The Harmonized Landsat and Sentinel-2 surface reflectance data set

Claverie, Martin; Ju, Junchang; Masek, Jeffrey G.; Dungan, Jennifer; Vermote, Éric; Roger, Jean-Claude; Skakun, Sergii; Justice, Christopher O.

doi:10.1016/j.rse.2018.09.002

Cited by 847 publications

(572 citation statements)

References 56 publications

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“…; Claverie et al. ) has the potential to reduce impacts of data gaps and further improve the ability to capture seasonal habitat dynamics.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, for areas and temporal windows with very high levels of cloud cover, some of our spectral-temporal metrics exhibited striping artifacts relating to scan line errors of Landsat 7. Integrating Landsat imagery with data from other sensors, such as Sentinel 2 (Wulder et al 2015;Claverie et al 2018) has the potential to reduce impacts of data gaps and further improve the ability to capture seasonal habitat dynamics.…”

Section: Discussionmentioning

confidence: 99%

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Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Oeser

Heurich

Senf

et al. 2019

Remote Sens Ecol Conserv

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Up‐to‐date and fine‐scale habitat information is essential for managing and conserving wildlife. Studies assessing wildlife habitat commonly rely on categorical land‐cover maps as predictors in habitat models. However, broad land‐cover categories often do not adequately capture key habitat features and generating robust land‐cover maps is challenging and laborious. Continuous variables derived directly from satellite imagery provide an alternative for capturing land‐cover characteristics in habitat models. Improved data availability and processing capacities now allow integrating all available images from medium‐resolution sensors in compositing approaches that derive spectral‐temporal metrics at the pixel level, summarizing spectral responses over time. In this study, we assessed the usefulness of such metrics derived from Landsat imagery for mapping wildlife habitat. We categorize spectral‐temporal metrics into habitat metrics characterizing different aspects of wildlife habitat. Comparing the performance of these metrics against categorical land‐cover maps in habitat models for lynx, red deer and roe deer, we found that models using habitat metrics consistently outperformed models based on categorical land‐cover maps, with average improvements of 13.7% in model AUC and 9.7% in the Continuous Boyce Index. Performance increases were larger for seasonal habitat models, indicating that the habitat metrics capture intra‐annual variability in habitat conditions better than land‐cover maps. Comparing suitability maps to ancillary data further revealed that our habitat metrics were sensitive to fine‐scale heterogeneity in habitat associated with forest structure. Overall, our study highlights the considerable potential of Landsat‐based spectral temporal metrics for assessing wildlife habitat. Given these metrics can be derived directly and in an automatized fashion from globally and freely available Landsat imagery, they open up new possibilities for monitoring habitat dynamics in space and time.

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“…; Claverie et al. ) has the potential to reduce impacts of data gaps and further improve the ability to capture seasonal habitat dynamics.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Oeser

Heurich

Senf

et al. 2019

Remote Sens Ecol Conserv

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“…The spatial concordance correlation coefficient we developed and presented here summarizes and accounts for the spatial information in the images, permitting more rigorous characterization of agreement between high-resolution digital images recorded by different sensors. Other applications include using images from different satellite remote-sensing platforms as part of ongoing efforts to harmonize , for example, imagery with different spatial resolution, spectral sensitivity, and angular characteristics (e.g., Landsat-Sentinel efforts Claverie et al, 2018). Calculation of concordance statistics before and after sensor harmonization could provide critical and objective information about the success of different harmonization methods.…”

Section: Discussionmentioning

confidence: 99%

A spatial concordance correlation coefficient with an application to image analysis

et al. 2020

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In this work we define a spatial concordance coefficient for second-order stationary processes. This problem has been widely addressed in a non-spatial context, but here we consider a coefficient that for a fixed spatial lag allows one to compare two spatial sequences along a 45 • line. The proposed coefficient was explored for the bivariate Matérn and Wendland covariance functions. The asymptotic normality of a sample version of the spatial concordance coefficient for an increasing domain sampling framework was established for the Wendland covariance function. To work with large digital images, we developed a local approach for estimating the concordance that uses local spatial models on non-overlapping windows. Monte Carlo simulations were used to gain additional insights into the asymptotic properties for finite sample sizes. As an illustrative example, we applied this methodology to two similar images of a deciduous forest canopy. The images were recorded with different cameras but similar fields-of-view and within minutes of each other. Our analysis showed that the local approach helped to explain a percentage of the non-spatial concordance and to provided additional information about its decay as a function of the spatial lag.As a consequence of the asymptotic normality of the sample CCC, an approximate hypothesis testing problem of the form H 0 : ρ c = ρ 0 versus H 1 : ρ c = ρ 0 for a fixed ρ 0 can be constructed. Alternatively, an approximate confidence interval of the form ρ c ± z α/2 σ 2 Z can be used, where z α/2 is the upper quantile of order α/2 of the standard normal distribution. Applications and extensions of Lin's coefficient can be found in Lin et al. (2012), among others.

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“…Landsat 8/OLI captures images of the Earth's surface in nine spectral bands at a 30 m spatial resolution (15 m for panchromatic band) [34], while Sentinel-2A/B/MSI captures images of the Earth's surface in 13 spectral bands at a 10, 20, and 60 m spatial resolution [35]. We used NASA's Harmonized Landsat Sentinel-2 (HLS) product [36], which provides a Level-2 Nadir BRDF (Bidirectional Reflectance Distribution Function)-Adjusted surface Reflectance (NBAR) at a 30 m spatial resolution. HLS combines, in a single data set, observations of the land surface from the Landsat 8's OLI [37] and Sentinel-2's MSI [38].…”

Section: Landsat-8/oli and Sentinel-2a /Msi Datasetsmentioning

confidence: 99%

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

et al. 2019

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A combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3-5 days) at moderate spatial resolution (10-30 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA's Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016-2018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4%) and coefficient of determination R 2 = 0.73 on cross-validation.is that methods for generating products from coarse spatial resolution sensors can be ported to moderate (Landsat 8/OLI, Sentinel-2/MSI) or high (Planet/PlanetScope) spatial resolution sensors. However, the practice shows that such a transition is not always straightforward due to larger data gaps because of clouds and uneven coverage, sensor characteristics, and increased spatial resolution (at least at the order of 100, when going from 250 to 30 m).Consider, for example, a crop yield assessment/forecasting application [4]. The hypothesis is that satellite-based features, such as vegetation indices (VIs) or biophysical parameters derived at a single date or accumulated over some time period, can be correlated to crop yields [5,6]. Since the reference data on crop yields are mainly available at the regional scale, the corresponding empirical models are built by averaging satellite-based features over those regions and correlating these derived variables with crop yields [7][8][9]. It is assumed that there is a homogeneity within the region in terms of crops grown and agricultural practices applied and, therefore, the averaging should be performed for satellite data acquired at the same (or approximately the same) stages of crop growth, meaning that the data are normalized. This is usually the case for coarse spatial resolution remote sensing sensors, which enable a higher likelihood of obtaining a high temporal frequency of cloud-free data over the Earth's surface [10,11]. This is also evidenced by multiple successful applications of coarse spatial resolution satellite data to crop yield assessment and forecasting [5,[7][8][9][12][13][14][15].However, this is not the case for moderate spatial resolution sat...

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The Harmonized Landsat and Sentinel-2 surface reflectance data set

Cited by 847 publications

References 56 publications

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

A spatial concordance correlation coefficient with an application to image analysis

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

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