Landsat - Science topic

If you are using Landsat Collection 2 Level 2 surface temperature data, then this is the correct equation. The USGS has already processed this analysis-ready ST data. You can find the reference here:

Landsat imagery provided by the USGS—specifically the Surface Reflectance (SR) products for Landsat 5 and Landsat 8—comes pre-processed with both radiometric and atmospheric corrections applied. These corrections are conducted through standardised processing algorithms, such as LEDAPS for Landsat 5 and LaSRC for Landsat 8, delivering surface reflectance data that is geometrically corrected, topographically adjusted, and radiometrically calibrated. As such, further radiometric correction is typically not required for most terrestrial remote sensing applications.

However, the necessity for additional correction may arise depending on the specific analytical goals of a study. For example, high-precision time-series analysis, inter-sensor calibration (e.g., between TM and OLI sensors), or biophysical parameter estimation in aquatic or snow-covered environments may require further normalisation or correction for sun-glint, adjacency effects, or terrain-induced reflectance variability. In such cases, supplemental preprocessing steps—such as BRDF correction or topographic normalisation—may enhance analytical accuracy.

In summary, while Landsat SR products are sufficiently corrected for general analysis, researchers should assess the sensitivity of their application to determine whether further radiometric preprocessing is warranted.

I used Sentinel-2 data. It was very effective.

This may help

• Check Pixel Values: Use the Identify Tool in

ArcGIS to inspect pixel values.

• Apply Contrast Stretching: Use Min-Max or

Histogram Equalization.

• Re-download the Data: If corruption is

suspected, get a fresh copy from the source.

I hope this will help.

Before calculating LST, you need to prepare the data by selecting the required bands, applying necessary corrections, and masking clouds.

In thermal infrared bands such as Band 6 (Landsat 5 and 7) and Band 10 (Landsat 8), clouds typically appear bright (white or light gray) because they are colder than the Earth's surface. However, if clouds appear dark in your downloaded images, it is likely due to inverse thermal stretching, where higher DN values (colder temperatures) are mapped as dark, or due to software-specific auto-stretching that affects contrast.

Additionally, if you are working with raw DN values or unprocessed thermal data, you may need to apply radiance-to-temperature conversion and adjust the display settings to properly visualize the cloud temperature. Try modifying the grayscale stretch or inverting the contrast to verify the correct thermal representation.

I didn't have time to examine this in depth. But they don't seem to have any correspondence with what might be happening on the ground ( like a forest fire, etc. ... I'm not familiar with the area, but before, during, and after seasonal field burns can cause anomalies) or manifest in the visible bands. The scene is basically an extract of a strip, so you might look up and down the path to see if the artifacts are also appearing there. If it is data loss or sensor malfunction, sometimes these are logged ( example https://ladsweb.modaps.eosdis.nasa.gov/alerts-and-issues/163894 ).

Realizar una clasificación supervisada de imágenes satelitales Landsat y Sentinel en QGIS utilizando el complemento Semi-Automatic Classification Plugin (SCP) requiere seguir una serie de pasos cuidadosamente para garantizar la precisión del resultado. Aquí tienes un desglose detallado y recomendaciones de manuales útiles:

Si necesitas ayuda más específica en alguna parte del proceso, no dudes en pedírmelo. 😊

Hello, Hawbash Abdalla Ahmed

Differences in LST values between Landsat 9 Level-1 and Level-2 images can be attributed to several factors related to the processing levels of the data:

Level-1 Data: These are raw, unprocessed images that have been radiometrically calibrated and geometrically corrected. They contain Digital Numbers (DN) that represent the raw sensor measurements. Level-2 Data: These images have undergone additional processing to convert the DN values into surface reflectance and temperature values. This includes atmospheric correction, which adjusts for the effects of the atmosphere on the recorded signal.

2. Atmospheric Correction:

Level-1 Data: Atmospheric effects are not corrected in Level-1 data. This means that the LST values derived from Level-1 data can be influenced by atmospheric conditions such as water vapour, aerosols, and other atmospheric constituents. Level-2 Data: These images include atmospheric correction, which removes the influence of the atmosphere, leading to more accurate surface temperature measurements.

3. Geometric Corrections:

Level-1 Data: These images are geometrically corrected using ground control points and digital elevation models to correct for relief displacement. Level-2 Data: Further geometric corrections may be applied to ensure higher spatial accuracy, which can affect the precise location of the temperature measurements.

Based on the above, Level-2 data generally provide more accurate and reliable LST values due to the additional corrections and processing steps applied.

Read here:

Best Regards,

Ali YOUNES

I'm very grateful for your detailed explanation

Hi Forqan Khalid Al-Daraji,

To estimate the shape of water bodies using remote sensing, several important indicators and algorithms can be employed. Key indices like the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and the Automated Water Extraction Index (AWEI) are effective for detecting and delineating water bodies in satellite imagery. These indices enhance the contrast between water and land features, making it easier to identify the boundaries of rivers and streams.

For algorithmic approaches, object-based image analysis (OBIA) can be particularly useful, especially when dealing with high-resolution imagery. Additionally, machine learning algorithms such as random forests or support vector machines have been applied successfully to classify water bodies and extract river morphology from satellite data.

However, when analyzing the morphology of rivers and narrow streams, spatial resolution is a critical factor. Landsat imagery provides a 30 m resolution, while Sentinel-2 offers a 10 m resolution. This may still be insufficient for capturing narrow water bodies accurately, especially when land adjacency effects become significant.

For more detailed comparisons of these methods, you might find the following papers helpful:

I hope this information assists you in your research.

Great question... I wonder the same (looking the answer and didn´t find it) !

@Pooja Yadav You may prefer using Collection 2 Surface Temperature products available globally for the

Landsat 9 Operational Land Imager 2 (OLI-2): February 2022 to present.

Dear Kévin Ella-Ekabane,

To identify wetland areas in Landsat imagery, a combination of spectral indices is highly effective. Water-sensitive indices like NDWI and MNDWI are crucial for highlighting water features, as you mentioned, while vegetation indices such as NDVI help capture wetland vegetation characteristics. For more specialized detection, indices like WAVI (Water Adjusted Vegetation Index) or WITI (Wetland Inundation Tendency Index), designed specifically for wetland environments, can provide enhanced accuracy. This multi-index approach allows for a comprehensive assessment of wetlands, capturing both their aquatic and vegetative components, and thereby improving the overall accuracy of wetland identification and mapping.

I hope this will help!

Good Luck!

Thank you, Ali Younes, for your response. I have tried the approaches you suggested, but I am facing the specific issue with Landsat 8 C2L2T1 data. Although I have assessed the percentages of cloud cover over each kml, I am struggling to map the distribution of the cloud pixels. Without this mapping, masking the clouds becomes quite challenging.

You may explore geostatistical approaches due to their appealing property of perfect coherence. Since you didn't mention anything about your dataset (i.e., if you have any ancilliary data to assist the downscaling), my recommendation is to do a little bit of research online about this topic (downscaling) and come back with a more focused question.

you have to analyze on emittance value of the reflected wave

Band 5 in Landsat 8 and 9 represent NIR wich is more appropriate to calculate NDVI, for the Sentinel 2 the Band 8 (835.1nm (S2A) / 833nm (S2B)) is also in the NIR range its more appropriate than Band 7 (782.5nm (S2A) / 779.7nm (S2B)) which represent Red Edge 3. you can check the Sentinal 2 Wavelengths Bands in the link bellow :

To the best of my knowledge this is not a common practice in remote sensing to mix thematic rasters and continues rasters to perform supervised classifications,where did you learn this from if I may ask and what is the aim of doing that?

Tafsirul Islam have a look at my research:

After trying many, I had better results with NDBI and DBI. You can also create your own index taking into account the spectral signature of built-up area and the specs of the satellite you intend to use.

Hello

You can read about this problem in this topic:

Good luck

By monitoring a database of satellite images that have undergone atmospheric and geometric corrections, you can obtain results on the evolution of the coastline over time and space.

I recommend using the Digital Shoreline Analysis System (DSAS).

Matej Žgela did you find an answer? I am interested to know, please

Yes, Landsat 5 and Landsat 8 data are generally compatible with each other. Both satellites are part of the Landsat program and have similar imaging capabilities, although there are some differences in sensor characteristics and data processing methods. While Landsat 5 was operational until 2013 and used the Thematic Mapper (TM) sensor, Landsat 8, launched in 2013, carries the Operational Land Imager (OLI) sensor. Despite these differences, efforts are made to ensure data continuity and compatibility between different Landsat missions, allowing for consistent analysis and comparison of imagery collected over time. However, it's always a good practice to verify compatibility based on specific analysis requirements and any potential differences in data characteristics.

I am intrested

You may find the solution in the document:

https://www.usgs.gov/media/files/lsds-1928-landsat-8-olitirs-solar-view-angle-generation-add

Subhadeep Mandal detailed data on the spatial distribution of soils are rather a national feature, and such data can be found in national resources, but they are rarely open access

Mohammed Jalal Tazi

Salama Alaikum

For geological lineament extraction, both Landsat imagery and Shuttle Radar Topography Mission (SRTM) Digital Elevation Models (DEMs) can be useful, but they provide different types of information and have their own advantages and disadvantages.

1. **Landsat Imagery**:

- Landsat imagery provides multispectral data in the visible, near-infrared, and shortwave infrared regions of the electromagnetic spectrum.

- It is particularly useful for detecting lithological and mineralogical variations, which can help identify lineaments associated with different rock types or geological structures.

- The high spatial resolution (30 meters for most bands) allows for the detection of relatively small-scale lineaments.

- However, Landsat imagery may not always clearly reveal topographic features or lineaments that are not associated with spectral variations.

2. **SRTM DEM**:

- SRTM DEMs provide a representation of the Earth's topography, which can be effective for lineament extraction based on topographic expressions.

- DEMs are particularly useful for identifying lineaments associated with faults, fractures, or other structural features that are expressed in the terrain.

- The shaded relief and slope maps derived from DEMs can enhance the visibility of lineaments, especially in areas with limited vegetation cover.

- However, SRTM DEMs have a relatively coarse spatial resolution (30 meters or 90 meters), which may limit the detection of small-scale lineaments.

In practice, the best approach for geological lineament extraction often involves integrating both Landsat imagery and SRTM DEMs. The combination of spectral information from Landsat and topographic information from SRTM DEMs can provide complementary insights and improve the accuracy of lineament mapping.

Here are some common techniques for lineament extraction using both datasets:

1. **Edge Enhancement Techniques**: Apply edge detection filters (e.g., Sobel, Prewitt, or Canny) to both Landsat imagery and SRTM DEMs to enhance lineaments.

2. **Principal Component Analysis (PCA)**: Perform PCA on Landsat bands and SRTM DEMs to identify the components that best represent lineaments.

3. **Lineament Extraction Algorithms**: Use specialized lineament extraction algorithms, such as Line Segment Detector (LSD), Hough Transform, or Radon Transform, on both datasets and combine the results.

4. **Manual Interpretation**: Visually interpret and digitize lineaments by combining the information from Landsat imagery (for spectral variations) and SRTM DEMs (for topographic expressions).

The choice between Landsat imagery and SRTM DEMs, or the combination of both, ultimately depends on the specific geological setting, the scale of the study area, and the availability of resources. In many cases, integrating both datasets can provide the most comprehensive and accurate lineament mapping results.

Waqar Ar

Hi , The Level 0 products of Landsat 1-9 are not publicly available for download. The Level 0 data refers to the raw, unprocessed instrument data from the Landsat satellites, which is not typically distributed to end-users.

The lowest level of Landsat data that is publicly available is the Level 1 data product. Level 1 data is the most fundamental level of Landsat data that has been processed to remove radiometric and geometric distortions, and is typically the level of data used for most applications.

You can download Landsat Level 1 data from the following sources:

While Level 0 data is not publicly available, the Level 1 data products are suitable for most remote sensing applications and have been processed to a level that allows for further analysis and interpretation.

To publish a paper in the most easily seen or most needed journal, OA is good, but it is more important to pay attention to whether it is a hardcore journal, which is more important than IF.

Based on my experience, I used Landsat 8 OLI/TIRS Collection 2 atmospherically corrected surface reflectance data for my purpose of extracting NDVI, LST, NDBSI and Wetness. You can see more about the detail of Landsat Product from this site: https://developers.google.com/earth-engine/datasets/catalog/landsat-8

When performing land use and land cover classification using satellite imagery, distinguishing between urban and barren land can indeed be challenging, especially when relying solely on visual interpretation of color composites. However, there are several techniques and additional data sources you can utilize to improve the accuracy of your classification:

1. "Additional spectral indices": Explore other spectral indices that are specifically designed to highlight urban areas or barren land. For example, the Normalized Difference Built-Up Index (NDBI) or the Normalized Difference Bareness Index (NDBaI) can help differentiate between urban and barren areas.

2. "Texture analysis": Incorporate texture analysis metrics into your classification process. Urban areas tend to have a different texture compared to barren land. Texture measures such as entropy, standard deviation, or GLCM (Gray Level Co-occurrence Matrix) features can provide valuable information for distinguishing between land cover classes.

3."Machine learning algorithms": Consider using machine learning algorithms such as Random Forest, Support Vector Machines (SVM), or Convolutional Neural Networks (CNNs) to automatically learn and differentiate between land cover classes based on a combination of spectral and contextual information.

4. "Higher-resolution imagery": If available and within your project scope, consider using higher-resolution imagery (e.g., Sentinel-2) to improve the visibility of urban features and finer details in barren land areas.

5. "Temporal analysis": Analyze temporal changes in land cover over multiple time periods to identify patterns and changes specific to urban expansion or vegetation dynamics in barren areas.

6. "Incorporate ancillary data": Utilize ancillary data sources such as population density maps, land use maps, or digital elevation models (DEM) to provide additional contextual information for classification.

7. "Data fusion": Combine multispectral imagery with other data sources such as radar data or LiDAR data to improve classification accuracy, especially in complex landscapes.

By incorporating these techniques and data sources into your land cover classification workflow, you can enhance the accuracy and reliability of distinguishing between urban and barren land areas in your study area. Experiment with different approaches and combinations to find the most suitable method for your specific project requirements.

Hello Salauat Kalabaev

To fill in the gaps and blur in Landsat 7 images, you can use several techniques:

Read here:

As for your second question, it depends on your specific needs and the quality of data you require.

Read here:

Best Regards,

Ali YOUNES

Arc Gs pro or Envi or Matlab.

I have got the same problem. After I changed the "Output Directory for FLAASH files", it got the right result. But I think your workflow may be wrong. After using "Build Layer Stack", the band order has changed. And I can't make sure whether FLAASH can solve it or not.

Hello Javid Hojabri

Including the most recent data available is generally recommended. This will ensure that your article reflects the most up-to-date information and findings in the field.

Generally, the necessity to update the study period depends on the focus of the article. The updating may be unnecessary if the article discusses new methodologies. However, it would be beneficial if it addresses a problem related to the study area.

Best Regards,

Ali YOUNES

Nikolaos Tziokas

Dear Nikolaos Tziokas,

I wanted to express my sincere gratitude for your valuable insights and recommendations. Your suggestion to explore the paper on the evaluation of spectral built-up indices for impervious surface extraction using Sentinel-2A MSI imageries in Addis Ababa has been immensely helpful. I have downloaded the paper and am in the process of thoroughly reviewing it.

I am particularly intrigued by the mention of the SWIR band and the indices NBAI and BRBA, which do not rely on the NIR band. Your guidance aligns perfectly with my objective to investigate indices without the NIR band for NDBI analysis. I appreciate your willingness to share knowledge and point me in the right direction.

Thank you once again for your assistance. I am confident that the information from the paper will significantly contribute to my research and analysis.

Anitha

In IDRISI Selav and TerrSet yo can calclate Tasseled Cap for Landsat 8, but unfortunately, it is not available for Sentinel 2. See the attached screen capture

To solve this problem, kindly open the MTL. txt file with Notepad in you PC, at the first row, replace the "Landsat" with "L1_" save the file and try importing it again, it will work this time.

NB: don't forget to put underscore "_"after typing "L1"

Enjoy your multispectral image afterwards.

I hope this solves your problem.

It would be nice to share your code so others can replicate your issue. The way your question is currently written we can only make assumptions.

Thanks Glicherie Caraivan

UTC

Field Definition: The date and time at the beginning of the data acquisition period in UTC time zone.

https://www.usgs.gov/centers/eros/science/sentinel-2-data-dictionary#acquisition_date

Thought I'd do an update to my comments about scaling factors for the Landsat C2L2 data product. It appears that perhaps the problems seen with the NDVI images at pixels with dark reflectance (water and cloud shadows) and bright reflectance (clouds) after applying the scaling corrections to the C2L2 data may be a function of the atmospheric correction. The relationship between the red and nir bands ends up being such that it might be over correcting the red band and / or under correcting the nir band for the dark and bright pixels (water, cloud shadows, clouds). This can happen when a non realistic atmospheric model is applied to the data. I discuss the fact that realistic vs non realistic atmospheric scattering models can affect the haze values selected and impact the results:

More food for thought.

Pat

Check Google

Zainab Khan

Hi,

Acquiring global data on the Normalized Difference Moisture Index (NDMI) in raster format presents a significant challenge, especially given the large area of interest (AOI) and the constraints related to computation from satellite imagery sources like Landsat, ASTER, or Sentinel. While obtaining such data directly as a raster product can be intricate due to the required computational processes

There are alternative approaches that you might consider to address this issue:

Surface Temperature product generated from Landsat Collection 2 Level-2 data measures the temperature of the surface of the Earth in Kelvin (K). This product is generated using various data sources, including thermal infrared bands, reflectance data, brightness temperature, emissivity data, and atmospheric profiles.

To accurately interpret the temperature values, it's essential to refer to the metadata of the dataset. The metadata should provide information about any scaling, offsets, or transformations applied to the data. If the dataset is supposed to represent temperature in Kelvin, then the metadata should clarify how the raw temperature measurements were converted to pixel values.

The Landsat Level-2 Surface Temperature product is generated from Landsat Collection 2 Level-1 thermal infrared bands, Top of Atmosphere (TOA) Reflectance, TOA Brightness temperature, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Database (GED) data, ASTER Normalized Difference Vegetation Index (NDVI) data, and atmospheric profiles of geopotential height, specific humidity, and air temperature extracted from reanalysis data.

You don't need to do the calculations we used to do on the Level 1 product. You can use the Level-2 product by multiplying the thermal band (ST_B10) by a scaling factor (0.00341802) and then adding an offset (149.0) to convert the raw values to temperature values in Kelvin.

The Raster Calculator expression could look something like this:

(("ST_B10" * 0.00341802) + 149.0)

Replace "ST_B10" with the actual name of your surface temperature raster dataset.

If you use Google Earth Engine, you can use the following code:

var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0)

#Remote Sensing of Environment (Recommended) (link: https://www.sciencedirect.com/journal/remote-sensing-of-environment)

#Environmental Remote Sensing (link: https://www.mdpi.com/journal/remotesensing/sections/environmental_remote_sensing)

#Journal of Remote Sensing (in partnership with science) (link: https://spj.science.org/journal/remotesensing)

Access the link and find out if there is any other: https://www.gisvacancy.com/remote-sensing-journals/

Lea Lea let me give you an example, which will clarify your doubts. I selected a C2 L2 Landsat 8 image from USGS EarthExplorer over Israel and downloaded the Surface Temperature (ST) and the Emissivity (Emis) bands. Here are the details of the Landsat 8 Scene:

ID: LC08_L2SP_174038_20230716_20230725_02_T

Date Acquired: 2023/07/16

Path: 174

Row: 038

After downloading the ST and Emis bands, I did the following:

I used the Multiplicative Scale Factor and the Additive Offset from the science guide to get the actual LST values in Kelvin. To get the values in °C, subtract it with 273.15

To get the actual Emissivity values, I used the Multiplicative Scale Factor from the science guide

Check attached images for reference.

In summary, when you download C2 L2 product, Land Surface Temperature (LST) or Surface Temperature (ST) is already provided as a ready to use product and by utilizing the scaling and offset values you get actual LST range. L2 products don't provide you with Brightness Temperature (BT) band or B10 Thermal band. As I said earlier, if you wish to separately calculate LST/ST, you can download the Landsat 8/9 thermal bands (B10 or B11) from the Collection 2 Level 1 data catalog and go through the manual calculation of B10/11 --> Radiance --> BT --> Emissivity --> LST.

Let me know if you need further clarification?

The equations provided are variations of the radiative transfer equation used for estimating Land Surface Temperature (LST) from satellite imagery, such as that from the Landsat series of satellites. The equation is also known as the Radiative Transfer Equation for Temperature (RTE for T), and it's frequently used in remote sensing applications.

The formula includes variables as follows:

LST represents the land surface temperature,

BT is the at-sensor brightness temperature,

w represents the wavelength of emitted radiance,

p is the constant Planck's constant, and

ε is the emissivity of the surface.

Formula 1 is a general form of the radiative transfer equation for temperature, while Formula 2 is a specialized form specifically tailored for Landsat 8 data.

Comparing Formula 1 and Formula 2, you can see that the terms w(BT/p) and 0.00115BT/1.4388 are similar in their purpose. They are both corrections for the wavelength of emitted radiance, but the actual values used (and their unit) will differ because the second formula is specifically calculated for Landsat 8's thermal bands. The 1.4388 value in Formula 2 represents the Wien's displacement constant in micrometers Kelvin units.

The other difference is that Formula 1 uses a natural logarithm of ε (emissivity), while Formula 2 doesn't include this term. This suggests that Formula 2 assumes a constant emissivity (ε) for Landsat 8, which might not necessarily be the case for all land cover types.

So, if you use Formula 1 and Formula 2 separately to calculate the LST of one Landsat 8 image, the results are likely not to be exactly the same due to the differences in the assumptions made in each formula. The difference in results would be based on how much the assumptions made for each formula match the reality of the particular Landsat image being processed.

In conclusion, the choice of formula should be guided by the specific details of your Landsat image, and your knowledge about the land cover types present, their emissivity, and the specific spectral characteristics of the Landsat platform being used.

@Syed Hussain Yes, it is made by Chat GPT, I just want to help you

hi, Jude Paho Nteinmusi Another simple way to verify the

success of the atmospheric correction is to analyze the

spectral values ​​of a pixel where there is healthy vegetation

and verify that the values ​​have the expected pattern for

healthy vegetation. I attach a sheet about it

Hello Ali Younes

Thanks for the valuable explanation about Landsat Collection 2 Level 1 and Level 2 data. So, now I can use these data directly for analysis without any further preprocessing.

Hello Javid,

You're seeking, apparently, the point of inflection for a presumed function which links the two variables summarized in the display.

If you've fitted a model, then solve for the first derivative being set to zero.

If you haven't fitted a model, then you'll need to:

1. Define some measure of slope change. This could be [(Y for the kth case - Y for the k - 1th case)] / [(Y k - 1th case) - Y k - 2th case)]

2. Define some degree of slope change that represents a slope "shift" and not just a graduated increase.

3. Compute the slope change (#1 above) for each point on the graph, going out to the right.

4. When you come to a point at which the slope change exceeds your criterion level (#2 above), then this is your shift point.

Good luck with your work.

Javid Hojabri

The correct value to use in the brightness temperature calculation is -273.15. This is because the Kelvin scale is based on absolute zero, which is defined as -273.15 degrees Celsius. Therefore, all temperatures in the Kelvin scale must be converted to Celsius by subtracting 273.15.

The reason why some articles use 272.15 is because they are using the Celsius scale instead of the Kelvin scale. The Celsius scale is based on the freezing point of water, which is 0 degrees Celsius. Therefore, all temperatures in the Celsius scale must be converted to Kelvin by adding 273.15.

However, it is important to note that the brightness temperature calculation is typically used in remote sensing, where the temperatures are measured in Kelvin. Therefore, it is important to use the correct value of -273.15 when converting brightness temperatures to Celsius.

Did you Sotirios Soulantikas find an solution for this problem?

You can obtain Landsat images of Kathmandu before 2000 from the USGS EarthExplorer website, which is the official archive for Landsat data. Here are the steps to follow:

Note that Landsat imagery before 2000 may not be freely available, and you may need to pay a fee for access to the data. Additionally, be aware that Landsat imagery from this time period may not have the same level of quality or resolution as more recent images.

Jihane Guenoun

while satellite remote-sensing techniques can provide valuable insights into crop water use and water availability, there are several limitations to their accuracy and reliability. These limitations must be carefully considered when using satellite-derived ETa estimates for monitoring and managing crop water use in different locations and for different crops.

There are several limitations to estimating actual crop evapotranspiration (ETa) using satellite remote-sensing techniques such as the Simplified Surface Energy Balance (SSEB) approach and Landsat Collection 2 (C2) Provisional ETa Science Products.

Spatial resolution: The spatial resolution of satellite imagery may not be fine enough to capture small-scale variations in ETa, particularly in heterogeneous landscapes with multiple crop types, varying topography, and soil characteristics.

Atmospheric interference: Atmospheric conditions such as cloud cover, haze, and aerosols can interfere with satellite measurements of ETa, particularly in regions with high levels of atmospheric pollution.

Sensor limitations: The accuracy of ETa estimates can be affected by sensor limitations, such as saturation of sensor values, band-to-band misregistration, and sensor noise.

Surface characteristics: The accuracy of ETa estimates can also be affected by surface characteristics, such as the presence of vegetation canopies, soil moisture, and surface temperature variations.

Crop variability: Crop variability, including differences in planting dates, crop management practices, and genetic traits, can result in variations in crop growth and water use that are difficult to capture using satellite remote-sensing techniques.

Calibration and validation: Accurate calibration and validation of satellite-derived ETa estimates is critical to ensure the accuracy and reliability of the data. This requires ground-based measurements of ETa, which can be difficult and expensive to obtain, particularly in remote or inaccessible areas.

Cost and accessibility: The cost of acquiring, processing, and analyzing satellite imagery can be prohibitive, particularly for small-scale farmers or resource-limited agribusinesses. Additionally, satellite imagery may not be readily accessible in some regions, particularly in areas with limited internet connectivity or data infrastructure.

Massimiliano Alvioli sorry for the delay !

Here is the result:

Harish Dangi

When I use this formula, LST = K2 / (ln(K1/Lλ + 1)) - 273.15 after getting the Lλ, i get the LST values of more than 800 which is weird. I used data from Landsat 8, collection 2, and LEVEL 2. However, if I use the given scaling factor of Surface Temperature which is "Band 10* 0.00341802 + 149.0", I get the values between -16 to 57. Since I used level 2, does it mean that using the scaling will give me the LST and no need to use further algorithms?

Here are a few resources that can help you with these steps:

I hope these resources help you with your bathymetry project!

Thank you for your responses. I will appreciate if you find any other helpful information in the future.

You can process MODIS data using GIS. You can do research on Landsat8 data, one of the band on Landsat8 too may give you temperature data. Processing the data to generate maps is very easy using GIS.

Use https://gdal.org/programs/gdal_translate.html#gdal-translate.

The command template is

gdal_translate -of RST -ot Int16 src_TIFF dst_IDRISI_RST

.

Insert your filenames for src_TIFF dst_IDRISI_RST.

To implement the METRIC model for Landsat 8 using Python, you will need to perform the following steps:

Acquire Landsat 8 data: Download Landsat 8 images for your area of interest from the USGS EarthExplorer (https://earthexplorer.usgs.gov/) or other satellite data providers. You'll need the Level-1 or Level-2 surface reflectance data.

Install required libraries: Ensure you have installed the necessary Python libraries, such as rasterio, numpy, gdal, and others.

Preprocess Landsat 8 data: Convert the digital numbers (DN) in the Landsat 8 images to top-of-atmosphere reflectance and brightness temperature values.

Calculate vegetation indices: Compute the Normalized Difference Vegetation Index (NDVI) and other indices, such as the Enhanced Vegetation Index (EVI), if needed.

Estimate land surface temperature (LST): Calculate land surface temperature using the thermal bands of Landsat 8.

Estimate surface emissivity: Calculate the surface emissivity using NDVI and LST values.

Calculate net radiation (Rn): Estimate net radiation using the Landsat 8 images and meteorological data, such as incoming solar radiation.

Estimate soil heat flux (G): Calculate soil heat flux using NDVI and net radiation values.

Obtain meteorological data: Acquire local meteorological data for air temperature, wind speed, and relative humidity from weather stations or other sources.

Estimate reference evapotranspiration (ET0): Calculate reference evapotranspiration using the FAO Penman-Monteith equation with the acquired meteorological data.

Compute ET fraction (ETf): Calculate the ET fraction using the surface energy balance equation, which includes net radiation, soil heat flux, and sensible heat flux.

Estimate actual evapotranspiration (ETa): Multiply the reference evapotranspiration (ET0) by the ET fraction (ETf) to obtain the actual evapotranspiration (ETa) values.

Export results: Save the calculated ETa values as a raster file or other desired formats.

import rasterio

import numpy as np

from osgeo import gdal

def download_landsat_data():

pass

def preprocess_landsat_data():

pass

def calculate_vegetation_indices():

pass

def estimate_lst():

pass

def estimate_surface_emissivity():

pass

def calculate_net_radiation():

pass

def estimate_soil_heat_flux():

pass

def acquire_meteorological_data():

pass

def calculate_reference_et():

pass

def compute_et_fraction():

pass

def estimate_actual_et():

pass

def export_results():

pass

if __name__ == "__main__":

download_landsat_data()

preprocess_landsat_data()

calculate_vegetation_indices()

estimate_lst()

estimate_surface_emissivity()

calculate_net_radiation()

estimate_soil_heat_flux()

acquire_meteorological_data()

calculate_reference_et()

compute_et_fraction()

estimate_actual_et()

export_results()

You'll need to fill in the functions with the appropriate calculations and methods. Some existing Python libraries, such as PyMETRIC, can help you.

There are several reasons why Landsat 9 may show higher surface temperature (LST) values than Landsat 8 in the same place and month:

Overall, the differences in LST values between Landsat 8 and 9 in the same place and month may be due to a combination of these factors, and further investigation may be necessary to determine the specific causes of the differences.

Hu Xiao I've some general thoughts. It's possible that the difference in reflectivity between Landsat 5 TM and Landsat 8 OLI/TIRS for the same object at different times could be due to several factors, such as differences in sensor calibration, changes in atmospheric conditions, or differences in the processing algorithms used to convert raw data into reflectance values. It's also important to note that Landsat 5 TM and Landsat 8 OLI/TIRS have different spectral bands, so they may be capturing different aspects of the object's reflectance.

Anyway, to determine the exact cause of the difference in reflectivity between the two sensors, further analysis would need to be conducted, such as comparing the raw data, examining the atmospheric correction algorithms used, and considering other factors that could be affecting the reflectance values.

Also, ensure that the data you downloaded has contents in it I.e. not an empty data.

unzip and preview as a whole or its individual bands using a GIS software (could be QGIS, ArcGIS, or ENVI)

Relative radiometric normalization using pseudo invariant features (PIF) in Google Earth Engine can be done by following these steps:

1. Add the input imagery to Earth Engine.

2. Identify potential PIFs by exploring the image and selecting features that do not change significantly in the image over the time period of interest.

3. Label PIFs and assign each one a unique identifier.

4. Select the sample points to be used for radiometric normalization.

5. Calculate the mean and standard deviation of the radiometric values for the sample points.

6. Calculate the radiometric value of the PIFs in the input imagery.

7. Calculate the normalization factor for each PIF by dividing the radiometric value of the PIF by the mean and standard deviation of the sample points.

8. Apply the normalization factor to the input imagery in order to achieve relative radiometric normalization using the PIFs.

Script:

// Define a function to calculate the relative radiometric normalization using PIF

var relativeRadiometricNormalization = function(image){

// Calculate the normalization factor

var normalizationFactor = image.normalizedDifference(['B7','B5']).select('B7');

// Select the bands to be used for the normalization

var bands = ['B2','B3','B4','B5','B6','B7'];

// Map the normalization over the bands

var normalized = image.select(bands).multiply(normalizationFactor).divide(100);

// Return the normalized image

return normalized;

};

// Apply the function to the image

var normalizedImage = relativeRadiometricNormalization(image);

Hope this will work.

Is your expectation that soil salinity has some distinctive signature you will be able to extract directly from the image's bands? Perhaps a more detailed description of your methodology will yield a helpful answer - especially if you provide some citations / references for your analysis.

I don't know about this tool bit you could use Raster calculator to compute the indices.

A faster way would be to use R and the function tasseledCap from the RSToolbox library. Here is the link to the function: https://rdrr.io/cran/RStoolbox/man/tasseledCap.html

Did you get the appropriate reference. Kindly share the articles with reference to the equation

You can use landscape fragmentation tools of ArcGIS 10.5 extension for calculating urban growth types. You have to reclassify lulc map. Settlement areas value will be 2 and other lulc value will be 1 then only you can calculate urban growth patterns.