Invited newsletter and book chapter
  1. Yang, K., H. Lu, and T. Koike, 2009: Microwave LDAS improves soil moisture and land flux estimates, GEWEX News, 19(3), 2-3.

    This newsletter introduces the dual-pass land data assimilation system of University of Tokyo developed. Two examples at CEOP reference sites show its potential in improving soil moisture and land fluxes estimates. Additional references are Yang et al. (2007, JMSJ) and Yang et al. (2009, JHM).

    介绍了东京大学发展的双通道陆面数据同化系统,并通过两个在CEOP参考站的应用实例说明该系统对提高土壤水分和地表通量估计精度的潜力。相关研究已发表于Yang et al. (2007, JMSJ)和Yang et al. (2009, JHM)。

  2. Liang, S. and J. Qin, 2008: Data assimilation methods for land surface variable estimation. In Advances in Land Remote Sensing: System, Modeling, Inversion and applications: Springer, (ed. Liang, S.), pp. 319-339.

    In this study, all kinds of assimilation algorithms based upon the optimal estimation and the optimal control theories are introduced. Their applications for data assimilation in meteorology, hydrology, ecology, and agriculture are reviewed. Moreover, research directions for the future assimilation are also discussed.


Journal papers
  1. Han, M., K. Yang, J. Qin, R. Jin, Y. Ma, J. Wen, Y. Chen, L. Zhao, Lazhu, and W. Tang, 2015: An algorithm based on the standard deviation of passive microwave brightness temperatures for monitoring soil surface freeze/thaw state on the Tibetan Plateau, IEEE Trans. Geosci. Remote Sens., 53(5), 2775-2783, doi:10.11-09/TGRS.2014.2364823.

    With the support of three soil moisture and temperature networks in the Tibetan Plateau, a microwave algorithm with AMSR-E data is developed for the detection of soil surface freeze/thaw state. The classification accuracy of this algorithm is more than 90% for the semi-humid and semi-arid regions, and misclassifications mainly occur at the transition period between unfrozen and frozen seasons.


  2. Lu, H., K. Yang, T. Koike, L. Zhao, and J. Qin, 2015: An Improvement of the Radiative Transfer Model Component of a Land Data Assimilation System and Its Validation on Different Land Characteristics, Remote Sens., 7(5), 6358-6379, doi:10.3390/rs70506358.

    The volume scattering effects of dry soil media and the surface scattering effects of rough surface are represented and coupled in a radiative transfer model (RTM). The RTM is tested through serving as the observation operator of a Land Data Assimilation System (LDAS). The LDAS results are validated at two stations with different weather and land cover conditions.


  3. Zhao, L., K. Yang, J. Qin, Y. Chen, W. Tang, H. Lu, and Z. Yang, 2014: The scale-dependence of SMOS soil moisture accuracy and its improvement through land data assimilation in the central Tibetan Plateau, Remote Sens. Environ., 152, 345-355, doi:10.1016/j.rse.2014.07.005.

    We evaluated SMOS L2 and L3 soil moisture products in central Tibetan Plateau. Evaluation shows the accuracy of SMOS product is scale-dependent. Large biases exist at SMOS node scale while reduced at 100-km scale. We then assimilated the 100-km averaged SMOS L2 data into a land surface model, and robust surface soil moisture estimate is achieved.

    本文以青藏高原土壤水分观测网为基础,针对 SMOS L2 和 L3 土壤水分产品进行了评估和同化实验。发现了 SMOS 土壤水分产品精度与空间尺度的关系。同时,通过同化 100-km 平均的 SMOS 土壤水分可以得到更高精度和更高时间分辨率的土壤水分产品。

  4. Qin, J., K. Yang, N. Lu, Y. Chen, L. Zhao, and M. Han, 2013: Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia, Remote Sens. Environ., 138, 1-9, doi:10.1016/j.rse.2013.07.003.

    An original soil moisture upscaling algorithm has been developed by introducing MODIS-derived apparent thermal inertia (ATI). First, a functional relationship between the station-averaged soil moisture and the pixel-averaged ATI is constructed. Second, this relationship is used to calculate the representative soil moisture time series at a certain spatial scale. Last, the Bayesian linear regression is applied to obtain the upscaled area-averaged soil moisture by using in-situ measurements as independent variables. The algorithm is evaluated using a network of in-situ moisture sensors in the central Tibetan Plateau. The results are greatly encouraging.


  5. Yang, K., J. Qin, L. Zhao, Y. Chen, W. Tang, M. Han, Lazhu, Z. Chen, N. Lu, B. Ding, H. Wu, and C. Lin, 2013: A Multi-Scale Soil Moisture and Freeze-Thaw Monitoring Network on the Third Pole, Bull. Amer. Meteor. Soc., 94(12), 1907–1916, doi:10.1175/BAMS-D-12-00203.1.

    A multi-scale soil moisture and temperature monitoring network was established on the central Tibetan Plateau to support remote sensing, land hydrological modeling, and surface process studies. The network measures two state variables (soil moisture and temperature) at three spatial scales (1.0, 0.3, 0.1 degree) and four soil depths (0~5, 10, 20, and 40 cm). The data is accessible upon request.


  6. Zhao, L., K. Yang, J. Qin, and Y. Chen, 2013: Optimal Exploitation of AMSR-E Signals for Improving Soil Moisture Estimation Through Land Data Assimilation, IEEE Trans. Geosci. Remote Sens., 51(1), 399-410, doi:10.1109/TGRS.2012.2198483.

    This study presents several sensitivity studies on the assimilating of different microwave signals into a dual-pass land data assimilation system to improve near-surface soil moisture estimation. Investigated are different polarizations, over pass time, and frequency combinations of AMSR-E brightness temperatures. Results shows that the vertically polarized, nighttime signals are the optimal choice in current system. In addition, a simple frequency-based ensamble estimation can produce more robust estimate when using different forcing data.


  7. Lu, H., T. Koike, K. Yang, Z. Hu, X. Xu, M. Rasmy, D. Kuria, and K. Tamagawa, 2012: Improving land surface soil moisture and energy flux simulations over the Tibetan plateau by the assimilation of the microwave remote sensing data and the GCM output into a land surface model, Int. J. Appl. Earth Obs. Geoinf., 17, 43-54, doi:10.1016/j.jag.2011.09.006.

    LDAS-UT output is compared with NCEP re-analysis and SiB2 simulations at two Tibetan Plateau stations. For the surface soil moisture, the LDAS simulations were superior to both NCEP and SiB2, and there was more than a one-third reduction in RMSE. This study also reveals the potential of the LDAS to improving the land surface energy and water flux simulations in ungauged and/or poorly gauged regions.


  8. Rasmy, M., T. Koike, D. Kuria, C. Mirza, and K. Yang, 2012: Development of the Coupled Atmosphere and Land Data Assimilation System (CALDAS) and its application over the Tibetan Plateau, IEEE T. Geosci. Remote Sens., 50(11), 4227-4242, doi:10.1109/TGRS.2012.2190517.

    A land data assimilation scheme and a cloud microphysics data assimilation scheme were coupled with a mesoscale model. By assimilating AMSR-E data, the coupled system improves the estimates of both soil moisture and atmospheric conditions, and eventually improves predicted clouds and rainfall.


  9. Su, Z., J. Wen, L. Dente, R. Velde, L. Wang, Y. Ma, K. Yang, and Z. Hu, 2011: The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products, Hydrol. Earth Syst. Sci., 15(7), 2303-2316, doi:10.5194/hess-15-2303-2011.

    In this paper the details of the Tibetan Plateau observatory of plateau scale soil moisture and soil temperature are reported. Analysis and comparisons with several satellite products concluded that global coarse resolution soil moisture products are useful but exhibit till now unreported uncertainties in cold and semiarid regions - use of them would be critically enhanced if uncertainties can be quantified and reduced using in-situ measurements.


  10. Xu, T., S. Liu, S. Liang, and J. Qin, 2011: Improving Predictions of Water and Heat Fluxes by Assimilating MODIS Land Surface Temperature Products into the Common Land Model, J. Hydrometeorol., 12(2), 227-244, doi:10.1175/2010JHM1300.1.

    In this study, four assimilation strategies are implemented to assimilate the MODIS land surface temperatures for estimating the sensible and latent heat fluxes: two assimilation algorithms (Ensemble Kalman Filter and SCE-UA optimization algorithm) and two control variables (soil temperature and moisture). The results indicate that the scheme of the Ensemble Kalman Filter with the soil moisture being the control variable performs best in these four strategies.


  11. Qin, J., S. Liang, K. Yang, I. Kaihotsu, R. Liu, and T. Koike, 2009: Simultaneous estimation of both soil moisture and model parameters using particle filtering method through the assimilation of microwave signal, J. Geophys. Res. Atmos., 114, D15103, doi:10.1029/2008JD011358.

    In this study, the particle filter is used to couple a daily-scale land surface model with the microwave signal, realize estimation of both parameters in the model and observation operator and model state variables, and obtain the soil surface moisture content on a daily basis. This reliability of the assimilation system is verified by comparing the estimates against the in-situ measurements.


  12. Tian, X., Z. Xie, A. Dai, C. Shi, B. Jia, F. Chen, and K. Yang, 2009: A dual-pass variational data assimilation framework for estimating soil moisture profiles from AMSR-E microwave brightness temperature, J. Geophys. Res. Atmos., 114, D16102, doi:10.1029/2008JD011600.

    A dual-pass assimilation (DP-En4DVar) framework is designed to optimize the model state (volumetric soil moisture content) and model parameters simultaneously using the gridded AMSR-E satellite brightness temperature data. Experiment results show that volumetric soil moisture content can be significantly improved to be comparable with in situ observations by assimilating only daily satellite brightness temperature. Furthermore, the improvement in surface soil moisture also propagates to lower layers where no observations are available.


  13. Yang, K., T. Koike, I. Kaihotsu, and J. Qin, 2009: Validation of a Dual-Pass Microwave Land Data Assimilation System for Estimating Surface Soil Moisture in Semiarid Regions, J. Hydrometeorol., 10(3), 780-793, doi:10.1175/2008JHM1065.1.

    The LDAS uses a dual-pass assimilation algorithm, with a calibration pass to estimate major model parameters from satellite data and an assimilation pass to estimate the near-surface soil moisture. Results show that (i) the LDAS-estimated soil moistures are comparable to areal averages of in situ measurements; (ii) the satellite-based calibration does contribute to soil moisture estimations; and (iii) the LDAS produces more robust and reliable soil moisture when forcing data become worse and is less sensitive to precipitation.


  14. Boussetta, S., T. Koike, K. Yang, T. Graf, and M. Pathmathevan, 2008: Development of a coupled land-atmosphere satellite data assimilation system for improved local atmospheric simulations, Remote Sens. Environ., 112(3), 720-734, doi:10.1016/j.rse.2007.06.002.

    This study developed a coupled land-atmosphere satellite data assimilation system as a new physical downscaling approach, by coupling a mesoscale atmospheric model with a land data assimilation system (LDAS). Through the use of satellite brightness temperature, the system has shown potential ability to provide better initial surface conditions and its inputs to the atmosphere and to improve physical downscaling through regional models.


  15. Mirza, C., T. Koike, K. Yang, and T. Graf, 2008: Retrieval of Atmospheric Integrated Water Vapor and Cloud Liquid Water Content Over the Ocean From Satellite Data Using the 1-D-Var Ice Cloud Microphysics Data Assimilation System (IMDAS), IEEE Trans. Geosci. Remote Sens., 46(1), 119-129, doi:10.1109/TGRS.2007.907740.

    This study employs a 1-D variational Ice Cloud Microphysics Data Assimilation System (IMDAS) to assimilate the satellite microwave radiometer data set of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and retrieve integrated water vapor (IWV) and integrated cloud liquid water content (ICLWC). This new method significantly improved the performance of the cloud microphysics scheme by the intrusion of heterogeneity into the external global reanalysis data, which resultantly improved atmospheric initial conditions of numerical weather prediction (NWP) models, and the modeled microwave brightness temperatures agree well with the observations of the Wakasa Bay Experiment 2003 in Japan.


  16. Yang, K. and T. Koike, 2008: Satellite Monitoring of the Surface Water and Energy Budget in the Central Tibetan Plateau, Adv. Atmos. Sci., 25(6), 974-985, doi:10.1007/s00376-008-0974-8.

    satellite data are integrated into a land data assimilation system (LDAS-UT) to estimate the soil moisture and surface energy budget on the Plateau. The results show that this satellite data-based system has a high potential for a reliable estimation of the regional surface energy budget on the Plateau.


  17. Li, A., S. Liang, A. Wang, and J. Qin, 2007: Estimating Crop Yield from Multi-temporal Satellite Data Using Multivariate Regression and Neural Network Techniques, Photogramm. Eng. Remote Sens., 73(10), 1149-1157.

    In this study, both the artificial neural network and the multivariate linear regression are used to construct the mathematical relationship between the yields of corn and soybean and the NDVI derived from the AVHRR and MODIS sensors. Both the stability and reliability of these two methods are verified by comparing with the ground statistics in the Midwest and the Great Plains of America.


  18. Qin, J., S. Liang, R. Liu, H. Zhang, and B. Hu, 2007: A Weak-Constraint Based Data Assimilation Scheme for Estimating Surface Turbulent Fluxes, IEEE Geosci. Remote Sens. Lett., 4(4), 649-653, doi:10.1109/LGRS.2007.904004.

    In this study, a simple land surface model is built. Then its adjoint model is constructed using the auto-differential technique and coupled with the conjugate descent algorithm to set up an assimilation system to assimilate the land surface temperature based on the weak-constraint concept. The surface latent and sensible heat flux can be retrieved through this system and furthermore they are verified by comparing with the ground measurements.


  19. Qin, J., R. Liu, S. Liang, H. Zhang, and B. Hu, 2007: A new method based on remote sensing observations and data assimilation for estimation of evapotranspiration over field crops, N. Z. J. Agric. Res., 50(5), 997-1004, doi:10.1080/00288230709510378.

    In this study, the auto-differential technique is used to construct the adjoint model of a land surface process model and then the adjoint model is coupled with an optimization algorithm to set up an assimilation system based upon the concept of the strong constraint. The validation results indicate that the system can retrieve the ground sensible and latent heat fluxes with satisfactory accuracy by comparing with the ground measurements at a cropland station of the American flux net.


  20. Yang, K., T. Watanabe, T. Koike, X. Li, H. Fujii, K. Tamagawa, Y. Ma, and H. Ishikawa, 2007: Auto-calibration system developed to assimilate AMSR-E data into a land surface model for estimating soil moisture and the surface energy budget, J. Meteor. Soc. Japan, 85A, 229-242, doi:10.2151/jmsj.85A.229.

    This study presents a new variational land system used to assimilate AMSR-E brightness temperature of vertical polarization of 6.9 GHz and 18.7 GHz. A major feature of this system is a dual-pass assimilation technique, which can auto-calibrate model parameters in one pass and estimate the soil moisture and energy budget in the other pass. The system not only detected the effect of precipitation event that were missing in the forcing data, but also led to a significant improvement in modeling of the surface energy budget.


  21. Qin, J., G. Yan, S. Liu, S. Liang, H. Zhang, J. Wang, and X. Li, 2006: Application of Ensemble Kalman Filter to remote sensing inversion of land surface parameters, Sci. China Ser. D, 49(6), 632-640, doi:10.1007/s11430-006-0632-x.

    in this study, the parameters of semi-physical kernel BRDF model are estimated by Ensemble Kalman Filter. Then, these parameters are used to retrieve the ground surface albedo. The in-situ measurements are used to validate the inversion results, showing that this method can retrieve the ground surface with satisfactory accuracy.