Soil freeze-thaw transition monitoring is essential for quantifying climate change and hydrologic dynamics over cold regions, for instance, the Third Pole. We investigate the L-band (1.4 GHz) microwave emission characteristics of soil freeze-thaw cycle via analysis of tower-based brightness temperature (T p B ) measurements in combination with simulations performed by a model of soil microwave emission considering vertical variations of permittivity and temperature. Vegetation effects are modeled using Tor Vergata discrete emission model. The ELBARA-III radiometer is installed in a seasonally frozen Tibetan grassland site to measure diurnal cycles of L-band T p B every 30 min, and supporting micrometeorological as well as volumetric soil moisture (θ) and temperature profile measurements are also conducted. Soil freezing/thawing phases are clearly distinguished by using T p B measurements at two polarizations, and further analyses show that: 1) the four-phase dielectric mixing model is appropriate for estimating permittivity of frozen soil; 2) the soil effective temperature is well comparable with the temperature at 25 cm depth when soil liquid water is freezing, while it is closer to the one measured at 5 cm when soil ice is thawing; and 3) the impact on T p B caused by diurnal changes of ground permittivity is dominating the impact of changing ground temperature. Moreover, the simulations performed with the integrated Tor Vergata emission model and Noah land surface model indicate that the T p B signatures of diurnal soil freeze-thaw cycle is more sensitive to the liquid water content of the soil surface layer than the in situ measurements taken at 5 cm depth. Index Terms-L-band microwave radiometry, Noah land surface model (LSM), Soil Moisture and Ocean Salinity (SMOS)/Soil Moisture Active Passive (SMAP), soil freeze/thaw (F/T), Tibetan Plateau, Tor Vergata model.

Abstract:The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ • ) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson's correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m 3 /m 3 for the ascending data, and are 0.012, 0.026, and 0.055 m 3 /m 3 for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites.

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Abstract. The Tibetan Plateau observatory (Tibet-Obs) of plateau scale soil moisture and soil temperature was established 10 years ago and has been widely used to calibrate/validate satellite- and model-based soil moisture (SM) products for their applications to the Tibetan Plateau (TP). This paper reports on the status of the Tibet-Obs and presents a 10-year (2009–2019) surface SM dataset produced based on in situ measurements taken at a depth of 5 cm collected from the Tibet-Obs that consists of three regional-scale SM monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15 min in situ measurements collected by multiple SM monitoring sites of the three networks and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks. Comparisons between four spatial upscaling methods – i.e. arithmetic averaging, Voronoi diagrams, time stability, and apparent thermal inertia – show that the arithmetic average of the monitoring sites with long-term (i.e. ≥ 6-year) continuous measurements is found to be most suitable to produce the upscaled SM records. Trend analysis of the 10-year upscaled SM records indicates that the Shiquanhe network in the western part of the TP is getting wet, while there is no significant trend found for the Maqu network in the east. To further demonstrate the uniqueness of the upscaled SM records in validating existing SM products for a long-term period (∼10 years), the reliability of three reanalysis datasets is evaluated for the Maqu and Shiquanhe networks. It is found that current model-based SM products still show deficiencies in representing the measured SM dynamics in the Tibetan grassland (i.e. Maqu) and desert ecosystems (i.e. Shiquanhe). The dataset would also be valuable for calibrating/validating long-term satellite-based SM products, evaluation of SM upscaling methods, development of data fusion methods, and quantifying the coupling of SM and precipitation at a 10-year scale. The dataset is available in the 4TU.ResearchData repository at https://doi.org/10.4121/12763700.v7 (Zhang et al., 2020).

Abstract. A ground-based scatterometer was installed on an alpine meadow over the Tibetan Plateau to study the soil moisture and -temperature dynamics of the top soil layer and air-soil interface during the period August 2017–August 2018. The deployed system measured the amplitude and phase of the ground surface radar return at hourly and half-hourly intervals over 1–10 GHz in the four linear polarization combinations (vv, hh, hv, vh). In this paper we describe the developed scatterometer system, gathered datasets, retrieval method for the backscattering coefficient (σ0), and results of (σ0) for co-polarization. The system was installed on a 5 m high tower and designed using only commercially available components: a Vector Network Analyser (VNA), four coaxial cables, and two dual polarization broadband gain horn antennas at a fixed position and orientation. We provide a detailed description on how to retrieve the co-polarized backscattering coefficients σ0vv & σ0hh for this specific scatterometer design. To account for the particular effects caused by wide antenna radiation patterns (G) at lower frequencies, σ0 was calculated using the narrow-beam approximation combined with a mapping the function G2/R4 over the ground surface. (R is the distance between antennas and the infinitesimal patches of ground surface.) This approach allowed for a proper derivation of footprint positions and -areas, and incidence angle ranges. The frequency averaging technique was used to reduce the effects of fading on the σ0 uncertainty. Absolute calibration of the scatterometer was achieved with measured backscatter from a rectangular metal plate as reference target. In the retrieved time-series of σ0vv & σ0hh for S-band (2.5–3.0 GHz), C-band (4.5–5.0 GHz), and X-band (9.0–10.0 GHz) we observed characteristic changes or features that can be attributed to seasonal or diurnal changes in the soil. For example a fully frozen top soil, diurnal freeze-thaw changes in the top soil, emerging vegetation in spring, and drying of soil. Our preliminary analysis on the collected σ0 time-series data set demonstrates that it contains valuable information on water- and energy exchange directly below the air-soil interface. Information which is difficult to quantify, at that particular position, with in-situ measurements techniques alone. Availability of backscattering data for multiple frequency bands allows for studying scattering effects at different depths within the soil and vegetation canopy during the spring and summer periods. Hence further investigation of this scatterometer data set provides an opportunity to gain new insights in hydro-meteorological processes, such as freezing and thawing, and how these can be monitored with multi-frequency scatterometer observations. The data set is available via https://doi.org/10.17026/dans-zc5-skyg (Hofste and Su, 2020). The effects of fading, calibration, and system stability on the uncertainty in σ0 are estimated to vary from ± 1.3 dB for X-band with vv-polarization to ± 2.7 dB for S-band with hh-polarization through the campaign. The low angular resolution of the antennas result in additional σ0 uncertainty, one that is more difficult to quantify. Estimations point out that it probably will not exceed ± 2 dB with C-band. Despite these uncertainties, we believe that the strength of our approach lies in the capability of measuring σ0 dynamics over a broad frequency range, 1–10 GHz, with high temporal resolution over a full-year period.

An unexplored aspect of L-band microwave emission is the impact of soil moisture and soil temperature (SMST) profile dynamics on diurnal brightness temperature (T B ) signatures of frozen soil. This study investigates this effect by comparing the T B simulations of layered (T B,l ) and uniform (T B,u ) soils using a newly developed integrated land emission model. The multilayer Wilheit model and the single-layer Fresnel model are adopted to compute the smooth soil reflectivity for the layered and uniform soils, respectively. A four-phase dielectric mixing model is used to calculate the soil permittivity (ε s ). A data set of concurrent ELBARA-III T B and SMST profile measurements performed in a seasonally frozen Tibetan meadow ecosystem is used for the analysis. The simulated T B,l considering SMST profile information captures well the ELBARA-III measurements with low biases (≤6 K) and high correlations (R 2 ≥ 0.88). T B,u produced based on the Fresnel model using the soil moisture of 2.5 cm is more consistent with the T B,l . The sensitivity test of averaging SMST profile below 2.5 cm leads to maximum differences of 2 K in T B,l simulations, indicating that the T B variations are primary dominated by the SMST dynamics at the surface layer. A sensitivity test of the Wilheit model to different ε s parameterizations shows that the dielectric model of Zhang et al. is comparable to the four-phase dielectric model in simulating T B,l , while the Mironov et al.'s model demonstrates larger biases Manuscript

Abstract. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) was established ten years ago, which has been widely used to calibrate/validate satellite- and model-based soil moisture (SM) products for their applications to the Tibetan Plateau (TP). This paper reports on the status of the Tibet-Obs and presents a 10-year (2009–2019) surface SM dataset produced based on in situ measurements taken at a depth of 5 cm collected from the Tibet-Obs that consists of three regional-scale SM monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15-min in situ measurements collected by multiple SM monitoring sites of the three networks, and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks. Comparisons between four spatial upscaling methods, i.e. arithmetic averaging, Voronoi diagram, time stability and apparent thermal inertia, show that the arithmetic average of the monitoring sites with long-term (i.e. ≥ six years) continuous measurements are found to be most suitable to produce the upscaled SM records. Trend analysis of the 10-year upscaled SM records using the Mann-Kendall method shows that the Maqu network area in the eastern part of the TP is drying while the Shiquanhe network area in the west is getting wet that generally follow the change of precipitation. To further demonstrate the uniqueness of the upscaled SM records in validating existing SM products for long term period (~ 10 years), comparisons are conducted to evaluate the reliability of three reanalysis datasets for the Maqu and Shiquanhe network areas. It is found that current model-based SM products still show deficiencies in representing the trend and variation of measured SM dynamics in the Tibetan grassland (i.e. Maqu) and desert ecosystems (i.e. Shiquanhe) that dominate the landscape of the TP. The dataset would be also valuable for calibrating/validating long-term satellite-based SM products, evaluation of SM upscaling methods, development of data fusion methods, and quantifying the coupling strength between precipitation and SM at 10-year scale. The dataset is available in the 4TU.ResearchData repository at https://doi.org/10.4121/uuid:21220b23-ff36-4ca9-a08f-ccd53782e834 (Zhang et al., 2020).