Comparison of Different Techniques to Estimate Surface Soil Moisture
Land subsidence is a gradual settling or sudden sinking of the land surface from changes that take place underground. There are different causes of land subsidence; most notably, ground-water overdraft and severe weather conditions. Subsidence of the land surface due to ground water overdraft is caused by an increase in the intergranular pressure in unconsolidated aquifers, which results in a loss of buoyancy of solid particles in the zone dewatered by the falling water table and accordingly compaction of the aquifer. On the other hand, exploitation of underground water may result in significant changes in degree of saturation of soil layers above the water table, increasing the effective stress in these layers, and considerable soil settlements. This study focuses on estimation of soil moisture at surface using different methods. Specifically, different methods for the estimation of moisture content at the soil surface, as an important term to solve Richard’s equation and estimate soil moisture profile are presented, and their results are discussed through comparison with field measurements obtained from Yanco1 station in south-eastern Australia. Surface soil moisture is not easy to measure at the spatial scale of a catchment. Due to the heterogeneity of soil type, land use, and topography, surface soil moisture may change considerably in space and time.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339676Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1295
 Y. Cui, et al. "Development and application of a regional land subsidence model for the plain of Tianjin." Journal of Earth Science 25.3 (2014): 550-562.
 Y. Najjar, Z. Musharraf, “Surface subsidence prediction by nonlinear finite-element analysis.” Journal of geotechnical engineering 119.11 (1993): 1790-1804.
 H. Bouwer, “Land Subsidence and Cracking Due to Ground-Water Depletion.” Ground Water 15.5 (1977): 358-364.
 Thu, Trinh M., and Delwyn G. Fredlund. “Modelling subsidence in the Hanoi city area, Vietnam.” Canadian Geotechnical Journal 37.3 (2000): 621-637.
 Ahmad, M. F., S. Runping, and Y. Jing. “Soil Moisture Retrieval through Satellite Data for Gansu and Xinjiang Region of China.” Pakistan Journal of Meteorology (Pakistan) (2012).
 Engman, Edwin T. "Progress in microwave remote sensing of soil moisture." Canadian Journal of Remote Sensing 16.3 (1990): 6-14.
 Patel, N. R., et al. "Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status." International Journal of Remote Sensing 30.1 (2009): 23-39.
 Sandholt, Inge, Kjeld Rasmussen, and Jens Andersen. "A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status." Remote Sensing of environment 79.2 (2002): 213-224.
 Sun, W., et al. "Using the vegetation temperature condition index for time series drought occurrence monitoring in the Guanzhong Plain, PR China."International Journal of Remote Sensing 29.17-18 (2008): 5133-5144.
 Fung, A.K., Li, Z., and Chen, K.S. 1992. Backscattering from a randomly rough dielectric surface. IEEE Transactions on Geoscience and Remote Sensing, Vol. 30, No. 2, 356369. doi: 10.1109/36.134085.
 Dubois, P.C., van Zyl, J., and Engman, T. 1995. Measuring soil moisture with imaging radar. IEEE Transactions on Geoscience and Remote Sensing, Vol. 33, No. 6, pp. 915926. doi: 10.1109/TGRS.1995.477194.
 Oh, Y., Sarabandi, K. and Ulaby, F.T., 1992, An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 30, pp. 370–382.
 Khabazan, S., M. Motagh, and M. Hosseini. "Evaluation of Radar Backscattering Models IEM, OH, and Dubois using L and C-Bands SAR Data over different vegetation canopy covers and soil depths." ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2013): 225-230.
 Leconte, Robert, et al. "Mapping near‐surface soil moisture with RADARSAT‐1 synthetic aperture radar data." Water Resources Research 40.1 (2004).
 Famiglietti JS, Wood EF. 1994. Multiscale modeling of spatially variable water and energy balance processes. Water Resources Research 11: 3061–3078.
 Brocca, L., F. Melone, and T. Moramarco. "On the estimation of antecedent wetness conditions in rainfall–runoff modelling." Hydrological Processes22.5 (2008): 629-642.
 Baghdadi, N., Gaultier, S., and King, C.: Retrieving surface roughness and soil moisture from SAR data using neural networks, Can. J. Remote Sens., 28, 701–711, 2002a.
 Notarnicola, C., Angiulli, M., and Posa, F.: Soil moisture retrieval from remotely sensed data: neural network approach versus Bayesian method, IEEE T. Geosci. Remote Se., 46, 547–557, 2008.
 Paloscia, S., Macelloni, G., Santi, E., and Tedesco, M.: The capability of microwave radiometers in retrieving soil moisture profiles: an application of Artificial Neural Networks, Proceeding IEEEIGARSS, Firenze, Italy, 3, 1390–1392, 2002.
 Paloscia, S., Pampaloni, P., Pettinato, S., and Santi, E.: A comparison of algorithms for retrieving soil moisture from ENVISAT/ASAR images, IEEE T. Geosci. Remote Se., 46, 3274– 3284, doi:10.1109/TGRS.2008.920370, 2008.
 Paloscia, S., Pampaloni, P., Pettinato, S., and Santi, E.: Generation of soil moisture maps from ENVISAT/ASAR images in mountainous areas: a case study, Int. J. Remote Se., 31, 2265–2276, 2010.
 Santi, E., Paloscia, S., Pampaloni, P., Pettinato, S., and Poggi, P.: Retrieval of Soil Moisture from Envisat ASAR Images: A Comparison of Inversion Algorithms. Proceedings of the 2004 Envisat & ERS Symposium (ESA SP-572), 6–10 September 2004, Salzburg, Austria, 2004.
 Chai, Soo-See, et al. "Backpropagation neural network for soil moisture retrieval using NAFE’05 data: a comparison of different training algorithms." Int Archives Photogramm, Remote Sens Spatial Inf Sci (China) 37 (2008): 1345.
 Lakhankar, Tarendra, et al. "Non-parametric methods for soil moisture retrieval from satellite remote sensing data." Remote Sensing 1.1 (2009): 3-21.
 Smith, A. B., Walker, J. P., Western, A. W., Young, R. I., Ellett, K. M., Pipunic, R. C., Grayson, R. B., Siriwidena, L., Chiew, F. H. S. and Richter, H. The Murrumbidgee Soil Moisture Monitoring Network Data Set. Water Resources Research, vol. 48, W07701, 6pp., 2012 doi:10.1029/2012WR011976.
 Mekonnen, D. F. "Satellite remote sensing for soil moisture estimation: Gumara catchment, Ethiopia." International Institute for Geo-Information Science and Earth Observation, Enschede (2009).
 Carlson, Toby. "An overview of the" triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery." Sensors7.8 (2007): 1612-1629.
 Carlson, Toby N., Robert R. Gillies, and Eileen M. Perry. "A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover." Remote sensing reviews 9.1-2 (1994): 161-173.
 Gillies, R. R., W. P. Kustas, and K. S. Humes. "A verification of the'triangle'method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e." International journal of remote sensing 18.15 (1997): 3145-3166.
 Quattrochi, Dale A., and Jeffrey C. Luvall. Thermal remote sensing in land surface processing. CRC Press, 2004.
 Oh, Y., Sarabandi, K. and Ulaby, F.T., 1994, An inversion algorithm for retrieving soi moisture and surface roughness from polarimetric radar observation. Proceedings IGARSS’94, Pasadena, USA. IEEE catalog no. 94CH3378-7, III, pp. 1582– 1584, (New York: IEEE).
 Oh, Y., Sarabandi, K. and Ulaby, F.T., 2002, Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 40, pp. 1348–1355.
 Oh, Y., 2004, Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 42, pp. 596–601.
 Doorenbos J, Pruitt WO. 1977. Background and development of methods to predict reference crop evapotranspiration (ETo). In Crop Water Requirements. FAO Irrigation and Drainage Paper No. 24, FAO: Rome; 108–119 (Appendix II).
 Weather Forecast & Reports – Long Range & Local | Wunderground | Weather Underground, http://www.wunderground.com/ Accessed on 2/05/2016.
 Australia’s official weather forecasts & weather radar, http://www.bom.gov.au/ Accessed on 9/05/2016.