We present results of mid-infrared spectroscopic mapping observations of six star-forming regions in the Small Magellanic Cloud (SMC) from the Spitzer Spectroscopic Survey of the SMC ((SMC)-M-4). We detect the mid-IR emission from polycyclic aromatic hydrocarbons (PAHs) in all of the mapped regions, greatly increasing the range of environments where PAHs have been spectroscopically detected in the SMC. We investigate the variations of the mid-IR bands in each region and compare our results to studies of the PAH bands in the SINGS sample and in a sample of low-metallicity starburst galaxies. PAH emission in the SMC is characterized by low ratios of the 6-9 mu m features relative to the 11.3 mu m feature and weak 8.6 and 17.0 mu m features. Interpreting these band ratios in the light of laboratory and theoretical studies, we find that PAHs in the SMC tend to be smaller and less ionized than those in higher metallicity galaxies. Based on studies of PAH destruction, we argue that a size distribution shifted toward smaller PAHs cannot be the result of processing in the interstellar medium, but instead reflects differences in the formation of PAHs at low metallicity. Finally, we discuss the implications of our observations for our understanding of the PAH life-cycle in low-metallicity galaxies-namely that the observed deficit of PAHs may be a consequence of PAHs forming with smaller average sizes and therefore being more susceptible to destruction under typical interstellar medium conditions.

Topographic correction methods rarely consider the canopy parameter effects directly and explicitly for sloping canopies. In order to address this problem, the topographic correction method MFM-GOST2 was developed by implementing the second version of the Geometric-Optical model for Sloping Terrains (the GOST2 model) in the multiple forward mode (MFM) inversion framework. First, a look up table (LUT) was constructed by multiple forward modeling of the GOST2 model; second, the radiance of a remotely sensed image and its corresponding topographic data were used for searching potential canopy parameter combinations from the LUT; and third, the corrected radiance was determined by averaging potential radiances of horizontal canopies from the LUT according to the canopy parameter combinations. The MFM-GOST2 and twelve generally used topographic correction methods were evaluated via a case study by visual analysis, linear relationship analysis, and the rose diagram analysis. The result showed that the MFM-GOST2 method successfully removed most of the topographic effects of a subset image of the Landsat-8 image in a case study. The case study also illustrates that the rose diagram analysis is a good way to evaluate topographic corrections, but the linear relationship analysis cannot be used independently for the evaluations because the decorrelation is not a sufficient condition to determine a successful topographic correction.

Rugged terrain distorts optical remote sensing signals, and land-cover classification and biophysical parameter retrieval over mountainous regions must account for topographic effects. Therefore, topographic correction is a prerequisite for many remote sensing applications. In this study, we proposed a semi-physically based and simple topographic correction method for vegetation canopies based on path length correction (PLC). The PLC method was derived from the solution to the classic radiative transfer equation, and the influence of terrain on the radiative transfer process within the canopy is explicitly considered, making PLC physically sound. The radiative transfer equation was simplified to make PLC mathematically simple. Near-nadir observations derived from a Landsat 8 Operational Land Imager (OLI) image covering a mountainous region and wide field-of-view observations derived from simulation using a canopy reflectance model were combined to test the PLC correction method. Multi-criteria were used to provide objective evaluation results. The performances were compared to that of five other methods: CC, SCS + C, and SE, which are empirical parameter-based methods, and SCS and DS, which are semi-physical methods without empirical parameter. All the six methods could significantly reduce the topographic effects. However, SCS showed obvious overcorrection for near-nadir observations. The correction results from D-S showed an obvious positive bias. For near-nadir observations, the performance of PLC was comparable to the well-validated parameter-based methods. For wide field-of-view observations, PLC obviously outperformed all other methods. Because of the physical soundness and mathematical simplicity, PLC provides an efficient approach to correct the terrain-induced canopy BRDF distortion and will facilitate the exploitation of multi-angular information for biophysical parameter retrieval over mountainous regions.

Energy-harvesting-powered sensors are increasingly deployed beyond the reach of terrestrial gateways, where there is often no persistent power supply. Making use of the internet of drones (IoD) for data aggregation in such environments is a promising paradigm to enhance network scalability and connectivity. The flexibility of IoD and favorable line-of-sight connections between the drones and ground nodes are exploited to improve data reception at the drones. In this article, we discuss the challenges of online flight control of IoD, where data-driven neural networks can be tailored to design the trajectories and patrol speeds of the drones and their communication schedules, preventing buffer overflows at the ground nodes. In a small-scale IoD, a multi-agent deep reinforcement learning can be developed with long short-term memory to train the continuous flight control of IoD and data aggregation scheduling, where a joint action is generated for IoD via sharing the flight control decisions among the drones. In a large-scale IoD, sharing the flight cont rol decisions in real-time can result in communication overheads and interference. In this case, deep reinforcement learning can be trained with the second-hand visiting experiences, where the drones learn the actions of each other based on historical scheduling records maintained at the ground nodes.