Abstract
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.
Highlights
Rice is one of the most significant agricultural crops in many countries, and it is a primary food source for more than three billion people worldwide [1,2]
Various studies have found the potentiality of a regression model derived from remote sensing based Normalized Difference Vegetation Index (NDVI) and ground-based crop statistics to estimate the crop yield [38,72,73] and crop production [74] under different management condition with reasonable validity [75,76]
This research work concentrates on the development of an effective and easy to use technical system to forecast the rice production in advance where MODIS-maximum value composite (MVC) data product MOD13A1 seems to be useful in such purpose
Summary
Rice is one of the most significant agricultural crops in many countries, and it is a primary food source for more than three billion people worldwide [1,2]. The projected global rice consumption is to be 873 million tons in 2030 [3] but the issues of population growth (in particular in the major rice producing/consuming countries) [4] and climate change have created enormous pressure on the global food demand and its production in recent decades [5]. The impacts of climate change issues may create negative impacts on agricultural yield and production capacity of Bangladesh. The developing countries like Bangladesh need remote sensing technology-based scientific study for updated crop information such as crop area and yield estimation, crop biophysical property condition, weather conditions, and seasonal characteristics to support early warning decision support systems in order to maintain food sovereignty because of vulnerability to the abrupt climatic changes and frequent natural disasters
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
