Articles on interdisciplinary applications, theoretical innovation and photo-optical instrumentation design published in 2016 in SPIE’s Journal of Applied Remote Sensing (JARS) have received best paper awards. JARS communicates concepts, information and progress to improve monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and other commercial and scientific applications. "Optically thin midlevel ice clouds derived from Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations," authored by Vinay Kayetha and Richard Collins at the University of Alaska, Fairbanks, was chosen in the category of interdisciplinary applications. The research employs satellite observation measurements to identify optically thin midlevel ice clouds and determine their occurrence and properties. "Large patch convolutional neural networks for the scene classification of high spatial resolution imagery," by Yanfei Zhong, Feng Fei and Liangpei Zhang at Wuhan University, was selected for theoretical innovation. The paper proposes a practical convolutional neural network architecture for high-spatial-resolution remote sensing image scene classification. "Design and prototype of radar sensor with Vivaldi linear array for through-wall radar imaging: An experimental study," by Betül Yilmaz and Caner Özdemir at Mersin University, was selected in the category of photo-optical instrumentation and design. The paper presents a radar sensor designed to detect and image moving objects behind a wall. "The three papers selected for these best paper awards are highly deserving of this distinction and indicative of the important remote sensing research and applications published in JARS," said Eric Pepper, director of publications for SPIE. SPIE, the international society for optics and photonics, is an international society advancing an interdisciplinary approach to the science and application of light.