Boro Rice Yield Estimation Model Using Modis Ndvi Data for Bangladesh

• Published in: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium pp. 7330 - 7333
• Main Authors: Alam, Md. Samiul, KALPOMA, KAZI, Karim, Md. Sanaul, Al Sefat, Abdullah, Kudoh, Jun-ichi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: Data models; Feature extraction; MODIS; NDVI; Production; production estimation; regression analysis; Regression tree analysis; Rice model; Yield estimation
• DOI: 10.1109/IGARSS.2019.8899084

The aim of this study is to construct a rice yield estimation model for Bangladesh. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) images have been used. The MODIS NDVI images and ground truth data are acquired for the years 2011 to 2016. Since Bangladesh is divided into 8 divisions, several regression models are applied to predict rice yield for each division rather than a single model for the entire country, in order to get improved result in rice yield prediction. Firstly the rice field area is predicted by using NDVI threshold values. An improvised algorithm has been implemented to determine the NDVI threshold values. Four regression models (Linear, Ridge, Lasso, Decision Tree) are performed to estimate total Boro production of each district of Bangladesh. Among the regression models, maximum R 2 (co-effiecient of determination) values of 0.492, 0.790, 0.899, 0.891, 0.848, 0.942, 0.777 and 0.848 are acquired for Barisal, Chittagong, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur and Sylhet divisions respectively. Ridge regression worked better for Barisal and Chittagong divisions. For Mymensingh and Rangpur divisions Lasso regression performed the best. Decision Tree regression worked best for the four other divisions.