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I am using L08 and 09 dataset band is ST_B10, or band 10 for thermal data,
I got this formula from many places that states "LST =ST_B10 × 0.00341802 + 149.0", is this correct? The confusion arose as someone said that the calculated value is BT (brightness temperature) not true LST as LST needs emissivity correction.?
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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:
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I have downloaded landsat 5 and landsat 8 atmospheric correct images, as provided by USGS nowadays.
Do I need to perform radiometric correction of these images?
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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.
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I asked this question to use satellite imagery to study urban ecosystem changes and find the best satellite imagery suitable for urban planning research. On the other hand, I know that Landsat 8-9 and Sentinel 2 can provide decent images, but the scale obtained from these images is too large.
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I used Sentinel-2 data. It was very effective.
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Why LST calculate from Landsat 8 Level 1 and LST from Landsat 8 Level 2 are different (around 10 K)
In picture is results
LST (K)- EPv is calculate from LS8 level1 ------
from E=1.009*0.047ln(NDVI)
-> BT = K2/ln(K1/L+1)
-> LST = BT/(1+(a*BT/b)*ln(E)) ; a = wave length, b = constant from boltsmann, Plank, Velocity of light (b=0.01438 m.K)
and LST from LS8 - level 2 (K) is LST from LS8 level 2---- from LST = 0.00341802*DN+149 (K)
Why results have too much different? (around 10 K)
and Which one is correct?
Thank you
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I am analyzing a Landsat 5 TM image, and the Band 3 (red) appears almost completely black, with very little to no visible details. What could be the possible reasons for this issue? Could it be related to atmospheric conditions, sensor calibration, or data processing errors? I would appreciate any insights or suggestions on how to resolve this.
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• 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.
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How can I process Landsat Analysis Ready Data (ARD) to derive Surface Temperature (LST) in ArcGIS Pro? What are the required steps, including preprocessing, calculating Top of Atmosphere (TOA) reflectance, emissivity correction, and applying the Single Channel or Split-Window algorithm?
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1. Preprocessing Landsat ARD Data
Before calculating LST, you need to prepare the data by selecting the required bands, applying necessary corrections, and masking clouds.
Step 1.1: Load and Visualize the Data
  • Open ArcGIS Pro and add the Landsat ARD data.
  • Use the Composite Bands tool to stack the required bands.
Step 1.2: Mask Clouds (Optional)
  • If a Quality Assessment (QA) band is available, use the "Extract by Attributes" tool to remove cloudy pixels.
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i'm working with landsat images. in all downloaded images , The cloud show dark colour . that's why i confused .
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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.
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I am working with Landsat 5 and Landsat 8 thermal bands (B6 for Landsat 5, B10 for Landsat 8) in QGIS. However, I noticed that some areas in the image appear as white patches or empty white boxes. These areas seem to lack data or have unusual values.
What could be the possible reasons for this issue? Could it be due to missing data, atmospheric interference, or processing errors? How can I fix or interpolate these missing values for better analysis?
Your insights and suggestions would be greatly appreciated!
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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 ).
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Necesito los pasos para realizar una clasificación supervisada de imágenes landsat y sentinel, en QGIS, con SCP, de tal manera que la clasificación sea lo más precisa posible, algún libro o manual que recomienden
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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:
Pasos para la Clasificación Supervisada con SCP en QGIS
  1. Preparación del Entorno: Instala QGIS y el complemento Semi-Automatic Classification Plugin (SCP).Ve a Complementos > Administrar e Instalar Complementos y busca "Semi-Automatic Classification Plugin". Descarga las imágenes satelitales de Landsat (e.g., desde Earth Explorer) o Sentinel (e.g., desde Copernicus Open Access Hub).
  2. Preprocesamiento de las Imágenes: Corrección atmosférica: Utiliza las herramientas de SCP para convertir las bandas de las imágenes a reflectancia superficial (si no están ya corregidas).En SCP, selecciona Preprocessing > DOS1 Atmospheric Correction. Reproyección: Asegúrate de que todas las imágenes estén en el mismo sistema de referencia espacial (e.g., EPSG:4326).
  3. Cargar las Imágenes en SCP: En SCP, configura las bandas necesarias para la clasificación:Ve a SCP > Band Set > Add Band Set. Carga las bandas relevantes (e.g., RGB o multiespectrales según el análisis requerido). Define la composición de bandas para visualizar mejor las características del terreno.
  4. Definir Regiones de Entrenamiento (ROIs): Crea polígonos en las áreas representativas de las clases que deseas clasificar (e.g., agua, vegetación, suelo desnudo).Utiliza SCP > ROI Creation > ROI Tool para dibujar las regiones de interés (ROIs). Asigna nombres claros a las clases y asegúrate de cubrir toda la variabilidad dentro de cada clase.
  5. Entrenamiento del Modelo: Genera el archivo de firmas espectrales:En SCP, selecciona Training > Generate Signatures. SCP calculará las estadísticas espectrales de las ROIs seleccionadas. Ajusta las clases si es necesario para mejorar la diferenciación.
  6. Clasificación Supervisada: Realiza la clasificación supervisada utilizando el algoritmo de tu preferencia (e.g., Máxima Verosimilitud, Random Forest, SVM).Ve a SCP > Classification > Supervised Classification. Selecciona el método de clasificación y ejecuta el proceso. Guarda el resultado como un nuevo raster.
  7. Validación de la Clasificación: Crea puntos de control independientes para evaluar la precisión.Usa el módulo de validación en SCP > Accuracy Assessment para generar una matriz de confusión. Calcula indicadores como la precisión global, Kappa, precisión por clase, etc.
  8. Postprocesamiento:Limpia los errores de clasificación aplicando un filtro de mayoría (opcional). Combina clases similares si es necesario.
  9. Exportación:Exporta el resultado final en el formato deseado (e.g., GeoTIFF).
Consejos para Aumentar la Precisión
  • Usa imágenes de alta resolución temporal y espacial adecuadas para tu región de interés.
  • Aumenta la cantidad y diversidad de las ROIs para capturar mejor la variabilidad espectral de cada clase.
  • Prueba diferentes algoritmos de clasificación y compara resultados.
  • Realiza una validación exhaustiva para identificar errores sistemáticos.
Libros y Manuales Recomendados
  1. Documentación oficial de SCP: Semi-Automatic Classification Plugin ManualEs una guía completa paso a paso con ejemplos prácticos.
  2. Libros sobre análisis de imágenes en QGIS:Congedo, L. (2020). "Remote Sensing with QGIS and SCP: A Practical Guide to Land Cover Classification." (Disponible en línea en algunos sitios o plataformas académicas). Graser, A. (2022). "Learning QGIS."
  3. Tutoriales en línea:YouTube Channel de SCP: Contiene videos que explican los pasos detallados. GIS Stack Exchange: Comunidad para resolver dudas específicas.
  4. Manuales de Landsat y Sentinel:USGS Landsat Handbook: Landsat Handbook. ESA Sentinel User Guides: Sentinel-2 User Guide.
Si necesitas ayuda más específica en alguna parte del proceso, no dudes en pedírmelo. 😊
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Anyone can explain it?
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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:
1. Radiometric Calibration
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
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Thanks for your explanation
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I'm very grateful for your detailed explanation
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Question for researchers and specialists:
What are the most important remote sensing indicators and algorithms that can be used to estimate the shape of riverbeds and small streams? How can the accuracy of these estimates be improved using data available from satellites like Sentinel-2 and Landsat?
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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.
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I have two monthly rasters (LST landsat 8) for the months July and August. I want to create another raster for the month June.
How should I proceed? I was thinking to take the mean but it doesn't make so much sense because June is the first month of my analysis and the LST should be lower compared to July and August.
R 4.4.1, RStudio , Windows 11.
Please no ChatGPT answers without knowing if its response is correct or not.
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I'm not sure I can download a ready made monthly LST from Landsat 8 (or 9). At least I'm not aware of such data.
What if I scale down the July's raster by let's say 0.9 and produce the June's LST? Dies this make sense?
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USGS provide two kind of major Data sets, which are collection 01- Level 01 and Level-02 data. In Level-02 data All the other visual bands are process to surface reflectance but why panchromatic band isn't process? My question is how to process panchromatic band to surface reflectance? Can you suggest the method for me?
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Great question... I wonder the same (looking the answer and didn´t find it) !
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I am calculating LST for Landsat 9 with Landsat 8 formulas in ArcGIS, but the thing is am not sure about the constant values used. Please let me know if anyone has any idea about the same
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@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.
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The indexes NDWI and MNDWI are use to identify waterbody. Can I use these index to identify wetland also? Thank you for advance.
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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!
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To mask cloud cover, the following details are needed:
- Algorithm for Landsat 8 Level 2, Collection 2, Tier 1 data
- The algorithm should be applied to each KML to mask out cloud cover
- Credible sources for the algorithm
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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.
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I am working on a project that involves using Landsat remote sensing images for detailed urban planning at the house scale. The current spatial resolution of the Landsat images is too low for this purpose, and I need to increase it to 5 meters. I've explored various super-resolution techniques but haven't found a method that works effectively for my needs.
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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.
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How can I properly identify hot and cold pixels manually or automatically to calculate the SEBAL model from Landsat images using GRASS GIS, R, or MATLAB?
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you have to analyze on emittance value of the reflected wave
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A question to see who can answer it. If the red and infrared bands of the satellites are these:
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Satellite.................................Landsat 8 / 9.................................Sentinel 2...................................
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....Red..............................Band 4: 0.64-0.67 μm.....................Band 4: 0.53-0.68 μm
Infrared............................Band 5: 0.85-0.89 μm....................Band 6: 0.70-0.80 μm
...................................................................................................Band 7: 0.81-0.92 μm
...................................................................................................Band 8: 1.20-1.30 μm
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Why do you use band 5 in Landsat and band 8 in Sentinel to estimate NDVI? Wouldn't it be more appropriate to use band 7 in Sentinel?
Thanks.D
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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 :
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How Satellite Bands (Landsat/Sentinal) and indices (NDVI/NDBI) were composite together (Layer stacked) (In a single layer) before performing supervised classification (MLC/SVM/RF etc)? How it reduces the heterogeneity in data?
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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?
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There are different types of automated, built-up area extraction methods used in remote sensing. However, I am puzzled to find suitable indexes for use for all sensors such as a 'X index' can be universally employed to Landsat TM,ETM+, OLI or Sentinel (may be). I'm looking forward to know mostly used built-up extraction index with higher accuracy.
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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.
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I have used the formula
LST = (BT / (1 + (0.00115 * BT / 1.4388) * Ln(ε))) for finding the JJA (June, July, and August) LST for California.
Where for BT (at satellite brightness temperature) I have used the 'ST_B10' band directly without re-scaling it since there are no brightness temperature bands in LandSat8 level 2 collection 2 Tier 1 data products and for calculating the emissivity (ε) I've used the re-scaled i.e. (ST_B10 = ("ST_B10" * 0.00341802) + 149.0) and cloud corrected ST_B10 band. When I use the formula (("ST_B10" * 0.00341802) + 149.0) following the LandSat8 handbook for LST, it shows a high value ranging from 10 to 70-degree degrees Celsius for California. I don't know what is wrong here and how to correct it. I am new to this field. Thanks in advance for any help you provide.
References: Weng et al., 2004-Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies.
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Hello
You can read about this problem in this topic:
Good luck
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I'm doing my first research and I can't find a way to see the shoreline evolution.
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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).
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I used Google Earth Engine to retrieve LST for my research area based on Landsat 7 (for 2001.) and Landsat 8 (for 2019.) by using surface refectance dataset (atmospherically corrected). I'm trying to see how LST changed with newly built-up surfaces.
So, based on slightly different bands wavelengths between satellites, does it also mean LST is varying? For example, if I compare LST from 2001. and 2019. on the same urban surface is the measured LST going to be different? I'm also asking based on this: thermal Band 6 on Landsat 7 is 10.40 - 12.50μm and Band 10 (TIRS1) on Landsat 8 is 10.60 - 11.19 μm.
I am aware of influence of weather during researched time on LST, but aside of that I would like to know if I can compare these two LST's.
Thanks!
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Matej Žgela did you find an answer? I am interested to know, please
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After harmonizing and making all the radiometric corrections, I still get that Landsat 8 shows a wrong increase in vegetation intensity. Anybody with a similar problem that may have found a solution?
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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.
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Dear Colleague,
I hope this message finds you well.
I am excited to announce the Call for Chapters for our upcoming book project titled "Applying Remote Sensing and GIS for Spatial Analysis and Decision-Making," scheduled to be published by IGI Global.
We are seeking contributions from researchers and practitioners who are passionate about exploring the application of remote sensing and GIS technologies in spatial analysis and decision-making processes. Your expertise and insights would greatly enrich the content of our book, and we cordially invite you to submit a proposal for a chapter.
Submission Deadline: May 19, 2024
For more details about the submission process and guidelines, please visit the following link: [https://www.igi-global.com/publish/call-for-papers/call-details/7509]
Should you have any inquiries or require further information, please do not hesitate to contact me . I am more than happy to assist you throughout the submission process.
Thank you for considering this opportunity to contribute to our publication. We look forward to receiving your proposals and collaborating with you on this exciting project.
Best regards,
Adil Moumane
University of Ibn Tofail. Kenitra, Morocco
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I am intrested
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usgs has not given any scaling factor values to be applied for level 1 products. but it also doesn't make sense for satellite zenith angle to have values from 1-2768.
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1. I'm seeking soil property raster datasets with resolutions matching those of Sentinel or Landsat imagery, as SoilGrids data are currently available only at a 250m resolution. Therefore, I'm interested in finding options with finer resolutions of 10/30m. What are the best available alternatives with finer resolutions?
2. Is there a dataset specific to the Indian context that provides more accurate and locally optimized soil data?
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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
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Hello, community, I have a question about remote sensing lineament extraction. What is the best satellite imagery for this purpose, Landsat or SRTM DEM, and why?
Thank you
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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.
Pleae recommend my reply if it is useful .Thank you
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I want to download the Level 0 product which should only contain the DN values, with no-processing. From where can I download the files. Please guide. Thanks
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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:
  1. USGS EarthExplorer (https://earthexplorer.usgs.gov/): This is the primary source for downloading Landsat data from the United States Geological Survey (USGS). You can search and download Level 1 data products for Landsat 1-9 missions.
  2. USGS GloVis (https://glovis.usgs.gov/): Another data portal provided by the USGS for searching and downloading Landsat Level 1 data.
  3. Amazon Web Services (AWS) Public Dataset (https://registry.opendata.aws/landsat-8/): AWS hosts a public dataset of Landsat 8 Level 1 data, which can be accessed and downloaded through various AWS services.
  4. Google Earth Engine (https://earthengine.google.com/): Google Earth Engine provides access to the complete Landsat archive, including Level 1 data, through their cloud-based platform for planetary-scale geospatial analysis.
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.
Pls recommend this reply if it was useful .Thanks
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Which is preferable:
publishing an article in a close-access journal like Elsevier (or Springer), or publishing in an open-access journal with a low impact factor?
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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.
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I am conducting a study on the "Impact of Land Use and Land Cover (LULC) Changes on Land Surface Temperature (LST)" and plan to use Google Earth Engine (GEE) for my analysis. I am at a crossroads in deciding between the "USGS Landsat 8 Level 2, Collection 2, Tier 1" dataset and the "USGS Landsat 8 Collection 2 Tier 1 TOA Reflectance" dataset for LULC classification.
Could the community provide insights on:
  1. Which dataset would be more suitable for LULC classification, especially in the context of analyzing its impact on LST?
  2. What specific pre-processing steps would be recommended for the preferred dataset within the GEE environment to ensure data integrity and robustness of the classification?
Any shared experiences, particularly those related to the use of these datasets in GEE for LULC and LST studies, would be incredibly valuable.
Thank you for your contributions!
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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
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I am doing Land Use Land Cover Classification in GEE using Landsat 8 Level 2, Collection 2, Tier 1 dataset. While collecting training points in the composite image, The challenge I am encountering is that urban and barren land areas appear similarly in the color composites I've tried, which were based on combinations of bands 5, 4, 3 and 7, 6, 4.
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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.
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how to fill in blur and gaps in Landsat 7 images. What can I do if the Landsat images are blurred in my work to calculate the area of lakes?
the second question is whether it is better to use level-1 Landsat 7 image or level-2 when calculating the surface of lakes?
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To fill in the gaps and blur in Landsat 7 images, you can use several techniques:
  1. ENVI software: This software has a plugin called landsat_gapfill which can be used to fill gaps in one scene with data from another Landsat scene. A linear transform is applied to the “filling” image to adjust it based on the standard deviation and mean values of each band, of each scene.
  2. QGIS software: You can use the gdal_fillnodata tool in QGIS, which uses an inverse distance weighting (IDW) interpolation.
  3. Google Earth Engine: The USGS L7 Phase-2 Gap filling protocol can be used.
Read here:
As for your second question, it depends on your specific needs and the quality of data you require.
  • Level-1 Landsat 7 images are delivered as quantized and calibrated scaled Digital Numbers (DN). The Level-1 DN data can be rescaled to Top-of-Atmosphere (TOA) reflectance by applying radiometric rescaling coefficients. If you require the most recent satellite data, you will be limited to Level 1 data.
  • Level-2 Landsat 7 images contain surface reflectance values, i.e., reflectance as it would be measured at ground level in the absence of atmospheric effects. If you plan to use the best pre-processed (Surface Reflectance) Landsat data, it is recommended to use Collection 2 Level 2 Tier 1.
Read here:
Best Regards,
Ali YOUNES
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Hello
I want to estimate LST by Landsat 8 collection 2 level 2.
What is its steps and formula.
with regards
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Arc Gs pro or Envi or Matlab.
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* I am using ENVI 5.6.
* I do not want to use any other AC processor or L2 products.
I have already read several similar Q/A posts on several forums, but that did not help.
If you successfully were able to apply FLAASH on Sentinel 2 L1C images using ENVI, please share your workflow on this post.
This is my workflow, but I keep receiving this error:
1. Adding the data using the "MTD_MSIL1C.xml' file
2. Staking 10m, 20m, and 60m layers using "Build Layer Stack."
3. Converting BSQ encoding to BIL using "Convert Interleave."
4. Adjusting the necessary parameters on the FLAASH module
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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.
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Hi,
I worked on my master's thesis several years ago, which was related to the LST of a basin with two methods: Single Window and Sebal using Landsat images, during the study period of 1984 to 2017.
Now I want to change this thesis into an article. Is it necessary for me to update the years until 2023 or not?
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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
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Why did the Normalized Difference Built-Up Index (NDBI) values for a tropical urban area decrease from -0.03 in 1988 to -0.16 in 2020 when using Landsat 5 and Landsat 8 data? Given the expected urban growth, why do the NDBI values also differ (-0.82 to 0.62 in 1988 and -0.45 to 0.35 in 2020)? What factors could explain these changes, and how should we consider these differences when comparing data from different satellites and time periods?
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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
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Greetings: How can I performe the Tasseled Cap Transformation in IDRISI Selva for Landsat 8 Images? because the only options available for that are :MSS, TM & ETM+, and its showing the attached error when I select ETM+
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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
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Please I am having difficulty opening Landsat images in ENVI 5.1 using File>open as>Landsat> Geotiff with Metadata. It keeps flagging the following error message "Unable to read file as the specified filetype: LANDSAT_GEOTIFF_META"
Has anyone encountered this issue and how was it resolved?
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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.
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I downloaded the Landsat 8 Level 2 Collection 2 Tier 1 Surface Temperature image of February 25, 2019, multiplied it by 0.00341802 plus 149 to get the Kelvin temperature of the study area, and then processed it to degrees Celsius. The value range unit is -32.88℃~31.07℃. However, the actual measured value in the study area that day was -19.66℃~-1.11℃.
Has anyone encountered similar problems? How to handle this abnormal situation.
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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.
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Dear all, I work for a big irrigation district. It seems that some farmers are illegally using irrigation water from the main canals at night which is prohibited. The crop areas are around 5-20 ha and are located in the Valle del Cauca region (Southwest part of Colombia). I'd like to know if there is a way of using RADAR (Sentinel-1) or multispectral data (Sentinel-2) through Google Earth Engine to determine if some fields have been recently irrigated. Thanks in advance for any reference or tutorial you might share.
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In the metadata file of satellite images, image acquisition time is there. In which time zone format they are giving the data?
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UTC
Field Definition: The date and time at the beginning of the data acquisition period in UTC time zone.
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Hello Everyone,
The scaling factors used for Collection 2 and Level 2 Landsat data includes a multiplicative (0.0000275) and an additive (-0.2) term. When these factors are used to convert to non-scaled surface reflectance values for each band, and these data are used in various processing procedures (like the ratio used to compute the NDVI), it has both noise problems (pixels with water or clouds) and the data are smoothed so it appears that the spatial resolution is lowered.
I don't understand why this problem has been around so long and not fixed at this stage. Not sure what the benefit is to spread the range of digital numbers way beyond the original range to create an artificially enhanced dynamic range of numbers. The 'empty spaces' from valid number to valid number is then filled in with a smoothing type of procedure (perhaps the resampling using cubic or bi-linear methods), so when zoomed up the image looks smooth and appears to have lost resolution compared to the original image (get level 1 and level 2 data and compare them). This combined with the 'noise' problem introduced by the 'scaling factor corrections' for pixels with water or clouds is one reason why many users are downloading level 1 data and doing their own corrections instead.
Generating the NDVI with the not scaled corrected data (the data you download) DOES NOT give valid NDVI values. They are in the range of -1 to +1, however, they are not correct; the values will be quite a bit smaller than they should be. If only a multiplicative scaling was used (e.g., 0.0000275) and not an additive also (e.g., -0.2) then the scaling would not matter because they would cancel out in the ratio process; this does not happen when an additive term is included.
I think it is time to re-think the scaling of the data and use a method that does not create problems that were not there to begin with !
Pat Chavez
Northern Arizona University (retired USGS)
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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
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Could you please provide me with 2 or 3 Elsevier or Springer articles that utilize this formula:
LST = BT / (1 + w * (BT / p) * ln(e))?
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Check Google
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Dear fellow researchers!
I need global data on normalised difference moisture index (NDMI) in raster formats. Is it possible to get it? The AOI is considerably large and going for calculation from Landsat, ASTER or Sentinel is not a viable option.
Please help?
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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:
  1. Remote Sensing Archives: Explore existing remote sensing archives or data repositories that provide pre-processed NDMI data. Some organizations or projects might have already computed and compiled global or regional NDMI datasets, saving you the computational effort.
  2. Earth Observation Databases: Investigate platforms that host a variety of Earth observation datasets, such as the Google Earth Engine or NASA's Earthdata Search. These platforms often offer pre-processed indices, including NDMI, which can be accessed and downloaded in raster format.
  3. Research Institutions: Contact research institutions or universities that specialize in remote sensing and geospatial analysis. They might have developed or aggregated NDMI datasets for research purposes, which they could share or provide access to.
  4. Data Fusion: Utilize fusion techniques that combine multiple data sources to estimate NDMI. This approach can involve merging data from various satellite sources, weather data, and other relevant information to approximate NDMI values over the desired AOI.
  5. Collaboration: Collaborate with experts in the field of remote sensing and geospatial analysis. They might have insights or access to resources that could assist in obtaining or generating the required NDMI data.
  6. Custom Computation: Consider leveraging cloud-based computing resources to perform the calculations efficiently over your AOI. Services like Google Earth Engine offer the capabilities to compute indices like NDMI on large areas without the need for extensive local computing resources.
  7. It's important to consider the trade-offs between the data sources, computation requirements, and the accuracy of the generated NDMI data to ensure they align with the objectives of your analysis.
If you find my reply is useful , please recommend it , Thanks.
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can I use this formula ? LST=BT/1+w*(BT/p)*In(e)
in which way we can obtain BT? and e for this kind of data?
I used this formula for calculating surface temperature in kelvin by "0.00341802 * DN + 149.0"
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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)
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My article focuses on the changes in Land surface temperature, vegetation, and waterbodies over a long time in an area by using Landsat and Modis data with a new methodology.
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#Remote Sensing of Environment (Recommended) (link: https://www.sciencedirect.com/journal/remote-sensing-of-environment)
#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/
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How do i calculate Land Surface Temperature Landsat 8 level 2 image?
Do I have to convert DN (TOA) to radiance, then convert radiance to brightness temperature, then emissivity, and finally the LST calculation?
OR is it enough to rescale the thermal bands and convert kelvin to celsius degrees (since level 2 is already corrected)?
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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
  • ("LC08_L2SP_174038_20230716_20230725_02_T1_ST_B10.TIF" * 0.00341802 + 149 ) - 273.15
To get the actual Emissivity values, I used the Multiplicative Scale Factor from the science guide
  • "LC08_L2SP_174038_20230716_20230725_02_T1_ST_EMIS.TIF" * 0.0001
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?
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LST = BT / 1 + w (BT / p) * Ln (ε) (formula 1)
What is the name of this method?
Additionally, we use the following formula for Landsat 8:
LST = (BT / (1 + (0.00115 * BT / 1.4388) * Ln(ε))) (formula 2)
What are the differences between Formula 1 and Formula 2? If we use separately Formula 1 and Formula 2 to calculate the LST of one Landsat 8 image, will the results be the same?
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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.
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  1. Additionally, what factors should be considered when selecting an appropriate image processing algorithm for lineament extraction?
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@Syed Hussain Yes, it is made by Chat GPT, I just want to help you
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How to identify our corrected image for Landsat 8 after all the corrections including atmospheric and radiometric is good to use before deriving the depth using the stumpf algorithm in the satellites derived bathymetry work?
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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
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For a research, I have to calculate different indices like NDVI, NDWI, etc. by using Landsat data.
However, I am confused about the preprocessing. Because, the Landsat collection 1 data are no longer now. That's why I have to use Landsat Collection 2 Level 1 or Collection 2 Level 2 datasets for analysis.
Now, I have to know whether the Landsat Landsat Collection 2 Level 1 or Collection 2 Level 2 datasets need any further preprocessing/corrections or not?
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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.
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I have 23500 points, I sorted them in Excel from lowest to biggest, and then in a scatter plot, I create its chart, now I want to find the data that after that data (point) my chart starts to a high slope near 90 degrees, or in another word my chart begins growing up faster.
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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.
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Hi, in the Brightness temperature calculation, I saw two values for converting to Celsius.
Which one is correct? why in some articles use 272.15 and other articles use 273.15??
BT = K2 / ln (K1/ Lλ +1) -272.15
or
BT = K2 / ln (K1/ Lλ +1) -273.15
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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.
If you find my reply useful , please recommend it . Thanks
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Hi,
I have experience working with Landsat, Modis and Sentinel-2 data; Right now I need to use Sentinel-3 data also to fuse with Sentinel-2 data, however, I am finding converting the Sentinel-3 data which is in NCDF4 format to Geotiff format correctly very difficult. I have come across snappy API which can be used from python but found the documentation and examples a tad inadequate when it comes to Sentinel-3 data. Since the lat-long information is provided in a separate file, my main problem is how to overlay geo-coordinates and radiance values from S6 and write it in a tiff file with the correct projection and resolution. Any help would be highly appreciated.
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Did you Sotirios Soulantikas find an solution for this problem?
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I need to analyze LULC pattern of Kathmandu city before 2000. The Landsat Image of Kathmandu before 2000 is not available in USGS web site.
Could you tell me where can I find it ?
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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:
  1. Go to the EarthExplorer website: https://earthexplorer.usgs.gov/
  2. Create an account or log in if you already have an account.
  3. Click on "Search Criteria" and select "Landsat Archive" under the "Data Sets" tab.
  4. Choose the time range for which you want to obtain Landsat imagery.
  5. Enter "Kathmandu" in the "Place Name" field or specify the latitude and longitude coordinates for the area of interest.
  6. Click on "Search" to view the available Landsat images for your selected area and time range.
  7. Use the filters to narrow down your search based on sensor type, cloud cover, and other criteria as needed.
  8. Once you have identified the Landsat image(s) you want to download, click on the "Download Options" button and follow the prompts to download the data in the desired format.
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.
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Hi,
I am trying to understand the limitations of the Simplified Surface Energy Balance (SSEB) approach and Landsat Collection 2 (C2) Provisional ETa Science Products to estimate actual evapotranspiration of different crops in various locations.
These would be used by an agribusiness to monitoring water consumption and water availability for crops (wheat, rice and corn) grown in 14 different countries
I am struggling to understand if and how these can be applied to different crop / locations couples as Landsat Collection 2 (C2) Provisional ETa Science Products are yet to be validated.
Thanks for your help,
Best regards.
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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.
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Access to very high resolution satellite data is costly. Since researchers most of the times don´t have another alternative other than relying on freely available datasets, 30m, 10m resolution datasets (Landsat/Sentinel) have been used in many studies.
How high resolution datasets ( landsat 30m and sentinel 10m) perform in sparse built-up areas using pixel based classification?
What is considered an acceptable classification? Could you provide examples (images)?
How about developing countries?
Are there available reference maps for subsaharan african countries?
I don´t trust conventional accuracy assessment!!!! unless it is followed by details of the image!!!
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How do I use the scaling factor when deriving the LST with Landsat collection 2 product?
At what step do I apply the scaling factor?
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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?
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How to create satellite-derived bathymetry using Landsat 8 images through ENVI or ArcGIS Pro software? are any recommendation steps/video tutorials? appreciate it if you can attach any related file or link about it
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Here are a few resources that can help you with these steps:
  • A paper by Nguyen et al. (2019) titled "Estimating Bathymetry from Landsat 8 Imagery in Shallow Water Environment Using an Empirical Approach". This paper describes an empirical method for bathymetry estimation using Landsat 8 imagery and provides detailed instructions for the process.
  • A tutorial video by Harris Geospatial that demonstrates how to extract bathymetry information from Landsat 8 imagery using ENVI software. Here's the link: https://www.youtube.com/watch?v=ESNtPOnB-_I
  • A tutorial video by Esri that shows how to create a bathymetry map using the Benthic Terrain Modeler in ArcGIS Pro. Here's the link: https://www.youtube.com/watch?v=49Jnsrxp12o
I hope these resources help you with your bathymetry project!
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I have Landsat 8 Collection 2 Level 2 which is in Surface Reflectance and Surface Temperature (Thermal Band). After rescaling with one of the two methods highlighted below, Can I say the Surface Temperature from the Thermal Band is the same as Land Surface Temperature (LST) OR I will have to calculate LST separately?
Surface Reflectance for OLI Bands= SR * 0.0000275 - 0.2
Surface Temperature for TIRS Band 10 = SR * 0.00341802 + 149
OR
((SetNull(("SR_B4"<1) | ("SR_B4">65455),"SR_B4"))*(0.0000275))-(0.2)
((SetNull(("SR_B10"<1) | ("SR_B10">65455),"SR_B10"))*(0.00341802))+(149)
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Thank you for your responses. I will appreciate if you find any other helpful information in the future.
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Could anyone help how to do heat vulnerability mapping?
Steps in ArcGIS
and data required
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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.
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when I try to import LANDSAT 9 images with Idrisi Selva the program sent the message: compressed grey scale image not supported...
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The command template is
gdal_translate -of RST -ot Int16 src_TIFF dst_IDRISI_RST
.
Insert your filenames for src_TIFF dst_IDRISI_RST.
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How to calculate METRIC model from landsat 8 in python?
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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.
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Hello, I am trying to process images to estimate LST in 2021 for a multitemporal analysis, however, the excess clouds do not allow me to use Landsat 8 images, fortunately Landsat 9 images are available, however the LST values when processing the information are high when working in ArcGis, however, when processing in QGis I get lower LST data for the same image, it would help me to know if any of you are already processing Landsat 9 images in multitemporal analysis and if you have observed this phenomenon.
Best regards
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There are several reasons why Landsat 9 may show higher surface temperature (LST) values than Landsat 8 in the same place and month:
  1. Spectral bands: Landsat 9 has an additional band, the Coastal/Aerosol band, which is not available on Landsat 8. This band captures shortwave radiation in the blue spectral region and may impact the estimation of LST, especially in areas with high aerosol content.
  2. Spatial resolution: Landsat 9 has a higher spatial resolution (30 meters) than Landsat 8 (30 or 15 meters, depending on the band), which may lead to a better representation of surface features and a more accurate estimation of LST.
  3. Sensor calibration: Differences in the calibration of the thermal sensors between Landsat 8 and 9 may result in different LST values, even in the same location and time.
  4. Atmospheric correction: Differences in the atmospheric correction algorithms used for Landsat 8 and 9 may affect the estimation of LST, especially in areas with high atmospheric variability.
  5. Environmental factors: Differences in environmental factors such as cloud cover, vegetation cover, and soil moisture may impact the estimation of LST, even in the same location and time.
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.
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I did a small experiment to extract the landsat reflectance data of the same object in different periods (including landsat 5 TM and landsat 8 OLI/TIRS). The true color RGB observation shows that the ground object has not changed, but the reflectivity data does not maintain a certain value over time. The data I use is T1_ L2 data in GEE, I want to know which step I need to operate less to generate this situation?
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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.
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Hello researchers, I am having trouble displaying Landsat Collection 2 Level 1 images of my study area (LC08_L1TP_181063_20200114_20200823_02_T1) in ENVI 4.4.
How do I view them?
Please help me.
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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)
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I have two incidents (pre and post incidents) for which NDVIs will be calculated and used to detect any change by subtracting. Now before calculating NDVI, I want to normalize radiometrically the post images with respect to the pre images using regression which uses pseudo-invariant target (PIF). I am looking to do this whole process in Google Earth Engine.
My questions are:
  • Can anybody please kindly share the script?
  • During selecting the PIFs, should I select them from the reference/base images or the target image?
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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.
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I am not able to delineate salinity using NDSI indices.
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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.
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I'm looking to extract shorelines from satellite images. To find a suitable method I need to compare various methods of automatic shoreline extraction methodologies. If you have any suggestions please leave in reply...
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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
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LSE = 0.004Pv + 0.986
It's widely used for TM and ETM but I want some references to use this equation for Landsat 8.
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Did you get the appropriate reference. Kindly share the articles with reference to the equation
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I want to determine different types of urban Typology like Primary urban core, Secondary urban core, urban fringe, Scatter urban from landsat image GIS and remote sensing techniques
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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.
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It shows "[Deprecated]" for both Landsat 7 & 8 surface reflectance Tier 1 bands...
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Use the following link instead
ee.ImageCollection("LANDSAT/LC08/C02/T1_L2") for Landsat 8
ee.ImageCollection("LANDSAT/LE07/C02/T1_L2") for Landsat 7
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Actually, a full frame should be stacked in one Tif file after downloading, but I receive it more than 50 files.
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Dear Aldo
Many thanks for your response
I will try it
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Can I use the Level 1 landsat imagery to generate an accurate land surface temperature for my AOI.
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yes you can
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Hi. I have three questions about Landsat 8 and LST.
Q1.
I want to use Landsat 8 TIRS data to calculate Land Surface Temperature (LST) of the arctic regions.
So far, I have pre-processed BT and TOA from Level 1 DN, converted to LSE, and added NDVI to obtain the LST value.
However, I have some anxiety that this method is inaccurate than using Level 2.
Nevertheless, the reason why I could not use Level 2 ST (Band 10) product is because of the data gap due to missing of ASTER GED data (figure).
As you can see from the link below, the high latitude regions such as Greenland and Iceland have data gap issue in ST products.
I wonder if there are some methods or alternative to overcome the data gap.
Q2.
I know that Sobrino 2004's method is frequently cited in LST calculations, and I know that there was a stray light problem with Landsat 8 TIRS.
However, recently I got to know that there are so many algorithms for converting TIRS images to LST (e.g., Split-Window or Single Channel)
Also, I found out that the stray light issue has already been calibrated in 2019.
Now, I wonder which algorithm is most appropriate for converting Landsat 8 TIRS images to LST.
Q3.
Is there no need to preprocess Level 2 SR products for NDVI or other band combinations?
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If Landsat 8/9 Level-2 products express your AOI properly, use Level-2. They reduce preprocessing time so that you can spend more time on analysis and writing.
However, if the quality is not enough for your AOI, Level-1 may be the only choice. It is essential to choose the appropriate LST retrieval algorithm for your AOI's situation (e.g. land cover). I spent a lot of time selecting them.
Or, if your AOI is very wide, you can use alternatives such as Sentinel-3 data.
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I am working on a trend analysis of at-satellite temperatures (brightness temperature) using Landsat 5, 7 and 8 thermal bands. For the visual and infrared bands I used the sensor harmonization function proposed by Roy et al.
However, for the thermal bands no transformation coefficients were calculated. Is there the necessity to transform the thermal bands?
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From what I understand, yes, the infrared bands should still be harmonized. I had the same question and was unable to find any citations for how to do this. The most recent publication I found that mentions thermal harmonization says that this harmonization is "complicated" and leaves it at that. Harmonizing surface reflectance between Landsat-7 ETM + , Landsat-8 OLI, and Sentinel-2 MSI over China | SpringerLink
However, collection 2 level 2 data should have relatively minor errors when comparing between satellites. If the higest accuracy Landsat thermal data is required for something like a timeseries analysis, the best option is to use the ARD product provided by USGS. This dataset is (currently) only available for the United States with plans for a global release at an unspecified point in the future.
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Hello everyone,
I am doing LULC of the arid region, I have acquired landsate 8 image data from USGS website and I have done preprocess in qgis using semi-automatic classification plugin using the standard tutorial and I have converted by DN into reflections value for LULC. however, I facing difficulties in assigning classes for built-up area and bare soil as they have high overlap spectral values.
besides this, I have also used SAVI as well as Modified bare soil index though it's not helping in my problem.
Anyone can tell me what to do in that case.
Thank you.
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Kory Postma thank you very much for your response.
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I am working on my master thesis and want to quantify the NDWI using Landsat images.
After estimating the water surface area for my area of interest, the areas obtained from Landsat 7 overestimated the actual area due to the inclusion of gaps in my calculation. Now, I need to fill the gaps.
I am using Landsat 7 collection 2 level 2. However, in Landsat 7 collection 1 level 2, the filling can be made by using gap mask folder when downloading the data which is not the case in collection 2.
Thank you in advance,
Awad M. Ali
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I figured out the answer myself. So, I will answer it and maybe someone can benefit from it.
You can just directly fill the gaps using QGIS:
Raster --> Analysis --> Fill nodata
Good luck!
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Remote Sensing
ENVI. software,
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Very good answer.
Thanks a lot Dr. Akande.
Kindest regards.
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hope to get reply asap so it would help for my project.
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The CCI-LC project provides Land-cover maps at 300 m spatial resolution on an annual basis from 1992 to 2020.
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I have two raster dataset of NDVI and LST, both calculated from Landsat data. I have extracted the NDVI and LST values for each pixel. Now I want to calculate the soil moisture index using the LST values. For that I have plotted the LST vs NDVI scatter plot. But I am unable to put two different regression lines to the dry edge and the wet edge of the scatter diagram. How to draw the graph?
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Howdy Daniel,
I did the exercise for the XInjiang province in China. It was published as well. Have a look at the following paper to get a grasp on the problem approach.
Cheers,
Frank
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Here[0] I described an issue with the scaling factor for Landsat 8 Level 2 products. I've used the scaling factor from here[1] but when I compute the NDVI the result has many pixels out of the validity range.
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You can use the SREM method for surface reflectance estimation and then can perform NDVI calculation. If you need any help regarding NDVI calculation using SREM, feel free to contact me.
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I am looking for night images of Las Vegas in summer (june to august), taken by Landsat 8. The goal is a LST analysis.
However, there does not seem to be a single image available for the ascending direction path 137 / row 209 in Earthexplorer. It shows always that there was no image found.
Am I making a mistake here in the search?
A similar question from 2017 (https://www.researchgate.net/post/How_can_I_download_nighttime_landsat_8_images) mentioned to contact customer support for night time images. Is this still up to date and how can I reach them?
There are several ways to contact them, but I do not know who exactly to contact regarding this matter.
Thank you in advance!
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Hi everyone, i am facing problem during calculating land surface temperature by landsat 8 image. In my aoi the LST comes high 72 and low 55. In actual temperature never gone high above 50 in my aoi. Anyone can tell me whats problem. In 2000 2005 2010 and 2015 temperature results are accurate. Only problem in 2021 Lst.
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I'm facing the same problem with LS9 B10 C2 LSp2. The calculated LST is notably higher than normal (71 to 91 Celsius!!). I use the same formula I used to use with LS8 C1L1 B10 that used to return realistic LST.
What parameters I have to modify their values to get correct LST?
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Isn’t MSS Landsat not reliable? For example, to calculate NDVI.
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Rawaz Rostam Landsat MSS has fewer spectral bands, and lower spatial resolution, and this could limit its applications today.
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In which SCORPAN factor do the Landsat bands fit into? Without any ratios/indices, just the "pure" bands.
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It depends on what can be seen on the respective images. This in turn depends on the location the image shows and the time the image was taken. The scorpan factors can therefore be s (bare soil), o (vegetation) or p (parent paterial).
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does landsat 9 need atmospheric correction?
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You can use Landsat surface reflectance (i.e. atmospherically corrected) provided by the USGS: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-8-9-olitirs-collection-2-level-2. You might also check this reference:
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I want to map the structural lineaments (faults and folds) on DEM or Landsat image. I will be thank full for your guidance.
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the extraction of lineaments, in particular lineaments which represent fault, using in general remote sensing, it will not be efficient.
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Dears members, I am working on shoreline delineation using landsat 8 OLI,
Could you please provide me a link to download the tasseled cap with landsat for arcgis .tbx extension for arcgis 10.5.
Secondly how to extract the shoreline boundary from landsat 8 images using arcgis 10.5
Thanks in advance
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The tool works on landsat 7 ETM + and Landsat 5
For L8 OLI you have to manually calculate Brightness, greenness, wetness and NDVI then use tasseled cap function
Here's the link for it
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Landsat 8
Landsat 9
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The two spectral bands of the TIRS sensor in Landsat 9 are:
  • Band 10 TIRS 1 (10.6 - 11.19 µm) 100-m
  • Band 11 TIRS 2 (11.5 - 12.51 µm) 100-m
These bands are identical to the TIRS sensor in Landsat 8, according to
I am not an expert in this field so I hope this answers your question.
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The top sites for Landsat satellite imagery in terms of content and ease of use.
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Thank you Marcos Mendez
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please I need scientific reference for my answer.
I'm using Landsat 8 LT1 for fusion of MS and PAN bands of satellite images .
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The section on geometric corrections in this book chapter could be useful:
Christine Pohl, John van Genderen. 27 Sep 2016, Preprocessing from: Remote
Sensing Image Fusion, A Practical Guide CRC Press
Accessed on: 24 May 2022
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Greetings scholars, I am sharing a web based application that might be handy to dedicated researchers.   Using this application users can download:- ·                 Time series data and metadata of the satellite imagery selected and algorithm specified   (Users can specify the length of the period, location, scale factor, clouds cover) ·                 Time slider to visualize the changes ·                 Time series charts Currently this application supports four algorithms: NDVI, 2BDA, 3BDA, Turbidity Index based on 3 satellite imagery (Landsat, MODIS and Sentinel). This application has been super useful to my current research (time efficient) and I strongly believe it would be supportive to the researchers willing to conduct similar research. I will try my best to include other algorithms based on users’ recommendation and suggestion. I have attached a quick video demonstrating method to use this application. Suggestion will be highly appreciated. Thank you. Happy researching! website link: mapcoordinates.info
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Please follow this article to know more about the datasets used in this application;
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I am working on Forest Canopy Density. There is a parameter called "Scaled Shadow Index(SSI)" while computing Forest canopy density. In most of the papers I found that, SSI has been calculated by "Linearly Transforming" Shadow Index. I have computed the Shadow Index. But i am not getting the idea to compute Scaled Shadow Index. Kindly help me out. Moreover, If I am using Landsat 5 and 8 Surface Reflectance Image for FCD Mapping and as the Reflectance value ranges from 0 to 1, is it still mandatory to normalize these Surface Reflectance data before calculating Vegetation Indices?
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The density clustering with wavelength clustering algorithms and Clustering by Wavelet Analysis may help your work
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I want to compare percent vegetation cover on a reclaimed site vs. percent vegetation cover in a reference area (i.e., an adjacent, not disturbed area). Can I do this with Sentinel or Landsat imagery? If so, would it be best to just create a ratio between something like NDVI in the reclaimed area vs. that in the reference?
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Below two articles, decades apart, that may assist you in approaching your question from first principle. NDVI is a helpful proxy for green biomass up to certain amount. Whether green biomass is correlated with plant cover depends on the season, if any, and the type of rangeland (annuals, perennials, low evergreen shrub....). Please refer to my papers on typologies of rangelands. Finally, I am interested in your reason for the estimation of cover. The only reason that comes to my mind is cover as predictive variable for erosion and infiltration.
Of course, you may approach your estimation purely empirically. Measure cover on the ground and correlate cover values with various temporal NDVI variables.