**1. Introduction**

Amazon rainforests directly influences the terrestrial climate system due to the emission or absorption of carbon dioxide (CO2 ) and evapotranspiration (ET), that is, through the processes of transpiration of plants and evaporation of water contained in leaves, stems, litter and soil [1, 2]. In addition to providing water vapor to the environment, influencing the general circulation in the tropics and contributing to regional precipitation, the Amazon rainforests are important in the atmospheric carbon cycle [3, 4]. Consequently, deforestation in the Amazon can lead to changes in surface net radiation (Rn), resulting in higher or lower availability of energy for the evapotranspiration processes and in the amount of CO2 absorbed or released by the atmosphere [5–7].

the atmosphere (i.e. Amazonian Research Micrometeorological Experiment (ARME, 1983– 1985) [26], Amazonian Boundary-Layer Experiment (ABLE, 1985–1987) [27], Anglo-Brazilian Amazonian Climate Observational Study (ABRACOS, 1991–1995) [28], and Green Ocean Amazon Experiment (GO-AMAZON, 2014–2015) [29]). Currently, the main source of surface measurements in the region is the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) [30]. LBA has sites on different land-use locations in the states of Rondonia (RO), Amazonas (AM), Para (PA) and Tocantins (TO). LBA data have been used to analyze the current state of the Amazonian ecosystem, as well as to serve as input and validation param-

Methods to Evaluate Land-Atmosphere Exchanges in Amazonia Based on Satellite Imagery…

Typical variables collected at these surface experiments are incoming solar radiation (K↓),

tion, emitted TIR (L↑), net radiation (Rn) [34, 35], soil heat flux (G), sensible heat flux (H), latent heat flux (λE) [10, 36], and the net ecosystem exchange (NEE) [5, 37]. It is important to mention that most of the observational studies in the Amazon region have been performed over primary forest and pasture areas. In this context, one way to extend such analyses to the diverse ecosystems of the Amazon is the combined use of surface measurements (i.e. plot-

The frequency at which satellite data are obtained and processed, combined with the possibility of regional and global studies, provides an excellent cost-benefit ratio. In recent years, there has been a gradual advance in the technical characteristics of the sensors onboard orbital platforms, which present increasingly improved spatial, temporal, radiometric, and spectral resolutions. Within this context, the scientific community has used orbital data to estimate surface biophysical and hydrological parameters using different algorithms. Focusing on the

First studies to estimate energy fluxes using RS date back to the 1970s [40], driven by the limited spatial density of surface measurements, which prevented more robust large-scale studies [41]. Currently, studies are focused not only in the estimation but also on describing the land-vegetation-atmosphere energy exchange processes in order to better understand, for example, the feedback mechanisms between the surface and the boundary layer. This issue is

Energy fluxes models differ according to the input data, assumptions and accuracy of the results [43, 44]. However, a common aspect among the algorithms is the orbital input data, once all algorithms require information regarding the visible, near infrared and thermal infrared spectral regions. The primary estimates from such models are related to Rn, G, H, λE and,

main models available in the literature that can be applied in the Amazon region.

) [32, 33], incoming (L↓) thermal infrared (TIR) radia-

http://dx.doi.org/10.5772/intechopen.75194

69

 **fluxes combining remote sensing and** 

fluxes using RS and ground observations, this topic presents the

eters for climate prediction numerical models [31].

level and flux towers biometric studies) and RS data [38, 39].

outgoing solar radiation (K↑), albedo (α<sup>s</sup>

**3. Modeling energy and CO2**

estimation of energy and CO2

**3.1. Models to estimate energy fluxes**

gaining importance due to potential climate change [42].

**ground data**

The relevance of physical phenomena related to energy exchanges between the surface and atmosphere under climate change leads to the need for improving studies on both temporal as well as spatial scales [8, 9]. During the last three decades, intensive campaigns and experiments have been developed for acquiring micrometeorological data in the Amazonian ecosystems, which has increased our understanding of the variations, especially seasonally, of the total energy available for the atmospheric heating process by the surface, ET and atmospheric CO2 exchanges [10, 11]. However, measurements obtained by such experiments are usually local and representative of small areas, and therefore not representative of the spatial variability of these processes [12, 13].

In this context, new methodologies have been developed to obtain the components related to energy and CO2 exchanges between the surface and atmosphere, such as the use of remote sensing (RS). Usually, the use of orbital sensors to estimate energy and CO2 fluxes are performed using models that consider information obtained directly from the satellite images as inputs, such as reflectance and land surface temperature (LST) [14, 15]. Regarding the estimation of surface energy fluxes, several algorithms have been developed, such as the Simplified Surface Energy Balance Index (S-SEBI) [16] and Evapotranspiration Assessment from Space (EVASPA) [17]. To estimate CO2 fluxes, we can highlight Parametric Production Efficiency Model (C-Fix) [18] and Temperature and Greenness Rectangle Model (TGR) [19]. These models were applied in different terrestrial biomes; however, it is worth mentioning that in the Amazon region such approach for the determination of energy and CO2 fluxes using RS data is still incipient [20–25].

Based on the considerations above, this chapter aims to present and discuss several models developed to estimate surface energy and CO2 fluxes by combining satellite data and micrometeorological observations, highlighting the potentialities and limitations of such models for applications in the Amazon region.
