Geographic Information Systems

Regional Coordination on Improved Water Resources Management and Capacity Building

A training manual: Crop mapping using remote sensing data of Landsat 8 Ministry of Water and Irrigation, Amman, Jordan

Prepared by:

Prof. Jawad T. Al-Bakri

February 2015

Citation: Al-Bakri, J. T. 2015. Crop mapping using remote sensing data of Landsat 8: A Training Manual. Regional Coordination on Improved Water Resources Management and Capacity Building, Ministry of Water and Irrigation, Amman, Jordan.

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TABLE OF CONTENTS Contents

Page

1

Introduction ............................................................................................................................. 3

2

ERDAS Imagine software ......................................................................................................... 3

3

Background on Landsat 8 data ................................................................................................ 6

4

Processing steps to derive NDVI images ................................................................................. 7

5

Downloading Landsat 8 data ................................................................................................... 8

6

Processing Landsat 8 data to create a mask for cloud .......................................................... 11

7

Atmospheric correction for Landsat 8 data........................................................................... 16 7.1

Image based atmospheric correction (using ground measurements) ........................... 17

7.2

Relative Atmospheric correction (Image-to-image) ...................................................... 23

8

Creating NDVI image from Landsat 8 data ............................... Error! Bookmark not defined.

9

Stacking NDVI images in one file .............................................. Error! Bookmark not defined.

10

Extracting NDVI profiles for crops......................................... Error! Bookmark not defined.

10.1

Extracting NDVI profiles using ERDAS Imagine.............. Error! Bookmark not defined.

10.2

Extracting NDVI profiles using ArcGIS ........................... Error! Bookmark not defined.

10.3

Classifying NDVI image using results from NDVI profiles ............. Error! Bookmark not

defined. 10.4

Summary of decisions trees for crops in Azraq ............. Error! Bookmark not defined.

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Introduction

Remote sensing data is increasingly used in water resources management. In the last two decades, improvements in spatial, spectral and temporal resolution of the earth observation systems (EOS) data encouraged the adoption of this technology for managing water resources in countries with scarce water resources. The most important application with this regard is the use of medium and high resolution data to map agricultural crops and to estimate crop evapotranspiration as a major component of crop water requirements. The improvements in remote sensing technology and the progressive development in computer software and hardware resulted in the development, calibration and adoption of remote sensing models for managing water resources. Application of remote sensing for mapping the different crops involves the use of digital image processing techniques to derive layers of irrigated crops and to estimate their seasonal consumption of water. Since remote sensing technology proved its efficiency in crop mapping, the use of such technology will contribute to the efforts of the Ministry of Water and Irrigation (MWI) in managing water resources in Jordan. This can be achieved by building the capacity of the MWI staff through on-job training programs and joint projects with local and international partners. This project “Regional Coordination on Improved Water Resources Management and Capacity Building” is aiming at building the capacity of the MWI in the area of remote sensing and its use for crop mapping and water management. This manual includes guidelines on the processing of Landsat 8 data to map irrigated crops in Mafraq-Azraq area in Jordan. The manual include all steps of processing satellite imagery to derive vegetation index of the different irrigated crops and derive crop maps using the multi-temporal data for the cropping season of 2013-2014. The manual includes the download procedure, correction of images and applications of different algorithms for deriving crop maps. All steps and procedures are based on the use of ERDAS Imagine software and some open sources software and data. 2

ERDAS Imagine software

Image processing software is specialized computer software that aids in displaying and processing of images to obtain thematic maps that can be used for further analysis in GIS environment. ERDAS Imagine is a powerful software package that is used (by remote sensing specialists) for manipulating and analyzing data. The practical parts of this manual will contain different steps to achieve different purposes and functions. ERDAS is windows-based software with many interfaces and tabs that will be activated when the software is launched. The software interface includes various tabs across the top, the image viewer and a contents and shoebox panel on the left, where you can organize your files, and a black graphic window on the right, where images and other spatial data can be displayed. The interface is similar in structure to the ones used in MS office. As you will see, the main menu contains 8 tabs when no file is displayed yet. The number of tabs increases to 12 when you display an image with multispectral bands (See the plate on next page). The help menu is accessed by clicking on the little question mark (?) on the upper right side of the ERDAS interface. This will open an Internet Explorer window with a Table of Contents on

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the left leading you to common ERDAS functions (you may need to click “Allow blocked content” at the bottom). There are more detailed help documents available for specific ERDAS tools that you can access by clicking on the Help tab on the main interface. Field Guides explain some of the theory behind ERDAS routines. Tour Guides walk you through some ERDAS routines in a step by step fashion. User Guides offer tutorials on a wide range of ERDAS applications. Language Reference help offers information on programming syntax for customizing ERDAS or writing your own models (we’ll eventually do some of this).

Preferences: It is preferred to customize ERDAS defaults to save yourself a lot of pointing and clicking during the remainder of the semester by using the Preferences Editor. You can access this by clicking on the File tab and then clicking on the Preferences button at the very bottom of the window that appears. First set your default directories for reading and writing information (files). The former is your default data directory and the latter is the default output directory. After selecting the directories and folders of input data and output processed data, click on “User Save” to save your preferences for yourself (user) of for all users (Global save).

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Important terminology in ERDAS Imagine: 1- Raster is the technical term for image or GIS data that are composed of pixels arranged in rows (or lines or records) and columns (or samples). Each x,y (row, column) grid cell is called a “pixel” (just like in your digital camera). 2- Vector is the technical term for GIS point, line, and polygon data in computer (digital) format. Vector data are composed of the x,y coordinates of points and information about if and how the points are connected into lines or areas (polygons). 3- A layer in ERDAS Imagine is either a single raster satellite band or a vector dataset. 4- A file (e.g. an image file) can have one or several layers. The extension for an ERDAS Imagine file is (*.img). The file will have several layers, one for each band of satellite data. This may cause confusion because there is not necessarily a correspondence between layer number (in ERDAS) and band number (from the satellite). In other words, ERDAS will number the bands consecutively regardless of the band numbers assigned by the engineers who designed the satellite. This will make more sense to you after a few more practical. 5- AOI (area of interest): this is usually created by the user to limit his work and analysis to this area instead of using the whole file. 6- Projection: This is the file coordinate system or location on the earth using projection. Each country has its own “local” projections. In Jordan, we use Jordan Transverse Mercator (JTM), Universal Transverse Mercator (UTM) zones 36 and 37, the Palestine Grid (Cassini), in addition to the geographic coordinates. We will cover this when you start to work with GPS. In ERDAS, as well as in other software, you can convert from one projection into another.

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Background on Landsat 8 data

Landsat represents the world's longest continuously acquired collection of space-based moderate-resolution land remote sensing data. The mission started in 1972 with Landsat MSS sensor that provided two visible (Green and Red) and two infrared bands at a spatial resolution of 79m and a temporal resolution of about 26 days. The improvements of Landsat started in the early 1982 when Thematic Mapper was added to the mission and provided data at 30m for visible and near infrared bands. The mission continued till Landsat 7 with ETM+ sensor. This generation of data suffered from striping of data with no obvious intention to continue with the mission, till Landsat 8 mission was decided, supported at different levels and launched in 2013. The mission included Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) with the following specifications: Band

Wavelength Resolution (m) (µm)

Possible use

1 - Coastal aerosol

0.43 - 0.45

30

For aerosol and water pollution

2 - Blue

0.45 - 0.51

30

Urban, water pollution with B1

3 - Green

0.53 - 0.59

30

Urban planning

4 - Red

0.64 - 0.67

30

Urban, Vegetation

5 - Near Infrared (NIR)

0.85 - 0.88

30

Vegetation, water

6 - SWIR 1

1.57 - 1.65

30

7 - SWIR 2

2.11 - 2.29

30

8 - Panchromatic

0.50 - 0.68

15

Urban planning

9 - SWIR

1.36 - 1.38

30

For cirrus clouds

10 - Thermal Infrared (TIRS) 1 10.60 - 11.19

100

11 - Thermal Infrared (TIRS) 2 11.50 - 12.51

100

Soil moisture and temperature, pollution

Soil, rock, water stress

The use of Landsat data was very common in the early stages as it was the only historical EOS data that was commercial and available for all users. The second reason for the use of Landsat (particularly 5 and 7) is the fact that the data was free and recently provided in the form of spectral reflectance at ground level. Things to consider when using Landsat 8 data: 1. The data is free of charge and can be downloaded immediately after acquisition. 2. The data is provided in the form of digital numbers (DN) that can be converted to Top of Atmosphere (TOA) radiance using the coefficients provided in the MTL file. (http://landsat.usgs.gov/Landsat8_Using_Product.php). 3. At the level of Landsat 8 spatial resolution no geometric correction might be needed. 4. So far (July 2014), atmospheric correction is needed before using visible and NIR bands. It is expected that surface reflectance data will be available using the LEDAP system. 5. All required information prior to data use is available at: (http://landsat.usgs.gov/).

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Processing steps to derive NDVI images

Crop mapping will be based on deriving NDVI time series from Landsat 8 images for one growing season (2013/2014). This requires the application of different steps that are summarized in the following flowchart.

Download of Landsat 8 data http://earthexplorer.usgs.gov/ (Registration and login, location, date, Landsat Archive data of OLI/TIRS, download)

Cloud mask for each image

Extract data

(Install fmask on C drive, copy image to the folder, extract image, use command prompt to apply fmask)

DN of bands 4 and 5 to a separate folder

Absolute Atmospheric correction Radiometer (ground data)

Reclassify in ArcGIS

Relative Atmospheric correction

Master Image (Day 181)

(Spreadsheets to derive equations, model maker to apply equations

Reflectance (0-100%)

Bands 4 and 5 Reflectance (0-100%)

NDVI Image Apply in model maker

Stack and subset to study area

NDVI images Layers of NDVI 2013/2014 in one file 7

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Downloading Landsat 8 data

Data from the Landsat 8 satellite (launched as the Landsat Data Continuity Mission - LDCM- on February 11, 2013) became available on May 30, 2013. The data is available, free of charge, through the “Earth Explorer” gateway at the following link: http://earthexplorer.usgs.gov/. The steps you need to download the data are very simple and summarized as follows: 1- You need to register on the website to receive a user and a password through email. 2- After receiving your user and password you need to login into the website

3- After login in using the user and password, your webpage will look like the following:

4- If you know the path and row for the image that cover your study area, then you can enter them in the specified tab. For the project area of Mafraq, the path and row are 173/38. This system of nomination is known as WRS2, in which each place on earth is divided into path and row. You can also enter the coordinates of your study area or use a direct click inside the map to select your location. In our project and after selecting the location, your page will appear as follows:

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5- After selecting your area, you need to specify other parameters for “search”, these include the “Data Sets” and the date for data. Click on the tab of “Data Sets” and scroll down to the Landsat data. The data we need are listed under the “Landsat Archive”. The type is L8 OLI/TIRS. You can also select other historical data of Landsat 4,5 and 7.

Note: Up-to-date (August 2014), the data of Landsat 8 is presented in digital numbers (DN), while data of Landsat 5 and 7 are in surface reflectance units corrected with LEDAP system. You need to carry out atmospheric correction for Landsat 8 to derive physical parameters and to use multi-temporal images for comparisons. 6- After selecting the data type (L8 OLI/TIRS) click the tab of “Data Range” to specify the date range. For the purpose of this training, select the range of data for the first 6 months of 2014 and click results. Your web page will have the following appearance:

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The results (shown in the left panel) are sorted from the recent (newer date) to the historic (older). To download the image click on the icon of disk

The following interface will appear:

The data of Landsat is the last icon “Level 1 GeoTIFF Data Product (957.1 MB)” . The size of the file is about 1 GB. Other files include the outlook image and quality image that shows cloud, snow, water..etc., in the image. After clicking on the download icon the image will start to download. You need to download two images at the same time. The file of the image is compressed (zipped) and has the extension of “.tar.gz”.

Note: Our Training will be based on the file “LC81730382014181LGN00.tar” for NDVI. The name convention is LC8 path = 173, row = 038, year = 2014, day of year = 181. For cloud masking, the suggested image is LC81730382014133LGN00.tar

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Processing Landsat 8 data to create a mask for cloud

Masking of clouds is required prior to using Landsat 8 data. You will use “fmask” software, which is open source software that can be downloaded from the following website https://code.google.com/p/fmask/. You can also get the software using Google search “fmask google code”. After downloading the software, you will have the package Fmask_pkg.exe on your computer, now you need to install it as follows: 1- Create a folder on your machine and call it C:\fmask. 2- Copy the Fmask_pkg.exe to this folder and double click on it to proceed with the installation. Accept all options and install. After installation you will get an icon for the toolkit that you need as shown below.

3- Make sure that this application or icon is copied in the folder c:\fmask. 4- Direct creation of the cloud mask: copy the original Landsat 8 file to the directory of the fmask software and click on the icon of mask . The image file will be uncompressed and the mask will be created. 5- If this method did not work, then unzip the image file in the same folder using the option “Extract Here”. For cloud masking, the image is 2014133.

These files are the 11 bands of Landsat 8 image, the quality band and the header file (LC81730382014133LGN00_MTL.txt) with all necessary information on the image like coefficients to transfer the digital numbers to top of atmosphere (TOA) radiance and reflectance. Further details on these coefficients are included in the official website of Landsat 8 (http://landsat.usgs.gov/Landsat8_Using_Product.php). For our NDVI image, we will not use these equations. For the time being, we will just work on cloud masking. Fmask citation:

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Zhu, Z. and Woodcock, C. E., Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sensing of Environment (2012), doi:10.1016/j.rse.2011.10.028

6- To create the layer of cloud mask, you need to use the DOS command by clicking on “Command Prompt”. After the command prompt is launched, you need to change the directory by typing cd.. and clicking enter for one time or more. 7- After reaching the root directory c:\, type cd fmask. This shall take you to the subdirectory of “fmask”. Type fmask.exe to start the program on the extracted image stored in the same folder. The following interface shall appear with some information after a while from typing the command:

8- The process of creating a cloud mask will take some time and when it is finished you will see some lines of information. Do not close the prompt command until the elapsed time is shown, and then you can dismiss the Prompt Command.

9- Open the “fmask” folder in the windows explorer to see the mask layer. You will have two additional files added to the folder at the end of the files list; these are 1- LC81730382014133LGN00_MTLFmask (this is the mask layer) 2- LC81730382014133LGN00_MTLFmask.hdr (this is the header file for the mask layer) You need to keep both files on the same folder to be used for masking the cloud in the NDVI image. The first file can be opened in ERDAS Imagine or ArcGIS software after adding the extension of .tif to the end of the file name in the windows explorer. Now you can explore the mask layer in ArcGIS directly.

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10- In case the mask layer is not read or opened in ERDAS or ArcGIS, you can use ArcGIS to export into TIF format This can be achieved as follows: Drag the mask layer from ArcCatalog or just add it as a layer in ArcMap. Right click on the image name and select Data\Export Data

The menu of export will appear, identify the file name and the location of the output, keep all other options as they are. This can work with either TIFF or Imagine file (img).

A pop-up window will appear asking you to increase the pixel depth, click yes and when finished click yes to add the layer to your ArcMap display or TOC. The image will be displayed with the following values: 0 = clear land pixel 1 = clear water pixel, 2 = cloud shadow,

3 = snow, 4 = cloud 255 = no observation

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In the next step you will reclassify all values. 11- To reclassify cloudy pixels to become with zero values and non-cloudy pixels to have the value of 1, you need to use ArcToolbox, select spatial analyst tools and navigate to “Reclassify”:

When the interface of reclssify is activated, fill in the required fields as shown in the below dialog (you need to click unique option to show all values in the mask). Now you can enter the new values and give the output a specific name. the suggested name is cloud2014133.

You can unmask areas of water by keeping their value (1) as it is. So, 0 will become 1 and 1 will be 1, 2 4 and 5 (if present) shall be reclassified to zeros.

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12- Now, you can display the mask image in ERDAS Imagine (see the next plate). The white areas are those with no clouds (value or DN = 1) while the dark areas are clouds with data value or DN = 0.

The logic behind this is that masking in ERDAS is a multiplication process by which you remove certain data. In our NDVI images we will multiply NDVI with cloudy areas (data with zero) to obtain pixels with zero (representing clouds) while areas with no cloud will keep their values (multiplied with 1). 13- Repeat the above steps for the other image (day 181). You will notice that this particular image has nearly no clouds; only few spots. To see these cloud spots, display the layer created from mask, after adding the extension “tif” to the name, in ArcGIS. Change the properties of symbology to unique values. Zoom in to see the small clouds. 14- Repeat the steps for the time series of Landsat images that you are going to use in your project. Below are some of the output masks.

LC81730382014085LGN00_MTLFmask.tif

LC81730382014117LGN00_MTLFmask.tif

LC81730382014149LGN00_MTLFmask.tif

Important Notes: 1- After finishing the fmask for one Landsat image, all files (bands) shall be moved from the fmask folder to another folder. Only the fmask.exe shall be kept. You can then copy another file of Landsat (.tar) to this folder and carry out the procedure similar to the steps you used for the first image.

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2- Ask the instructor (Dr Jawad Al-Bakri) to get the raw files (other images) instead of downloading them. For this project, the data includes the images of 2014 and some images from 2013. 3- Keep all the files of masks in a separate folder. These will be used later after creating NDVI images.

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Atmospheric correction for Landsat 8 data

Remote sensing sensors record radiance at a high distance above ground (usually 500 km or more). The recorded radiance (signal) includes the radiance from the target (pixel) and the radiance from the path (atmospheric column above the target). The total radiance recorded by the sensor is: Lsensor = LT + LP .

The atmospheric components that contribute to the recorded radiance are mainly the water vapor, tropospheric ozone (O3), O2 and CO2. Atmospheric correction is not correcting for clouds. The impacts of atmosphere on recorded radiance are high in the blue wavelength, followed by green and red. The attenuation starts to decline in the NIR and SWIR bands. For the application of Landsat 8 data in vegetation mapping, we need to correct the red and the NIR bands as we are going to derive the NDVI from these two bands. Methods of atmospheric corrections are one of the following: 1- Absolute correction: this involves the conversion of DN to radiance and/or reflectance using one of the following: a. Image Based: - Empirical line using ground objects like water bodies and desert surfaces. - Dark Object Subtraction (DOS) b. Radiative Transfer models (RT Based): these work on calculating optical depth and transmission of atmosphere for the gases responsible for atmospheric scattering. Examples on these models are 6S, MODTRAN, LOWTRAN and Streamer

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c. Hybrid: atmospheric parameters are extracted from the image and used in a radiative transfer model. Examples on these models are the Dark Dense Vegetation (DDV) and LEDAPS. 2- Relative correction: this is based on normalization of satellite imagery data based on slave and master principle. This type is used when multi-temporal images are used. In this manual, you will be subjected to absolute atmospheric correction using image based method with ground objects. This will be carried out for the image of 30 June 2014 (LC81730382014181LGN00.tar). The previous and the subsequent images will be corrected using reference objects from the image of 181 and the image to be corrected. A linear equation (empirical line) will be used for correction. 7.1

Image based atmospheric correction (using ground measurements)

Background on ground data for atmospheric correction: To carry out atmospheric correction for LC81730382014181LGN00 image, reference objects were scanned with a handheld multispectral radiometer at the time of overpass of Landsat 8 in the same day. The locations for ground data collection were the car parks of Azraq Refugees Camp. These two surfaces (shown in the image below) were newly paved, empty and large enough to appear in several pixels in Landsat image. Intermediate object (car park) Dark object (car park)

Syrian Refugees camp (Azraq Road)

The second reference surface was a bright surface (a dry mud flat, known as Qa’a Khanna). The third reference surface was a densely vegetated field (Parsley) in Azraq. All data were collected within one hour from the overpass.

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Preparation of the handheld multispectral radiometer (left) and collection of measurements (right) Summary of the collected data is shown in the following table: Object CarPark_Black1 CarPark_Black2 CarPark_Black3 CarPark_White Qa'a_Bright Parsley

Coordinates OLI Landsat data (DN) Radiometer (Reflectance, %) X (deg.) Y (deg.) Red NIR Red band NIR band 36.5901 31.9152 12636 13135 15.2 16.2 36.59 31.915 12629 13077 13.9 14.6 36.5901 31.9148 12950 13594 14.5 15.0 36.5888 31.9156 21311 23957 43.3 46.7 36.4212 32.0693 29484 33880 65.7 73.4 36.415 32.0624 11587 32283 12.2 70.8

Constructing a linear realtionship (empirical line) between Landsat 8 data and the radiometer data will result in the following realtionships:

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Relationships between DN and surface reflectance for the red (Left) and the NIR (Right) bands. The above equations will be used to correct Landsat 8 data of the LC81730382014181LGN00 image. The output from this stage will be reflectance values at ground level. The output bands (red and NIR) will be master bands for relative atmospheric corrections. The procedure for applying the above equations in ERDAS is listed in the next steps using the “Model Maker”. Before proceeding with steps of atmospheric correction, you need to extract bands 4 and 5 of the image (LC81730382014181LGN00.tar.gz) to a separate folder by unzipping the file of the Landsat image of 30th of June 2014. This is the image (LC81730382014181LGN00.tar.gz) you want to carry out the atmospheric correction for. Therefore, you need to extract bands 4 and 5 of the image to a separate folder by unzipping the file. 1. On the main menu of ERDAS Imagine, click the Tab of Toolbox and select the menu of Model Maker. The following menu will appear.

Raster icon (Input File)

The Function Icon

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2. Click the Raster icon in the Model Maker tool palette the model.

Dr Jawad Al-Bakri

and drag it to the empty page of

3. Select the Function icon in the Model Maker tool palette and drag it to the empty page of the model, just below the Raster, leave a reasonable distance (See the next page). 4. Click on the Raster icon again and drag it to the page, You can also copy the icon of raster and paste it in the model. 5. Connect the raster with the function by clicking the connect icon connecting the raster (created in step 2) to the function.

in the palette and

6. Connect the function to the raster (created by step 4) by another connection icon. The Model shall look exactly as the one shown on the next page. 7. You can use the text icon to write any annotation that explains the model component. The model is simple and contains an input file, a function, and an output file.

8. Double click on the Icon of input file or raster, a dialog will appear and will ask you to identify the name of the input file. Navigate to the folder where bands 4 and 5 were extracted. Since the format is not img file, then you need to change the file type in the dialog to tif or type * and enter to see the file. Select the file “lc81730382014181lgn00_b4.tif” as the input file. This is the red band of Landsat 8. 9. Double click the Icon of the output file or raster, and give a name for the output file (2014181_b4ref.img). You have to take care for the data type; in this example the unsigned 8bit is proper as our reflectance values are ranging between 0 to 100 without negative signs. If we select the signed data, then borders will have the values of -24 (line intercept). 10. Double click the function icon, and write the equation for correcting band 4 (See the figures generated in Excel). Fill in the dialog by clicking on $n1_lc81730382014181lgn00_b4 in the available input and complete the equation as follows:

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$n1_lc81730382014181lgn00_b4*0.00308 - 24.115

11. After finishing the equation click OK, then run the model by clicking the icon selecting Process\Run.

or by

12. If there is no “syntax errors” in the model, then it will run and an output file can be generated. 13. Display the output file in ERDAS Imagine viewer. 14. Click on the icon of “Add views” under the “Home” Tab to have another view to the right of the one in which the corrected image is displayed. Display the raw image (lc81730382014181lgn00_b4.tif) in this view.

15. Click on each view and click the Metadat icon

to examine the image minimum

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and maximum, click on the histogram icon look as follows:

Histogram of the raw data (B4)

Dr Jawad Al-Bakri

to see the histogram of each image. These shall

Histogram of the atmospherically corrected B4

16. If the model maker is still open, then save the model (e.g. absolute_b4_corr). Correcting the NIR band: Now, you need to correct the NIR band following the same procedure while changing the equation of correction. 17. Double click on the Icon of input file or raster and change the file name to “lc81730382014181lgn00_b5.tif” as the input file. This is the NIR band of Landsat 8. 18. Double click the Icon of the output file or raster, and give a name for the output file (2014181_b5ref.img). Use the unsigned 8-bit data. (What is the expected output value for the borders? What would be the value if we select signed 8-bit or float data type?). 10. Double click the function icon, and write the equation for correcting band 4 (See the figures generated in Excel). Fill in the dialog by clicking on lc81730382014181lgn00_b5 in the available input and complete the equation as follows: $n1_lc81730382014181lgn00_b5 * 0.00287 - 22.63

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Repeat the steps 12 to 16 to compare the histograms for NIR band before and after correction. They shall look like the figure below.

Histogram of the raw data (B5) Histogram of the atmospherically corrected B5 Note: the histogram for corrected bands may contain values above 100%. These are few pixels and form less than 0.0003% of the total pixels. In some cases, clouds and snow could result in such values. These will be eventually removed using cloud masking. 7.2

Relative Atmospheric correction (Image-to-image)

Relative atmospheric correction is a good approach for correcting images to a reference or base image (known as master). Although there are several options for atmospheric correction, however, selection of the method shall be made with much care to avoid non reasonable results. One method which can be carried out automatically is the histogram matching. This method is included in ERDAS Imagine under the tab (raster\radiometric\histogram match) and is simply applied by identifying the input image and correcting the other images using the histogram matching. This method, however, is not a good option for images with clouds (like the image of 2014133). Instead, it is used for cloud free images or for part of images with no cloud cover. Dark object subtraction is another method which can be used. However, the use of about 1000 pixels in the lower tail may not work in the case of cloudy images. Therefore, in this manual, the method will be based on the use of image-to-image approach with reference

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A training manual: crop mapping from RS

Dr Jawad Al-Bakri

objects (dark and bright) to construct a linear relationship between both images, based on these objects. In this project, the image of 2014181 was corrected with ground measurements, taken by a handheld radiometer. Therefore, this image is a master for correcting other images for the same area. The procedure of relative atmospheric correction is based on constructing a relationship between the uncorrected image (slave) and the corrected image (master) using reference ground objects. Selecting these objects is the most difficult task that will influence the output. The objects (locations) shall represent pixels that are stable and would not change during the time periods of the images. Considering that resolution of Landsat 8 is 30m; then we can think about different objects and analyse their spectral properties. 1- A paved road will not serve our purposes, as it has mixed pixels. 2- Water bodies (desert dams) are not good to use as dark objects, since they are loaded with sediments. In the western parts of Jordan, we can use the Mediterranean sea, the Gulf of Aqaba, large dams for this purpose. In this image, no pure water is found in the study area. So, water bodies are not recommended. 3- Vegetation: Although it serves as a dark object in the red band, however the time difference makes it difficult to use these areas, as vegetative growth during the season will affect the results of correction. In other areas in Jordan, forests might serve the purpose of correction. 4- Large car parks in Azraq refugees’ camp are good, providing no cars are existing at the time of image acquisition. This location (used previously in absolute atmospheric correction) will not work for the image 2014133, as the area was covered by clouds. For other images, we might use it. 5- Desert mud flats (Qa’a) are good to use as bright surfaces, we need to check them through Google Earth to see whether they are homogenous or not? If vegetation is at the edges or not? If water is not collected in some ponds or desert dams? 6- Dark basalt rock: we will use some of these areas as dark objects. For the image 2014133, it is preferred to select areas in the eastern parts of the image away from cloud and cloud shadows. Using Google Earth will help to identify good locations. Remember: Selection of points will depend on the experience of the analyst and his knowledge about the area and its atmospheric conditions. You need to follow the coming steps to perform atmospheric correction for bands 4 and 5 for the image: LC81730382014133LGN00.tar.gz. You need to extract bands 4 and 5 to a separate folder and then you can proceed with steps of relative atmospheric correction. Steps 1- Open two viewers in ERDAS Imagine and display 2014181_b4ref.img in the left viewer and the raw band in the right viewer LC81730382014133LGN00_B4.TIF. 2- Link both viewers, using “View Link” with the first choice in the drop-down menu as shown in the figure below.

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A training manual: crop mapping from RS

Dr Jawad Al-Bakri

3- Click in the inquire cursor

4- Go to the location shown in the following dialog

Turn view button

This location is a black object (basalt hill). DN = 12802, search close to this location till you find a value for histogram (real DN). 5Record the DN from both images (2014181_b4ref.img and LC81730382014133LGN00_B4.TIF). Click on turn view button and record the value from the reference image or from the raw image. 6- Move to the south of the first point to the location shown in the next dialog: Record the DN from both images: Reflectance is 65% fromt the 2014181_b4ref.img and 29118 in the LC81730382014133LGN00_B4.TIF. You can collect other points if you like from bright and dark objects.

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A training manual: crop mapping from RS

Dr Jawad Al-Bakri

7- Use Excel Spreadsheet to construct the relationship between DN and reflectance. Use the type scatter plot after highlighting the range of data. Click on one of the points (labels) and select add trendline (linear) and enable equation and R2. For the above two points the relationship for band 4 and band 5 (following the same procedure) will be:

You may need to select other points for band 5, as darker objects in band 4 are not necessarily with same darkness level in band 5. The same is true for brighter objects. 8- After this step you need to apply the equation on each band using the model maker (Use the same steps on pages 18-20) with the new equations. Change the input and the output files -

Input file (raster) for correcting band 4 in the model maker is LC81730382014133LGN00_B4.

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Output file (raster) is 2014133_b4ref

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Equation of correction for b4: $n1_lc8173038201433lgn00_b4 * 0.00282 – 17.5

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Equation of correction for b5: $n1_lc8173038201433lgn00_b5 * 0.0027– 18.32

9- Display band 4 of the image 2014181 and compare it with 2014133: -

Large differences are observed for areas with cloud or under cloud shadow.In terms of some reference objects (basalt rocks, paved roads and bright desert surfaces) shall give

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A training manual: crop mapping from RS

Dr Jawad Al-Bakri

close values between the two images. Generally, the correction will improve the values as possible. -

In terms of image histogram, both will have different shapes and values ranges. This is mainly attributed to cloud impacts. This justifies why we avoided the use of image histogram matching.

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Summary of equations for correcting the set of images (kindly, fill in for new ones) #

Image

Date

Remarks

B4

B5

1.

2013306

02 Nov. 2013

Scattered clouds in Mafraq and Azraq

× 0.00531 - 39.79

× 0.005 - 39.84

2.

2013322

18 Nov. 2013

Cloudy except for West of Azraq

× 0.00552 - 32.54

× 0.0045 - 23.77

3.

2013338

04 Dec. 2013

Clouds in the north of Mafraq

× 0.00580 - 36.84

× 0.0057 - 35.49

4.

2013354

20 Dec. 2013

Scattered clouds south and west of Mafraq

× 0.00691 - 43.22

× 0.0054 - 26.06

5.

2014005

05 Jan. 2014

(Cloudy for Azraq, observations available for north of Mafraq)

× 0.00736 - 54.27

× 0.0078 - 65.42

6.

2014021

21 Jan. 2014

× 0.00630 - 42.15

× 0.0052 - 33.88

7.

2014053

22 Feb. 2014

Cloudy except east of Azraq (15 observations with centre pivots) Small clouds in Mafraq

× 0.00446 - 27.40

× 0.0039 - 18.92

8.

2014085

26 Mar. 2014

Scattered clouds in Mafraq

× 0.00408 - 30.25

× 0.0035 - 24.65

9.

2014101

11 Apr. 2014

Clear sky

× 0.00349 - 27.90

× 0.0029 - 17.43

10. 2014117

27 Apr. 2014

Scattered clouds in Mafraq

× 0.00318 - 22.90

× 0.0030 - 21.28

11. 2014133

13 May 2014

Many scattered clouds

× 0.0027 - 16.70

× 0.0025 - 14.20

12. 2014165

14 June 2014

Clear Sky

× 0.00290 - 20.16

× 0.00275 - 19.94

13. 2014181

30 June 2014

Master image for absolute correction

× 0.00308 - 24.12

× 0.00287 - 22.63

14. 2014197

16 July 2014

Clear Sky

× 0.00290 - 20.84

× 0.0028 - 21.20

15. 2014213

01 Aug. 2014

Clear Sky

× 0.00297 - 19.75

0.0027 - 17.74

16. 2014229

17 Aug. 2014

Clear Sky

× 0.00338 – 26.19

× 0.0030 - 20.37

17. 2014245

2 Sep. 2014

Clear Sky

× 0.00317 - 21.07

× 0.0030 - 19.53

18.

21 Nov. 2014

Clear sky in Mafraq

×0.00669- 48.786

× 0.0056 - 38.56

2014325

Previous Season (Clear sky):

2013242 (30 Aug. 2013), 2013258 (15 Sep. 2013)

Unavailable images:

2013274, 2013290 (Oct. 2013)

Cloudy images (no observation):

2014037, 201469, 2014149

Field data collection for main irrigated crops was carried out on 28 May, 11 June and 25 June. This means that for vegetables images of 11 and 25 June are close to the acquisition times. Therefore, alfalfa, irrigated barley and olives data will be based on Azraq.

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