Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product

Vermote, Éric; Justice, Chris; Claverie, Martin; Franch, B.

doi:10.1016/j.rse.2016.04.008

Cited by 1,239 publications

(708 citation statements)

References 34 publications

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“…Surface reflectance data were downloaded from USGS/EROS (https:// espa.cr.usgs.gov/); these images were calibrated using the LEDAPS algorithm (Landsat 4-5, 7; Masek et al, 2013), or the L8SR algorithm (Landsat 8; Vermote et al, 2016) and CFmask (Zhu and Woodcock, 2012). For the spring migration season from 1983 to 2015, we processed a total of 242 Landsat surface reflectance scenes from Landsat 4, 5, 7 and 8 ( Fig.…”

Section: Landsat Time Seriesmentioning

confidence: 99%

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

Schaffer‐Smith

Swenson

Barbaree

et al. 2017

Remote Sensing of Environment

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Satellite measurements of surface water offer promise for understanding wetland habitat availability at broad spatial and temporal scales; reliable habitat is crucial for the persistence of migratory shorebirds that depend on wetland networks. We analyzed water extent dynamics within wetland habitats at a globally important shorebird stopover site for a 1983-2015 Landsat time series, and evaluated the effect of climate on water extent. A range of methods can detect open water from imagery, including supervised classification approaches and thresholds for spectral bands and indices. Thresholds provide a time advantage; however, there is no universally superior index, nor single best threshold for all instances. We used random forest to model the presence or absence of water from >6200 reference pixels, and derived an optimal water probability threshold for our study area using receiver operating characteristic curves. An optimized mid-infrared (1.5-1.7 μm) threshold identified open water in the Sacramento Valley of California at 30-m resolution with an average of 90% producer's accuracy, comparable to approaches that require more intensive user input. SLC-off Landsat 7 imagery was integrated by applying a customized interpolation that mapped water in missing data gaps with 99% user's accuracy. On average we detected open water on ~26000 ha (~3% of the study area) in early April at the peak of shorebird migration, while water extent increased five-fold after the migration rush. Over the last three decades, late March water extent declined by ~1300 ha per year, primarily due to changes in the extent and timing of agricultural flood-irrigation. Water within shorebird habitats was significantly associated with an index of water availability at the peak of migration. Our approach can be used to optimize thresholds for time series analysis and near-real-time mapping in other regions, and requires only marginally more time than generating a confusion matrix.

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Section: Landsat Time Seriesmentioning

confidence: 99%

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

Schaffer‐Smith

Swenson

Barbaree

et al. 2017

Remote Sensing of Environment

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“…The aerosol inversion relies on the assumed relationship between the surface reflectance in the blue, red, and near-infrared bands, using an algorithm similar to the one used for the MODIS aerosol product. It is described in more detail in [33]. The code is run natively by a FORTRAN program, and, for workflow embedding purposes, a Python wrapper was built around it.…”

Section: Atmospheric Correctionmentioning

confidence: 99%

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

Stratoulias

Tolpekin

et al. 2017

Remote Sensing

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Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.

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“…The details of surface reflectance estimation for each surface type are described below. Previous attempts show the possibilities of using MODIS data product for surface parameterization and atmospheric parameters retrieval for instruments like Landsat (similar to GF4) [26,37]. One year (2008) cloud-free MODIS C6 TOA reflectance and corresponding MODIS surface reflectance product (MOD09A1) were collected over the study region as shown in Figure 1.…”

Section: Surface Parameterizationmentioning

confidence: 99%

“…Previous studies show that certain relationships between the surface reflectance at visible channels (0.67 and 0.47 µm) and reflectance at near-infrared/ infrared channels (e.g., 0.86, 1.6, 2.1 and 3.75 µm) can be found for certain surface types depending on instruments [5,6,[38][39][40]. Figure 2 shows an Previous attempts show the possibilities of using MODIS data product for surface parameterization and atmospheric parameters retrieval for instruments like Landsat (similar to GF4) [26,37]. One year (2008) cloud-free MODIS C6 TOA reflectance and corresponding MODIS surface reflectance product (MOD09A1) were collected over the study region as shown in Figure 1.…”

Section: Surface Parameterizationmentioning

confidence: 99%

“…The surface reflectances for selected wavelengths are calculated for each valid observation and subsequently fitted as a function of NDVI' and scattering angles. This manner allows to capturing the potential seasonal/annual variability of the surface reflectance, enables one to scale this quantity down to the spatial resolution of GF4 and uses it in the retrieval of AOD at GF4 pixel level [26]. At the meantime, the regression has been performed after filtering surface reflectance 'outliers' which can be caused by the strong inhomogeneity inside the 5 km land cover resolution.…”

Section: Surface Parameterizationmentioning

confidence: 99%

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SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm

et al. 2017

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Abstract:The recently launched Chinese GaoFen-4 (GF4) satellite provides valuable information to obtain geophysical parameters describing conditions in the atmosphere and at the Earth's surface. The surface reflectance is an important parameter for the estimation of other remote sensing parameters linked to the eco-environment, atmosphere environment and energy balance. One of the key issues to achieve atmospheric corrected surface reflectance is to precisely retrieve the aerosol optical properties, especially Aerosol Optical Depth (AOD). The retrieval of AOD and corresponding atmospheric correction procedure normally use the full radiative transfer calculation or Look-Up- Table (LUT) methods, which is very time-consuming. In this paper, a Simplified AtmospHeric correction AlgoRithm for gAofen data (SAHARA) is presented for the retrieval of AOD and corresponding atmospheric correction procedure. This paper is the first part of the algorithm, which describes the aerosol retrieval algorithm. In order to achieve high-accuracy analytical form for both LUT and surface parameterization, the MODIS Dark-Target (DT) aerosol types and Deep Blue (DB) similar surface parameterization have been proposed for GF4 data. Limited Gaofen observations (i.e., all that were available) have been tested and validated. The retrieval results agree quite well with MODIS Collection 6.0 aerosol product, with a correlation coefficient of R 2 = 0.72. The comparison between GF4 derived AOD and Aerosol Robotic Network (AERONET) observations has a correlation coefficient of R 2 = 0.86. The algorithm, after comprehensive validation, can be used as an operational running algorithm for creating aerosol product from the Chinese GF4 satellite.

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Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product

Cited by 1,239 publications

References 34 publications

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm

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