Evaluation of registration accuracy between Sentinel-2 and Landsat 8

Luigi Barazzetti*a, Branka Cucaa,b, Mattia Previtalia

aDept. of Architecture, Built environment and Construction engineering (ABC)

Politecnico di Milano, Via Ponzio 31, 20133 Milan

(luigi.barazzetti, mattia.previtali)@polimi.it

bDept. of Civil Engineering and Geomatics, Cyprus University of Technology

Limassol, Cyprus

branka.cuca@cut.ac.cy

ABSTRACT

Starting from June 2015, Sentinel-2A is delivering high resolution optical images (ground resolution up to 10 meters) to

provide a global coverage of the Earth’s land surface every 10 days. The planned launch of Sentinel-2B along with the

integration of Landsat images will provide time series with an unprecedented revisit time indispensable for numerous

monitoring applications, in which high resolution multi-temporal information is required. They include agriculture, water

bodies, natural hazards to name a few. However, the combined use of multi-temporal images requires an accurate

geometric registration, i.e. pixel-to-pixel correspondence for terrain-corrected products. This paper presents an analysis

of spatial co-registration accuracy for several datasets of Sentinel-2 and Landsat 8 images distributed all around the

world. Images were compared with digital correlation techniques for image matching, obtaining an evaluation of

registration accuracy with an affine transformation as geometrical model. Results demonstrate that sub-pixel accuracy

was achieved between 10 m resolution Sentinel-2 bands (band 3) and 15 m resolution panchromatic Landsat images

(band 8).

Keywords: Accuracy, Landsat 8, Registration, Sentinel-2

1. INTRODUCTION

Sentinel-2 mission is a component of Copernicus programme (former Global Monitoring for Environment and Security

programme), the most ambitious Earth observation initiative, headed by the European Commission (EC) in partnership

with the European Space Agency (ESA) [13].

The Sentinel-2A spacecraft was launched on June 2015 on a Vega vehicle from Kourou, in French Guiana. It is the first

in the two-satellite Sentinel-2 mission that will routinely provide multi-spectral high-resolution (10, 20 and 60 m) optical

images of land surfaces (Sentinel-2B launch is planned for mid-2016).

Sentinel-2 constellation will enhance continuity of Landsat- and SPOT- like information thanks to the higher revisit

frequency and a very large swath (290 km x 290 km), much larger than Landsat ETM+ (180 km x 172 km) and SPOT-5

(60 km x 60 km). It will provide optical images in 13 spectral bands with different spatial resolution (10 m, 20 m and 60

m), from the visible and near infrared (VNIR), to the short-wave infrared (SWIR - 4 spectral bands at 10 m), as well as 6

bands at 20 m and 3 bands at 60 m spatial resolution.

The pair of Sentinel-2 satellites (2A and 2B) will have an average revisit time which is about 2-3 days at mid latitudes

and 5 days at the equator. The coverage of this satellite pair is however not limited to land surfaces. It also includes

major islands, coastal and inland waters (from -56° to +83° latitudes). The designed lifetime of Sentinel-2 is 7 years with

propellant for 12 year operations.

The Multispectral Instrument (MSI) has an acquisition principle based on the push-broom camera model, i.e. linear

arrays of sensors that uses the motion of the platform along the orbit to obtain consecutives image rows. Signals are

converted into digital images with 12-bit radiometric resolution. More details about the different bands are shown in

Figure 1.

Bands can be grouped into 3 main classes:

10 m resolution: blue (490 nm), green (560 nm), red (665 nm), and near-infrared (842 nm);

20 m resolution: four bands in the vegetation red-edge spectral domain (705 nm, 740 nm, 783 nm and 865 nm)

and two large SWIR bands (1610 nm and 2190 nm);

60 m resolution: atmospheric corrections and cloud screening (443 nm for aerosol retrieval, 945 nm for water

vapor retrieval and 1375 nm for cirrus cloud detection).

Figure 1. Spatial and spectral resolution for the Sentinel-2 (from ESA Special Publication 1322/2).

S-2 was developed to deliver high-resolution optical images for land services and to provide enhanced continuity of

SPOT- and Landsat-type data. It is envisaged that the increased swath width along with the short revisit time allows

rapid changes to be monitored, such as vegetation during the growing season, making Sentinel-2 extremely suitable for

land monitoring purposes, including agriculture and landscape changes [1].

Additional applications for MRSI images concerns land monitoring, emergency response, and security services (see [2-

7]). As things stand at the present, more than 30 satellites deliver images for such purposes (ground resolution better than

100 m). However, only Landsat images are available on the Internet without extra costs, making the Landsat archive a

powerful source of information for Earth Observation with images with a span of more than 40 years.

Landsat images remain the only data available for such a long period of time with a ground sampling distance (GSD) of

some tens of meters, i.e. ranging from 80 m grid cell of the Multi-Spectral Scanner (MSS) up to 15 m spatial resolution

for the panchromatic band of the Enhanced Thematic Mapper Plus (ETM+).

New data are now available after the successful launch of Landsat 8 on February 11, 2013. Nowadays, a huge amount of

orthorectified Landsat images are available (free of charge) and can be used for remote-sensing applications thanks to the

U.S. Geological Survey’s Earth Resources Observation and Science Center (http://eros.usgs.gov) and NASA’s Land

Processes Distributed Active Archive Center (https://lpdaac.usgs.gov). Data are geo-referenced by using the UTM

projection (ellipsoid WGS84), i.e. the same projection adopted by Sentinel-2.

The resolution of Landsat imagery is sufficient to support a wide range of applications such as agricultural development,

deforestation, desertification, natural disasters, mineral exploration and classification, mining, and urbanization, land

cover and land cover change detection, agriculture, forestry changes, water quality analysis, geological applications, etc.

The high revisit frequency will provide data useful for near real-time monitoring. On the other hand, the combined use of

Landsat and Sentinel (with differences in bandwidth, number/location of bands, and signal to noise ratio) will require

different procedures and methods able to cross-calibrate OLI (Operational Land Imager) and MRSI, including geospatial

registration [7-11] and harmonized surface reflectance (e.g. calibration and radiometric normalization, and atmospheric

correction) [12].

This paper focuses on the geometric registration between Sentinel-2 and Landsat 8. According to the Data Quality

Report S2 MPC (Ref. S2-PDGS-MPC-DQR) (2015) for Level 1-C data quality status, the geo-location uncertainty of S-2

(without Ground Control Points) is better than 10 m at 2σ confidence level, and the inter-channel spatial co-registration

of any two spectral bands is 0.23 pixels at 3σ confidence level. This means that errors are below 1 pixel, well below the

initial requirements that were defined as: 20 m at 2σ confidence level, 0.30 pixels of the coarser achieved spatial

sampling distance of these two bands at 3σ confidence level, respectively.

The Level-1C product is composed of 100 km2 tiles as ortho-images in UTM/WGS84 projection, derived from a Digital

Elevation Model (DEM) to project the image in a cartographic space. Per-pixel radiometric measurements are provided

in Top Of Atmosphere (TOA) reflectance values, along with the parameters to transform them into radiance values.

Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 m depending on the

native resolution of the different spectral bands.

2. INTER-CHANNEL SPATIAL CO-REGISTRATION EVALUATION

In order to perform the geometric comparison between S-2 and Landsat 8, it was retained necessary to firstly assess the

inter-channel spatial co-registration accuracy of S-2 imagery. Inter-channel spatial co-registration accuracy refers to the

registration accuracy between any two bands (spectral band-to-band geo-registration).

The basic requirement of inter-channel spatial co-registration is an accuracy better than 0.3 pixels, for any two spectral

bands. The analysis was carried out on 2 datasets chosen among a total of 32 datasets considered in the second phase (see

Table 4). Here, datasets for (1) Yemen and (2) Bosnia and Herzegovina were assessed using the commercial software

ERDAS Autosynch. A set of corresponding points was automatically extracted with automated image matching tools,

then an affine transformation estimated via least squares was used to check band registration. Statics are provided in

terms of RMS, that depends on the residuals after least squares adjustment.

The analysis was carried out by using standard image-to-image matching based the following scheme:

band 4 (10 m) was compared to bands 2, 3, 8 (10 m), as well as band 5 (20 m) to take into consideration the

different ground resolution;

band 5 became the new reference for image matching with bands 6, 7, 8a, 11, 12 (20 m) and band 9 (60 m);

band 9 was compared to bands 1 and 10 (60 m). As can be seen in Table 2, 3 and 4, results are well below the

basic requirements (0.3 pix), except for the last band combination for which automated matching was not able

to provide a sufficient number of corresponding points.

The results are shown in Table 1 and Table 2, for Yemen and Bosnia and Herzegovina datasets respectively.

Table 1. Band-to-band matching results with ERDAS Autosynch: Yemen dataset

Yemen

Reference Band

Sensed Band

# matches

RMS (pixel)

4 (10 m)

2 (10 m) 287 0.19

3 (10 m) 289 0.09

8 (10 m) 282 0.14

5 (20 m) 238 0.15

5 (20 m)

6 (20 m) 250 0.07

7 (20 m) 247 0.15

8a (20 m) 234 0.15

11 (20 m) 220 0.15

12 (20 m) 240 0.18

9 (60 m) 160 0.55

9 (60 m) 1 (60 m) 257 0.26

10 (60 m) 15 4.93

Table 2. Band-to-band matching results with ERDAS Autosynch: Bosnia and Herzegovina dataset

Bosnia and Herzegovina

Reference Band

Sensed Band

# matches

RMS (pixel)

4 (10 m)

2 (10 m) 516 0.19

3 (10 m) 518 0.18

8 (10 m) 469 0.42

5 (20 m) 460 0.16

5 (20 m)

6 (20 m) 469 0.19

7 (20 m) 471 0.22

8a (20 m) 462 0.24

11 (20 m) 466 0.18

12 (20 m) 455 0.22

9 (60 m) 269 0.74

9 (60 m) 1 (60 m) 394 0.98

10 (60 m) 28 10.34

3. REGISTRATION ACCURACY BETWEEN SENTINEL-2 AND LANDSAT-8

In the following step S-2 and Landsat 8 images were compared to evaluate the registration accuracy by using

corresponding points automatically extracted with different commercial software. Table 3 shows a comparison between

L8 and S-2 bands in terms of wavelength and resolution. As the analysis carried out with different band-to-band

combinations of S-2 provided good results, the comparison between S-2 and Landsat 8 was carried out with band 3 for

S-2 (10 m) and band 8 for Landsat (15 m).

Table 3. Comparison between L8 and S-2 bands

Landsat 8 Wavelength

(micrometers)

Pixel sixe (m) Sentinel-2 Wavelength

(micrometers)

Pixel sixe (m)

Band 1 0.43 - 0.45 30 Band 1 0.443 60

Band 2 0.45 - 0.51 30 Band 2 0.490 10

Band 3 0.53 - 0.59 30 Band 3 0.560 10

Band 4 0.64 - 0.67 30 Band 4 0.665 10

Band 5 0.85 - 0.88 30 Band 5 0.705 20

Band 6 1.57 - 1.65 30 Band 6 0.740 20

Band 7 2.11 - 2.29 30 Band 7 0.783 20

Band 8 0.50 - 0.68 15 Band 8 0.842 10

Band 9 1.36 - 1.38 30 Band 8a 0.865 20

Band 10 10.60 - 11.19 100

(resampled to 30)

Band 9 0.945 60

Band 11 11.50 - 12.51 100

(resampled to 30)

Band 10 1.375 60

Band 11 1.61 20

Band 12 2.19 20

A set of 32 S-2 images was downloaded from the Scientific Sentinel Hub (https://scihub.copernicus.eu/) that provides

access to data through an interactive graphical interface. The distribution of the samples is shown in Figure 2. The full

dataset has a homogenous distribution on all five continents, including relatively flat areas and mountainous areas with

large variations in elevation. Landsat 8 images were downloaded from EarthExplorer (http://earthexplorer.usgs.gov/).

Figure 2. The distribution of images used for registration accuracy evaluation.

More details about the images are illustrated in Table 4, in which the nation and the location of the center of the image

are reported.

Table 4. Image locations by country and respective places of reference

1 San Carlos de Bolivar Argentina 1

2 Rosario Argentina 2

3 Barcaldine Australia 1

4 Victoria Rock Australia 2

5 Bicheln Austria

6 Stanojevici Bosnia and Herzegovina

7 Loumana Burkina Faso

8 Terranova and Labrador Canada 1

9 Harmon Valley Canada 2

10 Mano Central African Republic

11 Shuangbeixiang China 1

12 Dawanzhen China 2

13 Nicosia Cyprus

14 Nicosia Cyprus2

15 Dragor Denmark

16 Diaraguerela Guinea

17 Dalmau India

18 Telol Al Baj Iraq

19 Venice Italy

20 Tokyo Japan

21 El Mehiriz Morocco

22 Ngourti Niger

23 Estancia Laguna Verde Paraguay

24 Leboter Russia

25 Beograd Serbia

26 Vrede Sud Africa

27 Atamurat Turkmenistan

28 Joshua Tree National Park USA 1

29 Picayune USA 2

30 Tucson USA 3

31 Maroa Venezuela

32 Al Matammah Yemen

The first analysis regarded the use of the registration tools available in three commercial software used for remote

sensing and GIS applications: Exelis ENVI, ERDAS Imagine, and ESRI ArcMap. The Landsat image was always

assumed as reference (or master). Figure 3 shows the RMS values obtained for 6 images of the full dataset: ERDAS

Autosynch provided the best results for all the considered datasets, for which a RMS worse than 1 pixel was found only

for mountainous areas (China and Austria datasets). It is clear that the achievable precision depends on the technique

used for image matching, which in some cases is based on interest operators (e.g. ENVI gives the opportunity to work

with the Harris, Forstner and Moravec operators) and correlation technique. A test carried out on the Venice dataset, in

which ENVI was used with the three operators available, provided a final RMS of:

0.6 pixels → Forstner operator; 0.63 pixels → Harris operator; 0.65 pixels → Moravec operator.

Results are therefore quite similar, notwithstanding the different interest operators used for point detection. In addition,

142 corresponding points were always extracted for the three tests (using three different software) carried out over the

same area.

Figure 3. Registration results with different software.

The comparison for the full dataset was carried out using Excelis ENVI and its image registration tools, in which the

Forstner operator was chosen for matching. Landsat (band 8, panchromatic at 15 m) was always used as reference,

whereas S-2 band 3 was set as warp image. Matching was carried after setting the mathematical model to RST (i.e. affine

transformation) and a threshold of 0.6 pixels for outlier rejection. Results are shown in Figure 4, where a sub-pixel

accuracy (in terms of RMS) was found for most images. Some datasets acquired over steep slopes (mainly mountainous

areas) revealed slightly worse results (always smaller than 1.2 pixels).

Figure 4. RMS of automated image registration with ENVI: S-2 and L8.

Finally, Figure 5 shows the distribution of image residuals for additional six datasets across five different continents, i.e.

residuals of image coordinates (pixels), after the estimation of affine transformation (the blue circle has unary radius).

This last test was carried out with ERDAS Autosynch. As can be seen, most points are located inside a smaller area

inside the circle, demonstrating that most points have sub-pixel precision. On the other hand, some datasets highlight

points outside the circle and contribute to the overall worsening of the RMS values.

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Figure 5. Residual distribution with ERDAS Autosynch for six datasets across five continents. The blue circle has a unary radius (1

pixel). Points are mainly located inside the blue circle, notwithstanding some points exhibit larger residuals.

CONCLUSION

This paper presents an evaluation of registration accuracy between Sentinel 2 and Landsat 8 images. The work was

conducted in two main activities: (i) assessment of inter channel-spatial registration accuracy and (ii) geometric

comparison between S-2 and L8 imagery over the same area. The results highlight a good geometric correspondence

between different bands of S-2, better than the basic requirement (0.3 pixels). These findings allowed us to choose band

3 of S-2 for the comparison with the panchromatic channel of Landsat 8, which was assumed as reference during image

registration with a set of corresponding points automatically extracted via image matching and the estimation of

transformation parameters with an affine transformation. The registration tool of ENVI package revealed a sub-pixel

spatial correspondence for 31 images (out of 32) in terms of RMS. The largest error (smaller than 1.2 pix) was found for

areas with large variations in elevation.

Such results contribute to the initial statement for the combined use of Landsat 8 and Sentinel-2 images in environmental

monitoring, in particular for the domain of land applications. The analysis of activities affecting landscape modifications

(including the agriculture sector) usually requires monitoring scales compatible to a ground resolution of few tenths of a

meter. As the bands examined in this work have a resolution of 10 m and 15m, it is expected that a combination of L8

and S-2 data acquired over the same area is able to provide quite robust datasets for this kind of analysis, towards future

applications requiring a more systematic and integrated approach for satellite image processing.

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