Application of Geospatial Techniques in Evaluating Spatial Variability of Commercially Harvested Mangoes in Bangladesh

Md Moniruzzaman (), Md. Sorof Uddin, Md. Abdullah Elias Akhter, Akshar Tripathi and Khan Rubayet Rahaman
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Md Moniruzzaman: Department of Geography and Environmental Studies & Wicked Problems Lab, Saint Mary’s University, 923 Robie Street, Halifax, NS B3H 3C3, Canada
Md. Sorof Uddin: Pomology Division, Horticulture Research Centre, Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
Md. Abdullah Elias Akhter: Department of Physics, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
Akshar Tripathi: National Center for Geodesy (NCG), Indian Institute of Technology (IIT) Kanpur, Kanpur 208016, Uttar Pradesh, India
Khan Rubayet Rahaman: Department of Geography and Environmental Studies & Wicked Problems Lab, Saint Mary’s University, 923 Robie Street, Halifax, NS B3H 3C3, Canada

Sustainability, 2022, vol. 14, issue 20, 1-20

Abstract: Mango is widely known as a popular fruit in South Asia, including Bangladesh. The country is a significant producer of different local and exotic varieties of mangoes in different geographic locations. Therefore, a study of fruit maturity at diverse locations and climatic conditions becomes critical for a sustainable mango production. In responding to this need, this study evaluates the variability of a few selected commercial mango ( Mangifera indica L.) varieties and their maturity timeline with respect to spatial extent (longitudinal-latitudinal variations), elevation profile, and time. Remote sensing technology has been widely used for horticultural applications to study fruit phenology, maturity, harvesting time, and for mapping locational differences. In doing so, we have employed remotely sensed data, such as the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) of 30 m spatial resolution, GPM IMERGM precipitation datasets (0.1 × 0.1 degree), NASA GLDAS (Global Land Data Assimilation System) surface skin temperature (0.25 × 0.25 degree), and Noah Land Surface Model L4 3-hourly soil moisture content datasets (0.25 × 0.25 degree). Alongside these, an intensive field data collection campaign has been carried out for 2019 and 2020. It was found that 1° locational variations may result in approximately 2–5 days delay of mango harvesting. The outcome of this study may enhance the appropriate planning of harvesting, production, and the commercialization of mango selection in specific geographic setting for a sustainable harvest and production system in the country.

Keywords: digital elevation model (DEM); mango harvesting; remote sensing; precipitation; spatial characteristics; temporal considerations (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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