The advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

Remote sensing provides high accuracy/precision for quantifying forest biophysical parameters needed for ecological management. Although the significant impact of Bidirectional Scattering Distribution Functions (BSDF) on remote sensing of vegetation is well-known, current forest metrics derived from sensor data seldom take leaf BSDF into account, and despite the importance of BSDF effects, leaf directional scattering measurements are almost nonexistent. Previous studies have been limited in the spectral coverage and resolution of observed electromagnetic radiation (EMR) and lacked models to interpolate all source-sensor angles beyond measurements. This study captured deciduous broadleaf Bidirectional Reflectance Distribution Functions (BRDFs) from the visible through shortwave-infrared (SWIR) spectral regions (350-2500 nm) and accurately modeled the BRDF for extension to any illumination angle, viewing zenith, or azimuthal angle. We measured bi-conical directional reflectance factor (BCRF) of leaves from three species of large trees, Norway maple (Acer platanoides), American sweetgum (Liquidambar styraciflua), and northern red oak (Quercus rubra). We then fit the data through nonlinear regression to physical, microfacet BRDF models, resulting in normalized root mean square errors of less than 8%, averaged across all wavelengths (excluding low signal-to-noise spectral regions). We extracted leaf physical parameters, including the index of refraction and a relative physical roughness from the microfacet models delineating the three species. The implications for forestry remote sensing are important, as rigorous models to represent leaves allow for the creation of more accurate forest scenes for radiative transfer modeling. Such accuracy enables higher fidelity sensor evaluations and data processing algorithms.

A credit assignment approach to decision-based classifier fusion is developed and applied to the problem of land-cover classification from multiseason airborne hyperspectral imagery. For each input sample, the new method uses a smoothed estimated reliability measure (SERM) in the output domain of the classifiers. SERM requires no additional training beyond that needed to optimize the constituent classifiers in the pool, and its generalization (test) accuracy exceeds that of a number of other extant methods for classifier fusion. Hyperspectral imagery from HyMAP and PROBE2 acquired at three points in the growing season over Smith Island, VA, a barrier island in the Nature Conservancy's Virginia Coast Reserve, serves as the basis for comparing SERM with other approaches.

In March 2015, a joint field experiment and airborne measurement campaign was conducted in the Algodones Dunes, California. The Algodones Dunes has been selected by NASA as a potential site for satellite intercalibration, and the overall goal of the experiment was to better characterize the spectral variability of the dunes. The experiment was a multiinstitutional effort involving NASA, South Dakota State University, the University of Arizona, the University of Lethbridge, and the Rochester Institute of Technology (RIT). These dunes have significant topographic relief. This fact, combined with natural variations anticipated within a large dune system, such as Algodones, made the direct measurement of the bidirectional reflectance distribution function (BRDF), which characterizes the angular variation in scattered light from the surface for a given illumination geometry, of paramount importance.