Spatiotemporal Analysis of Precipitation in the Sparsely Gauged Zambezi River Basin Using Remote Sensing and Google Earth Engine

Zeng, Hongwei; Wu, Bingfang; Zhang, Ning; Tian, Fuyou; Phiri, Elijah; Musakwa, Walter; Zhang, Miao; Zhu, Liang; Mashonjowa, Emmanuel

doi:10.3390/rs11242977

Cited by 17 publications

(15 citation statements)

References 57 publications

(80 reference statements)

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“…The availability of climate products accompanied by surface products within GEE, make this platform a great tool for climate studies and air quality monitoring. These advantages create a rising interest in the research community to use GEE for air pollution analyses [142], [143], climate change and monitoring [144], [145], biophysical variable studies [21], [146], evapotranspiration estimation [147], and precipitation mapping [148]. For instance, GEE was employed to map exposed mine waste areas to estimate the corresponding emission of particulate matter to the atmosphere [142].…”

Section: G Atmosphere and Climatementioning

confidence: 99%

“…Finally, the authors reported that GEE was an invaluable platform for monitoring long-term emission from exposed mine waste. Furthermore, GEE was used along with version 1 Tropical Rainfall Measuring Mission (TRMM) precipitation products to study the spatial and temporal patterns of precipitation in the Zambezi River basin [148]. To this end, TRMM data from 1998 to 2017 were processed in GEE to investigate the precipitation trends and magnitudes by Kendall's correlation and Sen's slope reducer respectively.…”

Section: G Atmosphere and Climatementioning

confidence: 99%

See 1 more Smart Citation

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

508

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: G Atmosphere and Climatementioning

confidence: 99%

Section: G Atmosphere and Climatementioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

508

216

View full text Add to dashboard Cite

show abstract

“…Within GEE it is available as rainfall intensity (mm·hr −1 ) from 1999 to 2019. This dataset has been used in a large number of scientific studies carried out on GEE (Wang et al ., 2019; Zeng et al ., 2019; Banerjee et al ., 2020).…”

Section: Dataset and Methodsmentioning

confidence: 99%

Evaluation of precipitation datasets available on Google earth engine over India

Dubey

Gupta

Goyal

et al. 2021

Intl Journal of Climatology

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Monthly mean precipitation estimates of seven products (TerraClimate, TRMM, CHIRPS, PERSIANN‐CDR, GPM‐IMERG, ERA5 and CFSR) available on Google earth engine (GEE) are evaluated against gridded gauge‐based precipitation product available from Indian Meteorological Department (IMD) for their skills and presence of systematic biases (during 2001–2018). All these products represent the climatological features reasonably well. Presence of systematic biases in these products is also observed from their evaluation. Biases across the periphery of the country are relatively on the higher side in comparison to the central regions. The magnitude of spatial variability is represented better for winter precipitation in comparison to summer precipitation. During both winter and summer, ensemble mean of various products outperforms individual products in terms of both RMSE and correlation. Performance of these products is also assessed across various Indian states, elevation bands and climate zones. The ability of these products to represent the seasonality was observed to be highest for the states with mid‐ranged peaks (10–20 mm·day−1) which tend to decrease with both increasing and decreasing peaks. Ability of the precipitation products to resemble the annual cycle does not vary with the amount of precipitation, although individual disparity among the products exists. Additionally, an alternative approach for data evaluation using Multiple Triple Collocation (MTC) was performed for the period 2001–2015 using an additional dataset obtained from soil‐moisture‐based rainfall estimates (SM2RAIN). Results from MTC convey that ERA5 performs relatively poor in comparison to the other products for central India followed by CFSR. In brief, the comprehensive evaluation of precipitation products reported herein will act a valuable reference for the researchers as well as decision makers to select the optimal product for their intended application and will inform the users about the various uncertainties in the foundations and specification of these products.

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“…Random forest supervised, pixel-based classification in ArcGIS 10.5 was used to classify the Landsat images. This classification was robust, effective and produced better results in environments such as Gonarezhou, and its utility has been demonstrated in many studies [56][57][58][59]. A Juno Trimble hand global positioning device was used to collect training samples for the classification and for conducting accuracy assessment.…”

Section: Landcover Classification and Analysismentioning

confidence: 99%

Partnerships and Stakeholder Participation in the Management of National Parks: Experiences of the Gonarezhou National Park in Zimbabwe

Musakwa

Gumbo

Paradza

et al. 2020

Land

Self Cite

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National parks play an important role in maintaining natural ecosystems which are important sources of income and livelihood sustenance. Most national parks in Southern Africa are managed by their states. Before 2007, Gonarezhou National Park was managed by the Zimbabwe Parks Management and Wildlife Authority, which faced challenges in maintaining its biodiversity, community relations and infrastructure. However, in 2017 the Frankfurt Zoological Society and the Zimbabwe Parks Management and Wildlife Authority formed an innovative partnership under the Gonarezhou Conservation Trust (GCT). This study examines the relationship between GCT management, Gonarezhou National Park stakeholders and communities as well as the impact of the relationship on biodiversity and ecosystems. The study also highlights challenges faced and lessons learned in managing Gonarezhou as a protected area. To obtain the information, key informant interviews, Landsat satellite imagery, secondary data from previous studies and government sources were utilized. The results indicate that the concerted efforts of the Gonarezhou Conservation Trust to manage the park are starting to bear fruit in improving biodiversity conservation, ecosystem management and engaging communities. However, challenges such as governance obstacles, problematic stakeholder management, maintaining trust in community relations, ensuring sustainability, managing the adverse impacts of climate change and human-wildlife conflicts must still be navigated to ensure the park’s sustainable management. Notwithstanding challenges, we argue that a partnership arrangement such as the Gonarezhou Conservation Trust is a desirable model that can be applied in national parks in Zimbabwe and Africa for better biodiversity management and tourism.

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Spatiotemporal Analysis of Precipitation in the Sparsely Gauged Zambezi River Basin Using Remote Sensing and Google Earth Engine

Cited by 17 publications

References 57 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Evaluation of precipitation datasets available on Google earth engine over India

Partnerships and Stakeholder Participation in the Management of National Parks: Experiences of the Gonarezhou National Park in Zimbabwe

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