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.

Dynamic changes in Earth’s land cover characteristics and associated temporally evolving biophysical surface properties, as well as their ultimate impacts on surface radiative (surface albedo) and climatic properties (land surface temperature), have been studied. The study area includes a part of southwestern Bangladesh covering a period of about twenty years from 1988 – 2011. The widely used Surface Energy Balance Algorithm for Land (SEBAL) has been applied in conjunction with satellite-derived radiative measurements. Relatively important land use types such as water, soil, sand, settlement, shrimp farm, forest and agricultural crop have been considered. Feature type conversion of parameters i.e Normalized Difference of Vegetation Index (NDVI), surface albedo and land surface temperature have been noticed over the area under the present study. The highest surface albedo as well as surface temperature value has been noticed over the sandy area. Analysis revealed increases of surface temperature by about 1 °C and 3 °C for land cover conversion from (i) crop to settlement and (ii) water to soil, respectively. All other categories of landcover conversion generally experience decreases in surface temperature. Spatial vegetation coverage and amount of soil moisture play a dominant role in the radiative as well as climatic properties.