Global Revisit Interval Analysis of Landsat-8 -9 and Sentinel-2A -2B Data for Terrestrial Monitoring

Li, Jian; Chen, Baozhang

doi:10.3390/s20226631

Cited by 48 publications

(23 citation statements)

References 40 publications

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“…The Landsat program addresses this issue as it offers radiometric consistency within its historical archive and thus, is one of the few instruments that can act as a reference sensor for other optical satellite programs [9]. Synergistic international partnerships between medium spatial resolution satellites can result in a virtual constellations [36], such as between Landsat and Sentinel-2 [37], reducing the effective revisit rate, such as a latitude-dependent <3 days revisit time for Landsat-8, Sentinel-2A, and Sentinel-2B [38]. Additional improvements via combining Landsat and Sentinel-2 are found in improved geometric fidelity [35].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

et al. 2021

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With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

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Section: Introductionmentioning

confidence: 99%

A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

et al. 2021

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“…OLI and MSI products were sufficiently consistent to be merged and to provide comprehensive observations for the monitoring of aquatic systems [36]. The combination of the products of these 3 missions yielded a global median average revisit interval of 2.9 days since the repeat cycle is 16-days for Landsat-8 and 5-days for Sentinel-2A/B [37].…”

Section: Ground Reference Bathymetry Datamentioning

confidence: 94%

Performance and Uncertainty of Satellite-Derived Bathymetry Empirical Approaches in an Energetic Coastal Environment

et al. 2022

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Objectives of this study are to evaluate the performance of different satellite-derived bathymetry (SDB) empirical models developed for multispectral satellite mission applications and to propose an uncertainty model based on inferential statistics. The study site is the Arcachon Bay inlet (France). A dataset composed of 450,837 echosounder data points and 89 Sentinel-2 A/B and Landsat-8 images acquired from 2013 to 2020, is generated to test and validate SDB and uncertainty models for various contrasting optical conditions. Results show that water column optical properties are characterized by a high spatio-temporal variability controlled by hydrodynamics and seasonal conditions. The best performance and highest robustness are found for the cluster-based approach using a green band log-linear regression model. A total of 80 satellite images can be exploited to calibrate SDB models, providing average values of root mean square error and maximum bathymetry of 0.53 m and 7.3 m, respectively. The uncertainty model, developed to extrapolate information beyond the calibration dataset, is based on a multi-scene approach. The sensitivity of the model to the optical variability not explained by the calibration dataset is demonstrated but represents a risk of error of less than 5%. Finally, the uncertainty model applied to a diachronic analysis definitively demonstrates the interest in SDB maps for a better understanding of morphodynamic evolutions of large-scale and complex coastal systems.

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“…The combination of the three satellites presents a new solution for global moderate-resolution landcover monitoring. Compared with a single satellite, the combination of the three satellites, taking advantage of their complementary revisit interval patterns, provides a 2.9-day global median average revisit interval [4,5]. This would benefit numerous remote sensing applications, such as deforestation [6], fire monitoring [7], agriculture dynamics [8], and ice velocity detection [9].…”

Section: Introductionmentioning

confidence: 99%

Optimal Solar Zenith Angle Definition for Combined Landsat-8 and Sentinel-2A/2B Data Angular Normalization Using Machine Learning Methods

Chen

2021

Remote Sensing

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Data from Landsat-8 and Sentinel-2A/2B are often combined for terrestrial monitoring because of their similar spectral bands. The bidirectional reflectance distribution function (BRDF) effect has been observed in both Landsat-8 and Sentinel-2A/2B reflectance data. However, there is currently no definition of solar zenith angle (θsz) that is suitable for the normalization of the BRDF-adjusted reflectance from the three sensors’ combined data. This paper describes the use of four machine learning (ML) models to predict a global θsz that is suitable for the normalization of bidirectional reflectance from the combined data in 2018. The observed θsz collected globally, and the three locations in the Democratic Republic of Congo (26.622°E, 0.356°N), Texas in the USA (99.406°W 30.751°N), and Finland (25.194°E, 61.653°N), are chosen to compare the performance of the ML models. At a global scale, the ML models of Support Vector Regression (SVR), Multi-Layer Perception (MLP), and Gaussian Process Regression (GPR) exhibit comparably good performance to that of polynomial regression, considering center latitude as the input to predict the global θsz. GPR achieves the best overall performance considering the center latitude and acquisition time as inputs, with a root mean square error (RMSE) of 1.390°, a mean absolute error (MAE) of 0.689°, and a coefficient of determination (R2) of 0.994. SVR shows an RMSE of 1.396°, an MAE of 0.638°, and an R2 of 0.994, following GPR. For a specific location, the SVR and GPR models have higher accuracy than the polynomial regression, with GPR exhibiting the best performance, when center latitude and acquisition time are considered as inputs. GPR is recommended for predicting the global θsz using the three sensors’ combined data.

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Global Revisit Interval Analysis of Landsat-8 -9 and Sentinel-2A -2B Data for Terrestrial Monitoring

Cited by 48 publications

References 40 publications

A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

Performance and Uncertainty of Satellite-Derived Bathymetry Empirical Approaches in an Energetic Coastal Environment

Optimal Solar Zenith Angle Definition for Combined Landsat-8 and Sentinel-2A/2B Data Angular Normalization Using Machine Learning Methods

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