Dynamic Land Surface Classifcations using Microwave Frequencies

Tuesday, 16 December 2014
Hasan Jackson1,2, Yudong Tian2, Christa D Peters-Lidard2 and Kenneth W Harrison3, (1)University of Maryland College Park, College Park, MD, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)UMD ESSIC/NASA GSFC, Greenbelt, MD, United States
Land surface emissivity in microwave frequencies is critical to the remote sensing of soil moisture, precipitation, and vegetation. Different land surfaces have different spectral signatures in the microwave portions of the electromagnetic spectrum. Their spatial and temporal behaviors are also highly variable. These properties are yet not well understood in microwave frequencies, despite their capability in detecting water-related variables in the atmosphere and land surface. A classification scheme was developed to stratify the Earth's land surfaces based on their seasonally dynamic microwave signatures. An unsupervised clustering approach was used identify and distinguish data groupings along two microwave based indicies. Land surface data clusters were mapped to determine their spatial relationships to known land cover groupings. Differences in land surface clusters were analyzed in their spatial consistency and their direction and magnitude of land surface change. It was found that vegetation and topography were the predominant contributors to change between seasons. Land surface extremes of sandy desert and closed canopy tropical forest displayed minimal intra-annual variability while transitional zones, such as the Sahel and North American temperate forests, exhibited the most variability. Distinct microwave signatures varied between seasons along a latittudinal gradient. Overall variability in land surface types increased at high lattitudes. This classification will help inform research studies maniputlating the microwave frequencies of the electromagnetic spectrum to better characterize land surface dynamics, and will be very useful in the validation of radiative transfer models and quantification of uncertainty in global precipitation monitoring.