With radiofrequency exposure caused by electronic applications increasing, some members of the public are worrying about potential health risks. In this paper, methods of performing large-scale radiofrequency exposure evaluation are described. All studied sites were divided into three categories: commercial-area, residential-urban, and residential-rural. Then a series of site investigations were conducted on a car-mounted system in the years 2014 and 2015, aiming to characterize electric field exposure from 12 different radiofrequency sources. The results indicate that the studied environment is safe as indicated by exposure below guidelines and standards. The highest exposure measured in the 2 y of monitoring was from an FM source, 316.23 mV m. Telecommunication sources dominate exposure, contributing the most power density (65-90%). Meanwhile, intergroup differences are discussed and summarized. The spatial distributions of FM and GSM1800 exposure are demonstrated on a map. This study describes an approach for the assessment of the spatiotemporal pattern of radiofrequency exposures in Chengdu and facilitates the identification of any sources causing exposure above relevant guidelines and standards.

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.

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