

Ci a ion: Ja dim, A.M.d.R.F.; A aújo
Júnio , G.d.N.; Sil a, M.V.d.; San os,
A.d.; Sil a, J.L.B.d.; Pando i, H.;
Oli ei a-Júnio , J.F.d.; Teixei a,
A.H.d.C.; Teodo o, P.E.; de Lima,
J.L.M.P.; e al. Using Remo e Sensing
o Quan i y he Join E ec s o
Clima e and Land Use/Land Co e
Changes on he Caa inga Biome o
No heas B azilian. Remo e Sens.
2022,14, 1911. h ps://doi.o g/
10.3390/ s14081911
Academic Edi o s: Baojie He,
Ayyoob Sha i i, Chi Feng and
Jun Yang
Recei ed: 3 Ma ch 2022
Accep ed: 1 Ap il 2022
Published: 15 Ap il 2022
Publishe ’s No e: MDPI s ays neu al
wi h ega d o ju isdic ional claims in
published maps and ins i u ional a il-
ia ions.
Copy igh : © 2022 by he au ho s.
Licensee MDPI, Basel, Swi ze land.
This a icle is an open access a icle
dis ibu ed unde he e ms and
condi ions o he C ea i e Commons
A ibu ion (CC BY) license (h ps://
c ea i ecommons.o g/licenses/by/
4.0/).
emo e sensing
A icle
Using Remo e Sensing o Quan i y he Join E ec s o Clima e
and Land Use/Land Co e Changes on he Caa inga Biome o
No heas B azilian
Alexand e Maniçoba da Rosa Fe az Ja dim 1,2,*, Geo ge do Nascimen o A aújo Júnio 1,2, Ma cos Vinícius
da Sil a 1, Ande son dos San os 1, Jhon Lennon Beze a da Sil a 1, Héli on Pando i 1, JoséF ancisco
de Oli ei a-Júnio 3, An ônio He ibe o de Cas o Teixei a 4, Paulo Edua do Teodo o 5,
João L. M. P. de Lima 6,7 , Ca los An onio da Sil a Junio 8, Luciana Sand a Bas os de Souza 2,
Emanuel A aújo Sil a 9and Thie es Geo ge F ei e da Sil a 1,2
1Depa men o Ag icul u al Enginee ing, Fede al Ru al Uni e si y o Pe nambuco, Reci e 52171-900, B azil;
[email p o ec ed] (G.d.N.A.J.); ma [email p o ec ed] (M.V.d.S.);
[email p o ec ed] (A.d.S.); [email p o ec ed] (J.L.B.d.S.); [email p o ec ed] (H.P.);
[email p o ec ed] (T.G.F.d.S.)
2Academic Uni o Se a Talhada, Fede al Ru al Uni e si y o Pe nambuco, Se a Talhada 56909-535, B azil;
[email p o ec ed]
3Ins i u e o A mosphe ic Sciences, Fede al Uni e si y o Alagoas, Maceió57072-970, B azil;
[email p o ec ed]
4Wa e Resou ces Depa men , Fede al Uni e si y o Se gipe, São C is ó ão 49100-000, B azil;
[email p o ec ed]
5Depa men o Ag onomy, Fede al Uni e si y o Ma o G osso do Sul, Chapadão do Sul 79560-000, B azil;
[email p o ec ed]
6MARE—Ma ine and En i onmen al Sciences Cen e, Uni e si y o Coimb a, 3000-456 Coimb a, Po ugal;
[email p o ec ed]
7Depa men o Ci il Enginee ing, Facul y o Sciences and Technology, Uni e si y o Coimb a,
3030-788 Coimb a, Po ugal
8Depa men o Geog aphy, S a e Uni e si y o Ma o G osso (UNEMAT), Sinop 78555-000, B azil;
[email p o ec ed]
9Depa men o Fo es Sciences, Fede al Ru al Uni e si y o Pe nambuco, Reci e 52171-900, B azil;
[email p o ec ed]
*Co espondence: alexand e.ja [email p o ec ed]
Abs ac :
Caa inga biome, loca ed in he B azilian semi-a id egion, is he mos populous semi-a id
egion in he wo ld, causing in ensi ica ion in land deg ada ion and loss o biodi e si y o e ime.
The main objec i e o his pape is o de e mine and analyze he changes in land co e and use, o e
ime, on he biophysical pa ame e s in he Caa inga biome in he semi-a id egion o B azil using
emo e sensing. Landsa -8 images we e used, along wi h he Su ace Ene gy Balance Algo i hm
o Land (SEBAL) in he Google Ea h Engine pla o m, om 2013 o 2019, h ough spa io empo al
modeling o ege a ion indices, i.e., lea a ea index (LAI) and ege a ion co e (V
C
). Mo eo e , land
su ace empe a u e (LST) and ac ual e apo anspi a ion (ET
a
) in Pe olina, he semi-a id egion o
B azil, was used. The p incipal componen analysis was used o selec desc ip i e a iables and
mul iple eg ession analysis o p edic ET
a
. The esul s indica ed signi ican e ec s o land use and
land co e changes on ene gy balances o e ime. In 2013, 70.2% o he s udy a ea was composed o
Caa inga, while he lowes pe cen ages we e iden i ied in 2015 (67.8%) and 2017 (68.7%). Rain all
eco ds in 2013 anged om 270 o 480 mm, wi h alues highe han 410 mm in 46.5% o he s udy
a ea, concen a ed in he no he n pa o he municipali y. On he o he hand, in 2017 he lowes
annual ain all alues ( om 200 o 340 mm) occu ed. Low ege a ion co e a e was obse ed by
LAI and V
C
alues, wi h a ange o 0 o 25% ege a ion co e in 52.3% o he a ea, which exposes he
e ec s o he d y season on ege a ion. The highes LST was mainly ound in u ban a eas and/o
exposed soil. In 2013, 40.5% o he egion’s a ea had LST be ween 48.0 and 52.0
◦
C, aising ET
a
a es (~4.7 mm day
−1
). Ou model has shown good ou comes in e ms o accu acy and conco dance
(coe icien o de e mina ion = 0.98, oo mean squa e e o = 0.498, and Lin’s conco dance co ela ion
Remo e Sens. 2022,14, 1911. h ps://doi.o g/10.3390/ s14081911 h ps://www.mdpi.com/jou nal/ emo esensing
Remo e Sens. 2022,14, 1911 2 o 27
coe icien = 0.907). The signi ican inc ease in ag icul u al a eas has esul ed in he p og essi e
educ ion o he Caa inga biome. The e o e, mi iga ion and sus ainable planning is i al o dec ease
he impac s o an h opic ac ions.
Keywo ds:
opical d y o es ; su ace ene gy balance; B azilian semi-a id; SEBAL; ac ual e apo anspi a ion
1. In oduc ion
The Caa inga biome occupies a la ge po ion o he B azilian semi-a id egion. I has
a high ecological di e si y o plan species, and i is conside ed he la ges in he wo ld
unde semi-a id condi ions [
1
,
2
]. I occupies an a ea o 900,000 km
2
, which co esponds
o app oxima ely 70% o he no heas egion o B azil (NEB). Howe e , only 7.5% o his
habi a is p o ec ed by law [
3
–
5
]. The municipali y o Pe olina, PE, B azil, is loca ed in
he semi-a id egion o he Caa inga biome. I is wi hin he hyd og aphic basin o he São
F ancisco i e , which a o s he de elopmen o i iga ed ag icul u e in he egion, mainly
ui -g owing (e.g., g apes and mangoes) [
6
–
8
], and i leads o posi i e socioeconomic
implica ions, bu also inc eases con lic s ega ding wa e use [2,9].
Caa inga is loca ed in he wo ld’s mos popula ed d y a ea, wi h mo e han 53 million
inhabi an s and a popula ion densi y close o 34 inhabi an s pe km
2
. This biome has
been a ec ed by en i onmen al deg ada ion and loss o biodi e si y o e he las decades,
mainly by he in ensi ica ion o ag icul u e (e.g., ain ed and i iga ed c ops cul i a ion),
u ban expansion, and he ad ance o pas u e a eas eplacing he na u al ege a ion [
10
–
12
].
Expanding ag icul u al ac i i ies has led o he de o es a ion o na i e a eas, soil dis u -
bance, changes in he hyd ological cycle, and highe ca bon emissions [
13
–
15
]. Toge he
wi h clima e changes, such as educed ain all and in ensi ied d ough e en s, his makes
he Caa inga biome and he B azilian ecosys em he mos h ea ened and suscep ible o
dese i ica ion. Fu he mo e, hese changes ha e been inc easingly comp omising na u al
esou ces and en i onmen al sus ainabili y [
16
–
19
] due o he changes in su ace p ope ies
and biophysical a iables, such as ege a ion co e (V
C
), land su ace empe a u e (LST),
and e apo anspi a ion (ET) [
20
–
22
]. ET is one o he main esponse pa ame e s o ege-
a ed a eas as a unc ion o local wa e condi ions, and i is also an impo an componen o
he hyd ological cycle [23,24].
The e o e, moni o ing physical–wa e indica o s o en i onmen al change condi ions,
such as he loss o biodi e si y o biomes and land use and occupa ion, is i al in managing
sca ce wa e esou ces. Fu he mo e, hese indica o s may be help ul in he planning o
ag icul u al ac i i ies, as well as in he managemen o d y a eas and he sus ainable use
and managemen o na u al esou ces [17,25–30].
In his scena io, emo e sensing has been used as a ool ha p esen s as and low
ope a ional cos s, being e icien in calcula ing he biophysical pa ame e s used in he
ene gy, wa e , and ege a ion balances (e.g., [
31
–
33
]). In ecen yea s, emo e sensing has
also been conside ed a good al e na i e o eplacing expensi e and di icul - o-ob ain
equipmen used in in si u s udies [
34
]. Fu he mo e, he modeling used in hese balances
is pe o med wi h he help o algo i hms, which a e essen ial echniques in he ex ac ion
o in o ma ion om sa elli e images on a egional and global scale [
27
,
28
,
33
,
35
]. I may
ha e i s e iciency imp o ed by he use o open-sou ce cloud p og amming languages, e.g.,
Google Ea h Engine (GEE). In GEE, he use can e ec i ely implemen algo i hms and
p ocess la ge da a olumes [36].
In his con ex , he e a e se e al su ace ene gy balance models, such as Mapping
E apoT anspi a ion a high Resolu ion wi h In e nalized Calib a ion (METRIC), Su ace En-
e gy Balance Sys em (SEBS), Simple Algo i hm o E apo anspi a ion Re ie ing (SAFER),
A mosphe e–Land Exchange In e se (Alexi), and he Ene gy Balance Algo i hm o Land
(SEBAL), based on emo e sensing da a. The app oach is subs an ia ed on biophysical
pa ame e s, such as LST, albedo, No malized Di e ence Vege a ion Index (NDVI), and
Remo e Sens. 2022,14, 1911 3 o 27
emissi i y, ha a e c ucial o es ima ing ET [
16
,
37
–
42
]. The SEBAL algo i hm has been
widely and success ully applied o a ious wo ld ecosys ems, including semi-a id condi-
ions in B azil [
43
–
46
]. Mo eo e , his me hod seeks o elimina e he p opaga ion o e o s
in he pa i ioning o he ene gy balance and he need o a mosphe ic co ec ion in he
es ima e o su ace empe a u e. These in e ac ions allow he gene a ion o he sensible hea
lux co ec ed o a mosphe ic s abili y and ins abili y condi ions [
47
–
51
]. NDVI is one o
he mos widely used indices in he li e a u e o ege a ion co e analysis, en i onmen al
deg ada ion, and ege a ion p ima y p oduc ion esilience, and i also helps in moni o ing
ag icul u al c ops, o es ecosys ems, and d ough assessmen s [
52
–
55
]. Wi h applica ions
in se e al coun ies, Bas iaanssen e al. [
49
] ha e ob ained highly accu a e esul s using he
SEBAL algo i hm on di e en ege a ed su aces wi h o es s, ag icul u al c ops (i iga ed
and ain ed), and e en ex eme landscapes, such as dese a eas. P e ious s udies ha e
shown ha he SEBAL can be used in Pe olina, PE, B azil, o he algo i hm has al eady
been calib a ed and alida ed o he egion in a ious ecosys ems wi h good ag eemen
be ween o bi al images and ield measu emen s [44,56–59].
Based on he abo e, he main objec i e o his pape is o de e mine and analyze he
changes in land co e , land use, and occupa ion on biophysical pa ame e s in he Caa inga
biome in he semi-a id egion o B azil. In he p esen s udy, he changes we e assessed
om 2013 o 2019 using Landsa image y, and biophysical pa ame e s (ne adia ion, ene gy
balance, LAI, V
C
, ET
a
, and LST) we e es ima ed by u ilizing emo e sensing. Addi ionally, he
SEBAL model was applied o de e mine he su ace ene gy balance in di e en ege a ion
en i onmen s. A e applying he model, he da ase p o ided by SEBAL allowed us o
de e mine he u bulen luxes and ac ual e apo anspi a ion (ET
a
) o he di e en land
use and land co e (LULC) ypes. Thus, h ough he SEBAL p oduc s, he spa io empo al
a ia ion pa e ns o ETain ag icul u al and o es y a eas we e e alua ed.
2. Ma e ials and Me hods
2.1. S udy A ea
The s udy was ca ied ou in he municipali y o Pe olina, loca ed in he S a e o
Pe nambuco, B azil. The egion comp ises he domain o he Caa inga biome and i belongs
o he semi-a id egion o he sub-mean o he São F ancisco Valley. The own is conside ed
he la ges ui -g owing cen e o he B azilian semi-a id egion due o he easy access
o he São F ancisco i e , which supplies he i iga ed pe ime e s. The municipali y
comp ises a e i o ial a ea o 4561.870 km
2
(Figu e 1), wi h an es ima ed popula ion o
349,145 inhabi an s [38].
A cha ac e is ic o he Caa inga biome is he p esence o di e en lo is ic mosaics, con-
sis ing o an a ea o ee and sh ub ege a ion, which p esen s i s dis ibu ion condi ioned
o clima ic and en i onmen al a ia ions, especially ain all in ensi y and equency [
60
],
as well as geological con igu a ions and soil p ope ies [
5
]. The ege a ion o his biome
p esen s deciduous species adap ed o wa e de ici condi ions and wi h exp essi e biomass
p oduc ion in ainy seasons, esul ing om he local clima ic condi ions [
40
,
61
]. The canopy
co e o he species o he Caa inga biome p esen s discon inuous cha ac e is ics, making
possible he soil exposu e in d y pe iods, p esence o he baceous s a um, cac us species,
and sh ubs [
23
,
61
]. I is wo h no ing ha his egion p esen s an exp essi e modi ica ion
o he na i e landscape (Caa inga) in a eas o i iga ed ag icul u al cul i a ion.
Remo e Sens. 2022,14, 1911 4 o 27
Figu e 1.
Spa ial loca ion o he s udy a ea, municipali y o Pe olina, Pe nambuco, No heas B azil.
Acco ding o he Köppen–Geige clima e classi ica ion, he egion’s clima e is o he
BSh ype, cha ac e ized as semi-a id opical, wi h an a e age ai empe a u e o 26.4
◦
C,
a e age ela i e humidi y o 62%, and annual ain all o 520 mm [
62
,
63
]. Rain all pa e n is
i egula h oughou he yea , esul ing om i s geog aphical loca ion and he In e op-
ical Con e gence Zone (ITCZ) in luence, wi h ain all p edomina ing om Feb ua y o
May [
10
,
64
,
65
]. The p edominan soils in he municipali y a e Typic Qua zipsammen ,
Ul isol Plin hic, A enosol, and Haplic Ac isol [66–68].
2.2. Sa elli e Images and Wea he Da ase s
Annual eco ds o a e age ai empe a u e (
◦
C), global adia ion (MJ m
−2
), ela i e
humidi y (%), a mosphe ic p essu e (kPa), wind speed (m s
−1
), and ain all (mm) we e
ob ained om he da abase o he Na ional Ins i u e o Me eo ology [
69
] (Figu e 2). The
a e age ai empe a u e (T
a
) anged be ween 24.1 and 30.7
◦
C, in which 2015 and 2019 we e
he wa mes yea s s udied (T
a
28
◦
C). O e all, No embe , Decembe , Janua y, Feb ua y,
and Ma ch p esen ed Ta alues abo e 28 ◦C (Figu e 2).
The yea s 2013 (334.4 mm) and 2019 (221.6 mm) showed he highes ain all a es.
Mos o he ain all eco ded o Pe olina was concen a ed om Decembe o Ma ch
(Figu e 2). Such clima ic condi ions conce ning he municipali y we e also epo ed in he
li e a u e (e.g., [2,70]).
Rain all da a co esponding o he 30 days p io o he ou imaging da es s udied
used we e ob ained om he Clima e Haza ds G oup In aRed P ecipi a ion wi h S a ion
(CHIRPS). CHIRPS a e new p ecipi a ion p oduc s co e ing he coo dina es 50
◦
S–50
◦
N
and 180
◦
E–180
◦
W, wi h 0.05
◦
(
±
5.3 km) spa ial esolu ion and daily o seasonal, empo al
esolu ions, a ailable wo ldwide since 1981 [
71
]. CHIRPS da a we e ex ac ed om he
Google Ea h Engine pla o m (h ps://ea hengine.google.com/, accessed on 20 Augus
2021) using Ja aSc ip p og amming language. Then, hey we e expo ed in sp eadshee
o ma (*.xls), using he da ase since 1981 om he collec ion ee.ImageCollec ion (“UCSB-
CHG/CHIRPS/DAILY”).
Remo e Sens. 2022,14, 1911 5 o 27
Figu e 2.
Mon hly me eo ological a ia ions ( ain all, a e age ai empe a u e, and global sola
adia ion) o he municipali y o Pe olina, B azil, om 2013 o 2019.
We used Ope a ional Land Image (OLI) Collec ion 1 Le el 1 bands 2 (0.450–0.51
µ
m),
3 (0.53–0.59
µ
m), and 4 (0.64–0.67
µ
m) in he isible spec um, 5 (0.85–0.88
µ
m) in he
nea -in a ed, and 6 (1.57–1.65
µ
m) and 7 (2.11–2.29
µ
m) in he sho wa e in a ed, all
wi h a spa ial esolu ion o 30 m, as well as band 10 om he The mal In a ed Senso
(TIRS) wi h a 100 m spa ial esolu ion. Besides his, we used ou Landsa -8 OLI/TIRS
images, pa h 217 and ow 66, co esponding o he yea s 2013, 2015, 2017, and 2019 ( o
he da es and imes o he sa elli e o e pass, see Table 1). The choice c i e ia adop ed
we e he absence o clouds (10%) and he images co esponding o he ansi ion pe iod
be ween he d y and ainy seasons in he egion unde s udy, om he yea s o 2013 o
2019. This pe iod p esen ed se e e and ex eme d ough e en s in he no heas [
23
,
72
].
All he images we e ob ained om he Uni ed S a es Geological Su ey (USGS) pla o m
(h ps://ea hexplo e .usgs.go /, accessed on 10 Augus 2021) and p ocessed h ough he
Land Su ace Re lec ance Code (LaSRC).
Table 1.
Da e o he Landsa -8 sa elli e pass, ollowed by he Julian day (JD), Ea h–Sun dis ance
(d , as onomical uni s—AU), local ime o he equa o pass (h, hou ; min, minu es), zeni h angle
(
θ
,
◦
), sola ele a ion angle (E,
◦
), and sun azimu h angle (
ϕ
,
◦
) o he municipali y o Pe olina,
Pe nambuco, B azil.
Acquisi ion Da e JD d Local Time θEϕ
5 Oc obe 2013 278 0.99 9 h 49 min a.m. 0.90 65.12 82.93
12 No embe 2015 316 0.99 9 h 48 min a.m. 0.90 64.85 113.46
16 Oc obe 2017 289 0.99 9 h 48 min a.m. 0.91 65.81 92.82
7 No embe 2019 311 0.99 9 h 48 min a.m. 0.90 65.41 110.36
No e: zeni h angle (θ) = sin(E). Sou ce: USGS/NASA [73].
2.3. Vege a ion Indices
The No malized Di e ence Vege a ion Index (NDVI) was calcula ed o ep esen he
amoun and quali y o ege a ion p esen on he su ace, cha ac e ized as an indica o o
we condi ions, calcula ed using Equa ion (1).
NDVI =ρNIR −ρRed
ρNIR+ρRed
(1)
whe e
ρNIR
and
ρRed
a e he e lec ances measu ed in he nea -in a ed and ed bands (i.e.,
Landsa -8 mul ispec al bands 5 and 4 o he OLI senso ), espec i ely, hey ange om
−
1
o +1. Values close o 1 on a posi i e scale co espond o high pho osyn he ic ac i i y, and
when nega i e, gene ally co espond o wa e bodies.

Remo e Sens. 2022,14, 1911 6 o 27
Based on he NDVI, we calcula ed he ege a ion co e (V
C
) o he s udy a ea
(Equa ion (2))
,
acco ding o Gao e al. [54].
VC=NDVI −NDVIS
NDVIV−NDVIS
·100 (2)
whe e V
C
is he ege a ion co e , NDVI
S
is he minimum NDVI alue om ba e soil
pixels ob ained in he s udy a ea, and NDVI
V
is he maximum NDVI alue ound in
ege a ed a eas, i.e., om ully ege a ed pixels. The NDVI
S
and NDVI
V
used o calcula e
V
C
we e ob ained om he domain o each NDVI image pe cen ile map ob ained om he
NDVI his og ams.
Soil-Adjus ed Vege a ion Index (SAVI) was calcula ed o obse e he ege a ion co e
o he a ea (Equa ion (3)).
SAVI =(1+L)·(ρNIR −ρRed)
(L+ρNIR+ρRed)(3)
whe e Lis he adjus men ac o o he soil, which a ies be ween 0 and 1. The alue 0 does
no each change, and esembles he NDVI. In a eas wi h low-densi y ege a ion, he alue
1 is assigned; o in e media e-densi y ege a ion a eas, he alue o 0.5; and o a eas wi h
high-densi y ege a ion, he alue 0.25 is assigned [
74
]. The adjus men ac o o 0.5 was
adop ed due o he s udy egion indica ing an in e media e ege a ion co e age in mos o
he yea , wi h p edominan ege a ion o he Caa inga biome, in he B azilian semi-a id
egion [75–77].
To e alua e changes in ege a ion biomass, he lea a ea index (LAI, m
2
m
−2
) was
de e mined (Equa ion (4)), a undamen al biophysical a iable o moni o ing s udies o
ag icul u al land and ege a ion mois u e condi ions [50].
LAI =
−ln0.69−SAVI
0.59 
0.91 (4)
2.4. Me hodology o Es ima ing E apo anspi a ion Using Sa elli e Images
E apo anspi a ion was es ima ed using he Ene gy Balance Algo i hm o Land
(SEBAL). Fo his, ou ine me eo ological da a and spec al bands om he Landsa -8
sa elli e we e used. The SEBAL algo i hm was implemen ed using Ja aSc ip code h ough
he Google Ea h Engine (GEE) pla o m. The da a we e expo ed in sp eadshee o ma
(*.xls). SEBAL uses ma hema ical modeling and ope a ions o calcula e he su ace ene gy
balance componen s and de e mine e apo anspi a ion. Thus, ene gy balance componen s
a e compu ed pixel-by-pixel, as desc ibed in Figu e 3. He e, we show ha he algo i hm
has a good pe o mance and high accu acy, as well as being calib a ed and alida ed wi h
simul aneous ield and Landsa sa elli e measu emen s [47–49,56,59,61,78,79].
Remo e Sens. 2022,14, 1911 7 o 27
Figu e 3.
Flowcha o he SEBAL model o es ima ing e apo anspi a ion. No e: LST and LSE
a e he land su ace empe a u e and land su ace emissi i y, espec i ely, NDVI is he No malized
Di e ence Vege a ion Index,
εa
is he a mosphe ic emissi i y, u* is he ic ion eloci y, T
a
is he
ai empe a u e, R
n
is he ne adia ion, Gis he soil hea lux, z
om
is he momen um oughness
leng h, z
1
and z
2
a e he wo heigh s be ween he su ace o he ancho pixels,
ah
is he nea -su ace
ae odynamic esis ance o hea anspo , DEM is he digi al ele a ion model, dT is he nea -su ace ai
empe a u e g adien , aand ba e he calib a ion coe icien s, LST o T
s
is he land su ace empe a u e,
ρai
is he ai densi y, C
p
is he speci ic hea o ai , His he sensible hea lux,
ψm
and
ψh
a e he s abili y
co ec ion ac o s o momen um and sensible hea , espec i ely, kis he on Ka man cons an , u
200
is
he wind speed a he heigh o 200 m, and
Λ
is he e apo a i e ac ion. These p e- and p ocessing
s eps we e pe o med inside Google Ea h Engine (GEE) cloud pla o m. The ac onyms and symbols
used in his s udy a e summa ized in he Abb e ia ions sec ion.
Su ace Albedo Adjus men
The su ace albedo (
αsup
) co esponds o a measu e o he e lec i i y o he Ea h’s
su ace, o each pixel, wi h a mosphe ic co ec ion ob ained acco ding o Equa ion (5) [
50
,
80
].
αsup =α oa −αpa h
τsw2(5)
whe e
α oa
is he albedo a he op o he a mosphe e, ha is, be o e a mosphe ic co ec ion,
αpa h
is he a mosphe ic e lec ance (se o 0.03, as used by Sil a e al. [
80
]), and
τsw
is he
a mosphe ic ansmissi i y o clea sky condi ions, acco ding o Equa ion (6) [50,80]:
τsw=0.35 +0.627 ·exp"−0.00146 ·Pa
K ·cos(θ)−0.075W
cos(θ)0.4#(6)
whe e P
a
is he a mosphe ic p essu e (kPa), wi h da ase a ailable eely in h ps://po al.
inme .go .b / (accessed on 10 Augus 2021), K
is he u bidi y coe icien o he a mosphe e
(K
= 1.0, o a clea sky day), acco ding o Allen e al. [
47
] and Sil a e al. [
80
],
θ
is he sola
zeni h angle, and Wis he p ecipi able wa e (mm), es ima ed om Equa ion (7) [81].
W=0.14 ·ea·Pa+2.1 (7)
whe e e
a
is he ac ual a mosphe ic wa e apo p essu e (kPa), es ima ed om Equa ion (8).
ea=HR ·es
100 (8)
Remo e Sens. 2022,14, 1911 8 o 27
whe e HR is he ins an aneous ela i e humidi y (%), and e
s
is he wa e apo sa u a ion
p essu e (kPa), es ima ed om Equa ion (9).
es=0.6108 ·exp17.27 ·T0
237.3 +T0(9)
whe e T0is he ins an aneous ai empe a u e (◦C) a he momen o he sa elli e pass.
Fo ob aining
α oa
, a linea combina ion o he spec al e lec ance o he six e lec i e
OLI bands was pe o med acco ding o Equa ion (10) [80]:
α oa =0.300 2+0.277 3+0.233 4+0.143 5+0.036 6+0.001 7(10)
whe e
2
,
3,
4
,
5,
6
, and
7
a e he su ace spec al e lec ances o bands 2, 3, 4, 5, 6, and 7
o he Landsa -8 OLI, espec i ely.
We use Equa ion (11) o ob ain each o he spec al e lec ances.
b=Addb+Mul b·DN
cos(θ)·d (11)
whe e he e ms Add
b
and Mul
b
belong o he adiome ic escaling g oup, speci ically
e lec ance_add_band (equal o
−
0.1) and e lec ance_mul _band (equal o 0.00002), espec-
i ely, p esen ed in he me ada a o each OLI—Landsa -8 image, DN is he digi al numbe
alue co esponding o he pixel,
θ
is he sola zeni h angle a he da a acquisi ion ime,
and d is he Ea h–Sun dis ance in as onomical uni s.
2.5. De e mina ion o Su ace-ene gy Pa i ioning
Based on he su ace ene gy balance componen s, he e apo a i e ac ion was de-
e mined. Ini ially, he su ace adia ion balance o ne adia ion—R
n
was calcula ed,
which is dis ibu ed by he ene gy pa i ioning in on o he sensible hea luxes—H,
la en —LE, and soil hea lux—G[
47
–
50
,
61
,
78
,
79
]. By pe o ming his p ocess, a linea
ela ionship be ween he su ace and ai empe a u e g adien was conside ed o exis .
F om his ela ionship and he in e nal calib a ion p ocess o ex eme condi ions such as
empe a u e and humidi y, i was es ablished he need o ob aining he knowledge o
he so-called “ancho pixels”, i.e., ho and cold pixels, which a e indica i e o ze o and
maximum e apo anspi a ion, espec i ely [47,48,61] (Figu e 3).
The land su ace empe a u e (LST) in K (Kel in) was ob ained using he spec al
adiance in band 10 o he TIRS senso and he emissi i y in he nea es band—
εnb
by he
modi ied Planck’s Law [82], as desc ibed in Equa ion (12).
LST =K2
ln”
nb·K1
L10 +1(12)
whe e K
1
and K
2
a e adia ion cons an s speci ic o he Landsa -8 TIRS band 10, equaling
774.89 W m−2s −1µm−1and 1321.08 K, espec i ely, p o ided by NASA/USGS; and L10
is he adiance a he wa eleng h ecei ed by he senso s (band 10, he he mal band).
The
εnb
was calcula ed based on he LAI o each pixel acco ding o Equa ion (13) [
41
].
”
nb=0.97 +0.0033 ·LAI (13)
Ini ially, he empe a u e a ia ion and ae odynamic esis ance o hea anspo in
all pixels o he s udy a ea (Pe olina, Pe nambuco) we e de e mined. The a mosphe e was
ini ially assumed o be in a neu al s abili y condi ion. Fo his s udy, he ho pixel was
conside ed in he exposed soil plo s (i.e., no ege a ion co e and/o li le ege a ion and
low mois u e con en ), assuming LE equal o ze o. The cold pixel was conside ed in g ape
o cha d plo s i iga ed by mic o-sp inkle s, when Hcan be conside ed ze o [
47
,
48
,
61
,
78
,
79
,
83
]
(see Figu e 3). Since u bulen e ec s a ec a mosphe ic condi ions and ai esis ance, he
Remo e Sens. 2022,14, 1911 9 o 27
Monin–Obukho simila i y heo y was applied and conside ed in he compu a ion o Hin
all pixels o he s udy a ea. I is wo h no ing ha he Monin–Obukho leng h was used o
co ec ions o he ini ial s able condi ion o he a mosphe e [47–49,78,79].
Calcula ion o Ene gy Fluxes (Hand LE) and E apo a i e F ac ion
The sensible hea lux (H) in SEBAL is calcula ed using an i e a i e p ocedu e om
he ae odynamic unc ion (Equa ion (14)) [47–49,78,79].
H=ρai ·Cp·(a+b·LST)
ah
(14)
whe e
ρai
is he mois ai densi y (kg m
−3
), C
p
is he ai speci ic hea a cons an p es-
su e (1004 J kg
−1
K
−1
), aand ba e calib a ion cons an s o he empe a u e di e ence
be ween wo heigh s (i.e., be ween he oughness leng h o hea ans e and he e e ence
heigh , usually 0.1 and 2.0 m abo e he displacemen plane), and
ah
is he nea -su ace
ae odynamic esis ance o hea anspo (s m
−1
). Fundamen ally, he coe icien s aand
ba e de e mined h ough an in e nal calib a ion o each sa elli e image by in e ac i e
p ocesses. We conside ex eme pixels o we /cold and d y/ho spo s. They we e selec ed
o de elop a linea ela ionship be ween he ae odynamic empe a u e o he su ace and
he ai empe a u e di e ence, and he LST.
By knowing he componen s o he su ace ene gy balance, such as he ne adia ion
(R
n
,Wm
−2
), sensible hea lux (H,Wm
−2
), and soil hea lux (G,Wm
−2
), he la en hea
lux (LE,Wm
−2
) was de e mined, bo h co esponding o he ime o he sa elli e pass o e
he s udy a ea, acco ding o Equa ion (15).
LE =Rn−H−G(15)
Subsequen ly, we de e mined he e apo a i e ac ion (
Λ
) acco ding o Equa ion (16).
Λ=LE
Rn−G(16)
2.6. Es ima e o ETaUsing SEBAL Me hod
Finally, as a SEBAL p oduc , we de e mine he ac ual e apo anspi a ion (ET
a
,
mm day
−1
) [
84
] based on Equa ion (17) below, o each sa elli e image used. In his s udy,
he implemen ed SEBAL model had al eady been ex ensi ely alida ed and calib a ed
unde o es s and ag icul u al land condi ions [56,61,85].
ETa=Λ·Rn24 ·86, 400
˘(17)
whe e R
n24
is he daily ne adia ion (W m
−2
), 86,400 is a cons an o daily imescale
con e sion (i.e., con e s om seconds o days), and
λ
is he la en hea o apo iza ion
o wa e (J kg
−1
). Then, he la en hea o apo iza ion allows he ET
a
exp ession in mm
day
−1
. Hence, accu a e es ima ion o R
n24
(Equa ion (18)) was de e mined acco ding o
Bas iaanssen e al. [49], and Lee and Kim [86]:
Rn24=Λ·(1−αsup)·Rn−a·τsw(18)
whe e ais a eg ession coe icien o he ela ionship be ween ne longwa e adia ion
and a mosphe ic ansmissi i y on a daily scale, o which we assigned he alue 143,
as p oposed by Teixei a e al. [
61
]. The ac onyms and symbols used in his s udy a e
summa ized in he Abb e ia ions sec ion.
Remo e Sens. 2022,14, 1911 16 o 27
dis ibu ion o ain all accumula ion in he p e ious days. On he o he hand, du ing
he da es s udied he e, he highes mean LAI alues s ood ou only in a eas o a bo eal
Caa inga (0.58
±
0.45 m
2
m
−2
) and ag icul u e (0.75
±
0.49 m
2
m
−2
), wi h he maximum
alues being associa ed wi h i iga ed ag icul u al a eas, mo e speci ically o cha ds, wi h
inc eased biomass p oduc ion. Howe e , in a eas o pas u e, u ban in as uc u e, and a eas
o a bo eal and he baceous Caa inga, he LAI alues we e close o o equal o ze o, making
i s a ia ion mo e homogeneous, ha is, close o he daily mean, wi h s anda d de ia ion
(SD) anging be ween 0.05 and 0.10 m2m−2wi hin he land co e classes (Figu e 7).
3.4. Land Su ace Tempe a u e (LST) in he S udied Classes
In he p esen s udy, he minimum LST alues we e seen in a eas o wa e bodies,
while he maximum LST alues we e seen in a eas o exposed soils, loca ed a poin s
o pas u e and deg aded Caa inga, u ban in as uc u e (asphal , conc e e, and g a el
su aces), and ag icul u al a eas unde going soil p epa a ion o cul i a ion (Figu e 8).
This esul is expec ed in ba e soil loca ions unde in ense an h opic ac i i y due o he
ans o ma ion o land use/land co e classes in o non-e apo a ing su aces. This makes
he place’s empe a u e highe and educes wa e a ailabili y in he soil, which causes
se ious p oblems in ag icul u al c ops. The compu ed LST map is shown in Figu e 8.
Figu e 8.
Spa io empo al dis ibu ion o he land su ace empe a u e—LST (
◦
C) in he municipali y
o Pe olina, Pe nambuco, B azil, on he imaging da es 5 Oc obe 2013 (
a
), 12 No embe 2015 (
b
),
16 Oc obe 2017 (c) and 7 No embe 2019 (d).
Due o he eplacemen o p ima y ege a ion wi h pas u es, ag icul u al c ops, and
u ban occupa ion, changes in land use can subs an ially a ec he hea and mass exchange
in he soil–plan –a mosphe e sys em, p opi ia ing he e en ion o a highe amoun o hea
by he Ea h’s su ace [
2
,
52
]. Land abandonmen and excessi e mechanical dis u bance o
he soil may also al e he hea exchange wi h he en i onmen and cause lowe he mal
and adian ene gy lag; hus, he land con e sion had inc eased LST in he a ea o he
non-e apo a ing su aces.
I can be obse ed ha he a e age LST alues o he da es s udied we e highe in
he a eas domina ed by pas u e (47.69
±
1.47
◦
C), he baceous Caa inga (47.28
±
1.27
◦
C),
and sh ub Caa inga (46.07
±
1.44
◦
C), e en highe han hose obse ed in a eas wi h u ban

Remo e Sens. 2022,14, 1911 17 o 27
in as uc u e (45.80
±
1.52
◦
C) (Figu e 8). Acco ding o Zhao e al. [
110
,
111
], si es wi h
di e en land co e ypes may ha e an LST inc ease g adien along he u ban o u al
p o ile. The high LST in he pas u e, he baceous, and sh ub Caa inga a eas is ela ed o he
lowe pe cen age o g ound co e ing by ege a ion (see Figu e 6), which esul s in d ie
exposed soil, wi h highe albedos and lowe e apo a i e cooling lux a es, a ac o ha
inc eases LST. Ano he ela ed ac o con ibu ing o he high LST o pas u es is ha g asses
ha e shallowe oo s. The e o e, hey can only access he wa e a ailable in he supe icial
soil laye s, which deple es as e han in deepe laye s [
2
]. Vege a ion canopy can e ain
ainwa e and dec ease g oundwa e echa ge by al e ing e apo a i e lux and aising he
land su ace empe a u e.
On he o he hand, i is obse ed, in gene al, ha in a eas domina ed by a bo-
eal Caa inga and ag icul u e, he a e age LST alues a e lowe (38.99
±
2.48
◦
C and
43.11
±
2.30
◦
C, espec i ely) (Figu e 8). The highes ege a ion co e and he highes
soil humidi y in he a eas domina ed by hese classes a o LST educ ion. Howe e , hey
p esen he g ea es spa ial a ia ions o LST among all land use and land co e classes,
acco ding o he s anda d de ia ion alues (
±
SD). The main ad an age o using LST da a
om sa elli e images is he o al su ace co e age. In his way, each ime se ies o pixels o
he LST map can be conside ed a “ i ual wea he s a ion” [112].
3.5. Va ia ions o he Ac ual E apo anspi a ion (ETa) o Land Use Classes
Rain all egime di ec ly in luenced ET
a
, so ha on 16 Oc obe 2017 (Figu e 9c), he
da e wi h he lowes ain all accumula ion in he p e ious days, he lowes ET
a
a es we e
ound, wi h an a e age alue o 2.02 mm day
−1
. On he o he hand, on 7 No embe 2019
(Figu e 9d), he pe iod wi h he highes ain all accumula ion, a e age ET
a
a es we e
2.62 mm day
−1
(Figu e 9). Acco ding o Teixei a e al. [
20
], high e apo anspi a ion alues
in Caa inga a eas occu igh a e ains. The e o e, he p e ious ain all aises soil wa e
a ailabili y and keeps na i e species wi h u gid s uc u es and g eene canopy.
Figu e 9.
Spa io empo al dis ibu ion o ac ual e apo anspi a ion (ET
a
, mm day
−1
), calcula ed wi h
Su ace Ene gy Balance Algo i hm o Land (SEBAL), in he municipali y o Pe olina, Pe nambuco,
B azil, on he imaging da es 5 Oc obe 2013 (
a
), 12 No embe 2015 (
b
), 16 Oc obe 2017 (
c
), and
7 No embe 2019 (d).
Remo e Sens. 2022,14, 1911 18 o 27
The ET
a
es ima ed by he SEBAL model showed a ia ion bo h wi hin and be ween
land use and land co e classes. The lowes ET
a
obse a ions in all he e alua ed da es we e
obse ed in he a eas occupied by pas u e and mosaic o ag icul u e and pas u e classes,
wi h a e age alues o 0.70
±
0.73 mm day
−1
and 1.01
±
0.96 mm day
−1
, espec i ely
(Figu e 9). These a eas ha e d yland cul i a ion p ac ices, which causes he lowe wa e
a ailabili y o a ec he e apo anspi a ion a es; mo eo e , he he e ogenei y o he a eas
causes sudden a ia ions in ETa.
On he o he hand, due o he e ec o he inc ease in ai empe a u e and a mosphe ic
demand e i ied h oughou he d y season in Pe olina, combined wi h he p esence o
p ese ed ipa ian o es s along s e ches o wa e bodies and con inuous i iga ion in c ops,
highe mean ET
a
alues we e obse ed in he a bo eal Caa inga (4.73
±
0.49 mm day
−1
)
and ag icul u e (3.07
±
1.23 mm day
−1
) classes. Fo p esen ing a eas wi h i iga ed and d y
cul i a ion, he a e age alues o his class become mo e a iable. When he e is a g ea e
con ibu ion o mois u e added o mo e dense ege a ion, he e is a a o ing o he local
mic oclima e in he egion [113,114], a phenomenon epo ed in a eas o a bo eal ege a ion.
The sh ub and he baceous Caa inga classes showed g ea e he e ogenei y indica ed
by he la ges s anda d de ia ions (2.42
±
0.76 mm day
−1
) and (1.46
±
0.71 mm day
−1
),
espec i ely, ela i e o he a bo eal Caa inga class (Figu e 9). Folhes e al. [
115
] epo ed
ha he e apo anspi a ion alues (2.0 mm day
−1
) du ing he d y season o species o
he baceous–sh ubby Caa inga. In d y pe iods, he Caa inga ege a ion uses he a ailable
ene gy as sensible hea lux (H), limi ing anspi a ion and pho osyn hesis, hus educing
e apo anspi a ion alues [
20
]. Howe e , a bo eal ege a ion is able o compensa e he
high apo p essu e de ici in he ai , e en in d y pe iods, when compa ed o sh ub and
he baceous ege a ion, due o he deep oo sys em keeping up wi h he wa e s o ed in
he soil [70,116,117].
The a bo eal and sh ub species play a undamen al eco-hyd ological ole, main aining
soil humidi y and s uc u ing i s po osi y, gua an eeing he main enance o in il a ion
capaci y and a o ing he su i al o species [116].
3.6. S a is ical Rela ions be ween he Va iables S udied and Land Use
In his s udy, we pe o med a PCA o he en i onmen al a iables in ela ion o land
use and land co e classes. The e o e, he i s wo componen s wi h eigen alues g ea e
han 1.0 we e ex ac ed sepa a ely o he yea s 2013, 2015, 2017, and 2019 (Figu e 10). In
2013, he wo p incipal componen s explained 94.77% o he o al a ia ion, wi h 70.39%
in he p incipal componen 1 (PC1) and 24.38% in he p incipal componen 2 (PC2). On
he o he hand, in 2015, 2017, and 2019, when added oge he , PC1 and PC2 ep esen ed
94.01, 92.34, and 94.56% o he o al a ia ion, espec i ely (Figu e 10). In addi ion, i can
be seen ha he LULC class, wi h he leas in luence on componen s 1 and 2 in all yea s,
is sh ub Caa inga, wi h a e age eigen alues (0.31 and 0.37, espec i ely). In addi ion, he
classes wi h he g ea es in luence on PC1 wi h posi i e and nega i e eigen alues a e wa e
bodies (2.69), ag icul u e (
−
2.35), a bo eal Caa inga (
−
1.92), pas u e (0.34), mosaic (0.16),
and u ban a ea (0.35). In PC2, he classes o LULC we e wa e bodies (
−
4.36), mosaic (2.10),
ag icul u e (−1.0), and a bo eal Caa inga (−3.40) (Figu e 10).
Remo e Sens. 2022,14, 1911 19 o 27
Figu e 10.
Sco es ob ained by p incipal componen analysis (PCA) o en i onmen al a iables and
land use and land co e . PC1 and PC2 a e he i s and second dimensions o PCA da a, espec i ely.
The ou inse ed panels below he PCA sco es plo s e e o he loadings plo s o he i s wo
p incipal componen s om 2013 o 2019.
Th ough PCA, we obse ed ha he o de ing o a iables in each p incipal componen
(PC) o he axes was in luenced by he deg ee o ege a ion co e and su ace wa e s a us
o he LULC classes (Figu e 10). Thus, PC1 con ibu ed mo e o he a iabili y o he
esponse o a iables ela ed o ene gy balance. The e o e, in PC2, he land use and land
co e classes (i.e., pas u e, mosaic, u ban a ea, and he baceous Caa inga) in luenced he
a iables R
n
,LE, ET
a
, and emissi i y wi h highe mean loadings (
−
0.95,
−
0.94,
−
0.99, and
−
0.80, espec i ely). On he o he hand, hey showed a high co ela ion wi h he a iables
H, LST, and albedo. Fo hese LULC classes, his may be ela ed o he p esence o ba e soil
and hin ege a ion co e s ha a ec he egional mic oclima e and soil–plan –a mosphe e
sys em luxes, esul ing in highe albedos, lowe a es o e apo a i e cooling luxes, and
highes LST [2,105].
On he o he hand, PC2 con ibu ed mo e o he a iabili y o he esponses o he
a iables ega ding he canopy in e ac ions (LAI and V
C
) due o he s ong nega i e
co ela ion wi h cul i a ed land (i.e., ag icul u e class) (Figu e 10). In all yea s, he e was
a p edominance o LE and LAI in a eas wi h a bo eal Caa inga and ag icul u e, wi h
ag icul u e p esen ing he highes V
C
. Caa inga p esen s a s ong ela ionship wi h LAI
in ainy pe iods due o he g ea e a ailabili y o wa e in he soil [
118
]. In he p esen
s udy, he samples we e aken in he pe iod wi h low ain all, so he LAI was no exp essi e
compa ed o ag icul u al a eas, which use i iga ion and he e o e inc ease he LAI. Thus,
Remo e Sens. 2022,14, 1911 20 o 27
he emissi i y in he Caa inga ege a ion is lowe , o he emissi i y o he soil is gene ally
lowe han ha o he lea es [2].
The esul s o he analysis o a iance (ANOVA) and mul iple eg ession analysis o
he es ablished model a e p esen ed in Table 3. Fo he combina ion o wo- a iable models,
he wo bes p e-es ablished pa ame e s based on he PCA esul s we e he a iables
LST and H, used o build he eg ession model. In pa icula , hese wo a iables a e
o g ea ele ance in ans e ing ene gy o he a mosphe e. The ANOVA esul s also
showed ha he model alues a e signi ican . Due o he obse a ions, a join analysis o he
coe icien o de e mina ion ob ained (R
2
= 0.98) can be pe o med, emphasizing he P- alue
ob ained om ou eg ession model, which was less han 0.001, hus indica ing g ea e
model accu acy and eliabili y (Table 3). No ably, i can be seen ha he model’s F- alue
was 16,692.84, being g ea e han he c i ical alue o F
0.05
= 3.018, which con i ms he
signi icance o he p oposed model. In addi ion, he a iables used p o ided a high R
2
and
LCCC, being essen ial o he model’s accu acy. Fu he mo e, he esul s showed ha he
mul iple linea eg ession model o de e mine ET
a
achie ed a coe icien o de e mina ion
o 0.98, RMSE o 0.498, MAE o 0.413, and dequal o 0.9620. I also esul ed in PBIAS, NSE,
and LCCC alues a e aged be ween
−
13.32%, 0.826, and 0.907, espec i ely (Table 3). F om
he s a is ical analysis, his model is desc ibed as ET
a
= 6.89
−
0.0527LST
−
0.0120H. Based
on he RMSE, LCCC, and do his applica ion, he use o he ET
a
model, besides p esen ing
a s ong co ela ion be ween he a iables Hand LST, as seen in Figu e 10, exp esses a
biophysical model wi h enough e iciency and high ag eemen o de e mine ETa.
Table 3. Analysis o a iance (ANOVA) and eg ession coe icien s esul s o he sugges ed model.
Sou ce o Va ia ion d SS MS F-Value p-Value
Reg ession 2 464.41 232.21 16,692.84 0.0001
LST 1 335.29 335.29 24,103.8 0.0001
H1 129.12 129.12 9281.9 0.0001
E o 397 5.52 0.01
To al 399 469.93
Reg ession s a is ics
P edic o s in model Reg ession coe icien s
β0β1β2R2
LST, H6.89 −0.0527 −0.0120 0.98
Model
S a is ical me ics
RMSE MAE PBIAS
(%) NSE LCCC d
0.498 0.413 −13.32 0.826 0.907 0.9620
d : deg ees o eedom, SS: sum o squa es, MS: mean squa e, LST: land su ace empe a u e, H: sensible hea lux,
β0
: in e cep ,
β1
and
β2
: es ima ed coe icien o he ac o x, R
2
: coe icien o de e mina ion, RMSE: oo mean
squa e e o , MAE: mean absolu e e o , PBIAS: pe cen bias, NSE: Nash–Su cli e e iciency coe icien , LCCC:
Lin’s conco dance co ela ion coe icien , and d: Willmo ’s index o ag eemen . Based on F- es , a a p obabili y o
0.05 (p< 0.05), signi icance o equa ion pa ame e s o each esponse a iable was de e mined.
The high d(0.9620) alues o ET
a
indica ed ha he e was a good ag eemen be ween
simula ed and measu ed ET
a
. In gene al, he ET
a
model showed excellen ag eemen (LCCC
0.9), high pe o mance, and low RMSE (0.498) (Table 3). The PBIAS and MAE alues o
ET
a
we e be ween
−
13.32% and 0.413, con i ming he close ag eemen . Consequen ly, his
esul desc ibes he abili y o simpli y and accu a ely p edic ET
a
in de ici en i onmen s.
Applica ions o eg ession model analysis wi h en i onmen al a iables a e common in he
li e a u e in d y o es s [
118
–
120
]. Howe e , hese implemen a ions o a iables in p e ious
s udies may be challenging o acqui e o speci ic loca ions, equi ing mo e simpli ied
models. Ou esul s also indica e he ela ionship be ween Hand he LST o ecosys ems o
de e mine ETa, an impo an a iable in he ene gy balances o en i onmen s wo ldwide.
Remo e Sens. 2022,14, 1911 21 o 27
4. Conclusions
In his s udy, we poin ou a signi ican endency o inc ease he ag icul u al a eas,
which esul s in he p og essi e dec ease o he B azilian Caa inga biome. The ege a ion
co e is di ec ly in luenced by he soil–wa e egime; yea s o highe ain all esul in
a lowe pe cen age o supp ession o he na i e o es in he municipali y o Pe olina,
Pe nambuco (B azil). The a eas wi h pas u e class p esen ed ho spo s due o deg ada i e
p ocesses and highe su ace empe a u es, in luenced by he sensible hea lux. A g adual
inc ease in LST is obse ed in he municipali y and i may cause u u e isks o o es a eas.
The SEBAL algo i hm used in a semi-a id en i onmen is a help ul ool o de e mine
he ene gy and mass luxes in di e en ecosys ems. No ably, he Caa inga biome has
pa icula i ies in biophysical pa ame e s, acco ding o he land co e and soil exposu e
on in a and in e -annual scales. The he e ogenei y o he su ace o he municipali y o
Pe olina, as a unc ion o land use and land co e pa e ns, al e s he ene gy exchange
wi h he a mosphe e. Ou esul s also sugges a simpli ied and alida ed model o ET
a
de e mina ion in a semi-a id en i onmen . The eg ession model could accu a ely p edic
he spa ial dis ibu ion o ETa, wi h high R2and LCCC and low RMSE alue.
Thus, i is possible o sugges ha he implemen a ion o ag icul u al ac i i ies in
he Pe olina should be ca ied ou in a planned and sus ainable way in o de o mi iga e
he impac s ha an h opic ac ion causes on he Caa inga, especially wi h he inc eased
ulne abili y o his biome o he dese i ica ion p ocess. Howe e , u he esea ch is
needed o in es iga e he spa ial a ia ions o he ypes o c ops co e ing he soil in he
municipali y, as well as he dynamics o i es and hei impac s on he di e si y o he
Caa inga biome. Field su eys and he use o unmanned ae ial sys ems (UAS) could
p o ide mo e de ailed in o ma ion a an in e media e and ine scale.
Supplemen a y Ma e ials:
The ollowing a e a ailable online a h ps://www.mdpi.com/a icle/
10.3390/ s14081911/s1, Figu e S1: Land use/land co e changes in Pe olina be ween 2013 and 2019.
Posi i e alues indica e an expansion o he espec i e land co e , nega i e alues a con ac ion.
The e ical axe is in million hec a es (Mha). Please see Supplemen a y Table S1 o access alues o
land use/land co e changes; Table S1: A eas o expansion and con ac ion o land use/land co e
changes in Pe olina be ween 2013 and 2019. Posi i e alues indica e an expansion o he espec i e
land co e , nega i e alues a con ac ion. The alues a e in million hec a es (Mha).
Au ho Con ibu ions:
Concep ualiza ion, A.M.d.R.F.J. and G.d.N.A.J.; me hodology, M.V.d.S.,
A.d.S., and A.M.d.R.F.J.; so wa e, M.V.d.S., A.d.S., and A.M.d.R.F.J.; alida ion, J.F.d.O.-J. and
A.H.d.C.T.; in es iga ion, J.L.B.d.S., H.P., P.E.T., L.S.B.d.S., and C.A.d.S.J.; da a cu a ion, M.V.d.S.,
A.d.S., A.M.d.R.F.J., and G.d.N.A.J.; w i ing—o iginal d a p epa a ion, A.M.d.R.F.J.; w i ing—
e iew and edi ing, T.G.F.d.S., J.L.M.P.d.L., and E.A.S.; isualiza ion, J.L.M.P.d.L. and A.H.d.C.T.;
supe ision, A.H.d.C.T., T.G.F.d.S., and J.F.d.O.-J.; p ojec adminis a ion, T.G.F.d.S. and J.L.M.P.d.L.;
unding acquisi ion, J.L.M.P.d.L. All au ho s ha e ead and ag eed o he published e sion o
he manusc ip .
Funding:
This esea ch was unded by he Po uguese Founda ion o Science and Technology (FCT),
h ough p ojec s ASHMOB (CENTRO-01-0145-FEDER-029351), GOLis (PDR2020-101-030913, Pa -
ne ship n . 344/Ini ia i e n . 21), MUSSELFLOW (PTDC/BIA-EVL/29199/2017), MEDWATERICE
(PRIMA/0006/2018), and h ough he s a egic p ojec UIDB/04292/2020 g an ed o MARE—Ma ine
and En i onmen al Sciences Cen e, Uni e si y o Coimb a, Coimb a, Po ugal.
Ins i u ional Re iew Boa d S a emen : No applicable.
In o med Consen S a emen : No applicable.
Da a A ailabili y S a emen :
Landsa -8 image cou esy o he USGS/NASA (h ps://www.usgs.
go /co e-science-sys ems/nli/landsa , accessed on 10 Augus 2021); MapBiomas da a p esen ed
in his s udy a e a ailable a websi es o B azilian Annual Land Use and Land Co e Mapping
P ojec (h ps://mapbiomas.o g/en/p ojec , accessed on 20 Augus 2021); and he me eo ological
da a p esen ed in his s udy a e a ailable a he websi e o he Na ional Ins i u e o Me eo ology
(h ps://po al.inme .go .b /, accessed on 10 Augus 2021).

Remo e Sens. 2022,14, 1911 22 o 27
Acknowledgmen s:
The au ho s would like o hank he Resea ch Suppo Founda ion o he Pe -
nambuco S a e (FACEPE, B azil—APQ-0215-5.01/10 and FACEPE - APQ-1159-1.07/14), he Na ional
Council o Scien i ic and Technological De elopmen (CNPq, B azil) and also unds h ough he
ellowship o he Resea ch P oduc i i y P og am (CNPq 305286/2015-3, 304060/2016-0, 309681/2019-
7, and 303767/2020-0), and he Coo dina ion o he Imp o emen o Highe Educa ion Pe sonnel
(CAPES, B azil - Finance Code 001) o he esea ch and s udy g an s. The au ho s a e also g a e ul
o inancial suppo om he Po uguese Founda ion o Science and Technology (FCT), and he
Uni e si y o Coimb a, Po ugal. In addi ion, we also would like o hank he anonymous e iewe s
o hei insigh ul commen s, o which signi ican ly inc eased he alue o his s udy.
Con lic s o In e es : The au ho s decla e no con lic o in e es .
Abb e ia ions
Summa y o all he symbols and ac onyms used in his pape .
I em Desc ip ion
aand bA e he calib a ion coe icien s
CpSpeci ic hea o ai
dWillmo ’s index o ag eemen
DEM Digi al ele a ion model
dT Nea -su ace ai empe a u e g adien
eaAc ual a mosphe ic wa e apo p essu e
esWa e apo sa u a ion p essu e
ETaAc ual e apo anspi a ion
GSoil hea lux
GEE Google Ea h Engine
HSensible hea lux
HR Ins an aneous ela i e humidi y
k on Ka man cons an
LAI Lea a ea index
LCCC Lin’s conco dance co ela ion coe icien
LE La en hea lux
LSE Land su ace emissi i y
LST Land su ace empe a u e
LULC Land use and land co e
MAE Mean absolu e e o
NDVI No malized Di e ence Vege a ion Index
NSE Nash-Su cli e e iciency coe icien
PBIAS Pe cen bias
PCA P incipal componen analysis
ah Nea -su ace ae odynamic esis ance o hea anspo
RMSE Roo mean squa e e o
RnNe adia ion
Rn24 Daily ne adia ion
R2Coe icien o de e mina ion
SAVI Soil-Adjus ed Vege a ion Index
T0Ins an aneous ai empe a u e
TaAi empe a u e
u* F ic ion eloci y
u200 Wind speed a he heigh o 200 m
VCVege a ion co e
WP ecipi able wa e
Remo e Sens. 2022,14, 1911 23 o 27
I em Desc ip ion
z1and z2A e he wo heigh s be ween he su ace o he ancho pixels
zom Momen um oughness leng h
αsup Su ace albedo
εaA mosphe ic emissi i y
ΛE apo a i e ac ion
λLa en hea o apo iza ion o wa e
ρai Ai densi y
ψmand ψhS abili y co ec ion ac o s o momen um and sensible hea , espec i ely
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