A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring

Li, Jian; Roy, David P.

doi:10.3390/rs9090902

Cited by 459 publications

(207 citation statements)

References 34 publications

(42 reference statements)

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“…The European Space Agency (ESA) followed the same policy and offers the Sentinel-2 scenes free of charge. Landsat and Sentinel-2 make up a synergistic system of global monitoring in which every place on the planet is revisited each 2.9 days on average [23]. 2D and 3D techniques are not exclusionary and using 2D high-frequency data to decide the most efficient moment to take the 3D data may be a clear case of synergy.…”

Section: Introductionmentioning

confidence: 99%

A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level

et al. 2019

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This paper presents a new methodological process for detecting the instantaneous land-water border at sub-pixel level from mid-resolution satellite images (30 m/pixel) that are freely available worldwide. The new method is based on using an iterative procedure to compute Laplacian roots of a polynomial surface that represents the radiometric response of a set of pixels. The method uses a first approximation of the shoreline at pixel level (initial pixels) and selects a set of neighbouring pixels to be part of the analysis window. This adaptive window collects those stencils in which the maximum radiometric variations are found by using the information given by divided differences. Therefore, the land-water surface is computed by a piecewise interpolating polynomial that models the strong radiometric changes between both interfaces. The assessment is tested on two coastal areas to analyse how their inherent differences may affect the method. A total of 17 Landsat 7 and 8 images (L7 and L8) were used to extract the shorelines and compare them against other highly accurate lines that act as references. Accurate quantitative coastal data from the satellite images is obtained with a mean horizontal error of 4.38 ± 5.66 m and 1.79 ± 2.78 m, respectively, for L7 and L8. Prior methodologies to reach the sub-pixel shoreline are analysed and the results verify the solvency of the one proposed.

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Section: Introductionmentioning

confidence: 99%

A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level

et al. 2019

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“…With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (F β around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.For instance, a combination of data from Sentinel-2 (2A and 2B) and Landsat 8 provides a global median average revisit interval of 2.9 days [9].Regarding PCG mapping from remote sensing, an increasing amount of scientific literature has been published during the last decade that has mainly focused on Landsat imagery [4,[10][11][12][13][14][15][16]. Novelli et al [17] compared single-date Sentinel-2 and Landsat 8 data to automatically classify PCG.…”

mentioning

confidence: 97%

“…For instance, a combination of data from Sentinel-2 (2A and 2B) and Landsat 8 provides a global median average revisit interval of 2.9 days [9].…”

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confidence: 99%

Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

Nemmaoui

Aguilar

et al. 2018

Remote Sensing

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M.A.A.); faguilar@ual.es (F.J.A.)Abstract: A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (F β around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.For instance, a combination of data from Sentinel-2 (2A and 2B) and Landsat 8 provides a global median average revisit interval of 2.9 days [9].Regarding PCG mapping from remote sensing, an increasing amount of scientific literature has been published during the last decade that has mainly focused on Landsat imagery [4,[10][11][12][13][14][15][16]. Novelli et al. [17] compared single-date Sentinel-2 and Landsat 8 data to automatically classify PCG. A few indices especially adapted to plastic sheet detection, such as the Index Greenhouse Vegetable Land Extraction (Vi) [18], Plastic-Mulched Landcover Index (PMLI) [10], Moment Distance Index (MDI) [12,19], Plastic Greenhouse Index (PGI) [13], and Greenhouse Detection Index (GDI) [16] have been recently proposed.In relation to the classification of crops via remote sensing, Badhwar [20] published one of the first works where Landsat imagery multi-temporal data (only three dates) were used for corn and soybean crops mapping. In fact, crop types classification from medium resolution satellite imagery was mainly conducted by using pixel-based approaches until approximately 2011. Just before Petitjean et al. [21] argued that the increasing spatial resolution of available spaceborne sensors was enabling the application of the object-based image analysis (OBIA) paradigm to extract crop types from satellite image time series, Peña-Barragán et al. [22] developed a methodology for outdoor crop identification and mapping using OBIA and decision tree algorithms. This methodology was also applied to a Landsat time series to map sugarcane over large areas [23]. This OBIA approach consisted of two main consecutive phases: (i) the delimitation of crop fields by image segmentation and, (ii) the application of decision rules based...

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“…Furthermore, STMDA showed also a good potential in denoising the time-series of displacement at the whole scale with respect to the application of standard DIC methods, thus providing displacement precision up to 0.01 pixels.The most common optical satellite missions (e.g., QuickBird, SPOT, LANDSAT, Sentinel-2, WorldView, Pléiades, just to mention few of them) are characterized by spatial resolution ranging between 0.3 and 30 m and a revisit time of some days [25][26][27], therefore, they are not fully adequate to detect landslides at a spatial and temporal scale suitable for continuous monitoring. However, as stated in [4,28], the fusion of images collected by different optical satellite sensors has certainly increased the opportunities for cloud-free surface investigation, thus allowing also a general improvement of the temporal resolution.In the last few years, EO has been affected by an astonishing sensor and platform development and improvement, thanks to small satellites. According to [27,29,30], among the main features of these satellites, properly named CubeSats, there are: (i) the general small size and weight (a single-unit of CubeSat normally measures 10 × 10 × 11 cm and typically weights less than 1.5 kg), (ii) high geometric resolution (~3-5 m/pixel) and (iii) daily/near-daily revisit time.…”

mentioning

confidence: 99%

“…The most common optical satellite missions (e.g., QuickBird, SPOT, LANDSAT, Sentinel-2, WorldView, Pléiades, just to mention few of them) are characterized by spatial resolution ranging between 0.3 and 30 m and a revisit time of some days [25][26][27], therefore, they are not fully adequate to detect landslides at a spatial and temporal scale suitable for continuous monitoring. However, as stated in [4,28], the fusion of images collected by different optical satellite sensors has certainly increased the opportunities for cloud-free surface investigation, thus allowing also a general improvement of the temporal resolution.…”

mentioning

confidence: 99%

Sliding Time Master Digital Image Correlation Analyses of CubeSat Images for landslide Monitoring: The Rattlesnake Hills Landslide (USA)

Caporossi

Muzi

2020

Remote Sensing

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Landslide monitoring is a global challenge that can take strong advantage from opportunities offered by Earth Observation (EO). The increasing availability of constellations of small satellites (e.g., CubeSats) is allowing the collection of satellite images at an incredible revisit time (daily) and good spatial resolution. Furthermore, this trend is expected to grow rapidly in the next few years. In order to explore the potential of using a long stack of images for improving the measurement of ground displacement, we developed a new procedure called STMDA (Slide Time Master Digital image correlation Analyses) that we applied to one year long stack of PlanetScope images for back analyzing the displacement pattern of the Rattlesnake Hills landslide occurred between the 2017 and 2018 in the Washington State (USA). Displacement maps and time-series of displacement of different portions of the landslide was derived, measuring velocity up to 0.5 m/week, i.e., very similar to velocities available in literature. Furthermore, STMDA showed also a good potential in denoising the time-series of displacement at the whole scale with respect to the application of standard DIC methods, thus providing displacement precision up to 0.01 pixels.The most common optical satellite missions (e.g., QuickBird, SPOT, LANDSAT, Sentinel-2, WorldView, Pléiades, just to mention few of them) are characterized by spatial resolution ranging between 0.3 and 30 m and a revisit time of some days [25][26][27], therefore, they are not fully adequate to detect landslides at a spatial and temporal scale suitable for continuous monitoring. However, as stated in [4,28], the fusion of images collected by different optical satellite sensors has certainly increased the opportunities for cloud-free surface investigation, thus allowing also a general improvement of the temporal resolution.In the last few years, EO has been affected by an astonishing sensor and platform development and improvement, thanks to small satellites. According to [27,29,30], among the main features of these satellites, properly named CubeSats, there are: (i) the general small size and weight (a single-unit of CubeSat normally measures 10 × 10 × 11 cm and typically weights less than 1.5 kg), (ii) high geometric resolution (~3-5 m/pixel) and (iii) daily/near-daily revisit time. CubeSats represent a cost-effective EO strategy, allowing unique chances for a wide variety of application fields [8,31].Planet Labs Inc. has recently made available a large constellation (> 130 units) of optical 3-U CubeSats (10 × 10 × 30 cm), also called PlanetScope or, more commonly, "Doves" [27,30,[32][33][34]. With an average orbit height of about 475 km a.s.l., the main CCD array sensor is able to collect images of 7000 × 2000 pixels, with a footprint of approximately 24 × 8 km 2 . Each Doves collects one image/second along his orbit with a slight overlapping between consecutive scenes. In this way, taking advantage of the whole constellation, images at a daily sampling rate are achievable, thus allowing to ...

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A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring

Cited by 459 publications

References 34 publications

A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level

A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level

Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

Sliding Time Master Digital Image Correlation Analyses of CubeSat Images for landslide Monitoring: The Rattlesnake Hills Landslide (USA)

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