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Soil Moisture Modelling Using TWI and Satellite Imagery in the Stockholm Region

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Soil Moisture Modelling Using TWI and Satellite Imagery in the Stockholm Region Soil moisture modelling using TWI and satellite imagery in the Stockholm region Jan Haas Master’s of Science Thesis in Geoinformatics TRITA-GIT EX 10-001 School of Architecture and the Built Environment Royal Institute of Technology (KTH) 100 44 Stockholm, Sweden March 2010 TRITA-GIT EX 10-001 ISSN 1653-5227 ISRN KTH/GIT/EX--10/001-SE Abstract Soil moisture is an important element in hydrological land-surface processes as well as land- atmosphere interactions and has proven useful in numerous agronomical, climatological and meteorological studies. Since hydrological soil moisture estimates are usually point-based measurements at a specific site and time, spatial and temporal dynamics of soil moisture are difficult to capture. Soil moisture retrieval techniques in remote sensing present possibilities to overcome the abovementioned limitations by continuously providing distributed soil moisture data at different scales and varying temporal resolutions. The main purpose of this study is to derive soil moisture estimates for the Stockholm region by means of two different approaches from a hydrological and a remote sensing point of view and the comparison of both methods. Soil moisture is both modelled with the Topographic Wetness Index (TWI) based on digital elevation data and with the Temperature‐Vegetation Dryness Index (TVDI) as a representation of land surface temperature and Normalized Difference Vegetation Index (NDVI) ratio. Correlations of both index distributions are investigated. Possible index dependencies on vegetation cover and underlying soil types are explored. Field measurements of soil moisture are related to the derived indices. The results indicate that according to a very low Pearson correlation coefficient of 0.023, no linear dependency between the two indices existed. Index classification in low, medium and high value categories did not result in higher correlations. Neither index distribution is found to be related to soil types and only the TVDI correlates alongside changes in vegetation cover distribution. In situ measured values correlate better with TVDIs, although neither index is considered to give superior results in the area due to low correlation coefficients. The decision which index to apply is dependent on available data, intent of usage and scale. The TWI surface is considered to be a more suitable soil moisture representation for analyses on smaller scales whereas the TVDI should prove more valuable on a larger, regional scale. The lack of correlation between the indices is attributed to the fact that they differ greatly in their underlying theories. However, the synthesis of hydrologic modelling and remote sensing is a promising field of research. The establishment of combined effective models for soil moisture determination over large areas requires more extensive in situ measurements and methods to fully assess the models’ capabilities, limitations and value for hydrological predictions. Keywords: TWI, TVDI, soil moisture i Acknowledgement I would like to express my sincere gratitude to my supervisors Ulla Mörtberg and David Gustafsson, Department of Land and Water Resources Engineering, School of Architecture and the Built Environment, KTH ‐ Royal Institute of Technology for their continuous help and guidance, data and literature provision as well as conducting fieldwork together and data post‐ processing. I also gratefully acknowledge the support and advice given to me by Dr. Yifang Ban, Head of Department of Urban Planning and Environment, School of Architecture and Built Environment, KTH ‐ Royal Institute of Technology. I would also like to express my gratitude towards Tuong Thuy Vu, Dorothy Furberg and Maria Irene Rangel Luna of the same department for their help, trust and technical support. Furthermore I would like to thank Else‐Marie Wingqvist from Sveriges Meteorologiska och Hydrologiska Institut (SMHI) for the provision of detailed temperature and precipitation data for several gauging stations in and around Stockholm. ii Table of Contents Abstract ................................................................................................................................................................................... i Acknowledgement ............................................................................................................................................................. ii Table of Contents ............................................................................................................................................................. iii List of Tables ....................................................................................................................................................................... vi List of Figures ................................................................................................................................................................... vii List of Appendices .......................................................................................................................................................... viii List of Acronyms ................................................................................................................................................................ ix 1 Introduction ............................................................................................................................................................. 11 2 Background .............................................................................................................................................................. 12 2.1 Topography‐based Hydrological Model (TOPMODEL) ................................................................ 12 2.2 Topographic Wetness Index (TWI) ...................................................................................................... 12 2.3 Digital Elevation Data ................................................................................................................................ 13 2.3.1 Gridded data (DEM) .......................................................................................................................... 13 2.3.2 Triangular irregular networks (TIN) ......................................................................................... 14 2.3.3 Contour Data ........................................................................................................................................ 14 2.3.4 DEM resolution ................................................................................................................................... 14 2.3.5 Depression removal in DEMs ........................................................................................................ 18 2.4 Flow direction algorithms ........................................................................................................................ 19 2.4.1 Single Flow Direction (SFD) algorithms ................................................................................... 19 2.4.2 Biflow Direction (BFD) algorithms ............................................................................................. 20 2.4.3 Multiple Flow Direction (MFD) algorithms ............................................................................. 21 2.4.4 Algorithm comparison ..................................................................................................................... 24 2.5 Soil moisture retrieval in remote sensing ......................................................................................... 26 2.5.1 Soil moisture retrieval techniques .............................................................................................. 27 2.5.2 Landsat program ................................................................................................................................ 28 2.5.3 Landsat 7 Enhanced Thematic Mapper (ETM+) ................................................................... 29 2.5.4 Normalized Difference Vegetation Index (NDVI) ................................................................. 30 iii 2.5.5 Temperature‐Vegetation Dryness Index (TVDI) ................................................................... 31 3 Study area and data description ..................................................................................................................... 35 3.1 Study area ....................................................................................................................................................... 35 3.2 Satellite image ............................................................................................................................................... 35 3.3 Digital Elevation Model ............................................................................................................................. 36 3.4 Contour data .................................................................................................................................................. 37 3.5 Land cover data ............................................................................................................................................ 37 3.6 Soil data ........................................................................................................................................................... 38 3.7 Precipitation and temperature data ...................................................................................................
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