A yearlong comparison of plot‐scale and satellite footprint‐scale 19 and 37 GHz brightness of the Alaskan North Slope

Abstract

[1] Subpixel heterogeneity remains a key issue in the estimation of land parameters using satellite passive microwave sensors; the scales of spatial variability on land are typically much smaller than sensor footprints (tens of km). Disaggregation is a necessary component of any successful assimilation or retrieval scheme attempting to exploit satellite passive microwave observations to estimate parameters at the local scale. This paper quantifies the similarity between ground-based brightness and satellite brightness observations at 19 and 37 GHz for Arctic tundra on the North Slope of Alaska, identifying and quantifying sources of the differences. To the extent that this very homogeneous area represents a limiting case, the impact of subpixel heterogeneity in less homogeneous areas may be gauged. The ground-based radiobrightness observations were collected during the Radiobrightness Energy Balance Experiment 3 (REBEX-3) conducted on the North Slope in 1994-1995. A comparison was made of 381 days of brightness observations from a tower-mounted Special Sensor Microwave Imager (SSM/I) simulator representing the full range of annual conditions with coincident satellite SSM/I observations. Issues such as instrument stability, the effects of atmospheric radiative transfer, and consistency of satellite pixel locations are considered. Linear correlations between tower-based and SSM/I brightness observations of 0.93, 0.94, 0.93, and 0.92 were observed for the 19V, 19H, 37V, and 37H channels, respectively. Footprint sizes were 2 Â 4 m for the tower-based observations and 43 Â 69 km for the resampled SSM/I observations. Atmospheric, topographic, and time-ofobservation effects can account for the differences between the best fit lines and the 1:1 lines, with calibration errors accounting for the residual differences.

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

“…When ∆T B = 10.65 − 36.5 GHz or ∆T B = 18.7 − 36.5 GHz are used to estimate terrestrial SWE, it is assumed that ∆T B is positively correlated with SWE because T B at 36.5 GHz, in general, decreases as SWE increases (i.e., snowpack deepens) due to volume scattering while T B at 10.65 GHz (or 18.7 GHz) is less sensitive to increasing snow depth as compared to 36.5 GHz. However, T B at 36.5 GHz can begin to increase after SWE reaches a threshold value; this is due to the transition from snow volume scattering to emission at 36.5 GHz [60,80]. SWE threshold values for the slope reversal in the correlations between SWE and ∆T B at 36.5 GHz vary depending on local snow conditions such as snow wetness, snow grain size, snow grain shape, and the presence of internal ice layers [81].…”

AMSR‐E algorithm for snowmelt onset detection in sub‐arctic heterogeneous terrain

“…The SSM/I sensor has been used in previous studies to establish the timing of the spring melt transition in the upper Yukon River basin (Ramage et al, 2006) as well as in the Juneau Icefield Isacks, 2002, 2003). Even though the SSM/I sensor provides twice-daily observations and has been shown to correlate well with groundbased brightness temperature measurements over fairly homogeneous terrain such as the Alaskan North Slope (Kim and England, 2003), the pixel resolution of greater 1589 than 25 ð 25 km 2 that results from the passive nature of the sensor is a problematic issue in monitoring dynamic changes over heterogeneous terrain. The AMSR-E sensor, recently launched aboard NASA's Aqua satellite in 2002, provides more observations of the study area per day and, with an improved pixel resolution over SSM/I, can provide a more accurate examination of snow characteristics over mixed terrain.…”

Snow stratigraphic heterogeneity within ground‐based passive microwave radiometer footprints: Implications for emission modeling

“…As variability in snow properties is so difficult to capture at the satellite scale, it seems reasonable to look at the much smaller scale of suborbital or ground‐based sensors to test microwave models, where the potential exists to characterize snow properties more thoroughly. The use of ground‐based passive microwave measurements to evaluate forward radiative transfer models, which use in situ snowpack properties, has been undertaken before [ Brogioni et al ., ; Durand et al ., ; Kim and England , ; Mätzler and Wiesmann , ; Montpetit et al ., ; Rees et al ., ; Roy et al ., ; Tedesco et al ., ]. However, measured snowpack properties are commonly limited to a single vertical profile, often in snow pits excavated outside of the sensor footprint because of the need to maintain a consistently undisturbed measurement area.…”