Bulletin UASVM Horticulture, 69(2)/2012 Print ISSN 1843-5254; Electronic ISSN 1843-5394 Mathematical Model for the Study of the Solar Radiation through Canopy Alina CIOBAN 1), Horia CRIVEANU 2), Florica MATEI 2), Ioana POP 2), Ancuta ROTARU 2) 1) Edmond Nicolau College, II/2 Campului St., Cluj-Napoca, Romania; alinacioban@yahoo.com. 2) Faculty of Horticulture, University of Agricultural Sciences and Veterinary Medicine, 3-5 Manastur St., 400327, Cluj-Napoca, Romania Abstract. The light energy is very important in the study of fundamental plants and biophysical processes such as photosynthesis, stomatal conductance, transpiration, leaf temperature, respiration. The mathematical model for the physics of the photon transport is given by Maxwell s equation that describes electromagnetic wave theory. There are many factors involved in the quantification of the light environment in a plant canopy due to the spatial and temporal variability of radiation (Baldocchi et al., 1985; Criveanu, 2005). In biometeorology there are important leaf energy balances and photosynthesis. The model considered in our paper assumes three dimensional canopy and stochastic theory for the transfer of the photons. Keywords: ArcGIS, canopy, solar radiation, Spatial Analyst. INTRODUCTION In the study of most natural phenomena it is necessary to analyze the influence of solar radiation. As we know, solar light is essential in stimulating the biophysical processes such as photosynthesis, photomorphogenesis and evapotranspiration, processes underlying plants growth and development (http://agricultura-prin-satelit.ro/?page_id=159). The action of the light can act on the electron s transport (within a molecule or between molecules), leading to the creation of the non-connected electrons. Such an electron transport under the action of the photon also takes place on plants within the chlorophyll molecules, during the photosynthesis process (Baldocchi et al., 1985). Those stipulated emphasize the fact that the solar radiation is one of the most important vegetation factors and the interaction of the solar radiation with the plants represents a major interest for creating the forecasts on the agricultural production. The development of the photosynthesis process on plants depends both on the absorption of the solar radiation and on its distribution inside a canopy. The radiation absorption influences the energetic balance, the stomatal conductance depending on the leaves type of illumination, while the morphogenesis is related to the variations of the spectral distribution of the solar radiation inside the canopy. It is highly important for the vegetal production that the phytomass formed in the time unit is as large as possible, which assumes the most profitable correlation of the following factors: a most possible extended leaf area, expressed by means of the leaf area index and the highest possible photosynthetic performance expressed by means of the efficiency of capturing the solar radiation inside the canopy (http://arhivadegeografie.wordpress.com, http://www.star-storage.ro/solutii/solutiigis). In this paper we aim to evaluate the beam of light transmission through the leaf canopy, using a mathematical model based on probability theory. Also, knowing the existence of various factors that influence the solar radiation distribution on Earth's surface, we propose 411
to represent the amount of direct and diffuse solar radiation on a geographical area (Stowe, Vermont), during a period of two months. In this respect we use a mathematical algorithm implemented in ArcGis (www.esri.com). MATERIALS AND METHODS The mathematical model for the physics of the photon transport is given by Maxwell s equation that describes electromagnetic wave theory. There are many factors involved in the quantification of the light environment in a plant canopy due to the spatial and temporal variability of radiation. In order to study the solar radiation interaction with plants canopy two aspects should be considered: diffuse radiation that penetrates the canopy gaps and the radiation that is generated by the interception and transmission through leaves and the reflection by leaves and soil. In order to analyze and to evaluate the beam of light transmission through the leaf canopy, we use differential analysis and elements of probability theory. In this respect we consider the plants canopy horizontally homogeneous turbid medium and the canopy divided into a number of statistically independent layers N. a Starting from the equation P0 1, that represents the probability of beam A penetration through n layers of leaves with an incremental amount of leaf area, a, relative to the total amount of area, A. We will reveal an equation that allows us to determine how much leaf area is needed to intercept at least 99% of the light, when the leaves are arrayed randomly in space. For this purpose we used the Poisson distribution, differential analysis and also Beer s Law exponential relation for light attenuation (http://nature.berkeley.edu/biometlab/ espm129/). RESULTS AND DISCUSSIONS Leaf area index (LAI) is used to describe the photosynthesis and transpiration surface of plant canopies and it is accurately considered to have large applications in modeling vegetation-atmosphere interaction. The physical significance of leaf area index is strongly correlated with solar radiation transmission through the plant canopy. Also, the mathematical models used in remote sensing data from agriculture are based on the temporal evolution of leaf area index (Teodosiu and Guiman, 2011). Leaf area index is defined as the amount of leaf surface area per unit ground. Taking the limit when n goes to infinity, in the equation that represents the probability of beam penetration through n layers and considering the fact that leaf area index is described by a N LAI= will obtain (Baldocchi et al., 1985): A a N I LAI exp ; I ( LAI) exp( LAI) ; (I is the light flux density). A di Using differential equation for the preset purpose we conclude that: dx n a k I The change in light flux density along a distance x is described by a function of its current intensity (I), extinction coefficient (k), and leaf area density (a). Integrating the two sides of the equation and then using the Beer-Lambert Law will obtain: 412
di k a dx k a x, I LAI I 0 exp k LAI, I Where, I 0 is the flux density of light at the top of the canopy. The resulting equation is useful because it allows us to determine the leaf area index required to intercept 99% of incoming light. Considering the case when k is equal to 1, the leaves are arranged horizontally and the sunlight coming directly from overhead will result L 4.6. Also, the solar radiation distribution on Earth's surface is strongly influenced by topographic factors and surface features such as elevation, surface orientation, slope and slope shape, shadows determined by topographic features. All these factors in turn affect the quantification of the light environment in a plant canopy (Povara, 2006). Modeling the solar radiation can be extremely efficient in understanding the spatial and temporal variation of solar radiation over landscape scales. Spatial Analyst extension provided by ArcGIS allows us to analyze the effects of sun on a geographical area for specific time periods (Dubayah and Rich, 1995). The Solar Radiation Analysis calculates isolation maps using digital elevation models (DEM) for input. In our paper, we consider a surface Digital Elevation Model from Stowe, Vermont. Fig.1.Digital elevation model for Stowe, Vermont To calculate insolation over a landscape, the solar radiation analysis uses an algorithm developed by Rich et al. (1994), as further developed by Fu and Rich (1999). The calculation method initially resides in the fact that the global radiation is defined as the amount of direct radiation and diffuse radiation. Dir Dif. The direct insolation R global from the sunmap sector with a centroid at zenith angle (θ) and azimuth angle (α) is calculated using the following equation: m( ) Dir Sconst SD, SG cos( AngI ) ; Dir Dir where: m is the relative S is the solar constant, is transmission of the atmosphere, const optical path length, measured as a proportion relative to the zenith path length, SD is the time duration represented by the sky sector, SG is the gap fraction for the sunmap sector; AngI, is the angle of incidence between the centroid of the sky sector and the axis normal to the surface. The diffuse radiation is calculated using the equation: 413
Dif, Rg Pdif D SkyG Weight cos( ) AngI ; Dif Dif where: Rg is the global normal radiation, Pdif is the proportion of global normal radiation flux that is diffused, D is the time interval for analysis, SkyG is the gap fraction for the sky sector, Weight is the proportion of diffuse radiation originating in a given sky sector relative to all sectors. Considering the aspects of the above described Solar Analysis, we will use herein a series of instruments made available by it in view of studying the Sun s effects on a geographical area during a certain time specific frame. In Fig. 2 (a), hemispherical view shed result can be noticed: angular distribution of sky visibility versus obstruction. (a) (b) (c) Fig.2. (a) Hemispherical view shed for input DEM from Stowe; (b) The result of SunMap calculation; (c) The result of SkyMap calculation The amount of direct and diffuse solar radiation from any sky direction is emphasized by creating a SunMap and a SkyMap rasters (Fig. 2b and Fig 2c). The time frame which was considered for creating the three rasters is March, 5 th May, 8 th. Using Area Solar Radiation analysis we created a map of insolation for a surface in Stowe, Vermont (Fig. 4.) specifying our input parameters (Fig. 3.). Fig.3. Input parameters for creating the insolation map 414
Fig.4. Map of insolation The results obtained in this paper are in agreement with the results obtained in scientific literature (Baldocchi et al., 1985, Fu and Rich, 1999) and consist in the use of mathematical model, physical concepts of solar radiation through canopy implemented into a geographical information system. CONCLUSIONS The solar radiation is one of the most important factors which stimulates the biophysical and biochemical processes of the plants, thus influencing the achievement of a large and high quality agricultural production input. The mathematical modeling of the solar radiation interaction with the plants allows data analysis and collection of information needed for an increased efficiency, as well as for a remote sensing estimation of the agro-technical measures required for various crops. In the matter of the plant canopy, the light beam s analysis and evaluation uses a series of techniques that are specific for the differential analysis and the probability theory. In the matter of the solar radiation distribution on Earth s surface, the analysis instruments provided by ArcGIS Spatial Analyst allow a map drawing and an analysis of Sun s effects on a geographical area during a certain specific time frame. REFERENCES 1. Baldocchi, D., B. Hutchison, D.R. Matt and R.T. McMillen, (1985). Canopy radiative transfer models for spherical and known leaf inclination distribution angles: a test in an oak-hickory forest. J. Appl. Ecol. 22:539-555. 2. Criveanu, R. (2005). Corelation between distribution on vertical of the solar energy and hops biology, yield. Bull. USAMV Cluj-Napoca. 25(1):79-84. 3. Dubayah, R. and P.M. Rich (1995). Topographic solar radiation models for GIS. Int. J. Geogr. Inf. Sci. 9(4):405-419. 415
4. Fu, P. and P.M. Rich (1999). Design and implementation of the Solar Analyst: an ArcView extension for modeling solar radiation at landscape scales. Proceedings of the Nineteenth Annual ESRI User Conference. 5. Povară, R. (2006). Meteorologie generală, Ed. Fundaţiei România de Mâine, Bucureşti. 6. Rich, P.M., R. Dubayah, W.A. Hetrick and S.C. Saving (1994). Using Viewshed models to calculate intercepted solar radiation: applications in ecology. American Society for Photogrammetry and Remote Sensing Technical Papers. 524 529. 7. Teodosiu, M. and G. Guiman (2011). Indicele suprafeţei foliare în suprafeţe de monitoring intensive (nivel II) din Romania, estimate prin metode directe şi indirecte. Revista pădurilor. 126 (3-4):58-69. 8. http://nature.berkeley.edu/biometlab/espm129/. 9. http://agricultura-prin-satelit.ro/?page_id=159. 10. http://arhivadegeografie.wordpress.com/category/coltul-cartografului/. 11. http://www.star-storage.ro/solutii/solutii-gis/solutii-gis-in-agricultura.aspx. 12. www.esri.com. 416