Surface water dynamics analysis based on sentinel imagery and Google Earth Engine Platform: a case study of Jayakwadi dam

Kandekar, Vidya. U.; Pande, Chaitanya B.; Rajesh, J.; Atre, A. A.; Gorantiwar, S. D.; Kadam, S. A.; Gavit, B. K.

doi:10.1007/s40899-021-00527-7

Cited by 54 publications

(18 citation statements)

References 19 publications

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“…For example, this is the case for Fenu et al [12] and Tseng et al [52], who used Python. Javascript was used by Kandekar et al [38].…”

Section: Discussionmentioning

confidence: 99%

“…Google Earth Engine (GEE) supports the processing of remote-sensing images with many excellent algorithms for remote-sensing image processing. From the Big Data system, vector data on the water mass are obtained, which provide a base for the later extraction of hydrological elements [38].…”

Section: Agricultural Big Data In the Context Of Climate Changementioning

confidence: 99%

“…Moreover, its mighty computing power supports PB-level remote-sensing data processing, suitable for large-scale and long-term research series. In addition, the rich API documents of GEE absorb many superior remote-sensing imageprocessing algorithms, which are very friendly for people who focus on data processing rather than programming [38].…”

Section: Data Visualizationmentioning

confidence: 99%

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Agricultural Big Data Architectures in the Context of Climate Change: A Systematic Literature Review

et al. 2022

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Climate change is currently one of agriculture’s main problems in achieving sustainability. It causes drought, increased rainfall, and increased diseases, causing a decrease in food production. In order to combat these problems, Agricultural Big Data contributes with tools that improve the understanding of complex, multivariate, and unpredictable agricultural ecosystems through the collection, storage, processing, and analysis of vast amounts of data from diverse heterogeneous sources. This research aims to discuss the advancement of technologies used in Agricultural Big Data architectures in the context of climate change. The study aims to highlight the tools used to process, analyze, and visualize the data, to discuss the use of the architectures in crop, water, climate, and soil management, and especially to analyze the context, whether it is in Resilience Mitigation or Adaptation. The PRISMA protocol guided the study, finding 33 relevant papers. However, despite advances in this line of research, few papers were found that mention architecture components, in addition to a lack of standards and the use of reference architectures that allow the proper development of Agricultural Big Data in the context of climate change.

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“…For example, this is the case for Fenu et al [12] and Tseng et al [52], who used Python. Javascript was used by Kandekar et al [38].…”

Section: Discussionmentioning

confidence: 99%

Section: Agricultural Big Data In the Context Of Climate Changementioning

confidence: 99%

Section: Data Visualizationmentioning

confidence: 99%

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Agricultural Big Data Architectures in the Context of Climate Change: A Systematic Literature Review

et al. 2022

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“…In this regard, Google Earth Engine (GEE) is the best-suited platform for large area analysis with pre-processed Sentinel-1 images integrated with land and inland water applications. In recent times, many researchers have used Sentinel-1 data and cloud-based platforms like GEE for robust mapping of big datasets over large-scale areas (S. S. Chen et al, 2021 ; Kandekar et al, 2021 ; Kouadri et al, 2021 ; Vanama et al, 2020 ). The analysis determines the processing of Sentinel-1 SAR data sets for automated flood mapping technique influencing the accuracy of the generated maps and validating results by implementing data from the optical sensor like Sentinel-2.…”

Section: Introductionmentioning

confidence: 99%

Google earth engine based computational system for the earth and environment monitoring applications during the COVID-19 pandemic using thresholding technique on SAR datasets

Ghosh

Kumar

Kumari

2022

Physics and Chemistry of the Earth, Parts A/B/C

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“…In the semi-arid region with various climatic factors affect the management of water resources (Kandekar et al 2021). Agriculture activates and makes an important contribution to the Indian economy.…”

Section: Introductionmentioning

confidence: 99%

Exploration of groundwater potential zones using analytical hierarchical process (AHP) approach in the Godavari river basin of Maharashtra in India

Rajesh¹,

Pande²,

Kadam³

et al. 2021

Appl Water Sci

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Total natural and groundwater resources play the most crucial role in developing ecological, biological and socioeconomic doings. Various parameters like land use, geology, elevation, slope, lineament, lineament density, drainage density and geomorphology affect the groundwater development of recharge and its accessibility. In this research, geographical information system (GIS), remote sensing, weighted overlay analysis and analytical hierarchy process (AHP) methods have been used for groundwater prospects mapping, and identifying suitable solutions site for soil and water conservation structures. To calculate the weights were assigned to every layers component in the determination of affecting factors. The weighted overlay analysis (WOA) tool is applied to give the sub-parameter ratings based on the scientific literature. The final map of potential groundwater zone map has prepared using Arc GIS 10.1 software and AHP methods. About 49.71% of the areas fall under the 'good potential zone, 41.05% falls under the 'moderate potential zone', and 9.22% falls under the 'poor zone by using AHP technique. Groundwater potential zone map is depend on the weighted overlay analysis and analytical hierarchy process (AHP) methods. The highest GWP is located in the lower part of the basin due to the best surface runoff gathering, infiltration situations and subsurface storage volume. The present study procedure, methods and outcomes can be valuable to estimate the suitable groundwater zones parallel to improve the dry land area in the semi-arid and arid regions of India.

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Surface water dynamics analysis based on sentinel imagery and Google Earth Engine Platform: a case study of Jayakwadi dam

Cited by 54 publications

References 19 publications

Agricultural Big Data Architectures in the Context of Climate Change: A Systematic Literature Review

Agricultural Big Data Architectures in the Context of Climate Change: A Systematic Literature Review

Google earth engine based computational system for the earth and environment monitoring applications during the COVID-19 pandemic using thresholding technique on SAR datasets

Exploration of groundwater potential zones using analytical hierarchical process (AHP) approach in the Godavari river basin of Maharashtra in India

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