The regression analysis has been performed to compare survey data with Different Landsat 8 Bands and Salinity Indices to test which bands have a strong correlation (R2 value greater than 0.65) with our study area EC samples.Root Mean Square Error (RMSE) and P-value have been calculated for all regression models. The highest correlated models with a low RMSE value andan acceptable P value ( less than 0.05) were used to estimate EC in dS/m unit directly from each cell value of salinity Indices. For ground truthing, this study has performed validation tests using estimated EC and survey EC using completely separate samples of previous samples. Comparing the Rankings of RMSE and R2this study found that SWIR1 and NIR bands have a better correlation than other bands and Salinity Index SI7 which is a combination of SWIR1, Red, and Green bandsshowsa good correlation with testing and validation samples. This study has prepared a soil salinity map of the present time using a satellite image-based salinity index. Themap from the salinity index SI7 visualizesthe salinity scenario very well. This study has prepared a qualitative map where classification has been provided as per local farmers' observation on salinity magnitude. For the study have prepared a salinity map for 2017, and 2013 to observe temporal variation with the SI7 Index. This study can conclude that in 2022, the soil salinity situation has improved slightly compared to a decade ago. However,almost 75% of the study area is still not good for rice production. Both natural and manmade reasons are responsible for soil salinity intrusion. Properlanduse planning is required to mitigate soil salinity intrusion in this study area.