Assessing the cropping intensity dynamics of the Gosaba CD block of Indian Sundarbans using satellite-based remote sensing

ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY(2024)

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摘要
Food availability is one of the dimensions of food security, and it is necessary to analyze the crop production scenario to estimate the availability of food in a region. Cropping sequence and cropping intensity indicate the seasonal crop production, thereby indicating the seasonal availability of food. Seasonal variation of per capita or per household availability of the cropped land determines the food security status of a given region. In Indian Sundarbans region, people's livelihood is seriously threatened by the food insecurity. The present study aimed to determine the seasonality of cropped land as well as the cropping intensities of Gosaba CD block of Indian Sundarbans during 2017-2018, 2018-2019 and 2019-2020 cropping years using Multi-dated Sentinel-2 data. Rule-based classification was applied for cropping sequence and cropping intensity mapping. Winter season cropped land was the lowest (< 16% of the village area). The area under crop-fallow-crop sequence (200% cropping intensity) decreased, while the area under crop-fallow-fallow (100% cropping intensity) sequence increased. Area under 300% cropping intensity gradually decreased. The average cropping intensity changed from 150% in 2017-2018 to 124% and 136% in 2018-2019 and 2019-2020, respectively. Large variation of the seasonal cropped land per household was estimated, and it became the worst during winter when it became less than 0.5 bighas (0.07 ha). Crop cultivation during dry season depended on the rainfall pattern and surface water availability. The present study successfully addressed the cropping scenario and food insecurity of the study area, and hopefully, it will help the planners and policy makers to take necessary actions for cropping intensification and ensuring food security in the Indian Sundarbans region.
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关键词
Cropping intensity,Cropped land per household,Sentinel-2,Rule-based classification,Indian Sundarbans
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