Soil moisture is an integral part of the Earth's hydrological cycle. Therefore, accurate estimation of the Earth's changing soil moisture is required to achieve sustainable land and water management to augment Numerical Weather Prediction (NWP) and forecasting skill, and to develop improved flood and drought monitoring capability. Unfortunately, most of the locations in the world do not have accurate soil moisture information on a relevant spatial and temporal scale. However, after latest advances in remote sensing and mesoscale models, it is now possible to estimate soil moisture using passive microwave satellite imaging such as Soil Moisture and Ocean Salinity (SMOS) and/or mesoscale model like Weather Research and Forecasting (WRF)-NOAH Land Surface Model (LSM). L-band passive remote sensing and WRF-NOAH LSM are potentially very useful for soil moisture sensing due to its all-weather capabilities and in-depth physics oriented relationship between soil emissivity and soil moisture, applicable for a diverse land use/land cover. In commensurate with new era in soil moisture remote sensing, this thesis explores the potential of SMOS satellite and WRFNOAH LSM for soil moisture retrieval over the temperate maritime climate. Also, soil moisture deficit (SM I) is found to be an integral component for irrigation scheduling, drought and flood prediction. Hence, the main focus of this thesis is the evaluation of soil moisture datasets as a method to effectively determine the SMD. All major areas of the improvement aided by the SMOS and WRF NOAH LSM arc addressed. Several novel approaches and investigations dealing with the SMOS soil moisture retrieval using Microwave Polarisation Difference Index (M PDI) and Radiative Transfer Equations arc examined. Input data (soil roughness, land surface temperature and vegetation opacity) sensitivity of different retrieval configurations are evaluated using the various algorithms. Thus, the thesis includes ( I) initial evaluation of SMOS satellite and ECMWF downscaled soil moisture using WRF-NOAH LSM with special reference to sensitivity of growing and non growing seasons; (2) assessment of land parameter retrieval model and tau-omega rationale; (3) a modified soil moisture retrieval algorithm from SMOS brightness temperature; and (4) sensitivity and uncertainty analysis of mesoscale model based product for SMD prediction. Further through this study, SMOS soil moisture downscaling schemes using artificial intelligence techniques with MODIS LST have also been proposed to improve the spatial resolution at a catchment scale and finally, data fusion techniques for improving soil l moisture deficit are presented with the SMOS and WRF-NOAII LSM. The overall finding indicates that the SMOS and WRF-NOAH LSM using ECMWF have been proven not only to improve data quality and soil moisture deficit estimation, but also have a great potential in fostering the soil moisture research and applications. The studies presented in this thesis will enhance our understanding of the Earth's water cycle, will help improve ECMWF forecast, SMOS algorithm, NWP and will lead to better water resource management practices.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:617590 |
| Date | January 2013 |
| Creators | Srivastava, Prashant K. |
| Publisher | University of Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
Page generated in 0.0019 seconds