# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 1 Introduction
As a result, Bangladesh is mainly affected by the pollutants and desert dust of India combined with sea salt particles blown from the Arabian Sea. Recently, experimental models of PM2.5 and PM10 estimation were proposed using ground-based PM measurements and AOD values derived from MODIS data products [8; 9; 10; 11; 12; 13; 14; 15]. The authors explored different regression models between ground-level PM mass concentration and AOD in different environmental regions. Mostly linear regression models presented a better fit than others based on their correlation coefficient. In addition to MODIS AOD, AOD derived from other optical satellite images was exploited to estimate PM mass concentrations with higher spatial resolution but lower temporal resolution [16; 17; 18; 19; 20]. For example, Landsat and ASTER short-wavelength images are, respectively, at 30 m and 15 m pixel sizes, but have both 16-day and revisiting periods. [PERSON] et al. conducted a study in 2009, where they compared the aerosol optical depth (AOD) derived from the GOES satellite and the MODIS Terra satellite with ground-based measurements of AOD from the Aerosol Robotic Network (AERONET) and the mass concentration of PM2.5 from ground-based measurements by the Interagency Monitoring of Protected Visual Environments at Bondville, Illinois [21]. Utilizing local experimental models based on linear regression, the researchers found that the correlation between AOD and PM2.5 mass concentration was strongest during autumn and weakest during winter. This paper introduces our smart environmental management system of ground-level sensors, and satellite image-derived land surface temperature and particulate matter concentrations for the Mekong Delta. We deploy multiple IoT stations in the system for ground measurement of various values, including land surface temperature (LST), PM10, PM2.5, CO\({}_{2}\), moisture, etc. Based on the telemetry data, we build computational models for extracting values of LST, PM10, and PM2.5 from MODIS images so that values for these parameters in the entire Mekong Delta area can be calculated. The combination of telemetry data and remote sensing images helps improve data quality and reduces building and operating costs. The main contributions of our paper consist of three aspects as follows: (1) We propose an architecture for smart environmental management based on two sources of data: telemetry data and remote sensing images; (2) We build regression models for estimating PM10 and PM2.5 and apply an experimental model for extracting LST from MODIS images for the Mekong Delta areas; (3) We deploy and test the proposed system with five IoT stations whose data can be provided for other studies. The rest of the paper is organized as follows. First, we present our system in Section 2. We then introduce materials and methods for extracting data from remote sensing images in Section 3. Section 4 discusses our computational models for extracting the above values from remote sensing images. We test and report our experiments in Section 5. Finally, Section 6 concludes our paper.