Thermal remote sensing of soil moisture in vineyards is a challenge. The grass-covered soil, in addition to a standing grape canopy, create complex patterns of heating and cooling and increase the surface temperature variability between vine rows. In this study, we evaluate the strength of relationships between soil moisture, mechanical resistance and thermal inertia calculated from the drop of surface temperature during a clear sky night over a vineyard in the Niagara region. We utilized data from two sensors, an airborne thermal camera (height ≈ 500 m a.g.l.) and a handheld thermal gun (height？≈ 1 m a.g.l.), to explore the effects of different field of views and the high inter-row temperature variability. Spatial patterns of soil moisture correlated more with estimated thermal inertia than with surface temperature recorded at sunrise or sunset. Despite the coarse resolution of airborne thermal inertia images, it performed better than estimates from the handheld thermal gun. Between-row variation was further analyzed using a linear mixed-effects model. Despite the limited spatial variability of soil properties within a single vineyard, the magnitudes of the model coefficients for soil moisture and mechanical resistance are encouraging indicators of the utility of thermal inertia in vineyard management.
Lamb, D.W.; Bramley, R.G.V. Managing and monitoring spatial variability in vineyard productivity. Nat. Resource Manage 2001, 4, 25–30.
[3]
Bramley, R. Generating Early Financial Benefits from Precision Viticulture through Selective Harvesting. Proceedings of the 5th European Conference on Precision Agriculture, Uppsala, Sweden, 9–12 June 2005.
[4]
Bramley, R.G.V.; Proffitt, A.P.B. Variation in Grape Yield and Quality in a Coonawarra Vineyard. Proceedings of the 5th International Symposium on Cool Climate Viticulture & Oenology, Melbourne, VIC, Australia, 16–20 January 2000.
[5]
Lamb, D.W. The use of qualitative airborne multispectral imaging for managing agricultural crops—A case study in south-eastern Australia. Aust. J. Exp. Agr 2000, 40, 725–738.
[6]
Lamb, D.W.; Bramley, R.G.V.; Hall, A. Precision viticulture—An Australian perspective. In Viticulture living with limitations. Acta. Hort 2002, 640, 15–25.
[7]
Hall, A.; Lamb, D.W.; Holzapfel, B.; Louis, J. Optical remote sensing applications in viticulture—A review. Aust. J. Grape Wine Res 2002, 8, 36–47.
[8]
Kaleita, A.L.; Tian, L.F.; Hirschi, M.C. Relationship between soil moisture content and soil surface reflectance. Trans. ASAE 2005, 48, 1979–1986.
[9]
Bramley, R.G.V. Progress in the Development of Precision Viticulture—Variation in Yield, Quality and Soil Properties in Contrasting Australian Vineyards. In Precision Tools for Improving Land Management; Currie, L.D., Loganathan, P., Eds.; Massey University: Palmerston North, New Zealand, 2001; pp. 25–43.
[10]
Lamb, D.; Mitchell, A.; Hyde, G. Vineyard trellising with steel posts distorts data from EM soil surveys. Aust. J. Grape Wine Res 2005, 11, 24–32.
[11]
Bennett, W.B.; Wang, J.; Bras, R.L. Estimation of global ground heat flux. J. Hydrometeorol 2008, 9, 744–759.
[12]
Tian, J.; Su, H.; Chen, S.; Zhang, R.; Yang, Y.; Rong, Y. Estimation of Soil Heat Flux by Apparent Thermal Inertia. Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada, 24–29 July 2011.
[13]
Wang, J.; Bras, R.L. A model of evapotranspiration based on the theory of maximum entropy production. Water Resour. Res 2011, 47, 1–10.
[14]
Wang, J.; Bras, R.L. Ground heat flux estimated from surface soil temperature. J. Hydrol 1999, 216, 214–226.
[15]
Wang, J.; Bras, R.L.; Sivandran, G.; Knox, R.G. A simple method for the estimation of thermal inertia. Geophys. Res. Lett 2010, 37, 1–5.
[16]
Price, J.C. Thermal inertia mapping: A new view of the Earth. J. Geophys. Res 1977, 82, 2582–2590.
[17]
Verhoef, A. Remote estimation of thermal inertia and soil heat flux for bare soil. Agric. For. Meteorol 2004, 123, 221–236.
[18]
Verstraeten, W.W.; Veroustraete, F.; Van der Sande, C.J.; Grootaers, I.; Feyen, J. Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests. Remote Sens. Environ 2006, 101, 299–314.
[19]
Pratt, D.A.; Ellyett, C. D. The thermal inertia approach to mapping of soil moisture and geology. Remote Sens. Environ 1979, 8, 151–168.
[20]
Minacapilli, M.; Iovino, M.; Blanda, F. High resolution remote estimation of soil surface water content by a thermal inertia approach. J. Hydrol 2009, 379, 229–238.
[21]
Minacapilli, M.; Cammalleri, C.; Ciraolo, G.; D’Asaro, F.; Iovino, M.; Maltese, A. Thermal inertia modeling for soil surface water content estimation: A laboratory experiment. Soil Sci. Soc. Am. J 2012, 76, 92–100.
[22]
Price, J.C. On the analysis of thermal infrared imagery: The limited utility of apparent thermal inertia. Remote Sens. Environ 1985, 18, 59–73.
[23]
Maltese, A.; Capodici, F.; Ciraolo, G.; La Loggia, G. Mapping soil water content under sparse vegetation and changeable sky conditions: Comparison of two thermal inertia approaches. J. Appl. Remote Sens 2013, 7, 73548.
[24]
Maltese, A.; Bates, P.D.; Capodici, F.; Cannarozzo, M.; Ciraolo, G.; La Loggia, G. Critical analysis of thermal inertia approaches for surface soil water content retrieval. Hydrol. Sci. J 2013, 58, 1–18.
[25]
Murray, T.; Verhoef, A. Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements—A universal approach to calculate thermal inertia. Agric. For. Meteorol 2007, 147, 80–87.
[26]
Murray, T.; Verhoef, A. Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements II. Diurnal shape of soil heat flu. Agric. For. Meteorol 2007, 147, 80–87.
[27]
Van Wijk, W.R. General Temperature Variations in a Homogeneous Soil. In Physics of Plant Environment, 1st ed ed.; North-Holland Publishing Co.: Amsterdam, The Netherlands, 1963; pp. 144–169.
[28]
Brunt, D. Notes on radiation in the atmosphere. I. Quart. J. R. Meteorol. Soc 1932, 58, 389–420.
[29]
Xue, Y.; Cracknell, A.P. Advanced thermal inertia modelling. Int. J. Remote Sens 1995, 16, 431–446.
[30]
Pratt, D.A.; Foster, S.J.; Ellyett, C.D. A calibration procedure for fourier series thermal inertia models. Photogramm. Eng. Remote Sensing 1980, 46, 529–538.
[31]
Johansen, O. Thermal Conductivity of SoilsPhD Thesis. University of Trondheim, Trondheim, Norway, 1975.
[32]
Mo, T.; Choudhury, B.J.; Schmugge, T.J.; Wang, J.R.; Jackson, T.J. A model for microwave emission from vegetation-covered fields. J. Geophys. Res 1982, 87, 11229–11237.
[33]
Oke, T.R. Leaves. In Boundary Layer Climates; Routledge: London, UK, 1988. Chapter 4.2; pp. 117–122.
[34]
Kim, E.J.; England, A.W. Radiobrightness Thermal Inertia Sensing of Soil and Canopy Moistures for Grassland Areas. Proceedings of Second Topical Symposium on Combined Optical-Microwave Earth and Atmosphere Sensing, Atlanta, GA, USA, 3–6 April 1995; pp. 39–41.
[35]
Wicklund, R.E.; Matthews, B.C. Soil Survey of Lincoln County, Ontario. Report Number 34; Canada Department of Agriculture. Research Branch: Guelph, ON, Canada, 1963; pp. 23–37.
[36]
Reedy, R.C.; Scanlon, B.R. Soil water content monitoring using electromagnetic induction. J. Geotech. Geoenviron. Eng 2003, 129, 1028–1039.
[37]
Soliman, A.; Brown, R.; Heck, R.J. Separating near surface thermal inertia signals from a thermal time series by standardized principal component analysis. Int. J. Appl. Earth Obs. Geoinf 2011, 13, 607–615.
[38]
Pradel, E.; Pieri, P. Influence of a grass layer on vineyard soil temperature. Aust. J. Grape Wine Res 1993, 6, 59–67.
[39]
Mira, M.; Valor, E.; Caselles, V.; Rubio, E.; Coll, C.; Galve, J.M.; Niclòs, R.; Sánchez, J.M.; Boluda, R. Soil moisture effect on thermal infrared (8–13 μm) emissivity. IEEE J. Sel. Top. Appl. Earth Obs 2010, 48, 2251–2260.
[40]
Flir Systems Inc. User’s Manual Flir Tools/Tools+ 3.1; Flir Systems Inc.: Wilsonville, OR, USA, 2013; pp. 54–60.
[41]
Endres, T. Personal Communication. 2012.
[42]
Zweig, M.H.; Campbell, G. Receiver operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clin. Med 1993, 39, 561–577.
[43]
Royall, R. M. The effect of sample size on the meaning of significance tests. Am. Stat 1986, 40, 313–315.
[44]
Pinheiro, J.; Bates, D. Mixed-Effects Models in S and S-PLUS, 1st ed ed.; Springer: New York, NY, USA, 2000; pp. 133–199.
[45]
The R Project for Statistical Computing. Available online: http://www.R-project.org/ (accessed on 20 March 2013).
[46]
Pinheiro, J.; Bates, D.; DebRoy, S.; Sarkar, D.; Team, T.R.C. nlme: Linear and Nonlinear Mixed Effects Models, 1st ed ed.; R Foundation for Statistical Computing: Vienna, Austria, 2009. R package; pp. 186–195.
[47]
Johnson, L.F.; Trout, T.J. Satellite NDVI assisted monitoring of vegetable crop evapotranspiration in California’s San Joaquin valley. Remote Sens 2012, 4, 439–455.
[48]
Reitberger, J.; Schn？rr, C.; Heurich, M.; Krzystek, P.; Stilla, U. Towards 3D mapping of forests: A comparative study with first/last pulse and full waveform LIDAR data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 2008, 37, 1397–1404.
[49]
Mathews, A.; Jensen, J. Visualizing and quantifying vineyard canopy LAI using an unmanned aerial vehicle (UAV) collected high density structure from motion point cloud. Remote Sens 2013, 5, 2164–2183.
