05 Jan 2022

05 Jan 2022

Review status: this preprint is currently under review for the journal TC.

A Novel Global Freeze-Thaw State Detection Algorithm Based on Passive L-Band Microwave Remote Sensing

Shaoning Lv1,2, Clemens Simmer2,5, Yijian Zeng3, Jun Wen4, Yuanyuan Guo1, and Zhongbo Su3 Shaoning Lv et al.
  • 1Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, 200438, Shanghai, China
  • 2Institute for Geosciences - Meteorology at the University of Bonn, Auf dem Huegel 20, 53121 Bonn, Germany
  • 3Department of Water Resources, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500AE, Enschede, The Netherlands
  • 4the Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225, Sichuan, China
  • 5Cloud and Precipitation Exploration Laboratory (CPEX-Lab) of Geoverbund ABC/J, Auf dem Huegel 20, 53121 Bonn, Germany

Abstract. Knowing the Freeze-Thaw (FT) state of the land surface is essential for many aspects of weather forecasting, climate, hydrology, and agriculture. Near-surface air temperature and land surface temperature are usually used in meteorology to infer the FT-state. However, the uncertainty is large because both temperatures can hardly be distinguished from remote sensing. Microwave L-band emission contains rather direct information about the FT-state because of its impact on the soil dielectric constant, which determines microwave emissivity and the optical depth profile. However, current L band-based FT algorithms need reference values to distinguish between frozen and thawed soil, which are often not known sufficiently well.

We present a new FT-state detection algorithm based on the daily variation of the H-polarized brightness temperature of the SMAP L3c FT global product for the northern hemisphere, which is available from 2015 to 2021. The exploitation of the daily variation signal allows for a more reliable state detection, particularly during the transitions periods, when the near-surface soil layer may freeze and thaw on sub-daily time scales. The new algorithm requires no reference values; its results agree with the SMAP FT state product by up to 98 % in summer and up to 75 % in winter. Compared to the FT state inferred indirectly from the 2-m air temperature of the ERA5-land reanalysis, the new FT algorithm has a similar performance as the SMAP FT product. The most significant differences occur over the midlatitudes, including the Tibetan plateau and its downstream area. Here, daytime surface heating may lead to daily FT transitions, which are not considered by the SMAP FT state product but are correctly identified by the new algorithm. The new FT algorithm suggests a 15 days earlier start of the frozen-soil period than the ERA5-land’s 2-m air temperature estimate. This study is expected to extend L-band microwave remote sensing data for improved FT detection.

Shaoning Lv et al.

Status: open (until 02 Mar 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review of Lv et al.', Simon Zwieback, 15 Jan 2022 reply

Shaoning Lv et al.

Shaoning Lv et al.


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Short summary
The freeze-thaw of the ground is an interesting topic to climatology, hydrology, and other earth sciences. The global freeze-thaw distribution is available by passive microwave remote sensing technique. However, the remote sensing technique indirectly detects freeze-thaw states by measuring the brightness temperature difference between frozen and unfrozen soil. Thus, we present different interprets of the brightness signals to the FT-state by using its sub-daily character.