Application of remote sensing imagery and algorithms in Google earth engine platform for drought assessment
Journal of Mining and Earth Sciences Vol. 62, Issue 3 (2020) 53 - 67
53
Application of Remote Sensing Imagery and
Algorithms in Google Earth Engine platform for
Drought Assessment
Hoa Thanh Thi Pham *, Ha Thanh Tran
Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Vietnam
ARTICLE INFO
ABSTRACT
Article history:
In Vietnam, drought is one of the natural disasters caused by high
temperatures and lack of precipitation, especially with El Nino and the
global warming phenomenon. It affects directly environmental,
economical, social issues, and the lives of humans. Many methods have
been used to assess drought, in which remote sensing indices are
considered the most commonly used tool today. They are used to analyze
spatio-temporal distribution of drought conditions and identify drought
severity. Especially with the launch of Google Earth Engine (GEE) - a
cloud-based platform for geospatial analysis, it is easy to access high-
performancecomputingresources for processingmulti-temporal satellite
data online. With the GEE platform, we focus on writing and running
scripts with the indicators suitable for evaluating drought phenomenon,
instead of calculating on software and downloading remote sensing
imagery with large size. In this study, we collected 26 Landsat 8 images in
the dry season in 2019 (from April to July) in Tay Hoa district, Phu Yen –
a region in the South Central Coast of Vietnam where agricultural
drought occurs frequently. We assessed the distribution of drought
conditions byusingadrought index (VHI index –Vegetation HealthIndex)
produced from Landsat satellite data in the GEE platform. The study
results indicated that the drought (from mild to severe) concentrated in
the North of the region, corresponding to high surface temperature and
NDVI low or NDVI moderate values. VHI maps were visually compared
with the drought map of the South Central Coast and the Central
Highlands. In general, the results also reflect the the method’s reliability
and can be used to support the managers to plan policies, making long-
term plans to cope with climate change in the future at Tay Hoa in
particular and other regions in general.
Received 16th Jan. 2021
Accepted 24th May 2021
Available online 30th Jun. 2021
Keywords:
Drought,
Google Earth Engine,
Remote sensing,
Tay Hoa,
VHI.
Copyright © 2021 Hanoi University of Mining and Geology. All rights reserved.
_____________________
*Corresponding author
E-mail: phamthithanhhoa@humg.edu.vn
DOI: 10.46326/JMES.2021.62(3).07
54
Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
is used to measure changes in NDVI and TCI
1. Introduction
In recent times, climate change are the main
determined the difference of LST over time.
Globally, many studies were conducted for the
assessment of drought intensity by application
this index with Landsat imagery (Masitoh et al.,
was applied in the research of (Nguyen Viet Lanh
that the availability of remote sensing data with
wide space coverage has enabled scientists to
study drought phenomenon around the globe.
Especially, thanks to the launch of Google
Earth Engine (GEE) - a cloud-based platform for
geospatial analysis, it is easy to access high-
performance computing resources for processing
multi-temporal satellite data online (Gorelick et
al., 2017). Since its appearance in 2010, GEE
abilities have been utilized for many applications
(Mutanga et al., 2019), including vegetation
mapping and monitoring, land cover/ land cover
Sunar et al., 2019). Besides, GEE with a large
amount of freely available satellite imagery and
direct image processing has been considered a
potential application in drought studies (Aksoy et
Space and temporal analysis have been flexibly
done on this platform. The availability of global
soil moisture data of the GEE data catalog and
web-based tools were used in the study (Sazib et
al., 2018) to enable users to assess the impact of
drought quickly and easily. Meanwhile, Aksoy et
al., (2019) analyzed the temporal distribution of
drought conditions in Turkey within 20 years
using different drought indices, such as
Vegetation Health Index (VHI), Normalized
Multiband Drought Index (NMDI), and
Normalized Difference Drought Index (NDDI).
These indices are produced from MODIS satellite
data in the GEE platform. Similar to (Aksoy et al.,
2019), algorithms on GEE were chosen to
calculate indices: Vegetation Condition Index
(VCI), Precipitation Condition Index (PCI), Soil
Moisture Condition Index (SMCI), and
Temperature Condition Index (TCI) (Khan et al.,
indices provide helpful spatial information for
assessing drought conditions from the regional
level to the country level. Significantly, they
reasons which caused global warming, the lack of
rainfall, making the drought more serious. This
phenomenon greatly impacts agriculture such as
reducing crop productivity, reducing cultivated
areas and crop yields, mainly food crops.
Therefore, identifying of drought extent is
considered an important program to assess the
drought occurrence and its severity to agriculture
development in Vietnam.
Although drought types occur at different
timescales as usual,it is detected in thedry season
with precipitation shortages, high temperatures
(Wilhite, 2000). Besides, it often happens in large
areas. Therefore, many scientists worldwide have
recognized the potential of using indices observed
from remote sensing data to monitor drought
effectively. The main reason was given as remote
sensing technology provides a synoptic view of
the Earth’s surface. The advantage of technology
is that image data is delivered continuously over
time and whole the globe, so the details of the
results are shown legibly with different regions,
more efficient than the measurement with the
monitoring point. The use of remote sensing data
to establish drought maps will provide an
overview of the space of drought areas for the
regions where there are no or few meteorological
stations and there is a variety of free satellite
imagery suitable for evaluating drought
conditions, such as MODIS and LANDSAT.
Among drought indices derived from remote
sensing data, the Normalized Difference
Vegetation Index (NDVI) combined with Land
Surface Temperature (LST) provides a strong
correlation. It gives valuable information to
Based on NDVI and LST relationship, many
drought indices were introduced, such as
Temperature – Vegetation Dryness Index (TVDI),
Vegetation Health Index (VHI), Water Supplying
Vegetation Index (WSVI), and tested successfully
a greater capability and better suitability in
combines twoindices:Vegetation Condition Index
(VCI) and Temperature Condition Index(TCI).VCI
Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
55
demonstrated that the tools on GEE allow easy
analysis and visualization. These tools help
explore spatial and temporal variations in
information and drought conditions for any
location in theworld withprocessing or managing
data toa minimum,instead of working withimage
processing software on laptop or computer which
are often time-consuming and labor-intensive.
In Vietnam, the research of GEE is still
relatively new. The applications have focused on
forest land monitoring (Nguyen Trong Nhan et al.,
2018). However, few studies evaluate drought
using mediumresolution imagerysuch as Landsat
in GEE in Vietnam. Therefore, in this study,
satellite-based drought indices of NDVI, LST, VCI,
TCI, VHI are calculated in the GEE using
algorithms and Landsat 8 in the local level to
assess drought conditions in the dry season in
2019. The results of the research may provide the
initial information about drought hazards for
authorities and regional planners.
2. Materials
2.1. Study Area
The study area is Tay Hoa – a rural district of
Phu Yen Province in the South Central Coastal
region of Vietnam.
It is extended from 12045’07” to 12045’15” N
latitude, 109015’13” to 109015’29” E longitude
(Figure 1). There are main types of terrain,
including mountains and plain. The hilly regions
are in the South, stretching from the West to the
East, accounting for over 50% of the natural area.
The West area is a red basalt land with an average
elevation of 30÷40 m, suitable for developing
short and long-term industrial crops. The plain is
located to the North and the East, in which the
East area is alluvial land, a large rice-growing
plain of Phu Yen Province.
Like some other localities in the region, Tay
Hoa has a tropical monsoon climate, hot and
humid, and is influenced by ocean climate. There
are two distinct seasons: the rainy season from
September to December and the dry season from
Figure 1. Location of the study area.
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Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
January to August (Department of Natural
2019. Tier 1 data (T1) have the highest
radiometric and positional quality and are
recommended for all time-series analysis (by
USGS). TOA data were converted from raw digital
numbers values using the calibration coefficients
The SR data were generated using the Land
Surface Reflectance Code (LaSRC) algorithm
data, the Red and Near-infrared (NIR) bands from
the SR data were chosen for spatial processing
analysis to compute LST and NDVI. The Landsat 8
image series was shown in section 4.
For the past few years, the drought situation
in Tay Hoa has been complicated. Significantly,
the dry season in 2019 had the most severe
recorded drought. The prolonged severe drought
and sweltering weather have dried up hundreds
of hectares of crops and forests. Because of the hot
weather and strong southwest wind, hundreds of
hectares of eucalyptus forest were destroyed.
Many communes could not practice agriculture
due to water scarcity, and many households lack
2.3. Google Earth Engine
Google Earth Engine is available via a web-
based JavaScript Application Program Interface
(API) called the Code Editor.
2.2. Data resources
Earth Engine provides an enormous amount
of data from satellites hosted by Google. Each data
source available on GEE has Image Collection and
ID (The data in GEE can be looked up at GEE
The center panel provides a JavaScript code
editor. The map in the bottom panel contains
the layers added by the script. The left panel
contains code examples, your saved scripts in
Scripts tab. The Docs tab of the Code Editor lists
the methods of each API class. The Asset
Manager is in the Assets tab in the left panel, is
used to upload and manage your image assets in
Earth Engine. Code Editor scripts can be shared
catalog
via
website
which, Landsat 8 imagery was added recently
when its satellitewas launched in 2013,witha 16-
day repeat cycle and resolution of imagery from
15 meters (Panchromatic) to 100 meters
(Thermal Infrared), the average one is 30 meter
with multispectral data. All Landsat 8 data are
directly available to GEE, including Tier 1, Tier 2,
raw scenes, top-of-atmosphere (TOA), and
surface reflectance (SR) data. All thermal bands
have been resampled to 30 m spatial resolution.
Table 1 describes the Landsat data in this
study. All Landsat 8 images which were covered
entirely the district, were retrieved from the
Image Collection in the GEE from April to July in
via
an
encoded
URL.
There are several ways to run operations in
the API: Calling methods attached to objects,
Calling algorithms, Calling Code Editor specific
functions, and Defining new roles. The Google
Earth Engine API provides a library of functions
that may be applied to data for display and
analysis.
Table 1. List of products in the GEE catalog used in the study.
ID
Description
Used
Bands
Spatial
Resolution range
100m, From
resampled April
Date
LANDSAT/LC08/C01/T1_TOA
Landsat 8, Collection 1,
Tier1, TOA (top-of-
TIR
atmosphere reflectance)
to 30 m.
to
July
2019
LANDSAT/LC08/C01/T1_SR
Landsat 8, Collection 1,
Tier1, SR (surface
reflectance)
NIR, Red
30 m
Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
57
Figure 2. Diagram of components of the Earth Engine Code Editor at
code.earthengine.google.com. (Source: https://developers.google.com/earth-engine).
2. Filter images by date range and the region
of interest: using filterDate() and filterBounds().
3. Remove the cloud from the TOA and SR
images using a module cloud mask with QA band.
4. Clip images according to the boundary of the
study area: using the clip(geometry).
3. Methodology
With the GEE platform, we used the
algorithms/ functions to writeand execute scripts
for indices as mention before in section 1:
Normalized Difference Vegetation Index (NDVI),
Land Surface Temperature (LST), Vegetation
Condition Index (VCI), Temperature Condition
Index (TCI), and Vegetation Health Index (VHI).
The red and the near-infrared bands
(respectively, bands 4 and 5) of Landsat 8 are
used to construct NDVI while the thermal band
calculates LST. From these indices, three other
indices as VCI, TCI, and VHI, were derived. All
math formulas were presented in sections 3.2 and
3.3.
5. NDVI was calculated with the existing
image
processing
function
in
GEE:
normalizedDifference(bandNames).
6. LST, VCI, TCI, and VHI were computed by
creating expression() with operators as Add,
Subtract, Multiply, Divide.
3.2. Formulas for calculating NDVI and LST
indices
- NDVI quantifies vegetation by measuring
the difference between near-infrared (which
vegetation strongly reflects) and red light (which
vegetation absorbs). The range of NDVI is −1 to
+1. The higher value of NDVI refers to healthy and
dense vegetation. Lower NDVI values show
sparse vegetation. The NDVI is calculated as
3.1. The image processing and analysis in GEE
for drought assessment
Figure 3 illustrates the processing chain for
generating the VHI index for drought assessment.
Our processing workflow consists of some steps
using coding by the JavaScript (JS) API:
1. Loading input data
- Load the collections of Landsat 8 TOA and
SR: using function ee.Image();
푁퐼푅 − 푅퐸퐷
(1)
푁퐷푉퐼 =
푁퐼푅 + 푅퐸퐷
Where:
- Load the study area with shapefile format:
using Table Upload in the Assets tab.
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Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
RED and NIR stand for the spectral
representing optimal or above-normal conditions
(Kogan, 1995). Meanwhile, Temperature
Condition Index (TCI) was created because
surface temperature is higher in dry years and
derived from the change of surface temperature
in a specific time series. TCI determines the stress
on vegetation caused by temperatures and shows
different vegetation responses.
reflectance measurements acquired in the red
(visible) and near-infrared regions, respectively.
- LST (Land Surface Temperature) estimation
푇퐵
퐿푆푇 =
.푇
(2)
퐵
(
)
∗ 푙푛 퐿푆퐸
1 +
The Vegetation Health Index (VHI) was
estimated using VCIand TCIforall observed times
Land SurfaceTemperature(LST) was derived
from the Top of Atmosphere Brightness
Temperature (TB) for the Landsat’s thermal
infrared (TIR) channels whichareprovided bythe
United States Geological Survey (USGS) and are
fully available and ready to use in GEE for Landsat
8, collection 1.
Besides, The LST retrieval algorithm used
here requires prescribed values of Land Surface
Emissivity (LSE). Values of LSE were calculated
based on the proportion of vegetation Pv. The
following formula is used:
푁퐷푉퐼 − 푁퐷푉퐼푚푖푛
푉퐶퐼 = 100 ×
(5)
푁퐷푉퐼푚푎푥 − 푁퐷푉퐼푚푖푛
퐿푆푇푚푎푥 − 퐿푆푇
푇퐶퐼 = 100 ×
(6)
(7)
퐿푆푇푚푎푥 − 퐿푆푇푚푖푛
푉퐻퐼 = 푎 × 푉퐶퐼 + (1 − 푎) × 푇퐶퐼
Where:
NDVI and LST - NDVI and LST values of each
month in the dry season in 2019;
퐿푆퐸 = 0.004푃푉 + 0.986
(3)
NDVI max and NDVI min - the maximum and
minimum value of NDVI;
LST max and LST min - the maximum and
minimum value of LST.
A and (1-a) are coefficients showing the
difference in weighting between VCI and TCI in
total vegetation health. Thevalueof “a”depends on
different conditions of environment and climate. In
unknown environmental conditions, “a” is selected
as 0.5 correspondings to the average condition,
assumingan equal contribution of bothvariablesto
were divided into 5 classes as, Table 2 (Kogan,
Whereas, Pv combined with NDVI are often
used as parameters to assess the emissivity while
lacking actual ground emissivity data. Pv is
2
푁퐷푉퐼 − 푁퐷푉퐼푚푖푛
(4)
푃푉 = (
)
푁퐷푉퐼푚푎푥 − 푁퐷푉퐼푚푖푛
In equation (2), ρ = 14380, ρ = h*c/s with h is
Plank’s constant (6,626*10-34 Js), s is Boltzmann’s
constant (1,38*10-23 J/K); c is velocity of light
(3*108 m/s).
3.3. VCI, TCI and VHI calculation
Vegetation Condition Index (VCI) is a derived
index from NDVI values. The VCI is expressed in
% from 0 to 100, with low values representing
stressed vegetation conditions, middle values
representing fair conditions, and high values
4. Results and discussion
Using GEE, we were able to produce data
quickly. From April to July 2019, 13 Landsat 8
No
1
VHI value
<10
Drought level
Extreme drought
Severe drought
Moderate drought
Mild drought
2
10÷20
20÷30
30÷40
3
4
Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
59
TOA images and 13 Landsat 8 SR images were
collected by coding. Figure 4 shows the Code
Editor scripts to extract drought indices from
satellite images. On the other hand, the VHI image
was also displayed directly in the Code Editor
interface (in the Layer section), the values (NDVI
min and max, LST min and max), chart of LST-
NDVI correlation presented in the Console
section. Final output tiff files (NDVI, LST, VCI, TCI,
VHI images) were in Tasks section and exported
to google drive.
4.1. NDVI, LST and LST-NDVI correlation
Using the LST–NDVI scatterplot in GEE, a
linear regression model was constructed to
determine the relationship between LST and
NDVI in the dry season. Correlation analysis has
been done to determine the relationship between
LST and NDVI, shown in Figure 5: the relationship
changed from month to month, a strong negative
correlation in April 2019 with the coefficient of
determination R2 (The total variance) 0.812.
Figure 3. The flowchart of image processing
and analyzing in the GEE platform for
retrieving Vegetation Health Index VHI.
Figure 4. Results in GEE.
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Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
Figure 5. Landsat 8 images of the study area from April to July 2019 in GEE (The false color
composite uses a band combination of SWIR-1 (B6), near-infrared (B5), blue (B4). The
study area is outlined in red.
April-2019
May-2019
June-2019
July-2019
Figure 6. LST-NDVI correlation.
Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
61
Generally, the NDVI-LST regression showed a
moderate fit (>0.5), exception of May 2019 (R2
was 0.491). However, the linear equation in May
2019 still presents the inverse relation between
LST and NDVI. The previous studies have also
et al., 2013). From these studies, it is evaluated
that the land surface temperature is high where
the vegetation cover is found low. Thus, this
study also confirms that the land surface
temperatures are higher and increase more
markedly in areas of sparse vegetation cover.
Conversely, dense vegetation cover absorbs the
land surface temperature.
The spatial distributions of LST and NDVI are
illustrated in Figure 7. High values of NDVI
indicate the information of health, dense
vegetation, and lower values represented
stressed vegetation. Negative values correspond
to areas with water surfaces. Overall, in Tay Hoa,
the distribution of high NDVI values was in the
South (the green area), corresponds to the forest,
the density of vegetation declined in the North
(yellow and red area). During the dry season,
nearly the entire studied area had surface
temperatures higherthan 20°C.Especiallyin April
2019, the surface temperature is mainly greater
than 250C (orange and red color), in which the
highest temperature is above 420C. Comparison
between NDVI and LST images from April to July
2019, in areas where high LST values were
observed, the NDVI diminished due to the
variation in vegetation state. Overall, they are also
considered tools for monitoring drought periods.
NDVI at a given pixel will typically be relatively
low, whereas LST is expected to be relatively high
because of vegetation deterioration.
Generally, the results show TCI, VCI, and VHI
had a similar pattern from April to July in 2019,
with values close to 0 in the North and values
increase to 100 in the South of the studied area.
Moreover, the distribution of drought
phenomenon over the dry season period in 2019
is shown in Figure 9 with four levels: from no
drought to severe(white tored, respectively). The
forests are distributed in the South of Tay Hoa
district and were not affected by drought (VHI
values >40). From April to July 2019, the
vulnerable to drought areas tended to increase,
mainly in Son Thanh Dong, Son Thanh Tay, Hoa
My Dong, Hoa Binh 1, Hoa Binh 2, and Hoa Phong,
where land is used for agriculture (by overlaying
VHI map with land use map of Tay Hoa). Besides,
the total drought area is recorded for 17÷20% of
the entire district, corresponding to the site with
highsurfacetemperaturefrom25÷300C and NDVI
low or NDVI moderate values (Figure 7). Overall,
the VHI index is chosen to assess the drought of
vegetation caused by temperature. Therefore, it is
appropriate to indicate the extent of agricultural
drought.
To assess the accuracy of the results of
agricultural drought using the VHI index by
coding in the GEE platform, we made the
following comparisons:
1. Due to the limitation of observational data
in the study area, we compared LST images in the
study with LST from MOD11A1 V6 data which
were provided directly on GEE (see
retrieved LST from Landsat 8 and Modis LST
revealed a good correlation (values R2 > 0.67).
Although the comparison is not entirely valid
because the resolution of the MOD11A1 V6
product is low, it shows that free Landsat 8
4.2. Spatial drought
Figure 8 represents the spatial distribution of
VCI, TCI, and VHI. TCI and VCI were created based
on the condition that the higher the temperature,
the worse the conditions for vegetation. High
values of VCI signify good vegetation; on the
contrary, its values decrease to 0 show extremely
unfavorable vegetation conditions. Low TCI
values indicate harsh weather conditions (due to
high temperatures), and high values (close to
100) reflect mostly favorable conditions.
imagery
sources
helpd
calculate
LST
approprivately in small areas.
2. Comparing drought conditions between
VHI index map of Tay Hoa district with the Palmer
map (PDSI -PalmerDrought SeverityIndex) of the
South Central Coast and the Central Highlands.
This map was published in 2016 and was a result
of the project of Vietnam Academy for Water
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Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
NDVI
LST
April
2019
May
2019
June
2019
July
2019
Figure 7. Spatial variation of NDVI and LST in April – July 2019.
Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
63
VCI
TCI
April
2019
May
2019
June
2019
July
2019
Figure 8. VCI and TCI in Tay Hoa district in dry season 2019.
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Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
Figure 9. Spatial distribution of drought with VHI index.
Resource. While VHI uses LST and NDVI extracted
from remote sensing for monitoring agricultural
drought, PDSI uses readily available temperature
1965). The drought in 2016 occurred in most
provinces of the south - central coast and the
central highlands in general and Tay Hoa district
in particular, while the drought results in 2019 in
the study mainly occurred in the northern regions
of Tay Hoa. Although there are no similarities in
space, time and index, the comparison is also
found that drought area in 2019, also existed in
2016. Besides, we overlayed the VHI map with the
land use map of Tay Hoa. Therefore, the results of
theareas identified as drought are consistent with
the reality of crop regions. Overall, the results also
reflect the method’s reliability, especially in the
absence of meteorological information in the area.
5. Conclusion
This study assessed drought conditions in a
relatively small rural area in the south - central
coastal of Vietnam during the dry season. The
method relies on Google Earth Engine and
algorithms/scripts to analyze and calculate the
Vegetation Health Index (VHI) - drought index.
Our results confirm that from April to July 2019,
the preliminary information about the spatial
distribution of mild, moderate, and severe
drought in the Tay Hoa district was provided
quickly. Futhermore, it shows the potential of
using GEE to monitor drought. The GEE data
catalog includes all the Landsat imagery and
replaces all the heavy computational processes
with advanced cloud computing technologies, the
results obtained fora short time. The GoogleEarth
Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67
65
Engine methodology that we developed in this
research will contribute to assessing and
monitoring drought for Tay Hoa district.
Agricultural and Forest Meteorology, 259, 286-
295. doi: 10.1016/j.agrformet.2018.05.014.
Chander, G., Markham, B. L.,Helder, D. L., (2009).
Summary of current radiometric calibration
coefficients for Landsat MSS, TM, ETM+, and
EO-1 ALI sensors. Remote Sensing of
However, this method also has the
disadvantages: it depends on selecting of suitable
Landsat 8 images for the study area. Images with
a large cloud cover will not be selected because
the information about drought at this time will be
lost. Therefore, to solve this combining different
types of satellite imagery in GEE (Landsat,
Sentinel, Modis) and adding the parameters to
indicate drought conditions, such as water
capacity and rainfall.
Environment,
113(5),
893-903.
doi:
Department of Natural Resources and
Environment of Phu Yen Province, (2019).
Climate assessment reports of Phu Yen
province (in Vietnamese).
DeVries, B., Huang, C., Armston, J., Huang, W.,
Jones, J.,Lang, M., (2020). Rapid and robust
monitoring of flood events using Sentinel-1
and Landsat data on the Google Earth Engine.
Remote Sensing of Environment, 240. doi:
10.1016/j.rse.2020.111664.
Acknowledgment
The authors would like to thank the Hanoi
University of Mining and Geology for funding this
research in Project No T20-10.
Author contributions
Ferrelli, F., Huamantinco Cisneros, M., Delgado,
A.,Piccolo, M., (2018). Spatial and temporal
analysis of the LST-NDVI relationship for the
study of land cover changes and their
contribution to urban planning in Monte
Hermoso, Argentina. Documents d'Anàlisi
Geogràfica, 64, 25. doi: 10.5565/rev/dag.355.
Pham Thi Thanh Hoa: Conceived the idea,
performed the analytic calculations, wrote the
manuscript. Tran Thanh Ha: analyzed the data
and formulas, commented and edited this
manuscript.
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