The split window technique has been used for over thirty years to derive surface temperatures of the Earth with image data collected from spaceborne sensors containing two thermal channels. The latest NASA/USGS Landsat satellites contain the Thermal Infrared Sensor (TIRS) instruments that acquire Earth data in two longwave infrared bands, as opposed to a single band with earlier Landsats. The United States Geological Survey (USGS) will soon begin releasing a surface temperature product for Landsats 4 through 8 based on the single spectral channel methodology. However, progress is being made toward developing and validating a more accurate and less computationally intensive surface temperature product based on the split window method for Landsat 8 and 9 datasets. This work presents the progress made towards developing an operational split window algorithm for TIRS. Specifically, details of how the generalized split window algorithm was tailored for the TIRS sensors are presented, along with geometric considerations that should be addressed to avoid spatial artifacts in the final surface temperature product. Validation studies indicate that the proposed algorithm is accurate to within 2 K when compared to land-based measurements and to within 1 K when compared to water-based measurements, highlighting the improved accuracy that may be achieved over the current single-channel methodology being used to derive surface temperature in the Landsat Collection 2 surface temperature product. Surface temperature products using the split window methodologies described here can be made available upon request for testing purposes.

Salt marsh vegetation density varies considerably on short spatial scales, complicating attempts to evaluate plant characteristics using airborne remote sensing approaches. In this study, we used a mast-mounted hyperspectral imaging system to obtain cm-scale imagery of a salt marsh chronosequence on Hog Island, VA, where the morphology and biomass of the dominant plant species, Spartina alterniflora, varies widely. The high-resolution hyperspectral imagery allowed the detailed delineation of variations in above-ground biomass, which we retrieved from the imagery using the PROSAIL radiative transfer model. The retrieved biomass estimates correlated well with contemporaneously collected in situ biomass ground truth data ( $R^2=0.73$ ). In this study, we also rescaled our hyperspectral imagery and retrieved PROSAIL salt marsh biomass to determine the applicability of the method across spatial scales. Histograms of retrieved biomass changed considerably in characteristic marsh regions as the spatial scale of the imagery was progressively degraded. This rescaling revealed a loss of spatial detail and a shift in the mean retrieved biomass. This shift is indicative of the loss of accuracy that may occur when scaling up through a simple averaging approach that does not account for the detail found in the landscape at the natural scale of variation of the salt marsh system. This illustrated the importance of developing methodologies to appropriately scale results from very fine scale resolution up to the more coarse-scale resolutions commonly obtained in airborne and satellite remote sensing.

The physical properties of a medium such as density, grain size and surface roughness all influence the angular dependence of spectral signatures. Radiative transfer models, such as the one developed by Hapke, can relate the angular dependence of the reflectance to these geophysical variables. This paper focuses on extracting geophysical parameters, fill factor (decreasing porosity) and the single scattering albedo (SSA), through the inversion of a modified version of the Hapke model of airborne and space-borne imagery. The inversion methodology was validated through controlled experiments within a laboratory setting, where a good correlation (R ${}^2$ = 0.72) between the retrieved fill factor and the measured density was obtained. Using the same approach, we also retrieved the sediment fill factor and SSA from airborne data collected by the NASA G-LiHT system, and space-borne data observed by the NOAA GOES imager. The results from these studies provide a mechanism to understand geophysical characteristics of the terrain and may potentially be used for long-term monitoring of the dynamic dunes system.

With the launch of Landsat-9 on 27 September 2021, Landsat continues its fifty-year continuity mission of providing users with calibrated Earth observations. It has become a requirement that an underflight experiment be performed during commissioning to support sensor cross-calibration. In this most recent experiment, Landsat-9 flew under Landsat-8 for nearly three days with over 50% ground overlap, from 13 to 15 November 2021. To address the scarcity of reference data that are available to support calibration and validation early-on in the mission, a ground campaign was planned and executed by the Rochester Institute of Technology (RIT) on 14 November 2021 to provide full spectrum measurements for early mission comparisons. The primary experiment was conducted in the Outer Banks, North Carolina at Jockey’s Ridge Sand Dunes. Full-spectrum ground-based measurements were acquired with calibrated reference equipment, while a novel Unmanned Aircraft System (UAS)-based platforms acquired hyperspectral visible and near-infrared (VNIR)/Short-wave infrared (SWIR) imagery data and coincident broadband cooled thermal infrared (TIR) imagery. Results of satellite/UAS/ground comparisons were an indicator, during the commissioning phase, that Landsat-9 is behaving consistently with Landsat-8, ground reference, and UAS measurements. In the thermal infrared, all measurements agree to be within 1 K over water and to within 2 K over sand, which represents the most challenging material for estimating surface temperature. For the surface reflectance product(s), Landsat-8 and -9 are in good agreement and only deviate slightly from ground reference in the SWIR bands; a deviation of 2% in the VNIR and 5–8% in the SWIR regime. Subsequent longer-term studies indicate that Landsat 9 continues to perform as expected. The behavior of Thermal Infrared Sensor-2 (TIRS-2) against reference is also shown for the first year of the mission to illustrate its consistent performance.

The increased sensitivity of modern hyperspectral line-scanning systems has led to the development of imaging systems that can acquire each line of hyperspectral pixels at very high data rates (in the 200–400 Hz range). These data acquisition rates present an opportunity to acquire full hyperspectral scenes at rapid rates, enabling the use of traditional push-broom imaging systems as low-rate video hyperspectral imaging systems. This paper provides an overview of the design of an integrated system that produces low-rate video hyperspectral image sequences by merging a hyperspectral line scanner, operating in the visible and near infra-red, with a high-speed pan-tilt system and an integrated IMU-GPS that provides system pointing. The integrated unit is operated from atop a telescopic mast, which also allows imaging of the same surface area or objects from multiple view zenith directions, useful for bi-directional reflectance data acquisition and analysis. The telescopic mast platform also enables stereo hyperspectral image acquisition, and therefore, the ability to construct a digital elevation model of the surface. Imaging near the shoreline in a coastal setting, we provide an example of hyperspectral imagery time series acquired during a field experiment in July 2017 with our integrated system, which produced hyperspectral image sequences with 371 spectral bands, spatial dimensions of 1600 × 212, and 16 bits per pixel, every 0.67 s. A second example times series acquired during a rooftop experiment conducted on the Rochester Institute of Technology campus in August 2017 illustrates a second application, moving vehicle imaging, with 371 spectral bands, 16 bit dynamic range, and 1600 × 300 spatial dimensions every second.

The leaf area index (LAI) is a structural parameter that plays a major role in modeling the exchange of energy, carbon dioxide, water, and other matter between plants and their surrounding environment. Direct means of measuring LAI are time-intensive and laborious, making indirect LAI retrieval methods desirable. In this study, we developed a method for retrieving structural metrics of erectophile plants that relies on open-source structure from motion software. We achieved this using a novel goniometer system to simultaneously retrieve high-resolution digital imagery and bidirectional spectroscopy measurements of Spartina alterniflora in a laboratory environment during both peak and late growing season. Retrieved LAI metrics correlated well with directional measurements of the Modified Chlorophyll Absorption Ratio Index 2 (MCARI2). Across all planting densities, the magnitude of the MCARI2 exhibited a high degree of variance, which depended on sensor viewing angle. MCARI2 values in the peak growing season were significantly higher. This result suggests that the MCARI2 metric is sensitive to seasonal changes in plant growth and senescence. We observed a high correlation between the LAIcontact values and the Total MCARI2 metric for the peak growth state plantings of R ² = 0.9, suggesting that summing multiple directional measurements of MCARI2 potentially provides a more accurate assessment of LAI and compensates for observed variance in MCARI2 as a function of sensor orientation. Future work will focus on validating the observed effects across a broader range of plant species.