Soil moisture variability over Odra watershed: Comparison between SMOS and GLDAS data

Abstract Monitoring of temporal and spatial soil moisture variability is an important issue, both from practical and scientific point of view. It is well known that passive, L-band, radiometric measurements provide best soil moisture estimates. Unfortunately as it was observed during Soil Moisture and Ocean Salinity (SMOS) mission, which was specially dedicated to measure soil moisture, these measurements suffer significant data loss. It is caused mainly by radio frequency interference (RFI) which strongly contaminates Central Europe and even in particularly unfavorable conditions, might prevent these data from being used for regional or watershed scale analysis. Nevertheless, it is highly awaited by researchers to receive statistically significant information on soil moisture over the area of a big watershed. One of such watersheds, the Odra (Oder) river watershed, lies in three European countries – Poland, Germany and the Czech Republic. The area of the Odra river watershed is equal to 118,861 km 2 making it the second most important river in Poland as well as one of the most significant one in Central Europe. This paper examines the SMOS soil moisture data in the Odra river watershed in the period from 2010 to 2012. This attempt was made to check the possibility of assessing, from the low spatial resolution observations of SMOS, useful information that could be exploited for practical aims in watershed scale, for example, in water storage models even while moderate RFI takes place. Such studies, performed over the area of a large watershed, were recommended by researchers in order to obtain statistically significant results. To meet these expectations, Centre Aval de Traitement des Donnes SMOS (CATDS), 3-days averaged data, together with Global Land Data Assimilation System (GLDAS) National Centers for Environmental Prediction/Oregon State University/Air Force/Hydrologic Research Lab (NOAH) model 0.25 soil moisture values were used for statistical analyses and mutual comparisons. The results obtained using various statistical tools unveil high scientific potential of CATDS SMOS data to study soil moisture over the Odra river watershed. This was also confirmed by reasonable agreement between results derived from CATDS SMOS Ascending and GLDAS data sets. This agreement was achieved mainly by using these data spatially averaged over the whole watershed area, and for observations performed in the period longer than three-day averaging time. Comparisons of separate three-day data in a given pixel position, or at smaller areas would be difficult because of data gaps. Hence, the results of the work suggest that despite of RFI interferences, SMOS observations can provide effective input for analysis of soil moisture at regional scales. Moreover, it was shown that CATDS SMOS soil moisture data are better correlated with rainfall rate than GLDAS ones.

[1]  Yann Kerr,et al.  SMOS Validation and the COSMOS Campaigns , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Y. Kerr,et al.  Catchment scale validation of SMOS and ASCAT soil moisture products using hydrological modeling and temporal stability analysis , 2014 .

[3]  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.

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

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

[6]  A. Al Bitar,et al.  An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data , 2010 .

[7]  T. Jackson,et al.  Watershed scale temporal and spatial stability of soil moisture and its role in validating satellite estimates , 2004 .

[8]  Y. Kerr,et al.  State of the Art in Large-Scale Soil Moisture Monitoring , 2013 .

[9]  Jasmeet Judge,et al.  Spatial Scaling and Variability of Soil Moisture Over Heterogeneous Land Cover and Dynamic Vegetation Conditions , 2013, IEEE Geoscience and Remote Sensing Letters.

[10]  B. Fang,et al.  Soil moisture at watershed scale: Remote sensing techniques , 2014 .

[11]  T. Jackson,et al.  Temporal persistence and stability of surface soil moisture in a semi-arid watershed , 2008 .

[12]  Yann Kerr,et al.  ESA's Soil Moisture and Ocean Salinity Mission: Mission Performance and Operations , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Iliana Mladenova,et al.  Validation of the ASAR Global Monitoring Mode Soil Moisture Product Using the NAFE'05 Data Set , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Y. Kerr Soil moisture from space: Where are we? , 2007 .

[15]  Sidharth Misra,et al.  RFI as Experienced During Preparations for the SMOS Mission , 2008 .

[16]  Yann Kerr,et al.  Soil Moisture , 1922, Botanical Gazette.

[17]  M. S. Moran,et al.  Estimating soil moisture at the watershed scale with satellite-based radar and land surface models , 2004 .

[18]  Thomas J. Jackson,et al.  Passive Microwave Soil Moisture Downscaling Using Vegetation Index and Skin Surface Temperature , 2013 .

[19]  J. Lipiec,et al.  Comparison of Surface Soil Moisture from SMOS Satellite and Ground Measurements , 2014 .

[20]  J. Martínez-Fernández,et al.  Mean soil moisture estimation using temporal stability analysis , 2005 .

[21]  M. Baghini,et al.  A critical review of soil moisture measurement , 2014 .

[22]  Yann Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle , 2010, Proceedings of the IEEE.

[23]  Yann Kerr,et al.  Validation of SMOS L1C and L2 Products and Important Parameters of the Retrieval Algorithm in the Skjern River Catchment, Western Denmark , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Performance comparison of a point-scale LSP model and the NOAH distributed SVAT model for soil moisture estimation using microwave remote sensing , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[25]  Luca Brocca,et al.  Catchment scale soil moisture spatial–temporal variability , 2012 .

[26]  B. Usowicz,et al.  Strategies for validating and directions for employing SMOS data, in the Cal-Val project SWEX (3275) for wetlands , 2010 .

[27]  Venkat Lakshmi,et al.  Soil moisture as an indicator of weather extremes , 2004 .

[28]  Luca Brocca,et al.  ASCAT soil wetness index validation through in situ and modeled soil moisture data in central Italy , 2010 .

[29]  Luca Brocca,et al.  Spatial‐temporal variability of soil moisture and its estimation across scales , 2010 .

[30]  Philippe Richaume,et al.  SMOS Radio Frequency Interference Scenario: Status and Actions Taken to Improve the RFI Environment in the 1400–1427-MHz Passive Band , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Z. Kundzewicz,et al.  The Great Flood of 1997 in Poland , 1999 .

[32]  Klaus Scipal,et al.  Temporal Stability of Soil Moisture and Radar Backscatter Observed by the Advanced Synthetic Aperture Radar (ASAR) , 2008, Sensors.

[33]  J. Famiglietti,et al.  Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE , 2007 .

[34]  José Martínez-Fernández,et al.  Temporal Stability of Soil Moisture in a Large‐Field Experiment in Spain , 2003 .

[35]  Thomas J. Jackson,et al.  Validation of AMSR-E soil moisture using L-band airborne radiometer data from National Airborne Field Experiment 2006 , 2011 .