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Lang K.,George Washington University | Seker S.,Bogazici University | Yuan Y.,George Washington University | Kurum M.,TUBITAK - Marmara Research Center | And 3 more authors.
2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014 | Year: 2014

Recent L band dielectric measurements of corn stalks have shown a periodic behavior between node and internode measurements. Based on these measurement results, a corn stalk has been modeled by a dielectric cylinder with a periodic dielectric variation along its length. The development of this model is part of an effort to understand how backscatter and emission from vegetation is related to its moisture content and to its physical structure. Researchers using L band satellite measurements of soil moisture can employ these and similar model results to determine the effects of vegetation on soil moisture measurements from space. © 2014 IEEE. Source

Parinussa R.M.,VU University Amsterdam | Wang G.,Nanjing University of Information Science and Technology | Holmes T.R.H.,Hydrology and Remote Sensing Laboratory | Liu Y.Y.,University of New South Wales | And 5 more authors.
International Journal of Remote Sensing | Year: 2014

Soil moisture retrievals from China’s recently launched meteorological Fengyun-3B satellite are presented. An established retrieval algorithm – the Land Parameter Retrieval Model (LPRM) – was applied to observations of the Microwave Radiation Imager (MWRI) onboard this satellite. The newly developed soil moisture retrievals from this satellite mission may be incorporated in an existing global microwave-based soil moisture database. To reach consistency with an existing data set of multi-satellite soil moisture retrievals, an intercalibration step was applied to correct brightness temperatures for sensor differences between MWRI and the radiometer of the Tropical Rainfall Measuring Mission’s (TRMM’s) Microwave Imager (TMI), resulting from their individual calibration procedures. The newly derived soil moisture and vegetation optical depth product showed a high degree of consistency with parallel retrievals from both TMI and WindSat, the two satellites that are observing during the same time period and are already part of the LPRM database. High correlation (R > 0.60 at night-time) between the LPRM and official MWRI soil moisture products was shown over the validation networks experiencing semiarid climate conditions. The skills drop below 0.50 over forested regions, with the performance of the LPRM product slightly better than the official MWRI product. To demonstrate the promising use of the MWRI soil moisture in drought monitoring, a case study for a recent and unusually dry East Asian summer Monsoon was conducted. The MWRI soil moisture products are able to effectively delineate the regions that are experiencing a considerable drought, highly in agreement with spatial patterns of precipitation and temperature anomalies. The results in this study give  confidence in the soil moisture retrievals from the MWRI onboard Fengyun-3B. The integration of the newly derived products into the existing database will allow a better understanding the diurnal, seasonal and interannual variations, and long-term (35 year) changes of soil moisture at the global scale, consequently enhancing hydrological, meteorological, and climate studies. © 2014, Taylor & Francis. Source

Crow W.T.,Hydrology and Remote Sensing Laboratory | Cosh M.H.,VU University Amsterdam
IEEE Transactions on Geoscience and Remote Sensing | Year: 2010

A recently developed data assimilation technique offers the potential to greatly expand the geographic domain over which remotely sensed surface soil moisture retrievals can be evaluated by effectively substituting (relatively plentiful) rain-gauge observations for (less commonly available) ground-based soil moisture measurements. The technique is based on calculating the Pearson correlation coefficient Rvalue between rainfall errors and Kalman filter analysis increments realized during the assimilation of a remotely sensed soil moisture product into the antecedent precipitation index (API). Here, the existing Rvalue approach is modified by reformulating it to run on an anomaly basis where long-term seasonal trends are explicitly removed and by calculating API analysis increments using a RauchTungStriebel smoother instead of a Kalman filter. This reformulated approach is then applied to a number of Advanced Microwave Scanning Radiometer soil moisture products acquired within three heavily instrumented watershed sites in the southern U.S. Rvalue-based evaluations of soil moisture products within these sites are verified based on comparisons with available ground-based soil moisture measurements. Results demonstrate that, without access to ground-based soil moisture measurements, the Rvalue; methodology can accurately mimic anomaly correlation coefficients calculated between remotely sensed soil moisture products and soil moisture observations obtained from dense ground-based networks. Sensitivity results also indicate that the predictive skill of the Rvalue metric is enhanced by both proposed modifications to its methodology. Finally,Rvalue calculations are expanded to a quasi-global (50° S50°N) domain using rainfall measurements derived from the Tropical Rainfall Measurement Mission Precipitation Analysis. Spatial patterns in calculated Rvalue fields are compared to regions of strong landatmosphere coupling and used to refine expectations concerning the global distribution of land areas in which remotely sensed surface soil moisture retrievals will contribute to atmospheric forecasting applications. © 2006 IEEE. Source

Igne B.,Iowa State University | Reeves III J.B.,Hydrology and Remote Sensing Laboratory | McCarty G.,Hydrology and Remote Sensing Laboratory | Hively W.D.,Hydrology and Remote Sensing Laboratory | And 2 more authors.
Journal of Near Infrared Spectroscopy | Year: 2010

Soil testing requires the analysis of large numbers of samples in the laboratory that is often time consuming and expensive. Mid-infrared spectroscopy (mid-IRI and near infrared (NIR) spectroscopy are fast, non-destructive and inexpensive analytical methods that have been used for soil analysis, in the laboratory and in the field, to reduce the need for measurements using complex chemical/physical analyses. A comparison of the use of spectral pretreatment as well as the implementation of linear and non-linear regression methods was performed. This study presents an overview of the use of infrared spectroscopy for the prediction of five physical [sand, silt and clayl and chemical - (total carbon and total nitrogen) soil parameters with near and mid-infrared units in bench top and field set-ups. Even though no significant differences existed among pretreatment methods, models using second derivatives performed better. The implementation of partial least squares IPLS], least squares support vector machines (LS-SVM) and locally weighted regression ILWRI for the development of the calibration models showed that the LS-SVM did not out-perform linear methods for most components while LWR that creates simpler models performed well. The present results tend to show that soil models are quite sensitive to the complexity of the model. The ability of LWR to select only the appropriate samples did help in the development of robust models. Results also proved that field units performed as well as bench-top instruments. This was true for both near infrared and mid-infrared technology. Finally, analysis of field moist samples was not as satisfactory as using dried-ground samples regardless of the chemometrics methods applied. © 2010 IM Publications LLP All right reserved. Source

Tugrul Yilmaz M.,Hydrology and Remote Sensing Laboratory | Crow W.T.,Hydrology and Remote Sensing Laboratory
Journal of Hydrometeorology | Year: 2013

It is well known that systematic differences exist between modeled and observed realizations of hydrological variables like soil moisture. Prior to data assimilation, these differences must be removed in order to obtain an optimal analysis. A number of rescaling approaches have been proposed for this purpose. These methods include rescaling techniques based on matching sampled temporal statistics, minimizing the least squares distance between observations and models, and the application of triple collocation. Here, the authors evaluate the optimality and relative performances of these rescaling methods both analytically and numerically and find that a triple collocation-based rescaling method results in an optimal solution, whereas variance matching and linear least squares regression approaches result in only approximations to this optimal solution. © 2013 American Meteorological Society. Source

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