GEE can prominently reduce uncertainties from input data and parameters of the remote sensing-driven distributed hydrological model

Pan, Zihao; Yang, Shengtian; Ren, Xiaodan; Lou, Hezhen; Zhou, Baichi; Wang, Huaixing; Zhang, Yujia; Li, Hao; Li, Jiekang; Dai, Yunmeng

doi:10.1016/j.scitotenv.2023.161852

Cited by 9 publications

(2 citation statements)

References 67 publications

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“…Studies using the GEE platform, for instance, have been done to generate high-quality NDVI time-series data products for real-time environmental monitoring. This shows that GEE has significant advantages for long-term series remote sensing analysis [21][22][23][24].…”

Section: Introductionmentioning

confidence: 85%

Fine-Scale Analysis of the Long-Term Urban Thermal Environment in Shanghai Using Google Earth Engine

Wang

Chen

et al. 2023

Remote Sensing

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Exploring the spatiotemporal patterns of urban thermal environments is crucial for mitigating the detrimental effects of urban heat islands (UHI). However, the long-term and fine-grained monitoring of UHI is limited by the temporal and spatial resolutions of various sensors. To address this limitation, this study employed the Google Earth Engine (GEE) platform and a multi-source remote sensing data fusion approach to generate a densely time-resolved Landsat-like Land Surface Temperature (LST) dataset for daytime observations spanning from 2001 to 2020 in Shanghai. A comprehensive analysis of the spatiotemporal patterns of UHI was conducted. The results indicate that over the past 20 years, the highest increase in average LST was observed during spring with a growth coefficient of 0.23, while the lowest increase occurred during autumn (growth coefficient of 0.12). The summer season exhibited the most pronounced UHI effect in the region (average proportion of Strong UHI and General UHI was 28.73%), while the winter season showed the weakest UHI effect (proportion of 22.77%). The Strong UHI areas gradually expanded outward over time, with a noticeable intensification of heat island intensity in the northwest and coastal regions, while other areas did not exhibit significant changes. Impervious surfaces contributed the most to LST, with a contribution of 0.96 °C, while water had the lowest contribution (−0.42 °C). The average correlation coefficients between LST and NDVI, NDWI, and NDBI over 20 years were −0.4236, −0.5128, and 0.5631, respectively.

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Section: Introductionmentioning

confidence: 85%

Fine-Scale Analysis of the Long-Term Urban Thermal Environment in Shanghai Using Google Earth Engine

Wang

Chen

et al. 2023

Remote Sensing

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“…4 Since the topography of the selected river section does greatly vary, there exists a one-to-one correspondence between the river discharge and river width [28]. According to the determined long time series surface width, the digital channel model was used to calculate the water depth, water level, flow area [29], wet circumference, and hydraulic radius corresponding to the surface width [29], and the velocity and discharge were then calculated according to the Manning Equation (1), the Chezy formula (2), and the flow formula (3) [26] (Figure 2 (Step 3)), as follows:…”

Section: A New Methods To Calculate the Meifrmentioning

confidence: 99%

Detection and Analysis of the Variation in the Minimum Ecological Instream Flow Requirement in the Chinese Northwestern Inland Arid Region by Using a New Remote Sensing Method

Yang,

Li,

Lou

et al. 2023

Remote Sensing

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With the development of human society, the balance between the minimum ecological instream flow requirement (MEIFR), which is an essential part of the ecological water demand in arid areas, and anthropogenic water depletion has received increasing attention. However, due to the lack of hydrological station data and river information on arid basins, previous researchers usually considered only the individual ecological water demand of rivers, lakes, or oases. To address this issue, a new method that combines river hydraulic parameters and the wet circumference obtained by an unmanned aerial vehicle (UAV) and remote sensing hydrological station (RSHS) technologies was applied to obtain the MEIFR and, then, systematically and quantitatively explore the balance from the perspective of the entire basin of Aiding Lake from 1990 to 2022, which is the lowest point of Chinese terrestrial territory. The results showed the following: (1) since 1990, the discharge of the seven rivers in the study area increased by 1–6%, and the MEIFR of these rivers increased by 15–100%; both quantities decreased by 3–5% from the upper to the lower reaches of the basin; (2) the surface area and water level of Aiding Lake decreased by 5% and 14%, respectively, but the MEIFR first decreased by 25% from 1990 to 2013 and, then, increased by 66.7% from 2013 to 2022; and (3) from 2011 to 2022, the MEIFR and anthropogenic water depletion exhibited a balance. Against the background of climate change, this research revealed that the MEIFR of the rivers in the Aiding Lake Basin have shown an upward trend over the past 30 years and quantitatively determined the above balance relationship and the period of its occurrence. This study supplied a method that could provide guidance for water resource management by decision-makers at a global level, thus helping achieve the sustainable development goals (SDGs).

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Tailoring the surface energy balance algorithm for land-improved (SEBALI) model using high-resolution land/use land cover for monitoring actual evapotranspiration

Mekonnen,

Alamirew,

Malede

et al. 2024

Agricultural Water Management

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GEE can prominently reduce uncertainties from input data and parameters of the remote sensing-driven distributed hydrological model

Cited by 9 publications

References 67 publications

Fine-Scale Analysis of the Long-Term Urban Thermal Environment in Shanghai Using Google Earth Engine

Fine-Scale Analysis of the Long-Term Urban Thermal Environment in Shanghai Using Google Earth Engine

Detection and Analysis of the Variation in the Minimum Ecological Instream Flow Requirement in the Chinese Northwestern Inland Arid Region by Using a New Remote Sensing Method

Tailoring the surface energy balance algorithm for land-improved (SEBALI) model using high-resolution land/use land cover for monitoring actual evapotranspiration

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