<p>The spatial variations of ABL depth has wide applications in aeronautical meteorology, urban meteorology, agricultural meteorology and hydrology. In the context of Indian subcontinent, it is more important where air pollution episodes, smog, fog etc. are getting worse over the years. The dispersal of smog and low-level pollutants depends strongly on meteorological conditions. Monitoring and management of air quality is closely associated with the transport and dispersal of atmospheric pollutants, including industrial plumes. Processes of pollutant transport include turbulent mixing in the ABL, particularly the role of convection, photochemistry and dry and wet deposition to the surface. The depth of the ABL determine the extent of thermal and mechanical mixing of pollutants. Further, mean ABL depth can be used to determine the average seasonal air pollution scenarios. Soil surface temperature is one of the major factors which derives the ABL depth. Thus, it is important to know - what is the spatial ABL depth and soil surface temperature variation, in which direction changes in ABL depth and soil surface temperature is more or less consistent, over Indian subcontinent.</p><p>To understand the spatial variability of ABL depth and soil surface temperature, a variogram analysis is performed taking 30 stations over Indian sub-continent. Data at 30 stations (Ahmadabad, Bhopal, Gwalior, Aurangabad, Nagpur, Raipur, New Delhi, Gorakhpur, Patna, Lucknow, Patiala, Siliguri, Karaikal, Vishakhapatnam, Machilipatnam, Lhasa, Minfeng, Jodhpur, Agartalla, Bengaluru, Bhubaneshwar, Chennai, Dibrugarh, Hotan, Hyderabad, Jagdalpur, Kolkata, Panjim, Port Blair, Srinagar) are collected for three years 1994, 1997 and 2000. ABL depths are computed using soundings obtained from the Integrated Global Radiosonde Archived (IGRA) by adopting the bulk Richardson method.</p><p>Both ABL depth and soil surface temperature are greater in central region, but low near shore and in hilly regions. By using both these parameters, omnidirectional variograms are drawn, which show the spatial distribution of ABL depths and surface soil temperature over India are determined for different years. The particular variogram demonstrates a well-suited spatial relationship for geostatistical analysis as pairs of points are more correlated the closer they are together and the greater the distance between points becomes less correlated. There are certain parameters of variogram (sill and range) that adjust iteratively to get the best fitted model. Then, models are fitted to the experimental variogram using least square approach between the experimental and modelled variogram values. The model with its corresponding parameters based on least square method is selected as the best variogram model. These parameters are finally used in the ordinary kriging analysis. Spherical variograms are fitted and found to have significant correlation for stations within a lags of 19, 18, 18 and 17, 17, 20 degrees latitude/longitude change for ABL depth and soil surface temperature and for the year 1994, 1997 and 2000 respectively. Utilizing variogram parameters, the spatial distributions are plotted using ordinary Kriging. A polynomial curve of order 3 fitted Cubic curve fitting on the scatter plots between soil surface temperature and ABL depth, yield R<sup>2</sup> value as 0.44, 0.52 and 0.53 in 1994, 1997, 2000 respectively.</p><p>&#160;</p>
Applying Infrared Thermography to Soil Surface Temperature Monitoring: Case Study of a High-Resolution 48 h Survey in a Vineyard (Anadia, Portugal)
