**2.1. Study region, crops, and agrometeorological data**

**Figure 1** shows the location of the study region in the northeastern side of São Paulo state, Southeast Brazil, together with the cropland masks and the agrometeorological stations used for the weather data gridding processes.

The agroecosystems are constituted by sugarcane (SC) and coffee (CO) interspaced with natural vegetation (NV). This last class is a mixture of Savannah and Atlantic Coastal Forest species. Some of the areas before occupied by coffee are nowadays being replaced by sugarcane crop.

The sugarcane areas present two well-defined seasons: the first one rainier and hotter and the other one drier and colder. According to Cabral et al. [13], the long-term maximum rainfall occurs in December (274 ± 97 mm month−1), and the minimum one is between July and August The Use of MODIS Images to Quantify the Energy Balance in Different Agroecosystems in Brazil http://dx.doi.org/10.5772/intechopen.72798 107

sugarcane is also replacing the coffee areas [1], as consequences of both sugar and alcohol

The negative effects of the sugarcane expansion could be more serious when compared with those from the fossil fuel exploration, regarding greenhouse gas emissions [3, 4]. Aiming bioenergy production, a crop has to grow fast presenting high yield, but its energy output must exceed fossil fuel energy input. Considering these issues, sugarcane is a good candidate for energy crop [5]. However, its expansion could affect the large-scale energy balance further influencing the carbon cycle [6–8]. Anderson-Teixeira et al. [9] have reported energy balance

Under land-use and climate change conditions, the use of tools for quantifying the large-scale energy balance components is relevant for supporting policy planning and decision-makings about the water resources. The difficulties of measuring and analyzing these components throughout only field measurements highlighted the importance of coupling remote sensing and weather data, which have been successfully done in commercial crops under different

Several algorithms have been developed for acquiring the large-scale energy balance components. The Simple Algorithm for Evapotranspiration Retrieving (SAFER) is applied in this chapter in sugarcane and coffee crops comparing the results with those for natural vegetation. The algorithm was developed and validated in Brazil based on simultaneous field radiation and energy balance data from experiments and remote sensing under strongly water and

Having cropland masks available, the energy balance components are analyzed in these mixed agroecosystems by the coupling MODIS images and weather data. The results may subsidize policies for a rational sugarcane and coffee water managements, being the analyses very useful under the actual scenario of water competitions between these crops and other

**Figure 1** shows the location of the study region in the northeastern side of São Paulo state, Southeast Brazil, together with the cropland masks and the agrometeorological stations used

The agroecosystems are constituted by sugarcane (SC) and coffee (CO) interspaced with natural vegetation (NV). This last class is a mixture of Savannah and Atlantic Coastal Forest species. Some of the areas before occupied by coffee are nowadays being replaced by sugarcane crop. The sugarcane areas present two well-defined seasons: the first one rainier and hotter and the other one drier and colder. According to Cabral et al. [13], the long-term maximum rainfall occurs in December (274 ± 97 mm month−1), and the minimum one is between July and August

sectors in the Southeast Brazil, as consequences of both climate and land use changes.

explorations, but also by stimulating renewable energy use [2].

alterations because of sugarcane expansion.

environmental conditions [3, 10].

106 Multi-purposeful Application of Geospatial Data

**2. Materials and methods**

for the weather data gridding processes.

**2.1. Study region, crops, and agrometeorological data**

vegetation contrasting conditions [11, 12].

**Figure 1.** Location of the study region inside the northeastern side of the São Paulo state, Southeast Brazil, together with the cropland masks and the agrometeorological stations used for the weather data gridding processes.

(27 ± 34 mm month−1); the annual value is 1517 ± 274 mm yr.−1. The mean air temperatures in January and July are, respectively, 24 and 19°C, and the annual average is 22°C.

The sugarcane phases may be divided into four [14]: Phase 1: Germination and establishment, from January to February, are influenced by soil moisture, soil temperature, and soil aeration, denoting activation and subsequent sprouting of the vegetative bud. Phase 2: Tillering is influenced by variety, solar radiation, air temperature, soil moisture, and fertilization, starting from around 40 days after the initiation of the growing cycle and may last up to 120 days (February–April). Phase 3: Grand growth is from 120 days after the starting of the growing cycle lasting up to 270 days in a 12-month crop (May–September). Both high soil moisture and solar radiation levels favor better cane elongation during this phase. Phase 4: Ripening and maturation are characterized by slower growth activity, lasting for about 3 months starting from 270 to 360 days after the growing cycle initiation (September–December). High solar radiation levels and low soil moisture conditions are favorable during this last phase [15].

The coffee crop concentrates at the right side of the study area (see **Figure 1**). The region presents also a rainy season and a dry winter somewhat similar to the sugarcane areas; however, due to higher altitudes, between 700 and 1100 m, the long-term annual air temperature ranges are lower, from 18 to 20°C [16].

The coffee crop in Brazil, differently from sugarcane, which completes its average growing cycle in 12 months, takes 2 years for its all crop stages. Six coffee phases are considered, starting in September of each year [17, 18]: Phase 1: Vegetation with bud formation, during 7 months, is normally from September to March. Phase 2: Vegetation is between April and August, when the transformation of the vegetative to reproductive buds occurs, when at the end of this phase, from July to August, the plants enter in relative dormancy stage. Phase 3: Flowering and grain expansion are normally from September to December. Phase 4: Grain formation is normally from January to March, when water stress can be detrimental to the grain development. Phase 5: Grain maturation. Moderate water stress can benefit the grains. Phase 6: Senescence and death of the non-primary productive branches generally occur in July and August. In this last stage, the self-pruning process represented by senescence occurs, when the productive branches wither and die, limiting plant development.

#### **2.2. Large-scale energy balance modeling**

For the large-scale modeling, the MODIS images were used during the year 2015 together with 10 agrometeorological stations from the National Meteorological Institute (INMET) in the study area, considering the cropland classes. Weather data were used to calculate the reference evapotranspiration (ET0) by the Penman-Monteith method [19]. The weather input modeling parameters, global solar radiation (RG), air temperature (T<sup>a</sup> ), and ET0 were up scaled for the 16-day period of the MODIS MOD13Q1 reflectance product (spatial resolution of 250 m) and gridded by using the moving average method generating pixels with the same spatial resolution as the satellite images.

**Figure 2** shows the steps for modeling the energy balance throughout the SAFER algorithm with the MODIS MOD13Q1 product.

Following **Figure 2**, the surface albedo (α0 ) was estimating according to Valiente et al. [20]:

$$\mathbf{a}\_0 = \mathbf{a} + \mathbf{b}\mathbf{a}\_1 + \mathbf{c}\mathbf{a}\_2 \tag{1}$$

The normalized difference vegetation index (NDVI) is a measure of the amount of vegetation

RR = α0 RG (3)

R<sup>a</sup> = <sup>A</sup> T<sup>a</sup> (4)

where εA is the atmospheric emissivity and σ is the Stefan-Boltzmann constant (5.67 × 10−8 W

εA = a<sup>A</sup> (−ln τ)bA (5)

where τ is the shortwave atmospheric transmissivity calculated as the ratio of RG to the incident solar radiation at the top of the atmosphere and aA and bA are the regression coefficients

Net radiation (Rn) can be described by the 24-hour values of net shortwave radiation, with a

Rn = (1 − α0)RG − aL τ (6)

where aL is the regression coefficient of the relationship between net longwave radiation and

Because of the thermal influence on longwave radiation via the Stephan-Boltzmann equation,

aL = dT<sup>a</sup> − e (7)

R<sup>S</sup> = RG − RR + R<sup>a</sup> − Rn (8)

, and Rn, the emitted surface longwave radiation (R<sup>s</sup>

where d and e are the regression coefficients found to be 6.99 and 39.93, respectively.

[11]:

) was acquired as

*α<sup>2</sup> + α<sup>1</sup>*

The Use of MODIS Images to Quantify the Energy Balance in Different Agroecosystems in Brazil

) was calculated by applying the Stefan-

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(2)

109

at the surface:

Boltzmann low:

0.94 and 0.10, respectively.

τ on a daily scale.

Having estimated RR, R<sup>a</sup>

residue in the radiation balance equation:

m−2 K−4).

NDVI <sup>=</sup> α2 <sup>−</sup> <sup>α</sup> *\_\_\_\_\_*<sup>1</sup>

The reflected solar radiation (RR) was estimated as

The longwave atmospheric radiation (R<sup>a</sup>

correction term for net longwave radiation [22]:

aL coefficient from Eq. (6) was correlated with the 24-hour T<sup>a</sup>

The parameter εA was calculated according to Teixeira et al. [21]:

where α1 and α2 are the reflectances from the bands 1 and 2, respectively, and a, b, and c are regression coefficients, considered as 0.08, 0.41, and 0.14, obtained under different Brazilian vegetation types and distinct hydrological conditions [10].

**Figure 2.** Flowchart for modeling the energy balance throughout application of the SAFER algorithm to the MODIS MOD13Q1 product.

The normalized difference vegetation index (NDVI) is a measure of the amount of vegetation at the surface:

$$\text{NDVI} = \frac{a\_i - a\_i}{a\_i + a\_i} \tag{2}$$

The reflected solar radiation (RR) was estimated as

$$\mathbf{R}\_{\mathbf{k}} = \alpha\_0 \mathbf{R} \mathbf{G} \tag{3}$$

The longwave atmospheric radiation (R<sup>a</sup> ) was calculated by applying the Stefan-Boltzmann low:

$$\mathbf{R}\_{\mathbf{a}} = \sigma \boldsymbol{\varepsilon}\_{\mathbf{A}} \mathbf{T}\_{\mathbf{a}} \tag{4}$$

where εA is the atmospheric emissivity and σ is the Stefan-Boltzmann constant (5.67 × 10−8 W m−2 K−4).

The parameter εA was calculated according to Teixeira et al. [21]:

Spectral reflectances

**2.2. Large-scale energy balance modeling**

108 Multi-purposeful Application of Geospatial Data

spatial resolution as the satellite images.

Following **Figure 2**, the surface albedo (α0

with the MODIS MOD13Q1 product.

where α1

and α2

Surface Albedo

Surface Temperature

vegetation types and distinct hydrological conditions [10].

ETr

MOD13Q1 product.

ET

**Figure 2.** Flowchart for modeling the energy balance throughout application of the SAFER algorithm to the MODIS

NDVI Rs

RG

ET0

RR

For the large-scale modeling, the MODIS images were used during the year 2015 together with 10 agrometeorological stations from the National Meteorological Institute (INMET) in the study area, considering the cropland classes. Weather data were used to calculate the reference evapotranspiration (ET0) by the Penman-Monteith method [19]. The weather input

scaled for the 16-day period of the MODIS MOD13Q1 reflectance product (spatial resolution of 250 m) and gridded by using the moving average method generating pixels with the same

**Figure 2** shows the steps for modeling the energy balance throughout the SAFER algorithm

α0 = a + bα1 + cα2 (1)

regression coefficients, considered as 0.08, 0.41, and 0.14, obtained under different Brazilian

modeling parameters, global solar radiation (RG), air temperature (T<sup>a</sup>

Ta

Rn

) was estimating according to Valiente et al. [20]:

), and ET0

were up

λE H

G

Ra

are the reflectances from the bands 1 and 2, respectively, and a, b, and c are

$$\varepsilon\_{\Lambda} = \mathbf{a}\_{\Lambda} \left( -\ln \pi \right)^{b\_{\Lambda}} \tag{5}$$

where τ is the shortwave atmospheric transmissivity calculated as the ratio of RG to the incident solar radiation at the top of the atmosphere and aA and bA are the regression coefficients 0.94 and 0.10, respectively.

Net radiation (Rn) can be described by the 24-hour values of net shortwave radiation, with a correction term for net longwave radiation [22]:

$$\mathbf{Rn} = (1 - \alpha\_0)\mathbf{R}\mathbf{G} - \mathbf{a\_t}\tau\tag{6}$$

where aL is the regression coefficient of the relationship between net longwave radiation and τ on a daily scale.

Because of the thermal influence on longwave radiation via the Stephan-Boltzmann equation, aL coefficient from Eq. (6) was correlated with the 24-hour T<sup>a</sup> [11]:

$$\mathbf{a}\_{\mathbf{L}} = \mathbf{d}\mathbf{T}\_{\mathbf{a}} - \mathbf{e} \tag{7}$$

where d and e are the regression coefficients found to be 6.99 and 39.93, respectively.

Having estimated RR, R<sup>a</sup> , and Rn, the emitted surface longwave radiation (R<sup>s</sup> ) was acquired as residue in the radiation balance equation:

$$\mathbf{R\_{S}} = \mathbf{R}\mathbf{G} - \mathbf{R\_{R}} + \mathbf{R\_{a}} - \mathbf{R}\mathbf{n} \tag{8}$$

Then, the surface temperature (T0 ) was estimated by the residual method [22]:

$$\mathbf{T}\_0 = \sqrt[4]{\frac{\mathbf{R}\_\*}{\sigma \epsilon\_\*}}\tag{9}$$

deviation (SD) staying between 11 and 12 mm yr−1. These values are below the historical value of the study area, and they were not well distributed along the year. A period from July to October, with several rainfall 16-day values bellow 10 mm, was noticed for all analyzed agroecosystems. The short rainfall amounts occurred from Phase 3 to Phase 4 of the generalized sugarcane growing cycle, which should have caused some water deficit, when its water requirements are high. Cabral et al. [13] reported a 13% of sugarcane biomass reduction in relation to the regional average in São Paulo state, Brazil, because of the lower water availability observed during the initial 120 days of cane regrowth. For rainfed sugarcane crop, a well-distributed growing season total precipitation between 1100 and 1500 mm is considered adequate. However, the P dropping during Phase 3 should have caused some water deficit, when the crop water requirements are high, further affecting the energy partition, by reducing leaf area and the number of tillers and leaves per stalk [23]. During Phase 4, rains are not desirable for sugarcane, because they lead to poor juice quality [15], and then the high

The Use of MODIS Images to Quantify the Energy Balance in Different Agroecosystems in Brazil

amounts at the end of the year, coinciding with this phase, were not favorable.

the agroecosystems when compared with precipitation. The corresponding ET0

(NV) but with small SD from 3 to 4 mm yr−1. The shortest ET0

001-016 081-096 161-176 241-256 321-336

SC CO NV

001-016 081-096 161-176 241-256 321-336

Precipitation (P), (b) reference evapotranspiration (ET0

SC CO NV

smallest one happening at the middle of the year, however, with lower differences among

were 1321, 1297, and 1293 mm yr−1 for the sugarcane (SC), coffee (CO), and natural vegetation

ET0 (m

0

28

56

84

112

140

Ta (oC)

Day of the Year (DOY)

**Figure 3.** Average 16-day values for the weather variables during 2015 in areas with sugarcane (SC), coffee (CO), and natural vegetation (NV) in terms of day of the year (DOY), located at the northeastern side of São Paulo state, Brazil. (a)

16.0

18.4

20.8

23.2

25.6

28.0

m)

values, one can see two atmosphere demand peaks with the

annual values

).

values in the middle of the year,

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111

001-016 081-096 161-176 241-256 321-336

(c) (d)

SC CO NV

001-016 081-096 161-176 241-256 321-336

), (c) incident global solar radiation (RG), and (d) air temperature (T<sup>a</sup>

SC CO NV

(a) (b)

Taking into account the ET0

P (mm)

0

28

56

84

112

140

RG (MJ

10.0

12.8

15.6

18.4

21.2

24.0

m-2 d-1)

where the surface emissivity (ε<sup>S</sup> ) was estimated as follows [22]:

$$\varepsilon\_{\rm s} = \mathbf{a\_{s}(ln N \text{NDVI}) + b\_{s}} \tag{10}$$

and aS and b<sup>S</sup> are the regression coefficients 0.06 and 1.00, respectively.

The SAFER algorithm is used to model water indicator represented by the ratio of the actual to the reference evapotranspiration (ET<sup>r</sup> ) based on the input remote sensing parameters, which is then multiplied by the 24-hour ET0 values to estimate the daily ET large-scale rates which in turn are transformed into latent heat fluxes (λE):

$$\rm{ET}\_{r} = \left\{ \exp \left[ \rm{f} + \rm{g} \left( \frac{T\_{\rm o}}{\alpha\_{\rm o} \rm{NDVI}} \right) \right] \right\} \frac{\rm{ET} \, 0\_{pr}}{5} \tag{11}$$

where f and g are the original regression coefficients, 1.8 and −0.008, respectively. The correction factor (ET0yr/5) is applied, ET0yr being the annual grids of reference evapotranspiration for São Paulo state in the year 2015 and 5 mm is the ET0yr value for the period of the original modeling in the Northeast Brazil [21].

For soil heat flux (G), the equation derived by Teixeira [12] was used:

$$\frac{\mathcal{G}}{\mathcal{R}\_n} = \mathbf{a}\_\mathbf{c} \exp(\mathbf{b}\_\mathbf{c} \mathbf{a}\_\mathbf{b}) \tag{12}$$

where the regression coefficients aG and bG are 3.98 and −25.47.

The sensible heat flux (H) is acquired as residue in the energy balance equation:

$$\mathbf{H} = \mathbf{R}\_p - \lambda \mathbf{E} - \mathbf{G} \tag{13}$$

#### **3. Results and discussion**

#### **3.1. Thermohydrological conditions and crop stages**

The driving weather variables for the surface energy balance are RG, T<sup>a</sup> , precipitation (P), and ET0 . They are presented in **Figure 3** on a 16-day time scale in terms of day of the tear (DOY), during 2015 as average pixel values for each agroecosystem class: sugarcane (SC), coffee (CO), and natural vegetation (NV).

Among the four weather parameters, P was the most variable along the year with the largest values occurring during the first and the last 3 months. The high-moisture conditions in the root zones during these periods affect the energy balance, increasing the latent heat fluxes (λE) for all agroecosystems. The rainfall annual totals were 1253, 1277, and 1245 mm yr−1 for the sugarcane (SC), coffee (CO), and natural vegetation (NV) with the range of standard deviation (SD) staying between 11 and 12 mm yr−1. These values are below the historical value of the study area, and they were not well distributed along the year. A period from July to October, with several rainfall 16-day values bellow 10 mm, was noticed for all analyzed agroecosystems. The short rainfall amounts occurred from Phase 3 to Phase 4 of the generalized sugarcane growing cycle, which should have caused some water deficit, when its water requirements are high. Cabral et al. [13] reported a 13% of sugarcane biomass reduction in relation to the regional average in São Paulo state, Brazil, because of the lower water availability observed during the initial 120 days of cane regrowth. For rainfed sugarcane crop, a well-distributed growing season total precipitation between 1100 and 1500 mm is considered adequate. However, the P dropping during Phase 3 should have caused some water deficit, when the crop water requirements are high, further affecting the energy partition, by reducing leaf area and the number of tillers and leaves per stalk [23]. During Phase 4, rains are not desirable for sugarcane, because they lead to poor juice quality [15], and then the high amounts at the end of the year, coinciding with this phase, were not favorable.

Then, the surface temperature (T0

110 Multi-purposeful Application of Geospatial Data

where the surface emissivity (ε<sup>S</sup>

and aS

and b<sup>S</sup>

T0 <sup>=</sup> <sup>4</sup>

ε<sup>S</sup> = aS

turn are transformed into latent heat fluxes (λE):

ET<sup>r</sup> <sup>=</sup> {exp[<sup>f</sup> <sup>+</sup> <sup>g</sup>(

the reference evapotranspiration (ET<sup>r</sup>

modeling in the Northeast Brazil [21].

\_\_<sup>G</sup>

**3. Results and discussion**

and natural vegetation (NV).

ET0

then multiplied by the 24-hour ET0

) was estimated by the residual method [22]:

(ln NDVI) + b<sup>s</sup> (10)

<sup>5</sup> (11)

, precipitation (P), and

) based on the input remote sensing parameters, which is

= a<sup>G</sup> exp(bG α0) (12)

values to estimate the daily ET large-scale rates which in

ET <sup>0</sup> \_\_\_\_\_yr

(9)

√

) was estimated as follows [22]:

The SAFER algorithm is used to model water indicator represented by the ratio of the actual to

where f and g are the original regression coefficients, 1.8 and −0.008, respectively. The correction factor (ET0yr/5) is applied, ET0yr being the annual grids of reference evapotranspiration for São Paulo state in the year 2015 and 5 mm is the ET0yr value for the period of the original

H = R<sup>n</sup> − E − G (13)

. They are presented in **Figure 3** on a 16-day time scale in terms of day of the tear (DOY), during 2015 as average pixel values for each agroecosystem class: sugarcane (SC), coffee (CO),

Among the four weather parameters, P was the most variable along the year with the largest values occurring during the first and the last 3 months. The high-moisture conditions in the root zones during these periods affect the energy balance, increasing the latent heat fluxes (λE) for all agroecosystems. The rainfall annual totals were 1253, 1277, and 1245 mm yr−1 for the sugarcane (SC), coffee (CO), and natural vegetation (NV) with the range of standard

For soil heat flux (G), the equation derived by Teixeira [12] was used:

where the regression coefficients aG and bG are 3.98 and −25.47.

**3.1. Thermohydrological conditions and crop stages**

The driving weather variables for the surface energy balance are RG, T<sup>a</sup>

Rn

The sensible heat flux (H) is acquired as residue in the energy balance equation:

<sup>T</sup> \_\_\_\_\_\_\_ <sup>0</sup> α0 NDVI)]}

are the regression coefficients 0.06 and 1.00, respectively.

\_\_\_ R\_\_\_s <sup>s</sup>

> Taking into account the ET0 values, one can see two atmosphere demand peaks with the smallest one happening at the middle of the year, however, with lower differences among the agroecosystems when compared with precipitation. The corresponding ET0 annual values were 1321, 1297, and 1293 mm yr−1 for the sugarcane (SC), coffee (CO), and natural vegetation (NV) but with small SD from 3 to 4 mm yr−1. The shortest ET0 values in the middle of the year,

**Figure 3.** Average 16-day values for the weather variables during 2015 in areas with sugarcane (SC), coffee (CO), and natural vegetation (NV) in terms of day of the year (DOY), located at the northeastern side of São Paulo state, Brazil. (a) Precipitation (P), (b) reference evapotranspiration (ET0 ), (c) incident global solar radiation (RG), and (d) air temperature (T<sup>a</sup> ).

from May to July, coincide with the sugarcane Phase 3, favoring cane elongation reduction during this phase.

In relation to RG and T<sup>a</sup> , the differences among the agroecosystems were smaller than those for P and ET0 , with average annual values around 17 MJ m−2 day−1 and 23.0°C, respectively. Then, the highest atmosphere demands in sugarcane could be probably attributed to low air humidity and/or high wind speed conditions.

The thermohydrological conditions also strongly affect the coffee crop stages [17]. As the growing cycle takes 2 years, some coffee phases will coexist. Rainfall should be well distributed for good yield. At the start of the year, for the period involving Phases 1 and 4, there was only a 16-day (DOY 001–016) period with P lower than 10 mm in January. In Phase 2 rainfall is important for the transformation of the vegetative to reproductive buds. During this period, P declined until values close to zero at the end of July (DOY 209–224). In Phase 3 (September–November), some water stress is desirable, as the main flowering happens during a period of water stress following by good water availability. However, only two 16-day periods with low rainfall amounts are verified from September to October (DOY 257–288). In Phase 4, water stress may wilt the fruits, but only during the period from DOY 001 to 016 the rainfall amount was low, bellow 10 mm. In Phase 5 the water requirements declined, and some water deficit during this phase could have favored the coffee plant growth. The period with low rainfall amounts from May to June was also inside this phase.

Conditions of low RG and ET0 levels from May to August coincided with low P amounts, thus reducing water consumption in coffee areas. Air temperature (T<sup>a</sup> ) regulates the vegetative growth and reproductive buds, being high values associated with water deficit during booming the reason for flower abortion and growth reduction [23]. However, the higher values, above 23°C, occurred under conditions of good rainfall availability.

Phases 1 and 4 of coffee plants. However, at the middle of the year, CO values were higher,

**Figure 4.** Composed net radiation (Rn) average values for sugarcane (SC), coffee (CO), and natural vegetation (NV) agroecosystems, during the year 2015, inside the northeastern side of São Paulo (SP) state, Southeast Brazil. The over bars


Rn 11.0 0.5 sc = ± Rn 11.1 0.4 co = ±



DOY: 161-176

DOY: 353-365 11.5

113

Rn (MJ m-2 d-1)

9.3

8.2

7.1

6.0




(b)


The Use of MODIS Images to Quantify the Energy Balance in Different Agroecosystems in Brazil

DOY: 097-112

DOY: 289-304 -24o

0 300 km

10.4 Rn 8.6 0.3 sc <sup>=</sup> <sup>±</sup>

Rn 6.4 0.4 sc = ± Rn 6.9 0.9 co = ±

http://dx.doi.org/10.5772/intechopen.72798

Rn 10.1 0.5 sc = ± Rn 9.7 0.5 co = ±

0 300 km



Regarding the ratio Rn/RG (**Figure 5b**), the higher mean pixel values were for the coffee (CO) class, mainly in the middle of the year. The values ranged from 0.49 to 0.55, from 0.50 to 0.57, and from 0.50 to 0.56, for, respectively, the SC, CO, and NV agroecosystems. The average annual Rn/RG of 50–55% is in agreement with field measurements in fruit crops and natural vegetation in the Northeast Region of Brazil [11] and with studies involving other distinct agroecosystems around the world [24, 25]. These results of similarities with national and international studies give confidence to the large-scale remote sensing methods tested here

Day of the Year - DOY

**Figure 5.** Daily net radiation (Rn) and their ratios to global solar radiation (RG) for sugarcane (SC), coffee (CO), and natural vegetation (NV) agroecosystems, during the year 2015, in the northeastern side of São Paulo (SP) state, Southeast

0.45

001-016 081-096 161-176 241-256 321-336

0.48

0.51

0.54

0.57

0.60

Rn/RG

(a)

when the plant stages were in mixed stages of the Phases 2, 5, and 6.



DOY: 033-048

DOY: 225-240

mean averages showed together with standard deviations (SD).



Rn 10.4 0.5 sc = ± Rn 10.2 0.4 co = ±

Rn 9.0 0.6 sc = ± Rn 9.6 1.5 co = ±


0 300 km

0 300 km


Rn 8.9 0.5 co = ±





0 300 km

0 300 km

Rn (MJ m-2 d-1)

5.0

6.6

8.2

9.8

11.4

13.0


by coupling the MOD13Q1 product and agrometeorological stations.

SC CO NV

001-016 081-096 161-176 241-256 321-336

Brazil. The over bars mean averages showed together with standard deviations (SD).
