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Global drought monitoring with big geospatial datasets using Google Earth Engine

DOI:10.1007/s11356-020-12023-0
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Ramla Khan at The Open University (UK)
Ramla Khan
  • The Open University (UK)
Hammad Gilani at International Water Management Institute
Hammad Gilani
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Abstract

Drought or dryness occurs due to the accumulative effect of certain climatological and hydrological variables over a certain period. Droughts are studied through numerically computed simple or compound indices. Vegetation condition index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the temperature condition index (TCI) is used for studying the temperature change. Dryness or wetness of soil is a major indicator for agriculture and hydrological drought and for that purpose, the index, soil moisture condition index (SMCI), is computed. The deviation of precipitation from normal is a major cause for meteorological droughts and for that purpose, precipitation condition index (PCI) is computed. The years when the indices escalated the dryness situation to severe and extreme are pointed out in this research. Furthermore, an interactive dashboard is generated in the Google Earth Engine (GEE) for users to compute the said indices using country boundary, time period, and ecological mask of their choice: Agriculture Drought Monitoring. Apart from global results, three case studies of droughts (2002 in Australia, 2013 in Brazil, and 2019 in Thailand) computed via the dashboard are discussed in detail in this research.
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RESEARCH ARTICLE
Global drought monitoring with big geospatial datasets using
Google Earth Engine
Ramla Khan
1
&Hammad Gilani
1
Received: 8 August 2020 /Accepted: 8 December 2020
#The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021
Abstract
Drought or dryness occurs due to the accumulative effect of certain climatological and hydrological variables over a certain
period. Droughts are studied through numerically computed simple or compound indices. Vegetation condition index (VCI) is
used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum
influence from cloud contamination and humidity in the air, so the temperature condition index (TCI) is used for studying the
temperature change. Dryness or wetness of soil is a major indicator for agriculture and hydrological drought and for that purpose,
the index, soil moisture condition index (SMCI), is computed. The deviation of precipitation from normal is a major cause for
meteorological droughts and for that purpose, precipitation condition index (PCI) is computed. The years when the indices
escalated the dryness situation to severe and extreme are pointed out in this research. Furthermore, an interactive dashboard is
generated in the Google Earth Engine (GEE) for users to compute the said indices using country boundary, time period, and
ecological mask of their choice: Agriculture Drought Monitoring. Apart from global results, three case studies of droughts (2002
in Australia, 2013 in Brazil, and 2019 in Thailand) computed via the dashboard are discussed in detail in this research.
Keywords Global drought .Satellite data .Drought indices .Interactive dashboard .Google Earth Engine .Case studies
Introduction
The Intergovernmental Panel on Climate Change (IPCC) is
working diligently on finding solutions for the impacts of
climate change (Downing et al. 2003), which is a major issue
of the world and is spurred on by anthropogenic activities
(Vera et al. 2006). More frequent natural disasters and extreme
weather are also courtesy of climate change (Marengo et al.
2009).
Drought is one of the costliest natural disasters known to
mankind (Kogan 1997). It has four types: agriculture drought
(crops when impacted by the dryness), meteorological
drought (the precipitation recorded less than normal), hydro-
logical drought (low water level in streams, reservoirs, and
groundwater), and socio-economic drought (the livelihood
and economy getting impacted) (Khan et al. 2020)
Droughts do not occur abruptly but rather develops slowly
over time (Palmer 1965). Rise and fall in certain climatic or
hydro-meteorological indicators (air temperature, humidity,
groundwater table, surface runoff, land surface temperature,
transpiration, evapotranspiration, soil moisture, precipitation,
and heat level, etc.) can become the consequent cause of
drought in a region (Svoboda and Fuchs 2017;Khanetal.
2020).
Researchers suggest numerically calculated drought indi-
ces for investigating impacts of drought in a region. Indices
describe drought’s qualitative state of a certain landscape for a
chosen time period and give a quantitative assessment of se-
verity and timespan of a drought (Svoboda and Fuchs 2017).
These indices are either computed from one hydro-meteoro-
logical/climatic variable or in combination (WMO and GWP
2016). Each of these indices has its own significance and plays
equally important roles in drought assessment (WMO 2012;
Svoboda and Fuchs 2017).
Droughts affect people from all walks of life. A farmer
perceives the drought as deficiency of moisture to his crops;
a hydrologist observes the declining datum in streams and
rivers while an economist would care for its effects on the
economy (Zargar et al. 2011;Palmer1965).
Responsible Editor: Philippe Garrigues
*Ramla Khan
ramla3903@gmail.com
1
Institute of Space Technology, Islamabad, Pakistan
https://doi.org/10.1007/s11356-020-12023-0
/ Published online: 4 January 2021
Environmental Science and Pollution Research (2021) 28:17244–17264
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... This happens due to an uneven balance between the soil surface evaporation and plant transpiration followed by low soil water content [1]. Khan et al. [6] monitored global drought for almost two decades (2001-2019) using big geospatial datasets from Google Earth Engine and calculated drought indices, namely vegetation condition index (VCI), temperature condition index ...
... According to the recent studies [6,10,11] and last reports of ICCP [2] and EEA, anthropogenic activities have significantly contributed towards climate change and drought that is the most important consequence of climate change in recent decades with a negative influence on our ecosystem, agriculture, and economy [6][7][8][9][10]. In this review article, we discussed various impacts of drought on soil microbial communities and plants. ...
... According to the recent studies [6,10,11] and last reports of ICCP [2] and EEA, anthropogenic activities have significantly contributed towards climate change and drought that is the most important consequence of climate change in recent decades with a negative influence on our ecosystem, agriculture, and economy [6][7][8][9][10]. In this review article, we discussed various impacts of drought on soil microbial communities and plants. ...
The Impact of Drought Stress on Soil Microbial Community, Enzyme Activities and Plants
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... In fact, few studies have taken advantage of the unique opportunities offered by satellite sensors such as wide spatial coverage in addition to availability of data at consistent temporal and spatial dimensions, suitable for farm-scale drought analysis (Unganai and Kogan 1998;Mutowo and Chikodzi 2014;Kuri et al. 2018;. Although these studies have applied remotely sensed data to characterise drought (see Liu and Kogan 1996;Sierra-Soler et al. 2016;Kogan et al. 2017;Aziz et al. 2018;Kogan et al. 2019;Javed et al. 2021;Khan and Gilani 2021;Kourouma et al. 2021;Zhang et al. 2021), few have characterised agricultural drought in across temporal scales relevant for agricultural planning e.g., monthly scale or subseasonal scale. For instance, Mutowo and Chikodzi (2014) estimated the spatial extent of agricultural drought in Zimbabwe across the whole agricultural season for the period between 2005 and 2010 using the vegetation condition index (VCI). ...
... In this study, the Vegetation Condition Index (VCI) was used to characterize drought occurrence in Zimbabwe. The VCI was chosen over other drought indices as it is one of the most commonly used remotely sensed index for drought and crop condition monitoring and has been successfully applied across a number of agricultural landscapes in Africa (Unganai and Kogan 1998;Frischen et al. 2020;Kourouma et al. 2021;Mupepi and Matsa 2022), Asia (Jain et al. 2010;Muthumanickam et al. 2011;Dutta et al. 2015, Khan et al., 2020Mikaili and Rahimzadegan 2022), Australia (Kogan et al. 2018;, North America (Quiring and Ganesh 2010;Kogan and Guo 2015;Kogan et al. 2017) and even at global scale (Kogan et al. 2020, Khan andGilani 2021). Advantages of the VCI are that as a satellite-based agricultural drought index, it provides near real time data across spatial scales and overcomes the problem of sparse network of meteorological stations that are characteristic of most developing countries such as Zimbabwe (Dhakar et al. 2013). ...
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... For example, Jesus et al. (2020) reportedly used GEE for image acquisition and derivation of several indices such as normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI) and visible atmospherically resistant index (VARI). In another study by Khan and Gilani (2021), GEE has assisted in monitoring drought indices such as vegetation condition index (VCI) for detecting changes in vegetation condition, temperature condition index (TCI) for examining temperature changes, soil moisture condition index (SMCI) for accessing degree of dryness and wetness of soil, alongside the precipitation condition index (PCI) computed from satellite derived data. ...
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This study evaluated regional vegetation dynamics and changes between 2015 and 2020 using Google earth engine (GEE) platform and normalized difference vegetation index (NDVI) derived from the multi-petabyte catalogue of sentinel-2 imageries. Using the computational capability of GEE, yearly mean NDVI from 2015 to 2020 were computed using level C-1 product. Subsequently, each of the NDVI images was classified into four land cover classes; water bodies, non-vegetated, grassland /cropland /shrubs, and forest using NDVI threshold values of < 0.01, 0.01-0.20, 0.20-0.30 and > 0.30, respectively. The classified maps allowed for the assessment of yearly variation in vegetation and changes between 2015 and 2020. Result showed that non-vegetated area increased from 18.53% in 2015 to 42.56% in 2020 (~ 25.00% gain), the forest area reduced to 6.78% in 2020 compared to 23.76% measured in 2015 (~ 17.00% loss in forest); whereas water bodies and grassland/cropland/shrubs remained relatively constant (0.21 and ~ 50.00%, respectively) across the years studied. Presently, the forest land was estimated to be about 2, 371.131 km2 (~ 6.70%) of the total land mass, grassland/cropland/shrubs occupied 17, 770.79 km2 (~ 50.07%), non-vegetated area was slightly less than half with 15, 274.85 km2 (~ 43.04%) and water bodies occupied 75.68 km2 (~ 0.21%).
... Oleh karena itu pemanfaatan GEE dapat mengurangi waktu pemrosesan dan menyediakan penyimpanann dataset untuk analisis spasial dan temporal. Dalam memantau kekeringan, Khan & Gilani (2021), menggunakan GEE untuk mengetahui kekeringan secara global dengan menggunakan berbagai indeks seperti Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), Precipitation Condition Index (PCI), dan Vegetation Condition Index (VCI). Pemantauan kekeringan berbasis cloud computing skala regional dan nasional baru sedikit dilakukan dan peneliti mayoritas menggunakan teknik konvensional. ...
Aplikasi Google Earth Engine Untuk Pemantauan Kekeringan Pertanian Di Kabupaten Lombok Tengah
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