International Journal of Advanced Engineering Research and Science (IJAERS) [Vol-6, Issue-4, Apr- 2019] https://dx.doi.org/10.22161/ijaers.6.4.6 ISSN: 2349-6495(P) | 2456-1908(O) www.ijaers.com Page | 59 Estimation of Energy Flux and Biomass in Pasture Areas through Remote Sensing Techniques Ricardo Guimarães Andrade, Marcos Cicarini Hott, Walter Coelho Pereira Magalhães Junior Brazilian Agricultural Research Corporation – Juiz de Fora, MG - Brazil Abstract — Pasture production is estimated through remote sensing techniques with the aid of models and algorithms. The application with no need for extensive field measurements is one of the advantages of the Surface Energy Balance Algorithm for Land (SEBAL). The objective of this work was to estimate energy fluxes and, subsequently, pasture biomass with the aid of remote sensing techniques. The study area is located on the Experimental Farm of Embrapa Beef Cattle, municipality of Campo Grande, State of Mato Grosso do Sul, Brazil. For the implementation of the SEBAL and estimation of energy fluxes and biomass of the pasture areas, meteorological data and Landsat 5 - TM image were used. It was found that the technique has the potential to be applied to indicate the forage availability and to support decision-making in the planning and management of the extensive production of beef and milk cattle, with economic and environmental sustainability of pasture areas. Keywords— geotechnology, livestock, SEBAL, sustainability, rural planning. I. INTRODUCTION The growing demand for food further reinforces the importance of increasing production with economic and environmental sustainability. Vilela et al. [1] have reported that intensifying and enhancing the efficiency of production systems may help to harmonize these interests. Hence, the sustainable use of pastures is a strategic issue, since most pasture areas are show some indication of degradation. The recovery of these areas may reduce the pressure for the opening of new farming and livestock frontiers, as well as contributing to diminishing the emission of greenhouse gases [2]. According to Dias-Filho [3], the support capacity would be the most flexible indicator to quantify the degradation of a given pasture. Currently, the only official parameter that is related to support capacity is the pasture stocking rate. This parameter has been presented by DIEESE [4], and it is the result of the division of the number of animals by the area occupied by pastures of a given geographical unit. However, Dias-Filho [5] warns that a priori, it is not possible to guarantee the degradation condition of pasture only by evaluating its instantaneous support capacity (maximum number of animals supported by pasture, with no harm to pasture and to the animal). Studies such as that carried out by Grigera et al. [6] highlight the potential of remote sensing techniques as a tool to assist in the implementation of systems for monitoring pasture production that makes it possible to identify, for example, the areas that require the adoption of acceptable management practices. Thus, models and algorithms are used to estimate pasture production using remote sensing techniques. One of these algorithms is the SEBAL (Surface Energy Balance Algorithm for Land) that was developed by Bastiaanssen et al. [7, 8]). One of the advantages of SEBAL is the flexibility in its structure so that other models can be coupled [9], facilitating the applications in studies carried out at a local and regional scale, with no need for extensive field measurements (Andrade et al. [10]). In order to estimate the above-ground biomass, Bastiaanssen and Ali [11] and Samarasinghe [12], among others, obtained good results by coupling the model of biomass accumulation proposed by Monteith [13] in the SEBAL associated with the model of efficiency use of the radiation that was structured by Field et al. [14]. As a result, the objective of this study was to estimate energy fluxes and, therefore, pasture biomass through the application of remote sensing techniques. II. MATERIAL AND METHODS The study area is located in the Experimental Farm of Embrapa Beef Cattle, municipality of Campo Grande, state of Mato Grosso do Sul, Brazil (Figure 1). According to the climatic classification of Köppen, the region is situated in a transition zone between humid temperate climate with hot summer (Cfa) and tropical climate with