A soil moisture assimilation scheme based on the microwave Land Emissivity Model and the Community Land Model

Applications of microwave remote-sensing data in land data assimilation are a topic of current interest and importance due to their high temporal and spatial resolution and availability. However, there have been few studies on land surface sub-grid scale heterogeneity and calculating microwave wetland surface emissivity when directly assimilating gridded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) satellite brightness temperature (BT) data to estimate soil moisture. How to assimilate gridded AMSR-E BT data for land surface model (LSM) grid cells including various land cover types, especially wetland, is worthy of careful study. The ensemble Kalman filter (EnKF) method is able to resolve the non-linearity and discontinuity in forecast and observation operators, and is widely used in land data assimilation. In this study, considering the influences of land surface sub-grid scale heterogeneity, a satellite data simulation scheme based on the National Center for Atmosphere Research (NCAR) Community Land Model version 2.0 (CLM2.0), microwave Land Emissivity Model (LandEM), Shuffled Complex Evolution (SCE-UA) algorithm and AMSR-E BT observation data is presented to simulate AMSR-E BT data and calibrate microwave wetland surface emissivity; then, a soil moisture data assimilation scheme is developed to directly assimilate the gridded AMSR-E BT data, which consists of the CLM2.0, LandEM and EnKF. The experimental results indicate that the calibrated microwave wetland surface emissivities possess excellent transportability, and that the assimilation scheme is practical and can significantly improve soil moisture estimation accuracy. This study provides a promising solution to improve soil moisture estimation accuracy through directly assimilating gridded AMSR-E BT data for various land cover types such as bare soil, vegetation, snow, lake and wetland.     

基于双EnKF的土壤水分与土壤属性参数同时估计

为提高土壤水分数据同化结果的精度,将基于双集合卡尔曼滤波(Dual Ensemble Kalman Filter,DEnKF)的状态-参数估计方案与简单生物圈模型(simple biosphere model 2,SiB2)相结合,同时更新土壤水分和优化模型参数(土壤属性参数)。选用2008年6月1日~10月29日黑河上游阿柔冻融观测站为参考站,开展了同化表层土壤水分观测数据的实验。研究结果表明:DEnKF可同时优化土壤属性参数和改进土壤水分估计,该方法对表层土壤水分估计的精度0.04高于EnKF算法的精度0.05。当观测数据稀少时,DEnKF算法仍然可以得到较高精度的土壤水分估计,3层土壤水分的估计精度在0.02~0.05之间。     

An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation

This paper aims to investigate several new nonlinear/non-Gaussian filters in the context of the sequential data assimilation. The unscented Kalman filter (UKF), the ensemble Kalman filter (EnKF), the sampling importance resampling particle filter (SIR-PF) and the unscented particle filter (UPF) are described in the state-space model framework in the Bayesian filtering background. We first evaluated those methods with a simple highly nonlinear Lorenz model and a scalar nonlinear non-Gaussian model to investigate the filter stability and the error sensitivity, and then their abilities in the one-dimensional estimation of the soil moisture content with the synthetic microwave brightness temperature assimilation experiment in the land surface model VIC-3L. All the results are compared with the EnKF. The advantages and disadvantages of each filter are discussed.The results in the Lorenz model showed that the particle filters are suitable for the large measurement interval assimilation and that the Kalman filters were suitable for the frequent measurement assimilation as well as small measurement uncertainties. The EnKF also showed its feasibility for the non-Gaussian noise. The performance of the SIR-PF was actually not as good as that of the UKF or the EnKF regarding a very small observation noise level compared with the uncertainties in the system. In the one-dimensional brightness temperature assimilation experiment, the UKF, the EnKF and the SIR-PF all proved to be flexible and reliable nonlinear filter algorithms for the low dimensional sequential land data assimilation application. For the high dimensional land surface system that takes the horizontal error correlations into account, the UKF is restricted by its computational demand in the covariance propagation; we must use the EnKF, the SIR-PF and other covariance reduction algorithms. The large computational cost prevents the UPF from being applied in practice.     

Modeling and assimilation of root zone soil moisture using remote sensing observations in Walnut Gulch Watershed during SMEX04

Soil moisture status in the root zone is an important component of the water cycle at all spatial scales (e.g., point, field, catchment, watershed, and region). In this study, the spatio-temporal evolution of root zone soil moisture of the Walnut Gulch Experimental Watershed (WGEW) in Arizona was investigated during the Soil Moisture Experiment 2004 (SMEX04). Root zone soil moisture was estimated via assimilation of aircraft-based remotely sensed surface soil moisture into a distributed Soil-Water-Atmosphere-Plant (SWAP) model. An ensemble square root filter (EnSRF) based on a Kalman filtering scheme was used for assimilating the aircraft-based soil moisture observations at a spatial resolution of 800 m × 800 m. The SWAP model inputs were derived from the SSURGO soil database, LAI (Leaf Area Index) data from SMEX04 database, and data from meteorological stations/rain gauges at the WGEW. Model predictions are presented in terms of temporal evolution of soil moisture probability density function at various depths across the WGEW. The assimilation of the remotely sensed surface soil moisture observations had limited influence on the profile soil moisture. More specifically, root zone soil moisture depended mostly on the soil type. Modeled soil moisture profile estimates were compared to field measurements made periodically during the experiment at the ground based soil moisture stations in the watershed. Comparisons showed that the ground-based soil moisture observations at various depths were within ± 1 standard deviation of the modeled profile soil moisture. Density plots of root zone soil moisture at various depths in the WGEW exhibited multi-modal variations due to the uneven distribution of precipitation and the heterogeneity of soil types and soil layers across the watershed.     

基于微波遥感和陆面模型的流域土壤水分研究

土壤水分是陆地水文的重要因子。微波遥感是测量土壤水分的一种重要方法。本文总结了基于微波遥感和陆面模型的土壤水分监测方法,包括被动微波法、主动微波法、主被动微波结合法、陆面模型模拟法和数据同化法五种。被动微波对表面土壤水分敏感,但其空间分辨率低;主动微波具有较好的分辨率但运作费用也较高;主被动微波结合则能够充分利用各自的优势。陆面模型在研究中也有重要作用,通过模型模拟能够得到根区土壤水分。而将观测值同化到模型的数据同化法,则能极大的提高土壤水分估计的能力。通过比较,指出数据同化是最有前景的研究领域。     

Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation

An integrated data assimilation system is implemented over the Red-Arkansas river basin to estimate the regional scale terrestrial water cycle driven by multiple satellite remote sensing data. These satellite products include the Tropical Rainfall Measurement Mission (TRMM), TRMM Microwave Imager (TMI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Also, a number of previously developed assimilation techniques, including the ensemble Kalman filter (EnKF), the particle filter (PF), the water balance constrainer, and the copula error model, and as well as physically based models, including the Variable Infiltration Capacity (VIC), the Land Surface Microwave Emission Model (LSMEM), and the Surface Energy Balance System (SEBS), are tested in the water budget estimation experiments. This remote sensing based water budget estimation study is evaluated using ground observations driven model simulations. It is found that the land surface model driven by the bias-corrected TRMM rainfall produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating TMI 10.67 GHz microwave brightness temperature measurements that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilating evapotranspiration estimated from satellite-based measurements.     

Improving Soil Moisture Estimation by Joint Assimilationof MODIS Land Surface Temperature and Airborne L band Microwave Brightness Temperature

In this work,a novel soil moisture data assimilation scheme was developed,which was based land surface model (CoLM,Common Land Model),microwave radioactive transfer model (L MEB,L band Microwave Emission of the Biosphere),and data assimilation algorithm (EnKS,Ensemble Kalman Smoother).This scheme is used to improve the estimation of soil moisture profile by jointly assimilatingMODIS land surface temperature and airborne L band passive microwave brightness temperature.The ground based data observed at DAMAN superstation,which is located at Yingke oasis desert area in the middle stream of the Heihe River Basin,are used to conduct this experiment and validate assimilation results.Three LAI products are used to analyze the influence of LAI on soil temperature.Three assimilation experiments are also designed in this work,including assimilation of MODIS LST,assimilation of microwave brightness temperature,and assimilation of MODIS LST and microwave brightness temperature.The results show that the uncertainties in LAI influence significantly soil temperature simulations in different soil layers.MODIS LAI product is seriously underestimated in this study area,which results soil temperature overestimation about 4~6 K.Three assimilation schemes can improve soil moisture estimations to different extend.Joint assimilation of MODIS LST and microwave brightness temperature achieved the best performance,which can reduce the RMSE of soil moisture to 31%~53%.     

Comparison of integrating LAS/MODIS data into a land surface model for improved estimation of surface variables through data assimilation

In this article, land surface temperature (LST) and sensible heat flux (H) data assimilation schemes were developed separately using the ensemble Kalman filter (EnKF) and the common land model (CoLM). Surface measurements of ground temperature, H, and latent heat flux (LE) collected at the Yucheng (longitude: 116° 36′ E; latitude: 36° 57′ N) and Arou (longitude: 100° 27′ E; latitude: 38° 02′ N) experimental stations were compared with the predictions by assimilating different observation sources into the CoLM. The results showed that both LST and H data assimilation schemes could improve the estimation of ground temperature and H. The root mean square error (RMSE) compared between the predictions and in situ measurements decreased more significantly with the assimilation of values of H measured by a large aperture scintillometer (LAS). Assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) LST only slightly improved the predictions of H and ground temperature. Daytime to night-time comparison results using both assimilation schemes also indicated that accurately quantifying model, prediction, and observation error would improve the efficiency of the assimilation systems. The newly developed land data assimilation schemes have proved to be a feasible and practical method to improve the predictions of heat fluxes and ground temperature from CoLM. Moreover, integrating multisource data (LAS and MODIS LST) simultaneously into the land surface model is believed to result in an efficient and robust way to improve the accuracy of model predictions from a theoretical point of view.     

AMSR-E亮温同化系统的数据分析方法研究

低频微波卫星观测信号由于其对土壤水分非常敏感,经常被同化到陆面模式来提高土壤水分和其它地表状态变量的模拟和预报。常用的同化算法主要利用统计学,优化理论等数学知识,对改进和理解模型的物理过程意义不大。通过研究发展一个数据分析方法,判断AMSR\|E亮温同化系统土壤水分的预报误差,为将来从物理角度定性分析提供基础。     

Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study

Predicted latent and sensible heat fluxes from Land Surface Models (LSMs) are important lower boundary conditions for numerical weather prediction. While assimilation of remotely sensed surface soil moisture is a proven approach for improving root zone soil moisture, and presumably latent (LE) and sensible (H) heat flux predictions from LSMs, limitations in model physics and over-parameterisation mean that physically realistic soil moisture in LSMs will not necessarily achieve optimal heat flux predictions. Moreover, the potential for improved LE and H predictions from the assimilation of LE and H observations has received little attention by the scientific community, and is tested here with synthetic twin experiments. A one-dimensional single column LSM was used in 3-month long experiments, with observations of LE, H, surface soil moisture and skin temperature (from which LE and H are typically derived) sampled from truth model run outputs generated with realistic data inputs. Typical measurement errors were prescribed and observation data sets separately assimilated into a degraded model run using an Ensemble Kalman Filter (EnKF) algorithm, over temporal scales representative of available remotely sensed data. Root Mean Squared Error (RMSE) between assimilation and truth model outputs across the experiment period were examined to evaluate LE, H, and root zone soil moisture and temperature retrieval. Compared to surface soil moisture assimilation as will be available from SMOS (every 3 days), assimilation of LE and/or H using a best case MODIS scenario (twice daily) achieved overall better predictions for LE and comparable H predictions, while achieving poorer soil moisture predictions. Twice daily skin temperature assimilation achieved comparable heat flux predictions to LE and/or H assimilation. Fortnightly (Landsat) assimilations of LE, H and skin temperature performed worse than 3-day moisture assimilation. While the different spatial resolutions of these remote sensing data have been ignored, the potential for LE and H assimilation to improve model predicted LE and H is clearly demonstrated.     

Development of a coupled land-atmosphere satellite data assimilation system for improved local atmospheric simulations

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). The LDAS consists of a land surface scheme as the model operator, a radiative transfer model as the observation operator, and the simulated annealing method for minimizing the difference between the observed and simulated microwave brightness temperature. The atmospheric model produces forcing data for the LDAS, and the LDAS produces better initial surface conditions for the modelling system. This coupled system can take into account land surface heterogeneities through assimilating satellite data for a better precipitation prediction. To assess the effectiveness of the new system, 3-dimensional numerical experiments were carried out in a mesoscale area of the Tibetan Plateau during the wet monsoon season. The results show significant improvement compared with a no assimilation regional atmospheric model simply nested from the global model. The surface soil moisture content and its distribution from the assimilation system were more consistent to in situ observations. These better surface conditions affect the land-atmosphere interactions through convection systems and lead to better atmospheric predictability as confirmed by satellite-based cloud observations and in situ sounding observations. Through the use of satellite brightness temperature, the developed coupled land-atmosphere assimilation 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.     

Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter

Proper estimation of initial state variables and model parameters are vital importance for determining the accuracy of numerical model prediction. In this work, we develop a one-dimensional land data assimilation scheme based on ensemble Kalman filter and Common Land Model version 3.0 (CoLM). This scheme is used to improve the estimation of soil temperature profile. The leaf area index (LAI) is also updated dynamically by MODIS LAI production and the MODIS land surface temperature (LST) products are assimilated into CoLM. The scheme was tested and validated by observations from four automatic weather stations (BTS, DRS, MGS, and DGS) in Mongolian Reference Site of CEOP during the period of October 1, 2002 to September 30, 2003. Results indicate that data assimilation improves the estimation of soil temperature profile about 1 K. In comparison with simulation, the assimilation results of soil heat fluxes also have much improvement about 13 W m^− 2 at BTS and DGS and 2 W m^− 2 at DRS and MGS, respectively. In addition, assimilation of MODIS land products into land surface model is a practical and effective way to improve the estimation of land surface variables and fluxes.     

Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach

An approach is evaluated for the estimation of soil moisture at high resolution using satellite microwave and optical/infrared (IR) data. This approach can be applied to data acquired by the Visible/Infrared Imager Radiometer Sensor Suite (VIIRS) and a Conical Scanning Microwave Imager/Sounder (CMIS), planned for launch in the 2009–2010 time frame under the National Polar-Orbiting Operational Environmental Satellite System (NPOESS). The approach for soil moisture estimation involves two steps. In the first step, a passive microwave remote sensing technique is employed to estimate soil moisture at low resolution (～25?km). This involves use of a simplified radiative transfer model to invert dual-polarized microwave brightness temperature. In the second step, the microwave-derived low-resolution soil moisture is linked to the scene optical/IR parameters, such as Normalized Difference Vegetation Index (NDVI), surface albedo, and Land Surface Temperature (LST). The linking is based on the ‘Universal Triangle’ approach of relating land surface parameters to soil moisture. The optical/IR parameters are available at high resolution (～1?km) but are aggregated to the microwave resolution for the purpose of building the linkage model. The linkage model in conjunction with high-resolution NDVI, surface albedo and LST is then used to disaggregate microwave soil moisture into high-resolution soil moisture. The technique is applied to data from the Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) acquired for the Southern Great Plains (SGP-97) experiment conducted in Oklahoma in June–July 1997. An error budget analysis performed on the estimation procedure shows that the rms error in the estimation of soil moisture is of the order of 5%. Predicted soil moisture results at high resolution agree reasonably well with low resolution results in both magnitude and spatio-temporal patterns. The high resolution results are also compared with in situ (0–5?cm deep) point measurements. While the trends are similar, the soil moisture estimates in the two cases are different. Issues involving comparison of satellite derived soil moisture with in situ point measurements are also discussed.     

迭代集合卡尔曼滤波方法的性能比较研究

针对数据同化过程中模型的非线性问题,通过分析对比得出了一种适合强非线性系统的迭代集合Kalman滤波(IEnKF)。在Lorenz\|63模型的框架内,比较分析集合Kalman滤波(EnKF)、迭代集合Kalman滤波(IEnKF)和迭代扩展卡Kalman滤波(IEKF)在集合数、观测误差方差、放大因子和模型步长不同时同化性能差异,由此探讨这3种方法的优劣。研究结果表明:随着集合数的增加,3种算法的同化性能都得到了一定的改善;放大因子的增大,使其同化性能变差且EnKF呈现出多重波峰波谷的现象;3种方法的均方误差(RMSE）随观测误差方差和模型步长的增大而增大,其同化精度都变差;而IEnKF同化性能最优,更具有鲁棒性。     

A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations

The backscattering and emission measured simultaneously by radar and radiometer show promise for the estimation of surface variables such as near-surface soil moisture and vegetation characteristics. In this paper, the 10.7 GHz Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) channel and 13.8 GHz precipitation radar (PR) observations are simultaneously used for the estimation of the near-surface soil moisture and vegetation properties. The Fresnel model for soil and a simple model for vegetation are used to simulate the passive microwave emission at 10.7 GHz. To determine the PR backscatter signal from a land surface, a theoretical approach is used based on the Geometric Optics Model for simulating bare soil and a semi-empirical water-cloud model for vegetation. The model parameters required in specifying the nature of the soil and vegetation are calibrated on the basis of in situ soil moisture data combined with remotely sensed observations. The calibrated model is subsequently used to retrieve near-surface soil moisture and leaf area index for assumed values of surface roughness and temperature. Algorithm assessment using synthetic passive and active microwave data shows a nonlinearity effect in the system inversion, which results in a varying degree of error statistics in soil wetness and vegetation characteristics retrieval. The technique was applied on TRMM radar/radiometer observations from three consecutive years and evaluated against in situ near-surface (5 cm) soil moisture measurements from the Oklahoma Mesonet showing a consistent performance.     

Monitoring agricultural soil moisture extremes in Canada using passive microwave remote sensing

Current methods to assess soil moisture extremes rely primarily on point-based in situ meteorological stations which typically provide precipitation and temperature rather than direct measurements of soil moisture. Microwave remote sensing offers the possibility of quantifying surface soil moisture conditions over large spatial extents. Capturing soil moisture anomalies normally requires a long temporal record of data, which most operating satellites do not have. This research examines the use of surface soil moisture from the AMSR-E passive microwave satellite to derive surface soil moisture anomalies by exploiting spatial resolution to compensate for the shorter temporal record of the satellite sensor. Four methods were used to spatially aggregate information to develop a surface soil moisture anomaly (SMA). Two of these methods used soil survey and climatological zones to define regions of homogeneity, based on the Soil Landscapes of Canada (SLC) and the EcoDistrict nested hierarchy. The second two methods (ObShp3 and ObShp5) used zones defined by a data driven segmentation of the satellite soil moisture data. The level of sensitivity of the calculated SMA decreased as the number of pixels used in the spatial aggregation increased, with the average error reducing to less than 5% when more than 15 pixels are used. All methods of spatial aggregation showed somewhat weak but consistent relationship to in situ soil moisture anomalies and meteorological drought indices. The size of the regions used for aggregation was more important than the method used to create the regions. Based on the error and the relationship to the in situ and ancillary data sets, the EcoDistrict or ObShp3 scale appears to provide the lowest error in calculating the SMA baseline. This research demonstrates that the use of spatial aggregation can provide useful information on soil moisture anomalies where satellite records of data are temporally short.     

Ensemble member generation for sequential data assimilation

Using an ensemble of model forecasts to describe forecast error covariance extends linear sequential data assimilation schemes to nonlinear applications. This approach forms the basis of the Ensemble Kalman Filter and derivative filters such as the Ensemble Square Root Filter. While ensemble data assimilation approaches are commonly reported in the scientific literature, clear guidelines for effective ensemble member generation remain scarce. As the efficiency of the filter is reliant on the accurate determination of forecast error covariance from the ensemble, this paper describes an approach for the systematic determination of random error. Forecast error results from three factors: errors in initial condition, forcing data and model equations. The method outlined in this paper explicitly acknowledges each of these sources in the generation of an ensemble. The initial condition perturbation approach presented optimally spans the dynamic range of the model states and allows an appropriate ensemble size to be determined. The forcing data perturbation approach treats forcing observations differently according to their nature. While error from model physics is not dealt with in detail, discussion of some commonly used approaches and their limitations is provided. The paper concludes with an example application for a synthetic coastal hydrodynamic experiment assimilating sea surface temperature (SST) data, which shows better prediction capability when contrasted with standard approaches in the literature.     

Pixel-scalesoil Moisture Monitoring Network and Its Preliminary Validation of L-band Soil Moisture Products

Soil moisture is a key variable in the process of crop growth,ground-air water heat exchange and global water cycle,which plays an important role in drought monitoring,hydrological land surface processes and climate change.Passive microwave remote sensing has become the main means of monitoring soil moisture with the sensitivity to soil moisture.In this study,the authenticity test of SMAP（Soil Moisture and Active and Passive） and SMOS（Soil Moisture and Ocean Salinity）passive microwave soil moisture products using the soil moisture sensor network monitoring data carried out against the underlying surface of farmlands in Jilin Province was carried out.The following conclusions were obtained：（1）Compared with the in situ measured data,SMOS L3(ascending and descending overpasses) and SMAP L3 passive microwave soil moisture products generally underestimated the ground data,but With the occurrence of rainfall events,there will be the phenomenon which is the value of soil moisture products is higher than the in situ data; although the unbiased root mean square error (unRMSE) of the two soil moisture products was greater than 0.07 m3/m3,the unRMSE of SMAP passive microwave soil moisture product data which was 0.078 m3/m3 was slightly lower;（2）Since the depth of induction of the L-band is lighter than the depth of detection of the sensor(5cm),and the dryness of the soil surface after rainfall causes the vertical inhomogeneity of soil moisture,which is one of the reasons why SMOS and SMAP passive microwave soil moisture products underestimate soil moisture; （3）SMOS has a higher value than the range of SMAP brightness temperature,which may be caused by radio frequency interference (RFI),which makes the error of soil moisture Retrieval and affects the validation accuracy.The comparison of bright temperature distribution of SMOS and SMAP shows that the effect of RFI on SMOS is more serious due to the influence of electromagnetic radio frequency interference (RFI),which may be the reason why the RMSE of soil moisture product of SMOS is higher than that of passive microwave soil moisture product of SMAP.     

小集合数条件下的数据同化策略研究

基于集合的数据同化方法近年来得到广泛的重视和研究,已经逐步实验在业务大气数据同化系统中来替代变分类方法。集合Kalman滤波方法高度依赖于集合的大小,集合数过小会带来欠采样,协方差低估,滤波发散和远距离的虚假相关等问题。局地化技术可以有效改善小集合带来的相关问题。在Lorenz-96模型的基础上,研究有无局地化的效果差异,探讨小集合条件下的局地化技术的优劣性;提出一种基于功率谱密度（PSD）判断集合数据同化效果的办法。实验证明：在有限集合数下,采用Kalman增益值和PSD可以评价同化效果,结合局地化技术,可以获得效率更高的同化算法。