Menglei Han

A MULTISCALE SOIL MOISTURE AND FREEZE-THAW MONITORING NETWORK ON THE THIRD POLE

Multisphere interactions over the Tibetan Plateau directly impact its surrounding climate and environment at a variety of spatiotemporal scales. Remote sensing and modeling are expected to provide hydrometeorological data needed for these process studies, but in situ observations are required to support their calibration and validation. For this purpose, we have established a dense monitoring network on the central Tibetan Plateau to measure two state variables (soil moisture and temperature) at three spatial scales (1.0°, 0.3°, and 0.1°) and four soil depths (0–5, 10, 20, and 40 cm). The experimental area is characterized by low biomass, high soil moisture dynamic range, and typical freeze–thaw cycle. The network consists of 56 stations with their elevation varying over 4470–4950 m. As auxiliary parameters of this network, soil texture and soil organic carbon content are measured at each station to support further studies. To guarantee continuous and high-quality data, tremendous efforts have been made t...

An Algorithm Based on the Standard Deviation of Passive Microwave Brightness Temperatures for Monitoring Soil Surface Freeze/Thaw State on the Tibetan Plateau

The land surface on the Tibetan Plateau (TP) experiences diurnal and seasonal freeze/thaw processes that play important roles in the regional water and energy exchanges, and passive microwave satellites provide opportunities to detect the soil state for this region. With the support of three soil moisture and temperature networks on the TP, a dual-index microwave algorithm with Advanced Microwave Scanning Radiometer-Earth Observing System data is developed for the detection of soil surface freeze/thaw state. One index is the standard deviation index (SDI) of brightness temperature (TB), which is defined as the standard deviation of horizontally polarized brightness temperatures at 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz. It is the major index and is used to reflect the reduction of liquid water content after soils get frozen. The other index is the 36.5-GHz vertically polarized brightness temperature (TB36.5V), which islinearly correlated with ground temperature. The threshold values of the two indices (SDI and TB36.5V) are determined with one grid from the network located in a semiarid climate, and the algorithm is validated with other grids from the same network. Further validations are conducted based on the other two networks located in different climates (semihumid and arid, respectively). Results show that the classification accuracy using this algorithm is more than 90% for the semihumid and semiarid regions, and misclassifications mainly occur at the transition period between unfrozen and frozen seasons. Nevertheless, the algorithm has limited capability in identifying the soil surface freeze/thaw state in the arid region because the microwave signals can penetrate deep dry soils and thus embody the bulk information beyond the surface layer.

Evaluation of Precipitable Water Vapor from Four Satellite Products and Four Reanalysis Datasets against GPS Measurements on the Southern Tibetan Plateau

AbstractThe southern Tibetan Plateau (STP) is the region in which water vapor passes from South Asia into the Tibetan Plateau (TP). The accuracy of precipitable water vapor (PWV) modeling for this region depends strongly on the quality of the available estimates of water vapor advection and the parameterization of land evaporation models. While climate simulation is frequently improved by assimilating relevant satellite and reanalysis products, this requires an understanding of the accuracy of these products. In this study, PWV data from MODIS infrared and near-infrared measurements, AIRS Level-2 and Level-3, MERRA, ERA-Interim, JRA-55, and NCEP final reanalysis (NCEP-Final) are evaluated against ground-based GPS measurements at nine stations over the STP, which covers the summer monsoon season from 2007 to 2013. The MODIS infrared product is shown to underestimate water vapor levels by more than 20% (1.84 mm), while the MODIS near-infrared product overestimates them by over 40% (3.52 mm). The AIRS PWV pr...

Evaluation of SMAP, SMOS, and AMSR2 soil moisture retrievals against observations from two networks on the Tibetan Plateau

Two soil moisture and temperature monitoring networks were established in the Tibetan Plateau (TP) during recent years. One is located in a semi-humid area of central TP and consists of 56 soil moisture and temperature measurement (SMTM) stations, the other is located in a semi-arid area of southern TP and consists of 21 SMTM stations. In this study, the station data are used to evaluate soil moisture retrievals from three microwave satellites, i.e. the SMAP of NASA, the SMOS of ESA, and the AMSR2 of JAXA. It is found that the SMAP retrievals tend to underestimate soil moisture in the two TP networks, mainly due to the negative biases in the effective soil temperature that is derived from a climate model. However, the SMAP product well captures the amplitude and temporal variation of the soil moisture. The SMOS product performs well in Naqu network with acceptable error metrics, but fails to capture the temporal variation of soil moisture in Pali network. The AMSR2 products evidently exaggerate the temporal variation of soil moisture in Naqu network, but dampen it in Pali network, suggesting its retrieval algorithm needs further improvements for the TP.

A surface soil temperature retrieval algorithm based on AMSR-E multi-frequency brightness temperatures

ABSTRACT In this study, a multi-frequency statistical algorithm is proposed for retrieving surface soil temperature () from AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observation System) brightness temperature () observations. The algorithm was developed based on a regression analysis between from all AMSR-E bands and the corresponding in situ observed by a soil moisture and temperature network in the central Tibetan Plateau. The new algorithm effectively utilizes information from the different bands provided by AMSR-E, lessening the influence of soil moisture, vegetation, and water vapour. Further validations were conducted based on seven soil moisture and temperature observation networks distributed globally. The results showed that the new multi-frequency algorithm can produce values with a mean bias of less than 2 K and a root mean square error of less than 3 K for different vegetation-covered areas of the globe. Compared with a widely used single-band inversion algorithm, the new multi-frequency algorithm has better accuracy in estimating and does not suffer from considerable overestimation or underestimation across these networks, indicating good transferability. This algorithm could contribute to research relating to the land energy balance by providing consistent and independent long-term estimates of daily global . Nevertheless, the new algorithm has limited ability to retrieve of frozen soil, given that AMSR-E values are affected by the deep soil temperature after a soil is frozen.

Surface roughness parameters estimation over the Tibetan Plateau: Optimization, calibration and validation in the dense soil moisture networks

Summary form only given. Surface roughness has significate impacts on the land surface microwave emission and is an important process that should be considered in the development of soil moisture retrieval algorithm. The surface roughness can be descripted by two parameters: rms height (h) and correlation length (l), with a distribution function either in Gaussian or exponential form. These two parameters can be observed at field scale (1 m ~ 10 m) by using roughmeters, but they are hard to measure at the scale of satellite footprint (~ 40 km). Currently, in the most of current soil moisture retrieval algorithm, the surface roughness parameters are either set as constant values or obtained from limited in situ observation. Consequently, the space heterogeneity of land surface roughness is not well handled. In this study, we developed a systemic method to estimate surface roughness parameters at satellite footprint scale and apply it to retrieve surface soil moisture over the Tibetan Plateau from AMSR-E/AMSR2 observation. At the first step, we optimized the surface roughness parameter over the Tibetan Plateau with using land surface model simulation and time series of microwave observation. At the same time, based on the accurate soil temperature moisture observations obtained at the Naqu dense soil network, surface roughness parameters were calibrated by using a radiative transfer model in which AIME was used. Finally, a regression relationship was developed between the optimized parameters and calibrated parameters and applied to correct the optimized parameter over whole Tibetan Plateau. With the new surface roughness parameters set, soil moisture over Tibetan Plateau was retrieved from AMSR-E/AMSR2 observation and validated at other two dense soil network: Maqu and Yadong. The study is carrying out now, more details will be reported in the conference.

Development of passive microwave retrieval algorithm for estimation of surface soil temperature from AMSR-E data

Soil temperature is one of the essential variables governing the land atmosphere interaction. In this study, we proposed a statistical algorithm to retrieve the surface soil temperature from AMSR-E brightness temperature (TB) observations. The algorithm was developed based on the regression relationship between AMSR-E TB and corresponding in situ soil temperature observed at the Naqu network in the central Tibetan Plateau (CTP-Naqu). The algorithm was validated by application in six soil observation networks distributed globally. The results demonstrate that the algorithm has high accuracy and good transferability and can be applied globally. Moreover, the new algorithm uses passive microwave observations and is able to retrieve soil temperature all-day and all-weather, which compensates the deficiency of thermal infrared-based algorithms that are vulnerable to cloud contamination.

Soil moisture and temperature measuring networks in the Tibetan Plateau and their applications in validation of microwave products

Soil moisture is a key parameter in the land-atmosphere interactions over the Tibetan Plateau (TP), which plays an essential role in the Asian monsoon processes. Validation of satellite observed and/or modeled surface soil moisture is a particularly challenging work due to the scale issues. Additional challenge in this area is the harsh environment and heavy workload to establish a Soil Moisture and Temperature Measurement System (SMTMS) network. In this paper, we introduced two soil moisture and temperature monitoring networks that have dense measurements. Several applications with the data are presented, including current microwave products evaluation, new algorithm development and soil parameter optimization.