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Over the past 24 years, the AErosol RObotic NETwork (AERONET) program has provided highly accurate remote-sensing characterization of aerosol optical and physical properties for an increasingly extensive geographic distribution including all continents and many oceanic island and coastal sites. The measurements and retrievals from the AERONET global network have addressed satellite and model validation needs very well, but there have been challenges in making comparisons to similar parameters from in situ surface and airborne measurements. Additionally, with improved spatial and temporal satellite remote sensing of aerosols, there is a need for higher spatial-resolution ground-based remote-sensing networks. An effort to address these needs resulted in a number of field campaign networks called Distributed Regional Aerosol Gridded Observation Networks (DRAGONs) that were designed to provide a database for in situ and remote-sensing comparison and analysis of local to mesoscale variability in aerosol properties. This paper describes the DRAGON deployments that will continue to contribute to the growing body of research related to meso- and microscale aerosol features and processes. The research presented in this special issue illustrates the diversity of topics that has resulted from the application of data from these networks.

Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters are important due to health impact. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. Seasonal aerosol optical depth (AOD) values at 550 nm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA’s Terra satellites, for the 10 years period of 2000 - 2010 were used to test 7 different spatial interpolation methods in the present study. The accuracy of estimations was assessed through visual analysis as well as independent validation based on basic statistics, such as root mean square error (RMSE) and correlation coefficient. Based on the RMSE and R values of predictions made using measured values from 2000 to 2010, Radial Basis Functions (RBFs) yielded the best results for spring, summer and winter and ordinary kriging yielded the best results for fall.

Obtaining continuous aerosol-optical-depth (AOD) measurements is a difficult task due to the cloud-cover problem. With the main motivation of overcoming this problem, an AOD-predicting model is proposed. In this study, the optical properties of aerosols in Penang, Malaysia were analyzed for four monsoonal seasons (northeast monsoon, pre-monsoon, southwest monsoon, and post-monsoon) based on data from the AErosol RObotic NETwork (AERONET) from February 2012 to November 2013. The aerosol distribution patterns in Penang for each monsoonal period were quantitatively identified according to the scattering plots of the Ångström exponent against the AOD. A new empirical algorithm was proposed to predict the AOD data. Ground-based measurements (i.e., visibility and air pollutant index) were used in the model as predictor data to retrieve the missing AOD data from AERONET due to frequent cloud formation in the equatorial region. The model coefficients were determined through multiple regression analysis using selected data set from in situ data. The calibrated model coefficients have a coefficient of determination, R2, of 0.72. The predicted AOD of the model was generated based on these calibrated coefficients and compared against the measured data through standard statistical tests, yielding a R2 of 0.68 as validation accuracy. The error in weighted mean absolute percentage error (wMAPE) was less than 0.40% compared with the real data. The results revealed that the proposed model efficiently predicted the AOD data. Performance of our model was compared against selected LIDAR data to yield good correspondence. The predicted AOD can enhance measured short- and long-term AOD and provide supplementary information for climatological studies and monitoring aerosol variation.

Direct geo-referencing system uses the technology of remote sensing to quickly grasp images, GPS tracks, and camera position. These data allows the construction of large volumes of images with geographic coordinates. So that users can be measured directly on the images. In order to properly calculate positioning, all the sensor signals must be synchronized. Traditional aerial photography use Position and Orientation System (POS) to integrate image, coordinates and camera position. However, it is very expensive. And users could not use the result immediately because the position information does not embed into image. To considerations of economy and efficiency, this study aims to develop a direct geo-referencing system based on open source software and hardware platform. After using Arduino microcontroller board to integrate the signals, we then can calculate positioning with open source software OpenCV. In the end, we use open source panorama browser, panini, and integrate all these to open source GIS software, Quantum GIS. A wholesome collection of data – a data processing system could be constructed.

The importance of soil water flow paths to the transport of nutrients and contaminants has long been recognized. However, effective means of detecting concentrated subsurface flow paths in a large landscape are still lacking. The flow direction and accumulation algorithm based on single-direction flow algorithm (D8) in GIS hydrologic modeling is a cost-effective way to simulate potential concentrated flow paths over a large area once relevant data are collected. This study tested the D8 algorithm for simulating concentrated lateral flow paths at three interfaces in soil profiles in a 19.5-ha agricultural landscape in central Pennsylvania, USA. These interfaces were (1) the interface between surface plowed layers of Ap1 and Ap2 horizons, (2) the interface with subsoil water-restricting clay layer where clay content increased to over 40%, and (3) the soil-bedrock interface. The simulated flow paths were validated through soil hydrologic monitoring, geophysical surveys, and observable soil morphological features. The results confirmed that concentrated subsurface lateral flow occurred at the interfaces with the clay layer and the underlying bedrock. At these two interfaces, the soils on the simulated flow paths were closer to saturation and showed more temporally unstable moisture dynamics than those off the simulated flow paths. Apparent electrical conductivity in the soil on the simulated flow paths was elevated and temporally unstable as compared to those outside the simulated paths. The soil cores collected from the simulated flow paths showed significantly higher Mn content at these interfaces than those away from the simulated paths. These results suggest that (1) the D8 algorithm is useful in simulating possible concentrated subsurface lateral flow paths if used with appropriate threshold value of contributing area and sufficiently detailed digital elevation model (DEM); (2) repeated electromagnetic surveys can reflect the temporal change of soil water storage and thus is a useful indicator of possible subsurface flow path over a large area; and (3) observable Mn distribution in soil profiles can be used as a simple indicator of water flow paths in soils and over the landscape; however, it does require sufficient soil sampling (by excavation or augering) to possibly infer landscape-scale subsurface flow paths. In areas where subsurface interface topography varies similarly with surface topography, surface DEM can be used to simulate potential subsurface lateral flow path reasonably so the cost associated with obtaining depth to subsurface water-restricting layer can be minimized.