A remote sensing method for retrieving land surface emissivity and temperature in cloudy areas: a case study over South China. Issue 5 (19th March 2019)

• Record Type: Journal Article
• Authors: Zhou, Fang-Cheng; Li, Zhao-Liang; Wu, Hua; Duan, Si-Bo; Song, Xiaoning; Yan, Guangjian
• Is Part Of: International journal of remote sensing. Volume 40:Issue 5/6(2019)
• Page Start: 1724
• Page End: 1735
• Publication Date: 2019-03-19
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2018.1519288
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Land surface temperature (LST) is an important parameter at the surface–atmosphere interface, and measurements of the LST at continuous temporal and spatial scales are necessary in many research fields. Passive microwave radiation can penetrate clouds and detect land surface information under clouds; consequently, passive microwave remote sensing has the potential to obtain LST under almost all weather conditions. In this study, the relationships between the brightness temperature polarization ratio (PR) and horizontally polarized emissivities, and between the horizontally and vertically polarized emissivities are combined to develop a three-stage LST retrieval algorithm from 19 GHz microwave brightness temperature observations in cloudy areas. During the first two stages, the horizontally and vertically polarized emissivities are solely obtained from the brightness temperature data. In the last stage, the LST is calculated with the known emissivity data by ignoring the atmospheric effect. In the validation of South China, the root-mean-square errors (RMSEs) of the estimated horizontally and vertically polarized emissivities at 19 GHz are 0.0078 and 0.0023, respectively. The retrieved LST is compared to single-point measurements of the meteorological stations, and the RMSE is 4.1200 K. The LST errors are mainly due to the propagation of estimated land surface emissivity (LSE) errors, and the poor representativeness of the validated data in mixed pixels. The superiority of this LST retrieval algorithm lies in simultaneously obtaining the LST and both polarized LSE under almost all weather conditions with little input data.