Cloud-based remote sensing datasets used in hydrological studies: A review and examples

Ceren Sıla Özdemir

doi:10.53030/tjags.1885422

Cloud-based remote sensing datasets used in hydrological studies: A review and examples

Abstract

Water resources are critically important for the continuity of ecosystems and the sustainability of human activities. However, climate change, population growth, and unplanned and rapid changes in land use are increasingly putting pressure on surface and groundwater resources. This situation necessitates an accurate assessment of the temporal and spatial distribution of water and its effective monitoring at the watershed scale. Traditional hydrological observation methods provide high accuracy but, due to their limited spatial coverage and prohibitive costs, they present significant limitations, particularly in regions with insufficient data. In this context, remote sensing technologies offer an approach that enables the acquisition of parameters represented directly and indirectly in hydrological analyses, covering large areas and providing continuity. This study examines the concept of water balance, which forms the basis of hydrology, within the framework of the hydrological cycle. It evaluates the contributions of different remote sensing datasets to the monitoring and assessment of water resources, as well as the limitations of these approaches, in light of studies in the literature. In addition, a representative study area encompassing the Hazar Lake Basin and the Behramaz Basin was selected, and analyses were conducted using the Google Earth Engine (GEE) platform to demonstrate the applicability of the available datasets.

Keywords

Ethical Statement

In the study, the author declares that there is no violation of research and publication ethics.

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Details

Primary Language

English

Subjects

Remote Sensing , Hydrology (Other)

Journal Section

Review Article

Authors

Ceren Sıla Özdemir ^*
0000-0003-0967-8179
Türkiye

Publication Date

September 30, 2026

Submission Date

February 9, 2026

Acceptance Date

June 10, 2026

Published in Issue

Year 2026 Volume: 8 Number: 2

DOI

https://doi.org/10.53030/tjags.1885422

IZ

https://izlik.org/JA64GE62AN

Cite

RIS / Bibtex

APA

Özdemir, C. S. (2026). Cloud-based remote sensing datasets used in hydrological studies: A review and examples. Turkish Journal of Applied Geoinformation Sciences, 8(2), 67-92. https://doi.org/10.53030/tjags.1885422

AMA

1.Özdemir CS. Cloud-based remote sensing datasets used in hydrological studies: A review and examples. Turk. J. Appl. Geoinf. Sci. 2026;8(2):67-92. doi:10.53030/tjags.1885422

Chicago

Özdemir, Ceren Sıla. 2026. “Cloud-Based Remote Sensing Datasets Used in Hydrological Studies: A Review and Examples”. Turkish Journal of Applied Geoinformation Sciences 8 (2): 67-92. https://doi.org/10.53030/tjags.1885422.

EndNote

Özdemir CS (September 1, 2026) Cloud-based remote sensing datasets used in hydrological studies: A review and examples. Turkish Journal of Applied Geoinformation Sciences 8 2 67–92.

IEEE

[1]C. S. Özdemir, “Cloud-based remote sensing datasets used in hydrological studies: A review and examples”, Turk. J. Appl. Geoinf. Sci., vol. 8, no. 2, pp. 67–92, Sept. 2026, doi: 10.53030/tjags.1885422.

ISNAD

Özdemir, Ceren Sıla. “Cloud-Based Remote Sensing Datasets Used in Hydrological Studies: A Review and Examples”. Turkish Journal of Applied Geoinformation Sciences 8/2 (September 1, 2026): 67-92. https://doi.org/10.53030/tjags.1885422.

JAMA

1.Özdemir CS. Cloud-based remote sensing datasets used in hydrological studies: A review and examples. Turk. J. Appl. Geoinf. Sci. 2026;8:67–92.

MLA

Özdemir, Ceren Sıla. “Cloud-Based Remote Sensing Datasets Used in Hydrological Studies: A Review and Examples”. Turkish Journal of Applied Geoinformation Sciences, vol. 8, no. 2, Sept. 2026, pp. 67-92, doi:10.53030/tjags.1885422.

Vancouver

1.Ceren Sıla Özdemir. Cloud-based remote sensing datasets used in hydrological studies: A review and examples. Turk. J. Appl. Geoinf. Sci. 2026 Sep. 1;8(2):67-92. doi:10.53030/tjags.1885422