FORECASTING OF POTATO YIELD ESTIMATION BY SATELLITE BASED REMOTE SENSING TECHNIQUE

Mohammad Mukhlesur Rahman (), Mohammad Amirul Islam, Md. Golam Mahboob, Nur Mohammad and Istiak Ahmed
Additional contact information
Mohammad Mukhlesur Rahman: Agricultural Statistics & ICT Division, Bangladesh Agricultural Research Institute (BARI), 1701 Gazipur, Dhaka, Bangladesh
Mohammad Amirul Islam: Department of Agricultural and Applied Statistics, Bangladesh Agricultural University, 2202 Mymensingh, Bangladesh
Md. Golam Mahboob: Department of Natural Resource Management, Bangladesh Agricultural Research Council, 1217 Dhaka, Bangladesh
Nur Mohammad: Agricultural Statistics & ICT Division, Bangladesh Agricultural Research Institute (BARI), 1701 Gazipur, Dhaka, Bangladesh
Istiak Ahmed: Agricultural Statistics & ICT Division, Bangladesh Agricultural Research Institute (BARI), 1701 Gazipur, Dhaka, Bangladesh

Acta Informatica Malaysia (AIM), 2024, vol. 8, issue 2, 49-55

Abstract: The goal of this research was to provide an operational technique with adequate technological components for monitoring and forecasting potato yield in Bangladesh. In the farmers’ fields of Shibganj upazila, the developed system investigates the combined use of satellite remote sensing (RS) and Geographic Information System (GIS) technology. The goal of the study was to construct a remotely sensed yield prediction model that used the high spatial resolution of Sentinel 2A and Landsat 8 satellite images to forecast potato yield one month ahead of harvest. Sentinel 2A (MSI) and Landsat 8 (OLI) satellite images with high spatial resolution data of 10m and 30m, respectively, were assessed, and 10-day and 16-day NDVI data were collected from these two satellites in this research. The study locations for the three potato growing seasons of 2018-2019, 2019-2020, and 2020-2021 were selected upazila. Sentinel 2A (MSI) and Landsat 8 (OLI) satellite data were used to get the NDVI values and yields for 20 farmers’ potato fields at Shibganj, Bogura district. The predicted percentages of the mean yield gap (or underestimation) for Sentinel 2A were 8.58%, while for Landsat 8, these were 9.56%, respectively, at Shibganj upazila, Bogura, during the potato growing season 2020–2021. The findings demonstrated that there is a substantial co-efficient of determination (R2 = 0.93 and 0.78) between remotely sensed NDVI and field-level potato yield. Therefore, remotely sensed NDVI data may be a useful tool for making early predictions about the yield of potatoes. Error of mean yield (%) of Sentinel 2A was better than Landsat 8 at Shibganj upazila, Bogura during the potato growing season 2020-2021.

Keywords: Remote sensing; Sentinel 2A; Landsat 8; GIS; NDVI (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://actainformaticamalaysia.com/archives/AIM/2aim2024/2aim2024-49-55.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zib:zbnaim:v:8:y:2024:i:2:p:49-55

DOI: 10.26480/aim.02.2024.49.55

Access Statistics for this article

Acta Informatica Malaysia (AIM) is currently edited by Associate Professor Dr. Shahreen Kasim

More articles in Acta Informatica Malaysia (AIM) from Zibeline International Publishing
Bibliographic data for series maintained by Zibeline International Publishing ( this e-mail address is bad, please contact ).