**4. Concluding remarks**

which may present reasonable inaccuracies depending on the biome, which, consequently, will result in the incorrect determination of parameters to calculate plants transpiration [90]. Most studies using MOD16 are focused on Asia and Middle East, aiming to evaluate watersheds [131, 132] and different land-uses, especially in agricultural areas [133]. Validation per-

Recent studies validated MOD16 in the Cerrado and Amazon biomes [45, 25]. Over Cerrado, the algorithm presented relative high correlation coefficients, ranging between ~0.78 and 0.81 [45]. Results obtained for the Amazon were less satisfactory. Validation performed using

0.76 [25]. It should be noted that simplifications in MOD16 algorithm regarding some parameters such as canopy conductance are defined as constant for a given biome (even in a heterogeneous one, such as the Amazon). This may be one of the reasons for low correlations between the estimated and observed data in the region. This is actually one of the main challenges of global algorithms, which need to be complex to accurately represent the physical processes on the surface, and simultaneously simple enough to be implemented globally [45]. Despite this, MOD16 was able to represent the spatial variability of ET in the Amazon. This is an important result and one interesting way to better evaluate the results of this model for the Amazon would be through the comparison between MOD16 outputs with more local estimates based

The MOD17 product [130] provides continuous estimates of GPP and NPP over the vegetated surface of the planet. As well as models described in Sections 3.2.1–3.2.4, the MOD17 algorithm is based on the RUE approach [92]. According to this approach, the productivity of vegetation under reasonable water and soil fertility conditions is linearly correlated with the amount of APAR. MOD17 is based on three basic relationships (Eqs. (11)–(13)) to estimate GPP and net photosynthesis (PSNet), on eight-day and monthly intervals, and annual NPP.

GPP <sup>=</sup> <sup>ε</sup>(Tair,min)f(VPD)APAR (11)

PSNet = GPP − Rlr (12)

NPP = ∑(PSNet) − Rg − Rm (13)

In the equations presented above, f(Tair,min) and f(VPD) are scale factors associated, respectively, to minimum air temperature and vapor pressure deficit, Rlr is the maintenance respira-

respiration of living cells in the woody tissue. It is worth mentioning that the algorithm defines distinct values for ε, depending on the vegetation. ε values are distinct for forest, savanna, pasture, and agricultural areas. Tair,min and VPD values, as well as respiration values, are based on a lookup table composed of specific physiological parameters for each terrestrial biome [134]. MOD17 product is estimated from MODIS standard products (i.e. fAPAR and LAI) and reanalysis data (i.e. air temperature and solar radiation) from the National Center

is the growing respiration, and Rm represents the maintenance

values between ~0.32 and

formed on such studies agree with results found by Mu et al. [90].

tower fluxes data located over forest and pasture areas showed R<sup>2</sup>

on the models described in Sections 3.1.1–3.1.4.

*3.3.2. MOD17*

80 Tropical Forests - New Edition

tion of leaves and thin roots, Rg

Micrometeorological studies in Amazonian ecosystems have limited spatial and temporal coverage, and therefore RS becomes a tool to enhance the comprehension of surface processes in the region. Models to estimate energy and carbon balance components from orbital data differ according to the input data, parametrizations and accuracy of the results. The algorithms to estimate energy fluxes use as inputs images from visible and infrared (near and thermal) spectral regions and are based on empirical and physical methods. In situ measurements are typically related to air temperature and wind speed, and most uncertainties are concentrated in the estimation of H and ET (when obtained as a residual term of the energy budget). On the other hand, CO2 fluxes models need data from the visible and near infrared spectral regions and are based on the RUE concept. Main challenges of such models consist in the estimation of RUE for different ecosystems, as well as to obtain surface solar radiation data with a reasonable spatial resolution.

Regarding the use of such models in the Amazon region, some difficulties emerge: (1) obtaining cloud free orbital data, and (2) availability of field observations. Therefore, the choice of the algorithm must consider the possibility of using daily composites, and minimal need of in situ data. Other issues, such as the complexity and operability of the models may be considered. It is then possible to point out algorithms that present greater potential of application in the region and/or where efforts for implementation should focus. Regarding energy balance, two models stand out: SEBAL [47], due to the reduced need for field measurements and because the model was previously validated in the region and showed good results, and EVASPA [17], due to the operability and possibility of generating estimates during days when there are no orbital data available. In relation to the carbon models, it is suggested the use of VPM [108], once the model was applied to distinct forest ecosystems (including the Amazon) showing good results, and TGR [19], due to the fact that the model is based on MODIS composites and has a low dependence of field data.

**References**

nature10717

hyp.10458

s41467-017-00306-z

**109**(3):1196-1206. DOI: 10.1073/pnas.1116706108

5-26. DOI: 10.1007/s00704-004-0041-z

Meteorology. 2006;**21**(3):42-49

DOI: 10.1007/s10980-015-0282-5

DOI: 10.1016/j.agrformet.2013.03.011

2007;**28**(20):4509-4535. DOI: 10.1080/01431160701241902

2002;**107**(D2070):1-17. DOI: 10.1029/2001JD000623

[1] Davidson EA, Araújo AC, Artaxo Netto PE, Balch JK, Brown IF, Bustamante MM, et al. The Amazon basin in transition. Nature. 2012;**481**(7381):321-328. DOI: 10.1038/

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

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

83

[2] Ahlstrom A, Canadell JP, Schurgers G, Wu M, Berry JA, Guan K, et al. Hydrologic resilience and Amazon productivity. Nature Communications. 2017;**8**:1-9. DOI: 10.1038/

[3] Swann AL, Fung IY, Chiang JC. Mid-latitude afforestation shifts general circulation and tropical precipitation. Proceedings of the National Academy of Sciences. 2012;

[4] Zanchi FB, Waterloo MJ, Tapia AP, Alvarado Barrientos MS, Bolson MA, Luizão FJ, et al. Water balance, nutrient and carbon export from a heath forest catchment in Central Amazonia, Brazi. Hydrological Processes. 2015;**29**(17):3633-3648. DOI: 10.1002/

[5] Von Randow C, Manzi AO, Kruijt B, Oliveira PJ, Zanchi FB, Silva RL, et al. Comparative measurements and seasonal variations in energy and carbon exchange over forest and pasture in south West Amazonia. Theoretical and Applied Climatology. 2004;**78**(1-3):

[6] Querino CAS, Moura MAL, Lyra RFF, Mariano GL. Evaluation and comparison of global solar radiation and albedo with zenith angle in the Amazon region. Brazilian Journal of

[7] Stark SC, Breshears DD, Garcia ES, Law DJ, Minor DM, Saleska SR, et al. Toward accounting for ecoclimate teleconnections: Intra-and inter-continental consequences of altered energy balance after vegetation change. Landscape Ecology. 2016;**31**(1):181-194.

[8] Houborg R, Soegaard H, Emmerich W, Moran S. Inferences of all sky solar irradiance using Terra and Aqua MODIS satellite data. International Journal of Remote Sensing.

[9] El-Masri B, Jain AK, Barman R, Meiyappan P, Song Y, Liang M. Carbon dynamics in the Amazonian basin: Integration of eddy covariance and ecophysiological data with a land surface model. Agricultural and Forest Meteorology. 2013;**182-183**(15):156-167.

[10] Malhi Y, Pegoraro E, Nobre AD, Pereira MGP, Grace J, Culf AD, et al. Energy and water dynamics of a central Amazonian rain forest. Journal of Geophysical Research.

[11] Zeri M, Sá LD, Manzi AO, Araújo AC, Aguiar RG, Von Randow C, et al. Variability of carbon and water fluxes following climate extremes over a tropical forest in southwest-

ern Amazonia. PLoS One. 2014;**9**(2):e88130. DOI: 10.1371/journal.pone.0088130

Regarding the use of global RS products in the Amazon, it is important to emphasize that such products usually enable the analysis of spatial patterns of surface parameters; however, they present inaccuracies when referring to the magnitude of the estimates. A noteworthy aspect is that studies conducted in tropical regions, among them the Amazon, have proposed methodologies based on integrating satellite images and reanalysis climate data in hydrological and ecosystem models based on local measurements [2, 22, 23, 45, 141, 142]. Although there are difficulties, for example those related to representing the ecophysiological processes from leaf to canopy scale, such approaches constitute promising opportunities for future research.

The use of models based on satellite images presents an important role in understanding the spatial and temporal patterns of biophysical surface parameters in a region where most of the information is local. Data generated from such algorithms may be used as inputs in earth system surface models allowing, among others, to evaluate the impact, both in regional and global scales, caused by land-use and land-cover changes.
