At present, as growing importance continues to be attached to atmospheric environmental problems, the demand for real-time monitoring of these problems is constantly increasing. This article describes the development and application of an embedded system for monitoring of atmospheric pollutant concentrations based on LoRa (Long Range) wireless communication technology, which is widely used in the Internet of Things (IoT). The proposed system is realized using a combination of software and hardware and is designed using the concept of modularization. Separation of each function into independent modules allows the system to be developed more quickly and to be applied more stably. In addition, by combining the requirements of the remote atmospheric pollutant concentration monitoring platform with the specific requirements for the intended application environment, the system demonstrates its significance for practical applications. In addition, the actual application data also verifies the sound application prospects of the proposed system.

The north and south poles of the earth (hereinafter referred to as the polar regions) are important components of the earth system. Changes in the material balance and movement of the polar ice shelf reflect the influence of the polar regions on global climate change and are also a response to global climate change. Through a comprehensive investigation of ice-shelf kinematics, with sufficient accuracy, it is possible to obtain ice-shelf elevation, movement-state data, ice-shelf material balance state, and the ice-shelf movement dynamics mechanism. Due to the extremely harsh environment in polar regions, remote sensing is currently widely used. Manual and equipment monitoring methods show insufficient accuracy or discontinuous time series. There is an urgent need to obtain continuous real-time ice-shelf kinematics-related parameters on the ground to verify the reliability of the parameters obtained by satellite remote sensing. These parameters should be combined with remote sensing monitoring to provide data support. In this paper, a monitoring system for the movement of polar ice and shelf ice cover is developed, and it is proposed that various data can be acquired by integrating high-precision GPS (global positioning system) and other sensors. Solutions to the problem of low-temperature power supply in the polar regions, data acquisition and storage strategies, and remote communication methods are proposed. Testing and remote sensing validation verified that the developed acquisition system can fulfill the requirements for monitoring the movement of the polar unmanned ice shelves and ice sheets.

Topographic correction of surface reflectance in rugged terrain areas is the prerequisite for the quantitative application of remote sensing in mountainous areas. Physics-based radiative transfer model can be applied to correct the topographic effect and accurately retrieve the reflectance of the slope surface from high quality satellite image such as Landsat8 OLI. However, as more and more images data available from various of sensors, some times we can not get the accurate sensor calibration parameters and atmosphere conditions which are needed in the physics-based topographic correction model. This paper proposed a semi-empirical atmosphere and topographic corrction model for muti-source satellite images without accurate calibration parameters.Based on this model we can get the topographic corrected surface reflectance from DN data, and we tested and verified this model with image data from Chinese satellite HJ and GF. The result shows that the correlation factor was reduced almost 85 % for near infrared bands and the classification overall accuracy of classification increased 14 % after correction for HJ. The reflectance difference of slope face the sun and face away the sun have reduced after correction.