Landsat 8 scenes are provided with band levels detailed para-

meters which allow the direct conversion of Digital Numbers (DN) to

the Top of Atmosphere (TOA) reflectance. Conversely, to measure the

surface reflectance, atmospheric effects must be taken into considera-

tion. OLI and TIRS band data were converted to TOA spectral radiance

(Alipour et al., 2003; Soliman et al., 2012; Barsi et al., 2012) using the

radiance rescaling factors provided in the metadata file (Landsat 8

Handbook, 2016).

= + L M Q A λ L cal L (1)

where:

Lλ is TOA spectral radiance (Watts/ (m

2

* srad * μm)), M L is Band-

specific multiplicative rescaling factor from the metadata, A L is Band-

specific additive rescaling factor from the metadata, Q cal is Quantized

and calibrated standard product pixel values (DN). OLI band data was

also converted to TOA planetary reflectance using reflectance rescaling

coefficients provided in the product metadata file (MTL file) of the

Landsat 8 satellite imagery (Landsat 8 Handbook, 2016). The following

equation was used to convert DN values to TOA reflectance for OLI data

(Landsat 8 Handbook, 2016):

′ = + ρλ M Q A ρ cal ρ (2)

Where:

ρλ ’

is TOA planetary reflectance, Mρ is Band-specific multiplicative

rescaling factor from the metadata, Aρ is Band-specific additive re-

scaling factor from the metadata.

Dark Object Substraction (DOS) method was applied to remove

apparently small values of reflectance caused by air diffusion which is

useful in calibrating the other parts of the image (Chavez, 1996). It is

worthy to note that DOS method can be used as alternatives atmo-

spheric measurements are not available. The equation for DOS cor-

rected image is given as

= − L P L min L DO ( 1%) 1 1 1 (3)

Where;

L min 1 is the radiance corresponding to the minimum DN from the

sum of all the pixels from the image, L DO ( 1%) 1 is the radiance of Dark

Object assumed to have a reflectance of 0.01.

Although TIRS has proven to be stable since its operation but there

have been some scene-dependent artifacts like banding and absolute

calibration errors observed in the imagery (Montanaro et al., 2014).

This is attributed to the stray light effects. The extent of both effects is

greater in band 11 than in band 10. Band 10 of the TIRS data is pre-

ferably used to band 11 because the artifacts in band 10 have demon-

strated satisfactory performance (Montanaro et al., 2014)

Hello,

The optical/hyperspectral L1B data acquired by any of the spaceborne sensor has radiance values. This shouldn't be directly used for estimating any of the indices in order to characterize an area. Rather, the reflectance values provide an accurate scenario in retrieving the geophysical properties of the surface.

Utilizing the reflectance rescaling coefficients of the Landsat OLI data (as mentioned in the products metadata file, .MTL), it's radiance (DN values) can be converted into TOA reflectance by employing the following equation:

pA' =Mp*Qcal + Ap

where:

pA' = TOA reflectance without any correction for sun angle.

Mp = Reflectance multiplicative rescaling factor

specified for each of the corresponding spectral band (REFLECTANCE_MULT_BAND_x, where x is the band number, as in the metadata).

Ap = Reflectance additive rescaling factor specified for each of the corresponding spectral band (REFLECTANCE_ADD_BAND_x, where x is the band number, as in metadata).

Qcal = Quantized and calibrated radiance DN values of the OLI data.

In order to correct TOA Reflectance for sun angle, the following needs to be executed:

pA = pA'/cos(zenith) = pA'/sin(elevation)

where,

pA = Corrected TOA Reflectance

elevation = Local sun elevation angle in degrees (SUN_E LEVATION, as in metadata).

zenith = Local solar zenith angle; zenith = (90 - elevation).

In this, you will find all the solar angles (elevation or zenith) with respect to the scene centre. However, for an even more accurate Reflectance calculations, per pixel solar angle should be utilized. Unfortunately, you won't find these parameters for Landsat 8 products. Atmospheric correction can be performed by utilizing the radiative transfer codes of FLAASH, ATCOR, QUAC, etc. Among all the techniques, FLAASH provides a reliable output as it incorporates a more detailed view of aerosol content and water retrieval parameters.

Pan sharpening can be done by integrating the high resolution Panchromatic (PAN) band image with the low resolution spectral data in order to produce the qualities of both (spatial and spectral) in one. You can use IDL console of ENVI software in order to process this step. If you are more comfortable with the tool, ENVI Classic and ERDAS Imagine provides an interactive way to perform this step. Moreover, you can use other image processing softwares like ILWIS, EnMAP-Box, etc.

I would recommend to use ENVI package and QGIS to perform processing of Satellite images along with geospatial and geostatistical analysis.

Kind Regards,

Shashwat

Hi Aydin,

You may find our article useful:

Article A survival guide to Landsat preprocessing

Befor you start atmospheric correction please perpend the following notes

you should know accurate meaning of DN,Radiance and Reflectance

Digital sensors record the intensity of electromagnetic radiation (ER) from each spot viewed on the Earth’s surface as a digital number (DN) for each spectral band. The exact range of DN that a sensor utilises depends on its radiometric resolution. For example, a sensor such as Landsat MSS measures radiation on a 0-63 DN scale whilst Landsat TM measures it on a 0-255 scale. Although the DN values recorded by a sensor are proportional to upwelling ER (radiance), the true units are W m-2 ster-1 µm-1 (Box 3.1).

The majority of image processing has been based on raw DN values in which actual spectral radiances are not of interest (e.g. when classifying a single satellite image). However, there are problems with this approach. The spectral signature of a habitat (say seagrass) is not transferable if measured in digital numbers. The values are image specific - i.e. they are dependent on the viewing geometry of the satellite at the moment the image was taken, the location of the sun, specific weather conditions, and so on. It is generally far more useful to convert the DN values to spectral units.

This has two great advantages:

1) a spectral signature with meaningful units can be compared from one image to another. This would be required where the area of study is larger than a single scene or if monitoring change at a single site where several scenes taken over a period of years are being compared.

2) there is growing recognition that remote sensing could make effective use of “spectral libraries” - i.e. libraries of spectral signatures containing lists of habitats and their reflectance (see Box 3.1).

While spectral radiances can be obtained from the sensor calibration, several factors still complicate the quality of remotely sensed information. The spectral radiances obtained from the calibration only account for the spectral radiance measured at the satellite sensor. By the time ER is recorded by a satellite or airborne sensor, it has already passed through the Earth’s atmosphere twice (sun to target and target to sensor).

Mr Aydin

At the this time Landsat8 consist of collection 1 Level 1 and Landsat collection 1 level2(On Demand)

collection 1 Level 2 is sourface reflectance and isn't for free download but you could email to USGS and demand it.

for Landsat collection 1 Level1(L1C) follow these notes for quickly atmospheric correction

after doing this stages your images Convert to sourface reflectance

1-Run ENVI 5.3(Not classic)

2-open MTL file

this is example of MTL file

------------------------------------------------------------------------------------------------

LC08_L1TP_165035_20180617_20180617_01_RT_MTL.txt

------------------------------------------------------------------------------------------------

3-Do Radiometric Calibration

you can search for finding it at toolbox

4-fill empty text box for Doing calibration in Radiometric calibration form

first you should select fron input files please select Multispectral

5-then Radiometric Calibration Form will be opened

click Apply FLAASH Setting button

don't alter others parameters

select output radiance file name

Cleck OK

5-now you have Radiance callibrated image and you ready to do final attmospheric correction

We use FLAASH atmospheric correction

6-run FLAASH atmospheric correction tool

and fill empty box for adjusting input parametrs

61.select the radiance calibrated file(dat file).

62.select name for reflectanse for example 'Sourfaceref'

63.select folder and path for converting files.

64.select Root name for FLAASH files for example 'flaash'

65.select sensor Type as Multispectral>>>Landsat 8 OLI

66.enter Flight Date (image date)

for this example is set to 2018 Jun 17

67.enter Flight Time GMT:

it written at MTL file open it via Excel application and read it

for IRAN is approximatly 7:15

68.Select Atmospheric Model from Tropical,Mid Latitude summer or ....

69.in the final you should adjust Multispectral Setting

70.you can set it for default or over water or over Land

71-Click OK button and wait to finish

Best Regards,

Hello Mr Khosravirad

thanks for your response there is problem about Pansharpening, as you know i used pan band and NN method after FLAASH and rescaling but my data values get above 1 (i think the accepted range is between 0-1).

Mr Aydin

Pan sharpening uses spatial information in the high-resolution grayscale band and color information in the multispectral bands to create a high-resolution color image, essentially increasing the resolution of the color information in the data set to match that of the panchromatic band.

Dear to make it easy to you

you just try the manual of OLI then read it

the Level 2 Surface Reflectance products CAN be downloaded for free from the Earth Explorer (USGS) website, contrary to what was stated above. I assume that the USGS either uses the equation they suggest for converting DN to reflectance............or a better one. The results are not without other challenges, but you can get them for free.

Dear Phil

I said the Landsat collection 1 level 2(sourface reflectance) in USGS is ON-Demand thus is not archived in USGS.

========================================================

https://landsat.usgs.gov/landsat-surface-reflectance-data-products

========================================================

Data Access

Surface Reflectance Level-2 data products can be ordered through the following pages:

USGS Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) On Demand Interface

To begin the order, upload a text file (*.txt) listing one Landsat Level-1 product or MODIS granule identifier on each line. Scene identifiers can be found in the search results on EarthExplorer or GloVis.

After uploading the scene list text file, a number of options can be selected, including:

• Source products (Original input Level-1 product or metadata)
• Top of atmosphere reflectance, surface reflectance (SR), or band 6 top of atmosphere brightness temperature products
• Surface reflectance-based spectral indices (NDVI, NDMI, NBR, SAVI, EVI)
• Customizable output options: data format, reprojection, modifying the image extents, and pixel resizing
• Intercomparison and output product statistics plotting

All orders submitted through ESPA are processed within 2-5 days, depending on the size of the order and the backlog already in the system. Email notifications are sent after the order is placed, and also after the data is processed and ready to download. *Note: Data requested through ESPA are not accessible using the EarthExplorer interface or Bulk Download Application (BDA).

EarthExplorer

Surface Reflectance (SR) data for individual scenes can be selected using EarthExplorer. These requests will be sent to the ESPA On-Demand interface for processing and data delivery.

please tell me how can i download landsat 8 sourface reflectance from USGS?

Best Regurds,

Dear the best way to do that just go to website of Landsat satellite series then download the landsat guide and follow its steps

regards

Landsat 8 scenes are provided with band levels detailed para-

meters which allow the direct conversion of Digital Numbers (DN) to

the Top of Atmosphere (TOA) reflectance. Conversely, to measure the

surface reflectance, atmospheric effects must be taken into considera-

tion. OLI and TIRS band data were converted to TOA spectral radiance

(Alipour et al., 2003; Soliman et al., 2012; Barsi et al., 2012) using the

radiance rescaling factors provided in the metadata file (Landsat 8

Handbook, 2016).

= + L M Q A λ L cal L (1)

where:

Lλ is TOA spectral radiance (Watts/ (m

2

* srad * μm)), M L is Band-

specific multiplicative rescaling factor from the metadata, A L is Band-

specific additive rescaling factor from the metadata, Q cal is Quantized

and calibrated standard product pixel values (DN). OLI band data was

also converted to TOA planetary reflectance using reflectance rescaling

coefficients provided in the product metadata file (MTL file) of the

Landsat 8 satellite imagery (Landsat 8 Handbook, 2016). The following

equation was used to convert DN values to TOA reflectance for OLI data

(Landsat 8 Handbook, 2016):

′ = + ρλ M Q A ρ cal ρ (2)

Where:

ρλ ’

is TOA planetary reflectance, Mρ is Band-specific multiplicative

rescaling factor from the metadata, Aρ is Band-specific additive re-

scaling factor from the metadata.

Dark Object Substraction (DOS) method was applied to remove

apparently small values of reflectance caused by air diffusion which is

useful in calibrating the other parts of the image (Chavez, 1996). It is

worthy to note that DOS method can be used as alternatives atmo-

spheric measurements are not available. The equation for DOS cor-

rected image is given as

= − L P L min L DO ( 1%) 1 1 1 (3)

Where;

L min 1 is the radiance corresponding to the minimum DN from the

sum of all the pixels from the image, L DO ( 1%) 1 is the radiance of Dark

Object assumed to have a reflectance of 0.01.

Although TIRS has proven to be stable since its operation but there

have been some scene-dependent artifacts like banding and absolute

calibration errors observed in the imagery (Montanaro et al., 2014).

This is attributed to the stray light effects. The extent of both effects is

greater in band 11 than in band 10. Band 10 of the TIRS data is pre-

ferably used to band 11 because the artifacts in band 10 have demon-

strated satisfactory performance (Montanaro et al., 2014)