Stand-alone error characterisation of microwave satellite soil moisture using a Fourier method
2014
Su, Chun-Hsu | Ryu, Dongryeol | Crow, Wade T. | Western, Andrew W.
Error characterisation of satellite-retrieved soil moisture (SM) is crucial for maximizing their utility in research and applications in hydro-meteorology and climatology. It can provide insights for retrieval development and validation, and inform suitable strategies for data fusion and assimilation. Su et al. (2013a) proposed a potential Fourier method for quantifying the errors based on the difference between the empirical power spectra of these SM data and a water balance model via spectral fitting (SF), circumventing the need for any ancillary data. This work first evaluates its utility by estimating the errors in two passive and active microwave satellite SM over Australia, and comparing the results against the triple collocation (TC) estimator. The SF estimator shows very good agreement with TC in terms of error standard deviation and signal-to-noise ratio, with strong linear correlations of 0.80–0.92 but with lower error estimates. As the two estimators are not strictly comparable, their strong agreement suggests a strong complementarity between time-domain and frequency-domain analyses of errors. A better understanding of the spectral characteristics of the error is still needed to understand their differences. Next, spatial analyses of the derived (SF and TC) error maps, in terms of error standard deviation and noise-to-signal ratio, for the two satellite data are performed with principal component analysis to identify influence of vegetation/leaf-area index (LAI), rainfall, soil wetness, and spatial heterogeneity in topography and soil type on retrieval errors. Lastly, seasonal analysis of the errors discovers systematic temporal variability in errors due to variability in rainfall amount, and less so with changing LAI.
Показать больше [+] Меньше [-]Ключевые слова АГРОВОК
Библиографическая информация
Эту запись предоставил National Agricultural Library