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Hi Remote sensing Experts,
I was looking for codes to generate time series NPP and GPP from Landsat images using Google Earth Engine. I am aware of the MODIS product. But I don't know about Landsat. Any suggestions would be highly appreciated.
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نعم ابحث لمثل هذه الموضوعات الرئيسه
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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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Hi,
Can any of you please share me Landsat tool box for ArcGIS 10.1 to fix scan line errors in landsat images?
Thanks.
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Please am in need of the Landsat toolbox for Arcmap 10.3. Thanks
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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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James Varghese Thank you for your detailed answer! I have read various references and compared the Mono-Window (MW), Single-Channel (SC), Radiative Transfer Model (RTM), and Split-Window (SW), and Weng's (Weng et al., 2004)* algorithms. Weng's algorithm was also used in the paper you attached (Mohan et al., 2021).
However, algorithms with high accuracy (=low RMSE compared to local LST observations) differed from paper by paper. This seems to be due to differences in each local situation (e.g., aerosol or vegetation). But roughly, the SW algorithm has stray light issue (although it was almost calibrated in 2019), and I think it has a consistency problem with the other sensors (Landsat 4-7). Weng's algorithm seems to be the most widely used because it is simple to calculate, but I think it has disadvantages because it based on the NDVI only, but does not reflect the atmospheric factors. I agree Weng's algorithm is simple to use, but I think it is necessary to apply atmospheric variables, given my research area with an oceanic climate.
As a result, I think MW and RTM are optimal, and if there is local MODIS or radiosonde data, it is easy to process while securing accuracy of MW or RTM. In my experience, except Weng's method, RTM seems to be widely used, and Sekertekin & Bonafoni (2020)* evaluates Sobrino et al. (2008)'s NDVI-based MW algorithm is most accurate. So I am considering using these two algorithms in processing Landsat TIR Level 1 data.
Meanwhile, I kept looking for a solution to the 'missing gap' problem of Level 2 data, but failed. Even USGS Landsat Science team couldn't answer. However, I found two literature on product quality. Pinto et al. (2020)* compared the accuracy of Level 1 and Level 2. However, this study is not for a thermal band, but a visual band, and Level 1 is compared to TOA and Level 2 is compared to BOA each. So there is a fundamental difference from the answer I want. The Korean paper (Park & Kim, 2021**) comparing Level 1 and Level 2 to local LST values reports that Level 2 showed higher accuracy (RMSE: L2=4.0degC, L1=5.2degC).
Several other papers using level 2 data do not used special algorithms other than converting ST products to Kelvin temperatures. There is a lack of research cases, but to sum these up, I consider Level 2 definitely has a value close to the actual LST. It is also consistent because it is calibrated globally with the same algorithm (LST algorithm v.1.3.0). However, since there are many areas missing pixel in Level 2 data, and it is corrected based on a global climate model, I guess MW or RTM using Level 1 data that inputs local weather variables may be more accurate. Therefore, I am working on checking the difference between MW and RTM based on Level 2.
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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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I am following Sadeghi et al., 2015 paper and try to use OPTRAM model for estimating soil moisture and have to use STR (SWIR transformed reflectance). I have no idea on how to calculate STR using SWIR band from Landsat 8. What is the software used for this process? Can you please help me step wise process to estimate soil moisture using OPTRAM model? It will be great help.
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Now there is also great material for estimating OPTRAM index in R using Sentinel-2, thanks to @iuliia-burdun
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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.
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I am trying to get seasonal changed land use and land classification for 1 km area study area. Most of the years in the wintertime period (November to January) have no image by Landsat 5 and Landsat 8. It will be nice if anyone can give me any hints
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It's clear now. Thank you very much
Dan Abudu
. Can you please give me an idea on another topic if you know about it ?
For the difficulty of getting images for specific seasons ( Spring, Summer, Autumn and Winter), I decided to spilt the yearly time period into two. Is there any gradient according to literature extending the time period and splitting the year into two parts rather than four seasons? I thought on growing and non-growing seasons for Urban forest, it difficult for me as it varies among study areas in the different regions of the world.
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How often the LIDAR satellites can revisit the same area: for example every day or 3 months? for example the landsat repeat the cycle every 16 days.
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Azzurra Lentini What is your end goal? If you look at the data products from the various platforms, there are also other sensors on the same. Also, not infrequently, observations from other platforms and sensors are used to complement and supplement any given single sensor. Satellite sensor suites are optimized for certain phenomena ( but find uses in other domains ) like atmospheric column characterization and coarse repeatable elevation change. But the mention of drones leads me to think maybe you have another domain in mind, which leads into the myriad of different types of Lidar and relevant signal processing chains in both drone and terrestrial applications. And perhaps Lidar may not be the best solution for what you are looking for.
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For a multi-temporal study (my master thesis) that uses satellite images to obtain indices, for example, is it right or wrong to use level 2 images? (to save time and avoid mistakes) Would it be better if I process the level 1 images? (Landsat)
Thank you
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Level 2 Landsat Surface Reflectance science products are definitely the preferred choice among researchers as these products are already atmospherically corrected (a crucial pre-processing step before multi-temporal studies/analysis) and as you pointed out it helps save time so that researchers can spend more time interpreting the results rather than spend time on data processing. Of course, if you have the time or for learning purposes, it is recommended to manually process Level 1 to Level 2 using various algorithms such as DOS, FLAASH etc. And if you still have more time, you can compare the USGS Level 2 products and the manually corrected data with the In-situ ground measurements (if available) for validation.
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Dear colleagues I'm trying to calculate the land surface temperature for the Bakun Catchment, Sarawak, Malaysia region using different landsat satellite images of different years with the help of Google Earth Engine Code Editor. I'm not sure why I'm getting incomplete results for just this region and even the pixel values are too high in some of the years (please check the attached screenshots). Yes, for the NDVI and NDWI the results are covering the whole region.
I tried the same code for other regions of Pakistan and the results are very well.
So, my question is can anyone help me to get to know that this region the Bakun Catchment, Sarawak, Malaysia comes under special consideration or are there any other satellite data issues or maybe something wrong with the code? Also, does this region's LST goes down to -3 to -10?
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Just curious, are you using the ready to use LST product from USGS or are using a separate algorithm to derive Landsat based LST? In the former0 case, most probably, the negative values in the Landsat Collection 2 ST products from USGS are due to the presence of 'clouds' in the satellite images. Cross check with the cloud mask/Quality Assessment band to make sure there are no clouds in your study area in Sarawak. If you are using the USGS Surface Temperature product, I assume, the reason for incomplete results could be because these LST products are based on ASTER Global Emissivity Database (GED). Wherever, GED has missing data, there will be missing data in the Landsat ST product as well.
I might be wrong, I will have to look at the ST products for further scrutiny.
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Dear researchers,
For our new project, we are considering the use of Thermal Infrared satellite imagery. While the temporal resoltion is not important for us, we are looking for the highest spatial resolution in terms of Thermal infrared sensor. Aprat the available satellite data of MOODIS, Landsat and Sentinel is there any commercial satellite that provide very high saptial resolution of Thermal Infrared images?
Any information would be grateful in this regard.
Many thanks,
Wajdi
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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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you can follow next process Saludos!
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I need specifically The method step by step to determine the:
1- Shoreline
2-Baseline
I have an image Landsat MSS dated in 1975 with 4 bands
Any tutorial, guideline,
Thank you for your help in advance
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Above answer is OK. Of course you have to do composition of bands for beter classification effect. Sugested unsupervised method of classification should be appropriate. You have to determine only the area of wather. Of course details of method will be depended from the program/tool you will use.
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I want to detect what cooling effect (intensity and distance) a group of small green areas can have, for example in one part of the town. And also for the the whole town, preferably from satellite images.
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Thank you for all of your suggestions.
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looking for yearly LST.
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Hello Santrupti Mahajan , what exactly do you mean by 'yearly LST'? Are you trying to get an average LST map from an entire year's (2021) LST data? If so, MODIS products do offer daily/8 day/monthly LST products that you might want to consider for the 'yearly' mean LST calculation. Similarly, you can use Landsat Surface Temperature products. Check relevant links below.
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I am planing to make a comparison of forest cover between 1984 and 2021. I will use Landsat 5 and Landsat 8 images.
Could some recommend any pre process corrections or calibrations for both satellites ?
If I do radiometric calibration and dark objects subtraction for Landsat 8, Should I do the same for Landsat 5?
Or do landsat 5 accept radiometric calibration and dark objects subtraction? Or just a Quick Atmospheric correction can fulfill it ?
Please help.
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Yes, all Landsat images (5, 7, and 8) should be corrected radiometrically and atmospherically.
As Dorian Gaboriau mentioned, the SCP plugin in QGIS could be useful. Please see this video :
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  • What is the difference between Landsat Collection 1, Collection 2 Level 1and Collection 2 Level 2?
  • What are the specifications included in each type?
  • Do these types include atmospheric correction?
  • What is the most appropriate collection for Land Use landcover and ecological related analysis?
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Dear Janaki,
Maybe you would be able to find your answer in the following USGS link:
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Which bands of Landsat 7 should I use (6A or 6B or both?) & similarly for Landsat 8 (10 or 11 or both?) for LST estimation?
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Which Level (L1 or L2) of LANDSAT 8 OLI images is best for estimating soil salinity?
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Can anyone help me on how to carry out an analysis of Urban Form from Landsat imagery?
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Landsat 9 will provide a high-quality and reliable stream of land imaging data for the next 10-plus years
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Landsat 9 carries the same instruments that are on the Landsat 8 satellite but with some improvements:
  • Operational Land Imager-2 (OLI-2) for reflective band data.
  • Thermal Infrared Sensor-2 (TIRS-2) for the thermal infrared bands.
  • OLI-2 has a slightly improved signal-to-noise ratio over Landsat 8's OLI.
Landsat 9 improvements include higher radiometric resolution for OLI-2 (14-bit quantization increased from 12-bits for Landsat 8) allowing sensors to detect more subtle differences, especially over darker areas such as water or dense forests.
Regards
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I have a shapefile which is the ground survey data of the forest type.
The shapefile data contained multiple kinds of land cover data, including the artificial conifer, artificial broadleaf, artificial bamboo, natural conifer, natural broadleaf, and natural bamboo.
I am trying to use the Landsat image with the Random Forest Classification and the ground survey shapefile data to classify the forest type.
The training sample of each forest type is 30. The area of each training sample larger is than 4500 m2
The variable I select to split is five, and the training tree is 1000.
The variables I use include band 1~5 & 7, NDVI, CTVI, EVI, RVI, TTVI, TVI, SoilSalinity_ndsi, SoilSalinity_s1~s6, SoilSalinity_si, SoilSalinity_si1~si3, Water_Index_lswi, Water_Index_mndwi, Water_Index_nddi, Water_Index_ndwi.
The software I used is TerrSet 2020(idrisi).
I have tried several times, but the result is not good.
The Kappa value only has 0.47 for conifer, 0.38 for broadleaf, and 0.37 for bamboo.
The OOB Accuracy is 0.49.
Is there any method to use Landsat image to classify the conifer and broadleaf?
Or are there any other soft wares recommended?
Thanks for helping
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Hey Chien-Hao Sung , very interesting topic!
I briefly comment on previous proposals and will add my comments, keeping in mind I am working mainly in central european forests. Thank you for the discussion!
In my oppinion, RF is supervised, always a good choice. The mathematical effect of using multiple decision trees is a base of this algorithm and is very robust. Here a nice overview: https://www.sciencedirect.com/science/article/abs/pii/S0034425716303169
Landsat is a good choice, adding Sentinel-2 will add additional information, due to higher spatial (10m and 20m) and spectral (e.g. red-edge1, 2, 3) resolutions, which .
Yes, we must consider the complex nature like Nasir Hameed explained. Totally agree with this. As well, that VI use information, which are already manifested in the bands, e.g. NDVI with nir and red. But we can ask, why we use the VIs, when all information is already there? Why do we use redundant information in well known masking algorithms? (https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm)
Suggestions on this topic are highly appreciated! Thank you!
I would use VIs at the moment, also based on https://www.mdpi.com/2072-4292/11/22/2599
Texture information seems to be useful, as well as using information from RADAR products. Fast solution might be using texture information, RADAR needs more effort.
I underline: "Its all on the approach",and hammer is still a hammer even when you name it banana ;)
Best of luck for your work!
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Hi,
I have been doing image classification of landsat 4-5 of year 1997 in ARC MAP, but it is difficult to proper classify the image. As i am using band 4, 3, 2 band combination, and settlements are mixing with bareland etc.. And there is a difference in bareland color, as some locations it is white, at some it is brown, and at some it is cyan color.
Is there any proper band combination to classify the landsat4-5 of year 1997.
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What satellite imageries are best for conducting flood assessment? Is it sentinel or Landsat?
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Hello,
I am currently doing research that finds the applicability of Landsat 8 satellite images for Soil Salinity Mapping.
01) In order to do that I generated a few soil salinity indexes based on a few methods such as NDSI, VSSI, etc.
02) As the second step, I generated the actual soil salinity map by collecting samples and interpolating that sample points.
Now I have a problem about what is the best way to find the correlation between 2 raster images?
Can I use Fishnet in ArcGis and generate points for each pixel and then use Microsoft Exel or Python to generate the correlation or are there any other methods to find that correlation?
Please be kind enough to enlighten me if You know any methods, thank you.
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As recommended by Mohammed el Hafyani, you can use Spatial Analyst ->Band Collection Statistics tool in ArcGis. You could also add SDMTools box to ArcGIS.
In R, you may use :
corLocal(raster1, raster2, method=c("pearson", "kendall", "spearman")
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Hi everybody! as I've asked in the question I'd need to know how to extract the landsat 8 image within the polygon I created. I've already tried with .clip() but it does not work. Here I leave the link to my code. Looking forwards for your answers. Many thanks.
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Sorry, may I ask you why I get error with the export?
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I am getting this error again & again.
Also I want to know what exactly is difference among bands, layers & channels of a geotiff image..? Kindly provide the reference documents which will give details about the bit frame of geotiff image format.
Thank you
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This is the website which enables us to download individual bands (even its interface is not that nice). It is Land viewer website (attached below)
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I am working on calculating NDVi and EVI indices from different years (50 year time period) and comparing them. I have a few questions regarding the datasets that I have found on Google Earth Engine (GEE).
  • In the Landsat 8 Surface Reflectance dataset that is given on Google Earth Engine, do I have to do any additional preprocessing as it is already atmospherically corrected or do i have to just scale it using the scaling factor?
  • What is the difference between the Landsat Surface Reflectance datasets and Landsat TOA Reflectance datasets?
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In the Landsat 8 Surface Reflectance dataset that is given on Google Earth Engine, do I have to do any additional preprocessing as it is already atmospherically corrected or do i have to just scale it using the scaling factor?
If you use Tier 1 Surface Reflectance, 'LANDSAT/LC08/C01/T1_SR', mentioned by Amirmohamad-Abasi, you don't need to do any preprocessing . You can also use Tier 1 Level 2 product "LANDSAT/LC08/C02/T1_L2" to get surface reflectance data.
What is the difference between the Landsat Surface Reflectance datasets and Landsat TOA Reflectance datasets?
TOA Reflectance datasets need atmospheric correction.
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After downloading the SLSTR Level 2 scene, I noticed the range of values is 0 to 255 i.e., 8bit. I need to convert this DN value into temperature reading in Kelvin units, of the respective pixels. Like Landsat has a coversion factor in the form of (a*x + b). Couldn't find relevant information in the website (https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/products).
Want to know if anyone has information on the conversion factors for senitnel 3 LST DN values to temperature in Kelvin.
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Is it possible to determine LST for agriculture field level area using sentinel data
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Hello everyone. I need to calculate the upwelling and downwelling radiances and atmospheric transitivity for Landsat images prior to 2000 for my research therefore I cannot use the NASA calculator (https://atmcorr.gsfc.nasa.gov/). Does anyone know a way how to determine these parameters for images between 1984 and 2000? I do not have MODTRAN software.
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Djamal, thank you for your idea
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Hello, I'm Arjun.
I am working on LU/LC change of India. What are Satellite dataset I can use for whole India LU/LC change with accuracy at district level for the period of 1961-2021?
Regards,
Arjun
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Landsat images the best choice for LU/LC study but only from 1972 (The first Earth-observing satellite Landsat 1 was launched on July 1972 and collected data until January 1978)
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Hi folks,
I wonder if anyone know how to calculate the fractional vegetation cover (FVC) from Landsat imagery? As far as I have read about that NDVI, NDVIs, and NDVIv are three required input for calculation FVC as follow equation:
FVC = ( NDVI − NDVIs )/( NDVIv − NDVIs ) (1)
where:
- NDVI is given by: NDVI = (nir − red)/(nir − red); nir and red are corrected reflectance obtained from the sensor bands located in the near infrared (NIR) and the red spectral regions for each pixel within an image
- The NDVIs and NDVIv are values of the NDVI for bare soil (FVC = 0) and pure green vegetation (FVC = 1) within an image, respectively
However, the major problem when applying Eq.(1) is how to accurately estimate the NDVIs and NDVIv values on the Landsat imagery. Therefore, I need you support on how to retrieve the values for NDVIs and NDVIv and the way we can calculate them from Landsat or other RS images pls?
Thank you very much with kind regards
Bang
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It is usually in such studies in references review, 0.2 for soil ndvi and 0.5 for vegetation ndvi .
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Hello everyone, I'm Arjun.
I am working on large scale LU/LC change in whole India. I want to use landsat imageries as it has data for almost 50 years. But in the selection procedure, when I am looking images for a particular time period I found in many scenes there are large cloud covers.
How can I search for cloud free data for whole India for a particular period (say January 1990)?
Secondly, what is the ignorable percentage of cloud cover which we can use for our analysis ?
Regards,
Arjun
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Thank you Adedeji Oluwatola and Paul Griesberger for your assistances.
Regards,
Arjun
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I'm MFSc scholar working in this area
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You might have to check the spectral signature across the band. To identify which of the bands can spot the sediment s
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Hey , I'm currently doing undergraduate research and checking the applicability of Remote Sensing images to obtain soil salinity over paddy areas? I derived few soil salinity indexes using few methods such as NDSI etc.
Now I wanted to know the general soil salinity index range (ex NDVI = -1~+1) to check my output is correct or not...
because,
I used Landsat 8 Level 1 it gives - 0.01 ~ 1. 49
(after apply TOA + Sun angle correction)
and
when used Landsat 8 Level 2 it gives - 1126 ~ 427115
(directly)
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Dear Thilina,
If I have not misunderstood your question, the results you are getting are expressed in radiance values (due to their radiometric correction) or digital radiation levels (bits), in the second case.
Regardless of these ranges, when you apply a spectral index, the data will be represented by dimensionless values, which you will have to interpret. Normally the data in the middle of the range obtained will define the change value for the analysed variable. But you should check in detail the formulation of the applied index.
Regards
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Is there any method available to extract air quality from Landsat data sets?
If yes. what is the procedure and what are the references to study?
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Want to mark High tide Line from Open source Satellite data series. Apart from Landsat is there any other data sources? If yes than what are the alternatives to mark or define HTL?
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Dear Gaonkar,
You can go for the Sentinal-2 RS data for better result.
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I want to do a long-term (1990-2018) study which Enhanced vegetation index (EVI) is needed. However, the temporal coverage of MODIS can't meet the demand of this study. Therefore, I want to use both Landsat 5 and Landsat 8 in one study. I don't know if it will influence the accuracy of the study or not be permmited.
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Hi,
Yes, you can.
You can have a look at our research, in which we produced LULC maps from Landsat 4-5, 7 and 8.
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I use ENVI 5.3. Recently in May 2021 I have downloaded several landsat images from landsat 5 to 8 for my research purpose. Unfortunately ENVI cannot open Geotiff and metadata file for these images.Showing error that this file format dooesnot support.
Does anyone face this problem? How can I solve this problem?
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you can easily overcome this problem by replacing the first two lines in the MTL.txt file by
GROUP = L1_METADATA_FILE
GROUP = METADATA_FILE_INFO
Good Luck
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I already saw this question somewhere, but haven't seen any answers to it. So I am asking it again.
For some reasons that I don't know (and I would be glad if anyone could tell), though Landsat 8 surface reflectance data exist and can be accessed directly using google earth engine, Several authors (including myself) still do their own atmospheric correction and radiometric calibration.
Is there any reason for that? Is there a reason to doubt the quality of ready to use surface reflectance data provided by USGS, available as lansat 8 surface reflectance images?
I recently obtained excessive values of EVI (circa 2) when I used surface reflectance in the calculation (I am sure of the formula). I would like to mention that all other indices I computed are in range (SAVI and several normalized difference vegetation indices such us NDVI, GNDVI, LSWI, MNDWI,...). I don't know if someone also encountered such a problem with ENVI.
In any case, to conform to one of my previous research, I would like to continue using Dark Object Substraction instead of ready to use Landsat 8 SR.
Question: Does anyone know how to perform dark object substraction in google earth engine?
Thank you.
Best regards,
Koffi
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Hi,
I found a code that might be helpful: https://github.com/aazuspan/geeTools
Cheers,
Daniela
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I needed to download two satellite maps of the year-2020 and year-2000 to study the land use changes. I visited usgs.gov site to download the LandSat map, where I found that LandSAT8 OLI/TIRS C2L2 is available for 2020 and LandSAT4-5 TM C2L2 is available for 2000.
Can I use these two different LandSat level for comparing land use changes?
I have used the natural color band, and here is the result.
LandSat_2000 is made by using LandSAT4-5 TM C2L2, while LandSat_2020 map is made by using LandSAT8 OLI/TIRS C2L2.
Are they Okay?
One more thing.
From 2000 (satellite) map, it is clear that the area has more vegetation, while from 2020 (satellite) map, it is clear that the area has rarely any vegetation.
When I performed NDVI analysis, I found that 2000 map has more water bodies than 2020 (though in satellite, we see that 2000 has more vegetation).
I don't understand what's wrong. I must have a gap. Can you please help?
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I am applying supervised classification on landsat 5 images. But there is certian problem in obtaining the results. As you can see in the picture that there are settlements along the stream but if I see on google map it doesn't show the settlements along the river.
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You can use random sampling method in google earth . First you can mark LULC types ( waterbodies, builtup areas,vegetation areas...) as points or polygons using google earth. Then you can export the data as training samples for supervised classification.
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Hello Experts from PCI Geomatica,
I have successfully installed PCI Geomatica 2017 software for automatic cloud and haze removal from landsat TM and santinel 2A_Level 2A images. But when i start processing after sometime the window of FOCUS automatically disappeared. how can i solve this problem? Thanks in advanced.
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Hello Cao Yinxia , I am learning about PCI, but don't know where to download it because when I find only information about crack software. Can you help me? Thank you so much.
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I am performing a time-series analysis and using Sentinel-2 for the latest data and Landsat-5 for the older data. Now in the time-series graph, I see a significant rise in the output values for the last two years' data (sentinel imagery). The first step of my algorithm is to derive NDVI so does the difference between the two sensors still matter? If so then how to correct the process?
P.S- I am using top-tier products for all the imagery so is there a question of DN to TOA conversion?
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After downloading the Landsat satellite image particular area how we can identify or detect tea throughout the agricultural land utilization. Are there any method?
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NDVI can help in that
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for example for Landsat I upload MTL format for some of the calculations such as spectral indices. about worldview, I get error for the calculation.
Best regards
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Hi Dear Dr
For this purpose, please try again by ENVI 5.3 IDL version.
It should be noted that the ENVI classic sometimes not responding to this process.
You can use this link for additional information.
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Hi,
I am new to DL and I'm trying to classify 1 Landsat8 image into 3 categories using VGG-19. I am using 8 bands (B2 to B7 ,B10 and Panchromatic). I performed the sampling procedure and my samples are named "1_id_b2" (category_id sample_Landsat band). I have my training and test samples into separate folders. The folder structure is similar to the image attached (folder_str). I've read that I need to create training and test labels. I don't understand why to create the labels, because I already labeled my samples.
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Also, the website mentioned in the 4th point does not work
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I think if anyone has the procedure for the calculation it shall help me understand it more by kindly sending it my e-mail. (eneerzack@gmail.com)
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Any persons who have knowledge about this may answer to help my some research student of Yemen.
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Dear Syed Ahmad Ali,
You have to register and log in on the link below for the source data to download.
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Steps that I need:
1. EVI L8 time series reduced by montly median values;
2. Fit a curve using Savitsky-Golay, Whitakker or Harmonic model;
3 - Extract phenometrics, for example, SOS (start of the growing season) and EOS (end of the growing season).
Do someone knows how to perform these steps using Google Earh Engine?
Thanks
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  • Hello. Think the paper entitled 'Spatiotemporal image fusion in Google Earth Engine for annual estimates of land surface phenology in a heterogenous landscape' (Nietupski et al 2021), which contains GEE codes, may be adapted to the problem.
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I'm preparing land use maps for various years using Landsat 8 and Landsat 7 imagery. For Landsat 7, I've decided to not fill the gaps for SLC as it might result in inaccuracies.
For the Land Change maps, I intend to use post classification approach. I'm going to do this in ArcGIS desktop.
How should I detail about the loss of area via SLC in my technical report? Can someone refer me to technical papers that haven't used gap filling for their analysis?
PS: I cannot use Landsat 5 data as it is not available for my AOT for some years.
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The earlier images of Landsat 8 (OLI) started from the late 2013, this could be used to perform an image fusion with the Landsat 7 SLC, or as a replacement. For example, If you require Landsat 2011 (ETM+) image which has Scan Line error, you could download the 2013 Landsat 8 (OLI) from the USGS EarthExplorer as an alternative.
Read through this article for further details, see attached document.
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I face a problem in LULC classification, like as an industrial area showing as a water body.
Please help me.
Thank you.
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band 4, 5, 6 is the best three-band combination and band 1, 2, 5, 7 is the best four-band combination which achieved almost identical performance as using all bands for LULC classification.
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hello
is there anyway to download big satellite imagery (like Landsat 8 or sentinel) by rectangle focused on just my study area of research not whole the scene!
i want to examine drought progress in last 20 years for example by NDVI but in smaller area of interest
so i need data like Landsat 8 or sentinel for more detailed investigations in smaller basins
MODIS like data are so big and universal
but i prefer smaller regions to be investigated by detail
is there any way or site that provide such huge data in such way
selective download of satellite data for regions of interest
best regards
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Solmaz,
Please go to https://earthdata.nasa.gov/ and click on "Feedback" at the top left corner. Pose your questions in as much detail as you can provide, mentioning AppEEARS as well. The NASA system will route your question to the right persons at the Land Processes DAAC who can help you.
Good luck with your project.
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Hello,
I just created a "Quick Stats" histogram to view my Landsat 8 data after I divided each band by 10,000, and I see that the maximum reflectance values of all 7 bands are between 3-6. Do I need to correct this? My mean values are between 0 and 1, but I am not sure if they are correct either.
The layer stack image of the 7 bands came out cyan, which means that these values may be skewed by cloud reflectivity. How can I correct this to get the BOA values?
Thank you!
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Al Bris , thanks for your question. The answers also interest me
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Hi Everyone!
I have already calculated the original NDSI index, and I would like to use PCA analysis for more accurate results. I read in a study that I need the brightest and the darkest component for the index calculation (NDPCSI). I also read that this corresponds to the PCA component 1 and component 2. That is also mean Band 1 and Band 2? (if not, which bands should I use) Because I did the PCA analysis with Landsat 8 Bands (1-8), but I'm not sure what I supposed to use. I am a little bit confused about it.
Somebody could help me in this case, please?
I would be very glad if you tell me.
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PCA normally use to remove the redundancy of satellite bands and to select appropriate bands for image classification.
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I am looking for satellite imagery of last 10 years so that i can find out change detection in such place. The possible solution to download the historical solution is by using google earth pro but due to the contrast problem in such imagery it become very difficult for me to classify it and another solution i have found was Declassified imagery in earth explorer but all those images were taken in range of 1970's and another landsat but due to the coarse resolution i find it is also unsuitable for this project, santinel-2 contains the datasets only after 2015. What may be the possible solution for this probelem ? Please tell me if you have any idea , i will apreciate your help
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Hello Yogesh! You didn't write what is your area of interest (entire world? Americas? Europe? Asia...?). One of the best places (except Earth Explorer) is Copernicus Acces Hub (https://scihub.copernicus.eu/dhus/#/home). places
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In metadata file Lmin and Lmax has been given. Also Saturation radiance and solar radiance has been given. I want to perform it in ENVI.
It is easy to perform this operation for satellites like landsat, sentinel as the method is predefined in software itself.
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Assuming that there has been an atmospheric correction to the data, then the formula for radiance L in a particular band is DN = [(L-Lmin)/(Lmax-Lmin)]2^n, where DN is the digital number recorded satellite value for each pixel, n is the dynamic range in bits (n = 8, 10,11,...). Solving this formula for L you get the radiance.
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I find it difficult using training samples to classify pixels into sample classes
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Whatever supervised classification algorithm you intend to use, its success depends on the sample's size, distribution, and representation ( number of samples, locations, and covered objects). You can use Random Forest, Support Vector Machine, Artificial Neural Network, or the newly implemented deep learning SO-UNet that does not require large or complicated samples check
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I want to extract SO2 and NO2 from Landsat or Sentinel images. Any help from the remote sensing community.
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There are already operational products from OMI, OMPS, TROPOMI and GOME2. See e.g. comparison between OMI and OMPS in Regarding algorithms, I suggest you look into doi:10.1002/2014GL060136.
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While Using a Landsat image of 30 mts resolution, a map of what scale will be produced?? So how do u derive the scale of a map to be produced from certain resolution of satellite image???
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This online tool may be helpful - https://www.maptools.com/scale_calculator
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  • I am wondering if you let me know the limitations of Landsat 8 to getting the bathymetry. 
  • LIDAR data is capable of giving us the water depth in relation to water surface. Can we get the water level in gauge station using LIDAR?
  • Interms of expense and availability of  data source should I prefer the LIDAR or the Landsat 8?
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Hello could you recommend me a paper for lake bathymetric estimation for purpose of groundwater?
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I am conducting an image classification of a Landsat 5 image and I generated 5 classes using training samples but each time I try to extract my area of interest, the output only shows 2 classes. How can I maintain the 5 classes I generated?
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Either may be in your area of interest contain only two class among five class, or you can try subset in ERDAS Imagine because many times comes problematic error in extract by mask or clipping in ArcGIS
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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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Hello
Dear Polash
Yes, you should make radiometric corrections based on the area you are studying