"The art, science, and technology of observing an object, scene, or phenomenon by instrument-based techniques"

Remote sensing is the process of detecting and monitoring an object or an area by measuring its reflected and emitted radiation. It has a wide range of applications in many different fields that are crucial to human activity. Data gained through remote sensing by cameras on satellites and airplanes take images of large areas on the Earth's surface, making the analysis of large-scale systems possible. It allows us to monitor shoreline changes for coastal mapping and erosion prevention, to monitor ocean circulation, temperature changes, and current systems, to better understand the oceans and how to best manage ocean resources. This kind of sensors makes it possible to extend the frame of the discovery to other geophysical systems. They can be used to identify the chemical composition of lakes or other planets and to detect their chemical and physical processes. 

Our Publications

• Watson, C. S., Kargel, J. S., Shugar, D. H., Haritashya, U. K., Schiassi, E., & Furfaro, R. (2020). Mass loss from calving in Himalayan proglacial lakes. Frontiers in Earth Science, 7. DOI: http://dx.doi.org/10.3389/feart.2019.00342
• Bishop, M. P., Young, B. W., Colby, J. D., Furfaro, R., Schiassi, E., & Chi, Z. (2019). Theoretical Evaluation of Anisotropic Reflectance Correction Approaches for Addressing Multi-Scale Topographic Effects on the Radiation-Transfer Cascade in Mountain Environments. Remote Sensing, 11(23), 2728. DOI: http://dx.doi.org/10.3390/rs11232728
• Schiassi, E., Furfaro, R., Kargel, J. S., Watson, C. S., Shugar, D. H., & Haritashya, U. K. (2019). GLAM Bio-Lith RT: A Tool for Remote Sensing Reflectance Simulation and Water Components Concentration Retrieval in Glacial Lakes. Frontiers in Earth Science, 7. DOI: http://dx.doi.org/10.3389/feart.2019.00267
• Furfaro, R., Linares, R., & Reddy, V. (2018). Space objects classification via light-curve measurements: deep convolutional neural networks and model-based transfer learning. In AMOS Technologies Conference, Maui Economic Development Board. DOI: https://doi.org/10.1007/s40295-019-00208-w
• Schiassi, E., Furfaro, R., & Mostacci, D. (2016). Bayesian inversion of coupled radiative and heat transfer models for asteroid regoliths and lakes. Radiation Effects and Defects in Solids, 171(9-10), 736-745. DOI: http://dx.doi.org/10.1080/10420150.2016.1253091
• Picca, P., & Furfaro, R. (2013). Analytical discrete ordinate method for radiative transfer in dense vegetation canopies. Journal of Quantitative Spectroscopy and Radiative Transfer, 118, 60-69. DOI: 10.1016/j.jqsrt.2012.12.007
• Picca, P., Furfaro, R., & Ganapol, B. D. (2012). On radiative transfer in dense vegetation canopies. Transport Theory and Statistical Physics, 41(3-4), 223-244. DOI: 10.1080/00411450.2012.671218
• Picca, P., Furfaro, R., Kargel, J., & Ganapol, B. D. (2008, April). Forward and inverse models for photon transport in soil-ice mixtures and their application to the problem of retrieving optical properties of planetary surfaces. In Space Exploration Technologies (Vol. 6960, p. 69600O). International Society for Optics and Photonics. DOI: 10.1117/12.777479