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A multi‐model ensemble provides useful information about the uncertainty of the future changes of climate. High‐emission scenarios using representative concentration pathways (RCP8.5) of the Fifth Phase Coupled Model Inter‐comparison Project (CMIP5) in the Intergovernmental Panel on Climate Change (IPCC) also aids to capture the possible extremity of the climate change. Using the CMIP5 regional climate modelling predictions, this study analyses the distribution of the temperature and precipitation in Bangladesh in the recent years (1971–2000) and in three future periods (2010–2040, 2041–2070 and 2070–2100) considering RCP8.5 scenarios. Climate changes are expressed in terms of 30‐year return values of annual near‐surface temperature and 24‐h precipitation amounts. At the end of the century, the mean temperature increase over Bangladesh among the 11 RCMs will vary from 5.77 to 3.24 °C. Spatial analysis of the 11 RCMs exhibited that the southwest and the south central parts of Bangladesh will experience a greater temperature rise in the future. Possible changes in rainfall are also exhibited both temporally and spatially. Based on the analysis of all the RCMs, a significant increase of rainfall in the pre‐ and post‐monsoon period is observed. It is also evident that monsoon rainfall will not increase in comparison with pre‐monsoon season. Zonal statistics of 64 districts of Bangladesh are also conducted for the 2020s, 2050s and 2080s to find out the most exposed regions in terms of the highest rise in temperature and changes in precipitation.

Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.

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Rotavirus is the most common cause of diarrheal disease among children under 5. Especially in South Asia, rotavirus remains the leading cause of mortality in children due to diarrhea. As climatic extremes and safe water availability significantly influence diarrheal disease impacts in human populations, hydroclimatic information can be a potential tool for disease preparedness. In this study, we conducted a multivariate temporal and spatial assessment of 34 climate indices calculated from ground and satellite Earth observations to examine the role of temperature and rainfall extremes on the seasonality of rotavirus transmission in Bangladesh. We extracted rainfall data from the Global Precipitation Measurement and temperature data from the Moderate Resolution Imaging Spectroradiometer sensors to validate the analyses and explore the potential of a satellite‐based seasonal forecasting model. Our analyses found that the number of rainy days and nighttime temperature range from 16°C to 21°C are particularly influential on the winter transmission cycle of rotavirus. The lower number of wet days with suitable cold temperatures for an extended time accelerates the onset and intensity of the outbreaks. Temporal analysis over Dhaka also suggested that water logging during monsoon precipitation influences rotavirus outbreaks during a summer transmission cycle. The proposed model shows lag components, which allowed us to forecast the disease outbreaks 1 to 2 months in advance. The satellite data‐driven forecasts also effectively captured the increased vulnerability of dry‐cold regions of the country, compared to the wet‐warm regions.