Simulation of the SMAP data stream from SMAPEx field campaigns in Australia
Date
2015
Authors
Wu, X.
Walker, J.
Rudiger, C.
Panciera, R.
Gray, D.
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Journal article
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IEEE Transactions on Geoscience and Remote Sensing, 2015; 53(4):1921-1934
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Xiaoling Wu, Jeffrey P. Walker, Christoph Rüdiger, Rocco Panciera, and Douglas A. Gray
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Abstract
NASA’s Soil Moisture Active Passive (SMAP) mission will provide a ∼10-km resolution global soil moisture product with a 2–3-day revisit by exploiting the synergy between active and passive observations. However, soil moisture downscaling techniques required to exploit this synergy have not yet received extensive testing, being limited to mostly synthetic data. Consequently, airborne field campaigns such as the SMAP Experiments (SMAPEx) have been designed to provide experimental data to fill this gap. The objective of this study is to assess the reliability of SMAP prototype data stream derived from airborne observations, with the aim of providing a simulated SMAP data set for prelaunch algorithm development of SMAP. Specifically, the reliability of incidence- angle normalization and spatial resolution aggregation for airborne observations was assessed for this purpose. The impact of azimuthal angle on active–passive observations was analyzed to assess the potential influence of SMAP rotating antenna on observations. Results showed that the accuracies of angle normalization were ∼0.8 dB for active and 2.4 K for the passive observations (1-km resolution), while the uncertainties associated with spatial upscaling were 2.7 dB (150-m resolution) and 2 K (1-km resolution). Although azimuthal signatures associated with the variable orientation of surface features were observed in the high-resolution observations, these tended to be smoothed when aggregating to coarser resolution. As these errors are expected to decrease further at the coarser resolution of SMAP, results suggested that data from SMAPEx can be reliably used to simulate SMAP data for subsequent use in active–passive soil moisture algorithm development.
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