This research work dealt with the development of an operational methodology with appropriate technical components for monitoring and forecasting of rice crop (Boro) production in Bangladesh. Designed system explores integrated application of remote sensing (RS) sciences and Geographic Information System (GIS) technology. Terra MODIS 16-day Normalized Difference Vegetation Index (NDVI) maximum value composite (MVC) image product MOD13A1 of 500 m spatial resolution covering Bangladesh have been utilized for a period 2011–2017. Hence the district-wise sum of NDVI on pixel-by-pixel has been calculated from Jan–April during 2011–2017. Regression analysis between district-based pixel-wise summation of MODIS-NDVI and district-wise BBS (Bangladesh Bureau of Statistics) estimated Boro production revealed strong correlation (R² = 0.57–0.85) where in March most of the regression coefficient shows significant correlation due to maximize photosynthetic activities. Therefore, the highest regression coefficient value from derived set of coefficient value (BCP-Boro Crop Production Model 2) has been utilized to obtain year-wise rice productions for all the years (2011–2017). Global Positioning System (GPS)-based field verification, accuracy assessment and validation operation have been carried out at randomly selected geographical positions over the country using various statistical tools. The results demonstrate good agreement between estimated and predicted yearly Boro rice production during 2011–2017 time period with Mean Bias Error (MBE) = −29,881 to 19,431 M.Ton; Root Mean Square Error (RMSE) = 5238 to 11,852 M.Ton; Model Efficiency (ME) = (0.86–0.94); Correlation Coefficients = 0.65 to 0.87. Therefore MODIS-NDVI based regression model seems to be effective for Boro production forecasting. The system generally appears to be relatively fast, simple, reasonably accurate and suitable for nation-wide crop statistics generation and food security issues.

This research work studies the integrated application of satellite Remote Sensing (RS) and Geographic Information System (GIS) for the monitoring and forecasting of rice crop (Aman) production in Bangladesh. Normalized Difference Vegetation Index (NDVI) images of Terra MODIS products MOD13A1 (h25v06 and h26v06) with 500 m spatial resolution, composed using Maximum Value Composite (MVC) techniques, were used to cover Bangladesh for the period of 2011–2017. Country scale NDVI (district-wise summation) was calculated pixel-by-pixel to draw a regression curve while using Bangladesh Bureau of Statistics (BBS) estimations of Aman production for the months of September–November. The regression study of district-wise pixel-based summation of MODIS-NDVI and ground-based BBS-estimated Aman production shows a strong correlation (R² = 0.54–0.78); for the months of September and October, most of the regression coefficient indicates significant correlation due to maximum photosynthetic activities. Therefore, based on the highest regression coefficient value of September and October, Aman Crop Production (ACP) models were developed and the ACP Model-2 was exploited (from the derived set of coefficient values) to acquire year-wise rice production for all the years (2011–2017). The simulated ACP Model-2 demonstrates good agreement between the estimated and predicted yearly Aman rice production for the 2011–2017 time period with Mean Bias Error (MBE) = (−9435 to 23,156) M.Ton; Root Mean Square Error (RMSE) = 253–4426 M.Ton; Model Efficiency (ME) = (0.89–0.93); and, Correlation Coefficients = (0.72–0.94). Hence, the MODIS–NDVI-based regression model seems to be effective for Aman crop production forecasting in the context of food security issues in Bangladesh. The applied system is simple, rationally accurate, and fit for the generation of nationwide crop statistics.