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Mapping Rice Paddy Cropping Patterns with MODIS and ALOS-2 ScanSAR Over Bangladesh from 2001 to 2018s

By: Md Rahedul Islam, Wataru Takeuchi, Kei Oyoshi

Key Words: GPS Field Data, K-means ++, Normalized Difference Vegetation Index (NDVI), Kalman’s Filtered, Maximum Likelihood Classifier (MLC).

IES-Vol10-No1-p33-59, February 2023.

Abstract

Bangladesh is one of the largest rice producer, importer, and consumer’s country in the world. The food security of the country depends on rice production. But the rice production of the country is facing several challenges like climate change, arable land loss, water scarcity and methane emission. The remote sensing-based rice area mapping of the country challenging due to the seasonal cloud contamination, fragmented field size, high cropping intensity and diversity. The objective of this study to mapped rice seasonal rice area as dry season boro, post-monsoon season amon and rainy season aus rice area from 2001 to 2018. The global positing system (GPS) field data on cropping pattern, ALOS-2 ScanSAR HH and HV backscattering coefficient and moderate resolution imaging spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset uses for this study. In methodology, firstly we use multidate ALOS-2 ScanSAR and GPS field data with unsupervised k-means ++ clustering for identifying seasonal rice area in 2018. Secondly, ALOS-2 ScanSAR rice paddy information and MODIS Kalman’s NDVI data uses for seasonal rice paddy area map in 2018 and extended it from 2001 to 2018. Thirdly, the ALOS-2 ScanSAR rice area map compared with GPS field data for validation and ALOS-2 ScanSAR map used for validating MODIS rice area map. Finally, the rice area compared with the national statistical data and relevant studies over the country. The result shows that the boro rice area increases (30.44%), amon (4.65%) and aus (11.90%) rice area decrease from 2001 to 2018. ALOS-2 ScanSAR seasonal rice area and GPS field data overall accuracy 78.65% and kappa value 0.76. The ALOS-2 ScanSAR and MODIS seasonal rice area show an overall accuracy of 83.5% with kappa value 0.77. The ALOS-2 and MODIS based estimated rice area compared with national statistics reported area and found R2 value 0.98 (boro), 0.87 (amon) and 0.62 (aus). Furthermore, we compared our result with the relevant studies and found very strong agreement.

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Md Rahedul Islam, Wataru Takeuchi, Kei Oyoshi

Mapping Rice Paddy Cropping Patterns with MODIS and ALOS-2 ScanSAR Over Bangladesh from 2001 to 2018s

IES-Vol10-No1-p33-59, February 2023.

https://jenvosci.com/ies-publish/mapping-rice-paddy-cropping-patterns-with-modis-and-alos-2-scan-sar-over-bangladesh-from-2001-to-2018s/

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