An examination of methods for estimating land surface microwave emissivity. Issue 21 (10th November 2015)

• Record Type: Journal Article
• Authors: Tian, Yudong; Peters‐Lidard, Christa D.; Harrison, Kenneth W.; You, Yalei; Ringerud, Sarah; Kumar, Sujay; Turk, F. Joseph
• Is Part Of: Journal of geophysical research. Volume 120:Issue 21(2015:Nov.)
• Page Start: 11, 114
• Page End: 11, 128
• Publication Date: 2015-11-10
• Subjects: microwave -- emissivity -- precipitation -- remote sensing -- brightness temperature; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2015JD023582
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Land surface emissivity is a critical variable for the passive microwave‐based remote sensing of the land and atmosphere. Driven by the Global Precipitation Measurement mission, we implemented and evaluated a variety of approaches for quantitative estimation of land surface emissivity and its variability, within a well‐defined common framework. These approaches fall into three classes: physical modeling, statistical modeling, and a hybrid of physical and statistical modeling. Every approach is subject to evaluation against retrieved emissivity over a large area in the Southern Great Plains for a period of 2 years. Physical modeling, based on two radiative transfer models coupled to a land surface modeling framework, produced reasonable estimates, with channel‐ and polarization‐dependent errors. Calibration of these models with historical data substantially improved their performance at lower frequencies. The statistical method was tested with five different regression models, and each of them consistently outperformed physical models by about 50%. The best statistical model had an average error of 0.9–2.1%. These statistical models were slightly improved when empirical orthogonal function analysis was incorporated in the emissivity data. The hybrid approach produced errors between the uncalibrated and calibrated physical model errors. In addition to their predictive performance, other aspects of each approach's strengths and weaknesses are discussed. Key Points: New real‐time methods are developed for land surface emissivity estimation New methods are evaluated against various existing ones Strengths and weaknesses of various methods are identified and documented