Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method

Multiple soil moisture products have been generated from data acquired by satellite. However, these satellite soil moisture products are not spatially or temporally complete, primarily due to track changes, radio-frequency interference, dense vegetation, and frozen soil. These deficiencies limit the application of soil moisture in land surface process simulation, climatic modeling, and global change research. To fill the gaps and generate spatially and temporally complete soil moisture data, a data assimilation algorithm is proposed in this study. A soil moisture model is used to simulate soil moisture over time, and the shuffled complex evolution optimization method, developed at the University of Arizona, is used to estimate the control variables of the soil moisture model from good-quality satellite soil moisture data covering one year, so that the temporal behavior of the modeled soil moisture reaches the best agreement with the good-quality satellite soil moisture data. Soil moisture time series were then reconstructed by the soil moisture model according to the optimal values of the control variables. To analyze its performance, the data assimilation algorithm was applied to a daily soil moisture product derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Microwave Radiometer Imager (MWRI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Preliminary analysis using soil moisture data simulated by the Global Land Data Assimilation System (GLDAS) Noah model and soil moisture measurements at a multi-scale Soil Moisture and Temperature Monitoring Network on the central Tibetan Plateau (CTP-SMTMN) was performed to validate this method. The results show that the data assimilation algorithm can efficiently reconstruct spatially and temporally complete soil moisture time series. The reconstructed soil moisture data are consistent with the spatial precipitation distribution and have strong positive correlations with the values simulated by the GLDAS Noah model over large areas of the region. Compared to the soil moisture measurements at the medium and large networks, the reconstructed soil moisture data have almost the same accuracy as the soil moisture product derived from AMSR-E/MWRI/AMSR2 for ascending and descending orbits.

[1]  John F. Hermance,et al.  Stabilizing high‐order, non‐classical harmonic analysis of NDVI data for average annual models by damping model roughness , 2007 .

[2]  Mehrez Zribi,et al.  Analysis of C-Band Scatterometer Moisture Estimations Derived Over a Semiarid Region , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Jindi Wang,et al.  Estimating the fraction of absorbed photosynthetically active radiation from the MODIS data based GLASS leaf area index product , 2015 .

[4]  Naoto Matsuura,et al.  DEVELOPMENT OF AN ADVANCED MICROWAVE SCANNING RADIOMETER (AMSR-E) ALGORITHM FOR SOIL MOISTURE AND VEGETATION WATER CONTENT , 2004 .

[5]  W. Crow,et al.  The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: a case study based on ESTAR measurements during SGP97 , 2003 .

[6]  Z. Samani,et al.  Estimating Potential Evapotranspiration , 1982 .

[7]  George H. Hargreaves,et al.  Reference Crop Evapotranspiration from Temperature , 1985 .

[8]  K. Moffett,et al.  Remote Sens , 2015 .

[9]  Lakshman Nandagiri,et al.  Performance Evaluation of Reference Evapotranspiration Equations across a Range of Indian Climates , 2006 .

[10]  José A. Sobrino,et al.  Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999 , 2006 .

[11]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[12]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[13]  Randal D. Koster,et al.  Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model , 2005 .

[14]  J. Townshend,et al.  Spatially and temporally continuous LAI data sets based on an integrated filtering method: Examples from North America , 2008 .

[15]  Jean-Pierre Wigneron,et al.  A parameterized multifrequency-polarization surface emission model , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Yi Liu,et al.  A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations , 2012, Environ. Model. Softw..

[17]  Philippe Richaume,et al.  Evaluation of SMOS Soil Moisture Products Over Continental U.S. Using the SCAN/SNOTEL Network , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Qiang Liu,et al.  Analysis of spatial distribution and multi-year trend of the remotely sensed soil moisture on the Tibetan Plateau , 2013, Science China Earth Sciences.

[19]  Qiang Liu,et al.  Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices , 2015, Remote. Sens..

[20]  Steven Kempler,et al.  Value-added Data Services at the Goddard Earth Sciences Data and Information Services Center , 2004 .

[21]  Luca Brocca,et al.  On the estimation of antecedent wetness conditions in rainfall–runoff modelling , 2008 .

[22]  Lazhu,et al.  A MULTISCALE SOIL MOISTURE AND FREEZE-THAW MONITORING NETWORK ON THE THIRD POLE , 2013 .

[23]  C. Justice,et al.  The generation of global fields of terrestrial biophysical parameters from the NDVI , 1994 .

[24]  J. Qin,et al.  Evaluation of AMSR‐E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau , 2013 .

[25]  E. Njoku,et al.  Vegetation and surface roughness effects on AMSR-E land observations , 2006 .

[26]  Samuel Buis,et al.  Global sensitivity analysis measures the quality of parameter estimation: The case of soil parameters and a crop model , 2010, Environ. Model. Softw..

[27]  Keiji Imaoka,et al.  The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies , 2003, IEEE Trans. Geosci. Remote. Sens..

[28]  Yann Kerr,et al.  Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission , 2001, IEEE Trans. Geosci. Remote. Sens..

[29]  Thomas R. H. Holmes,et al.  An evaluation of AMSR–E derived soil moisture over Australia , 2009 .

[30]  A. Belward,et al.  The Best Index Slope Extraction ( BISE): A method for reducing noise in NDVI time-series , 1992 .

[31]  Miaoling Liang,et al.  China land soil moisture EnKF data assimilation based on satellite remote sensing data , 2011 .

[32]  Steven Platnick,et al.  Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data , 2004 .

[33]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[34]  B. Venkatesh,et al.  Modelling soil moisture under different land covers in a sub-humid environment of Western Ghats, India , 2011 .

[35]  W. Wagner,et al.  Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers , 2008 .

[36]  Per Jönsson,et al.  Seasonality extraction by function fitting to time-series of satellite sensor data , 2002, IEEE Trans. Geosci. Remote. Sens..

[37]  W. Wagner,et al.  Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe , 2011 .

[38]  W. Wagner,et al.  Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT) , 2007 .

[39]  Shengli Wu,et al.  Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements , 2014, Remote. Sens..

[40]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[41]  Yaoming Ma,et al.  The Tibetan plateau observatory of plateau scale soil moisture and soil temperature, Tibet - Obs, for quantifying uncertainties in coarse resolution satellite and model products , 2011 .

[42]  W. Verhoef,et al.  Reconstructing cloudfree NDVI composites using Fourier analysis of time series , 2000 .

[43]  W. Wagner,et al.  An Intercomparison of ERS-Scat and AMSR-E Soil Moisture Observations with Model Simulations over France , 2009 .

[44]  Wade T. Crow,et al.  An ensemble Kalman filter dual assimilation of thermal infrared and microwave satellite observations of soil moisture into the Noah land surface model , 2012 .

[45]  R. Jeu,et al.  Multisensor historical climatology of satellite‐derived global land surface moisture , 2008 .

[46]  W. Wagner,et al.  Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data , 2003 .

[47]  Shunlin Liang,et al.  Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product , 2016, Remote. Sens..

[48]  Thomas J. Jackson,et al.  Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[49]  Yann Kerr,et al.  Validation of Soil Moisture and Ocean Salinity (SMOS) Soil Moisture Over Watershed Networks in the U.S. , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Mehrez Zribi,et al.  Analysis of surface and root-zone soil moisture dynamics with ERS scatterometer and the hydrometeorological model SAFRAN-ISBA-MODCOU at Grand Morin watershed (France) , 2008 .