Evaluating the consistency of surface brightness, greenness, and wetness observations between Landsat-8 OLI and Landsat-9 OLI2 through underfly images

Xu, Hanqiu; Ren, Mengjie; Yang, Lijuan

doi:10.1016/j.jag.2023.103546

Cited by 1 publication

(3 citation statements)

References 25 publications

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“…Moreover, one of its significant roles is to work in tandem with Landsat-8 to halve the Earth observation cycle from 16 d to 8 d [21,39]. This necessitates a high degree of consistency in the imagery data between Landsat-9 and Landsat-8 to ensure the results from both are strictly comparable [54]. The primary distinctions between them stem from differences in spectral reflectance attributed to their spectral response functions [21].…”

Section: Spectral Response and Reflectance Of Landsat-8 And Landsat-9...mentioning

confidence: 99%

“…They encompass all the challenging factors of identifying surface water: mountain shadows, urban areas (buildings), vegetation, and bare soil. Another critical metric for assessing the performance of remote sensing instruments is the signal-to-noise ratio (SNR) [54]. Under typical radiation conditions, the SNR of data transmitted by OLI-2 on Landsat-9 is improved by 25%, offering higher radiometric precision in observing the Earth's surface.…”

Section: Spectral Response and Reflectance Of Landsat-8 And Landsat-9...mentioning

confidence: 99%

“…This minimizes the potential discrepancies arising from atmospheric and illumination conditions. The results in [39,54] have leveraged these data to analyze the differences between the two sensors, illustrating this approach.…”

Section: Uncertainty and Perspectivementioning

confidence: 99%

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The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

Chen,

Wang,

Wang

et al. 2024

Remote Sensing

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The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

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Section: Spectral Response and Reflectance Of Landsat-8 And Landsat-9...mentioning

confidence: 99%

Section: Spectral Response and Reflectance Of Landsat-8 And Landsat-9...mentioning

confidence: 99%

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The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

Chen,

Wang,

Wang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

Evaluating the consistency of surface brightness, greenness, and wetness observations between Landsat-8 OLI and Landsat-9 OLI2 through underfly images

Cited by 1 publication

References 25 publications

The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

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