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Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Dist. - Nadia, West Bengal PIN - 741 252

The university established in 1974, has completed five decades of its existence as the pioneer institute of Agricultural Education, Research and Extension. The main objective of this Viswavidyalaya is to provide facilities for the study of Agriculture, Horticulture and Agricultural Engineering. It is also to conduct researches in these sciences and undertake the educational and extension programmes in agriculture among the rural clientele base, keeping in view the requirements of the state.

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Subject: Agricultural Meteorology × Start date: 2001 ×
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    ThesisItemOpen Access
    Mapping of Surface Water Resources of the Coastal Blocks of Indian Sundarbans Using Optical Remote Sensing
    (Department of Agricultural Meteorology and Physics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia-741252, 2023) Sarkar, Sreya; Dr. Manoj Kumar Nanda
    The Sundarbans is the largest continuous mangrove forest in the world, situated within the world's largest delta, formed by the Ganges- Brahmaputra-Meghna rivers. The unique geographic location along the coast of the Bay of Bengal renders the Sundarbans highly vulnerable from both biodiversity and climatological perspectives. About 4.43 million of people are settled in Sundarbans with agriculture and fishery as their main sources of livelihood. A part of the population depends on forest resources mainly for collection of honey. Crop production in the Sundarbans is challenging due to salinity build up in dry season and the scarcity of quality water for irrigation. Hence, surface water mapping through remote sensing is important for crop planning during pre- and post-monsoon seasons. Keeping this in view a study was carried out in 19 coastal blocks of Indian Sundarbans to analyse the seasonality of rainfall, to assess the spatiotemporal trend of seasonal water surfaces using Sentinel-2 satellite imageries and to find the possible linkage of satellite derived vegetation status with rainfall and surface water resources of coastal blocks of Indian Sundarbans. The investigation was carried out for last five years (2017 to 2022) using optical remote sensing data of Sentinel-2 and the open-source data products in the cloud computing platform of Google Earth Engine (GEE). The CHIRPS (Climate Hazards Group Infrared Precipitation with Station) rainfall data showed that the highest monsoon rainfall occurred in 2021-2022, whereas highest preand post-monsoon rainfall occurred in the year 2017-18. Both 2018-19 and 2020-21 experienced a lower amount of rain during this period. The extent of seasonal surface water coverage estimated by Modified Normalized Difference Water Index (MNDWI) with appropriate threshold (> 0.01) from seasonal median composite of Sentinel-2 imagery for the period from December to May, each year of the study period revealed that in North 24 Parganas the seasonal surface water extent was maximum in Minakhan block throughout the study period followed by Sandeshkhali-1. The minimum was found in Hingalganj block. Most of the blocks showed an increasing trend during last five years. The maximum permanent water surface area was in Sandeshkhali-1 (699 ha) followed by Minakhan (5686 ha). The Hingalganj block contains the minimum permanent water areas. In South 24 Parganas, Canning-2 contained the maximum extent of seasonal surface water while the lowest seasonal surface water area found in Jaynagr-1. The maximum and minimum permanent water surface area was found in Canning-2 and Jaynagar-1 blocks respectively. The trend vegetation status of the coastal Sundarbans blocks was analyzed using seasonal median composite of Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 imageries. The post-monsoon NDVI had strong relation with both monsoon and post-monsoon (winter) rainfall season. On the other hand, the summer NDVI has very strong relationship with summer rainfall whereas the winter and monsoon rain did not have any impact on summer NDVI. The present study demonstrated the capability of CHRIPS data and cloud computing platform, Google Earth Engine (GEE) to analyze the seasonality and spatial distribution of rainfall in the coastal blocks of Indian Sundarbans. The study also demonstrated the efficiency of conditional thresholding technique with Modified Normalized Difference Water Indices (MVDWI) and Normalized Difference Vegetation Index (NDVI) in accurately delineating the waterbodies.