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"Zeng, Yijian"
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Coupled dynamics in soil : experimental and numerical studies of energy, momentum and mass transfer
In arid and semi-arid areas, the main contributions to land surface processes are precipitation, surface evaporation and surface energy balancing. In the close-to-surface layer and root-zone layer, vapor flux is the dominant flux controlling these processes - process which, in turn, influence the local climate pattern and the local ecosystem. The work reported in this thesis attempts to understand how the soil airflow affects the vapor transport during evaporation processes, by using a two-phase heat and mass transfer model. The necessity of including the airflow mechanism in land surface process studies is discussed and highlighted.
Book
Optimizing Ad Recommendations Using A Bayesian Multi-Armed Bandit Approach
The contextual multi-armed bandit problem in online advertising recommendation represents a core challenge in recommender systems. Traditional approaches, including Linear Upper Confidence Bound (LinUCB) and Feature-based Collaborative Filtering (FeatureCF), demonstrate limitations in exploration-exploitation balance and high-dimensional feature modeling: Upper Confidence Bound (UCB) suffers from excessive exploration due to its upper confidence bound strategy, while FeatureCF struggles with convergence due to inefficient gradient updates. To address these deficiencies, this paper proposes the Bayesian Multi-Armed Bandit (MAB), which enhances performance through a three-layered progressive strategy: employing Thompson Sampling for precise reward estimation, integrating a temporal decay factor to dynamically adjust exploration bias, and utilizing a cold-start strategy to accelerate initial learning. Simulation experiments on the Criteo dataset over 10,000 steps reveal that Bayesian MAB outperforms its counterparts, achieving a final Click-Through Rate (CTR) of 0.4407, an optimal arm selection rate of 0.3682, and a Gini coefficient of 0.5731, demonstrating a superior exploration-exploitation balance. The effectiveness of Bayesian MAB in overcoming the shortcomings of traditional methods offers an efficient solution for online advertising recommendation.
Journal Article
Global long term daily 1 km surface soil moisture dataset with physics informed machine learning
Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. Here we use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics. Global Surface Soil Moisture (GSSM1 km) provides surface soil moisture (0–5 cm) at 1 km spatial and daily temporal resolution over the period 2000–2020. The performance of the GSSM1 km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1 km in testing set is 0.05 cm
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/cm
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, and correlation coefficient is 0.9. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. GSSM1 km product can support the investigation of large-scale climate extremes and long-term trend analysis.
Journal Article
Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau
It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10 yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.
Journal Article
Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model
This paper coupled a unified passive and active microwave observation operator—namely, an enhanced, physically-based, discrete emission-scattering model—with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture Active and Passive (SMAP) brightness temperature TBp (p = Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of TBH lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of TBV reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated.
Journal Article
A New Sea Ice Concentration (SIC) Retrieval Algorithm for Spaceborne L-Band Brightness Temperature (TB) Data
Sea ice concentration (SIC) is crucial to the global climate. In this study, a new single-channel SIC retrieval algorithm utilizing spaceborne L-band brightness temperature (TB) measurements is developed based on a microwave radiative transfer model. Additionally, its four uncertainties are quantified and constrained: (1) variations in seawater reference TB under warm water conditions, (2) variations in sea ice reference TB under extremely low-temperature conditions, (3) the freeze–thaw dynamics of sea ice captured by Diurnal Amplitude Variation (DAV) signals, and (4) Land mask imperfections. It is found that DAV has the most pronounced effect: eliminating its influence reduces RMSE from 10.51% to 8.43%, increases R from 0.92 to 0.94, and minimizes Bias from -0.68 to 0.13. Suppressing all four uncertainties lowers RMSE to 7.42% (a 3% improvement). Furthermore, the algorithm exhibits robust agreement with the seasonal variability of SSM/I SIC, with R mostly exceeding 0.9, RMSE mostly below 10%, and Biases mostly within 5% throughout the year. Compared to ship-based and SAR SIC data, the new L-band algorithm’s Bias and RMSE are only 2% and 2% (ship-based)/2% and 1% (SAR) higher, respectively, than those of the SSM/I product. Future algorithms can integrate the DAV signal more effectively to better understand sea ice freeze–thaw processes and ice-atmosphere interactions.
Journal Article
Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements
The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m³ m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m³ m−3 (0.030 m³ m−3) at the 9-km scale and 0.035 m³ m−3 (0.026 m³ m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m³ m−3 (0.032 m³ m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.
Journal Article
First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau
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 m3/m3 for the ascending data, and are 0.012, 0.026, and 0.055 m3/m3 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.
Journal Article
A simulation analysis of the advective effect on evaporation using a two-phase heat and mass flow model
The concept of enhanced vapor transfer in unsaturated soils has been questioned for its reliance on soil temperature gradient, which leads to consideration of other mechanisms of vapor transfer, e.g., advective vapor transfer due to soil air pressure gradient. Although the advective flux is an important portion of evaporation, there is a lack of knowledge of its effect on evaporation. In order to assess the dependence of evaporation on the soil air pressure gradient, a vertical one‐dimensional two‐phase heat and mass flow model is developed that fully considers diffusion, advection, and dispersion. The proposed model is calibrated with field measurements of soil moisture content and temperature in the Badain Jaran Desert. The proposed model is then used to investigate the advective effect in both low‐ and high‐permeability soils. The advective effect is reflected by underestimating evaporation when the airflow is neglected and is more evident in the low‐permeability soil. Neglecting airflow causes an underestimation error of 53.3% on the day right after a rainfall event in the low‐permeability soil (7.9 × 10−4 cm s−1) and 33.3% in the high‐permeability soil (2 × 10−3 cm s−1). The comparisons of driving forces and conductivities show that the isothermal liquid flux, driven by the soil matric potential gradient, is the main reason for the underestimation error. Key Points The two‐phase heat and mass flow model The underestimation error induced by neglecting air flow The mechanical analysis of the advective effect on evaporation
Journal Article
In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model
The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m−3 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m−3 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.
Journal Article