Rice Yield Loss Area Assessment from Satellite-derived NDVI after Extreme Climatic Events Using a Fuzzy Approach-Reference-Cited by-同舟云学术

Rice Yield Loss Area Assessment from Satellite-derived NDVI after Extreme Climatic Events Using a Fuzzy Approach

Published:2022-04-01 Issue:1 Volume:31 Page:32-46
ISSN:0916-9482
Container-title:Agricultural Information Research
language:en
Short-container-title:Agricultural Information Research

Author:

Shamsuzzoha Md¹,Noguchi Ryozo²,Ahamed Tofael²

Affiliation:

1. Graduate School of Life and Environmental Sciences, University of Tsukuba

2. Faculty of Life and Environmental Sciences, University of Tsukuba

Publisher

Japanese Society of Agricultural Informatics

Link

https://www.jstage.jst.go.jp/article/air/31/1/31_32/_pdf

Reference64 articles.

1. Ahamed, T. R. N., K. G. Rao and J. S. R. Murthy (2000) GIS-based for crop-land suitability analysis. Agricultural Systems, 63 (2): 75–95, DOI: https://doi.org/10.1016/S0308-521X(99)00036-0.

2. Alam, M. S., K. Kalpoma, M. S. Karim, A. A. Sefat and J. Kudoh (2019) Boro rice yield estimation model using Modis NDVI data for Bangladesh. IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan. 7330–7333 pp., DOI: https://doi.org/10.1109/IGARSS.2019.8899084.

3. BBS (2015) Population projection of Bangladesh: dynamics and Trends 2011–2061. Bangladesh Bureau of Statistics (BBS), ISBN-978-984-33-9960-1. Government of the People’s republic of Bangladesh, Dhaka, 〈http://203.112.218.65:8008/WebTestApplication/userfiles/Image/PopMonographs/PopulationProjection.pdf〉, browsed on Feb. 26, 2021.

4. Bhattacharyya, R. and S. Mukherjee (2020) Fuzzy membership function evaluation by non-linear regression: An algorithmic approach. Fuzzy Information and Engineering, 12 (4): 412–434, DOI: https://doi.org/10.1080/16168658.2021.1911567.

5. Bianchini, S., L. Solari, M. D. Soldato, F. Raspini, R. Montalti, A. Ciampalini and N. Casagli (2019) Ground subsidence susceptibility (GSS) mapping in Grosseto plain (Tuscany, Italy) based on satellite InSAR data using frequency ratio and fuzzy logic. Remote Sensing, 11 (17), DOI: https://doi.org/10.3390/rs11172015.

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