2.1. Study Area The research is carried out in the coastal area, the southern part of Bangladesh, composed of 18 districts, viz. Bagerhat, Barguna, Barishal, Bhola, Chattagram, Cox’s bazar, Feni, Gopalganj, Jessore, Jhalkathi, Khulna, Lakshamipur, Madaripur, Narail, Noakhali, Patuakhali, Pirojpur, and Satkhira. The whole study area is located between 89o93"0 E and 21o23"0 N, and the surface area is 47,150 km2. Though the people of the coastal areas are mostly dependent on agriculture, the cropland quality is already degraded and continuously degraded because of the occurrence of natural disasters and climate change impact. Coastal Bangladesh is a hotspot for hydrometeorological disasters, where cyclones, tidal waves, drought, floods, waterlogging, saltwater intrusion, and land subsidence are common phenomena. This has a direct impact on livelihoods, since agriculture employs more than 60% of Bangladesh’s population [38], and it is also a major source of income for the 40 million people who live along the coast.
2.2. Multicriteria Evaluation of the Land Suitability Analysis The assessment of land suitability for a specific use is known as land suitability analysis (Food and Agriculture Organization [FAO]. A land suitability assessment examines various criteria, such as the climatic, geographical, soil, vegetation, and other characteristics of lands, to determine suitable lands for specific uses. One of the most critical aspects of this assessment is the definition of parameters that influence land suitability. Land suitability for agricultural uses can be assessed using a variety of parameters that take into account a variety of factors, such as data availability, farming methods, the precision of evaluation, the crop type, and the environmental characteristics of the study region.
For evaluating the land suitability of pulse crop (green gram), eleven criteria are considered which belong mostly to topography (slope and elevation), climate (rainfall, land surface temperature), land use land cover (LULC), and soil characteristics (topsoil texture, soil drainage, soil salinity, soil pH, soil depth, and inundation land type) based on a relevant literature review and the opinions of experts like agriculturists, agronomists, and government personnel from the agriculture ministry.
2.2.1. Topography Data The topography of the study area refers to the slope and elevation properties of the land of the coastal area. The slope and elevation were calculated using the original Shuttle Radar Topography Mission (SRTM) and digital elevation models (DEM), which were downloaded from the USGS earth explorer in the ArcGIS environment. The topographical maps were produced and corrected the projection using the Universal Transverse Mercator (UTM) projection and the WGS 84 datum (WGS 84 46N) in the ArcGIS environment. The slope was determined by calculating the maximum rate of change between each cell and its neighbors. In the output raster, each cell had a slope value. A lower slope value means that the terrain is flatter, while a higher slope value indicates that the terrain is steeper. Flat fields had a smooth surface, which was better for crop cultivation because it made water distribution more even and fair. It is observed that the slope of the study area ranges from zero to 77.44%, and the altitude ranges from zero to 255 m.
2.2.2. Rainfall The rainfall data are collected from PERSIANN-CCS, of the (CHRS) data portal. The PERSIANN-Cloud Classification System (PERSIANN-CCS) is a real-time global high resolution (4 km × 4 km;) satellite precipitation product developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI). The PERSIANN-CCS system enables the categorization of cloud-patch features based on the cloud height, areal extent, and variability of the texture estimated from satellite imagery. Rainfall raster data were downloaded for the year 2020 for the whole country, followed by extraction by mask in ArcGIS to get the data for the study area for further reclassification. Before reclassification, the raster has been resampled to get the desired cell size, which is compatible with the cell size of other criteria.