A Comparison of Different Water Indices and Band Downscaling Methods for Water Bodies Mapping from Sentinel-2 Imagery at 10-M Resolution

Liu, Haiyang; Hu, Hsiao-Yun; Liu, Xulong; Jiang, Hao; Liu, Wanxia; Yin, Xianze

doi:10.3390/w14172696

Cited by 18 publications

(16 citation statements)

References 30 publications

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“…Fisher et al [17] reported -close to our results -overall accuracies of 0.98, 0.97, and 0.95 for WI, MNDWI, and NDWI for images from eastern Australia. Liu et al [30] also observed that WI achieved an overall accuracy of 0.96 and a kappa coefficient of 0.89, and AWEIsh also performed well with an overall accuracy of 0.96 and a kappa coefficient of 0.89. In this study, LSWI performs worse due to the limitation of the index in interpreting Sentinel-2 data.…”

Section: Discussionmentioning

confidence: 89%

“…3 and 4). The contrast between water and land is acceptable for MNDWI, although it is less accurate than WI and AWEI in predicting the fractional cover of small water bodies or small streams [30]. SWI is better than NDWI, AWEInsh, and LSWI for detecting small water bodies and river channels.…”

Section: Discussionmentioning

confidence: 95%

“…They are more effective to detect surface water in urban and vegetated areas, despite that the detection in such areas is commonly difficult due to shadows [25]. Shadows in urban areas are often misclassified as water bodies, as they have similar low reflectivity characteristics to water bodies [30]. Fischer et al [17] state that WI and AWEIsh performed best, the MNDW and AWEInsh performed less accurately, and NDWI the least.…”

Section: Discussionmentioning

confidence: 99%

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Performance of Water Indices for Water Resources Monitoring Using Large-Scale Sentinel-2 Data

Tesfaye¹

2023

Preprint

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Evaluating the performance of water indices and mapping the spatial distribution of water-related ecosystems are important for monitoring surface water resources. This is particularly the case for Ethiopia since there is limited information available on water resources development over time despite its relevance for the people and ecosystems. To address this problem, this paper evaluates the performance of seven water indices for country-scale surface water detection based on high spatial and multi-temporal resolution Sentinel-2 data, processed using the Google Earth Engine cloud computing system. Results show that the water index (WI) and automatic water extraction index with shadow (AWEIsh) are the most accurate ones to extract surface water. Comparisons are based on qualitative visual inspections and quantitative accuracy indicators. For the latter, WI and AWEIsh obtained kappa coefficients of 0.96 and 0.95, respectively, and an overall accuracy of 0.98 each. Both indices accounted for similar spatial coverages of surface waters with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

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Performance of Water Indices for Water Resources Monitoring Using Large-Scale Sentinel-2 Data

Tesfaye¹

2023

Preprint

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“…They were more effective to detect surface water in urban and vegetated areas, despite that the detection in such areas is commonly difficult due to shadows (Feyisa et al, 2014 ). Shadows in urban areas are often misclassified as water bodies, as they have similar low reflectivity characteristics to water bodies (Liu et al, 2022 ). Fischer et al (2016) stated that WI and AWEIsh performed best, whereas the MNDW and AWEInsh performed less accurately, and NDWI the least.…”

Section: Discussionmentioning

confidence: 99%

Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia

Tesfaye,

Breuer

2024

Environ Monit Assess

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Evaluating the performance of water indices and water-related ecosystems is crucial for Ethiopia. This is due to limited information on the availability and distribution of water resources at the country scale, despite its critical role in sustainable water management, biodiversity conservation, and ecosystem resilience. The objective of this study is to evaluate the performance of seven water indices and select the best-performing indices for detecting surface water at country scale. Sentinel-2 data from December 1, 2021, to November 30, 2022, were used for the evaluation and processed using the Google Earth Engine. The indices were evaluated using qualitative visual inspection and quantitative accuracy indicators of overall accuracy, producer’s accuracy, and user’s accuracy. Results showed that the water index (WI) and automatic water extraction index with shadow (AWEIsh) were the most accurate ones to extract surface water. For the latter, WI and AWEIsh obtained an overall accuracy of 96% and 95%, respectively. Both indices had approximately the same spatial coverage of surface water with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia. The results provide a valuable insight into the extent of surface water bodies, which is essential for water resource planners and decision-makers. Such data can also play a role in monitoring the country’s reservoirs, which are important for the country’s energy and economic development. These results suggest that by applying the best-performing indices, better monitoring and management of water resources would be possible to achieve the Sustainable Development Goal 6 at the regional level.

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“…The water body index method [35] enhances the contrast between features by using ratios of remote sensing image bands (NDWI uses the second and fourth band pixel values, MNDWI uses the second and fifth band pixel values, EWI uses the first and fourth band pixel values). The commonly used water body indexes include the normalized difference water body index (NDWI), the improved normalized difference water body index (MNDWI) and the enhanced water body index (EWI).…”

Section: Water Index Methodsmentioning

confidence: 99%

Temporal and Spatial Evolution of Eichmann Lake Wetland in Aksu River Basin and Its Response to Ecological Water Supply

Nie

Chen

Wang³

et al. 2022

IJERPH

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Timely understanding and quantitative analysis of the changing trend in natural ecosystems in arid and semi-arid areas and their response to the ecological water supply process are of great significance for maintaining the health of oasis ecosystems. Taking the Eichmann Lake wetland of the Aksu River Basin in Xinjiang as the research area, the temporal-spatial distribution characteristics of the lake and the response of ecological water in recent years were studied based on remote sensing images and monitoring data. The results show that: (1) The water surface area of Eichmann Lake is shrinking, from 61.57 km2 in 1996 to 27.76 km2 in 2020. The changes in water surface area have experienced three stages: rapid decline, slow decline, and slow recovery. After the ecological water supply, the water surface area has obvious seasonal changes with hysteresis; (2) In areas with a low average water level, the ecological water supply has a significant impact on the groundwater level. The higher the water supply is, the higher the groundwater level will be. There is a significant lag effect between the change in the groundwater level and the response of the ecological water supply, which is 1–2 months; (3) The response characteristics of different natural vegetation to the ecological water supply were different in interannual, seasonal, and spatial contexts. The response of Populus euphratica to the ecological water supply is obvious, and its growth is the best within the range of 100–500 m from the water supply outlet. This research can provide the basis for the rational allocation of the Aksu River Basin’s water resources, and also act as a valuable reference for the restoration and reconstruction of surrounding vegetation in the Aksu River irrigation area.

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A Comparison of Different Water Indices and Band Downscaling Methods for Water Bodies Mapping from Sentinel-2 Imagery at 10-M Resolution

Cited by 18 publications

References 30 publications

Performance of Water Indices for Water Resources Monitoring Using Large-Scale Sentinel-2 Data

Performance of Water Indices for Water Resources Monitoring Using Large-Scale Sentinel-2 Data

Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia

Temporal and Spatial Evolution of Eichmann Lake Wetland in Aksu River Basin and Its Response to Ecological Water Supply

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