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: 
Received 16th Jan. 2021 
Accepted 24th May 2021 
Available online 30th Jun. 2021 
 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-
performance computing resources for processing multi-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 by using a drought index (VHI index – Vegetation Health Index) 
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. 
Copyright © 2021 Hanoi University of Mining and Geology. All rights reserved. 
Keywords: 
Drought, 
Google Earth Engine, 
Remote sensing, 
Tay Hoa, 
VHI. 
_____________________ 
*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 
1. Introduction 
In recent times, climate change are the main 
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 the dry 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 
identify agricultural drought (Sruthi et al., 2015). 
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 
in many countries (Alshaikh, 2015; Schirmbeck et 
al., 2017; Sholihah et al., 2016). VHI demonstrated 
a greater capability and better suitability in 
monitoring drought (Bento et al., 2018). It 
combines two indices: Veget ... ation 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. 
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. 
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 to red, 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 
high surface temperature from 25÷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 
https://developers.google.com/earth-
engine/datasets/catalog/MODIS_006_MOD11A1
?hl=en). The comparison results between 
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 
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 - Palmer Drought Severity Index) 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 
62 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. 
64 Hoa Thanh Pham Thi and Ha Thanh Tran/Journal of Mining and Earth Sciences 62 (3), 53 - 67 
Resource. While VHI uses LST and NDVI extracted 
from remote sensing for monitoring agricultural 
drought, PDSI uses readily available temperature 
and precipitation data series (Alley, 1984; Palmer, 
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 
the areas 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 for a short time. The Google Earth 
Figure 9. Spatial distribution of drought with VHI index. 
 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. 
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. 
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 
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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