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Satellite Image Analysis - Science topic

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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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Can anyone explain me how can I create a grid of 1x1 km in satellite imagery of SPOT6/7 by using Arcmap?
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If you have ArcMap 10. then you can use Arctoolbox>Data Management Tools>Raster> Raster processing> Resampling
Resample (Data Management): Changes the spatial resolution of a raster dataset and sets rules for aggregating or interpolating values across the new pixel sizes.
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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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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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I have SPI values for each meteorological station but I should have SPI value for each pixel of satellite image.
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In my study area I have 153 stations. I have monthly precipitation data for all these for 40 years. I should I find SPI for WHOLE area? I know R studio.
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Respected sir/mam,
I want to simulate the urban expansion using different time series LULC based on satellite image. please suggest me most suitable model for urban simulation.
Thanks and regards.
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I am trying to delineate agricultural fields using Sentinel 2 imagery. I have been implementing different image segmentation algorithms to a time series of this data set. My best output so far has false positive errors of some non-agricultural zones (like forests). Hence, I'm looking for the best way to distinguish forests from ag-fields as a post-processing step.
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Hi!,
Given that the crop seasons are known and the availability of sentinel data, maybe instead of post-processing you can perform a pre-processing, select a date or dates when the crops are still not developed and classify the forests, then create a mask of forest areas and apply that mask to an image or images for the crops classification. Depending on the complexity and types of crops you may have to anlyse more than one date but in this way you remove the effect of forests.
Hope it helps, I have solved this way a similar problem I had with urban and soil with stubble mixed signatures.
Regards,
Soledad
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Dear friends
I am working on a project in which I have to calculate LST using Landsat imagery. I used different algorithms. However there is a significant difference between LSTs calculated on the imagery and those measured in field. The calculated brightness temperatures are in more accordance with field-measured LSTs. I think that this is because these methods are based on vegetation cover (NDVI) and the study area has a very poor vegetation cover. Is there any other approaches that do not use vegetation indices?
Sincerely Yours
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Surface temperature can be calculated using the two thermal bands of the Landsat 8 , this can be calculated by applied the radiometric calibration via ENVI software.
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Respected sir/mam,
I have using satellite image of Landsat 8 and 5 for LULC classification. I am confused that which software and method are the best for LULC classification?
Please help me.
Thanks and Regards
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Most GIS and RS software have the standard classification methods (ArcGIS, QGIS, ENVI, Eedas etc).
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In Terrset, an error encountered in executing Spatial Decision Modeller (MCE). During execution or run the MCE a massage come on the screen that reflects
"Your columns and rows are not same" , I have attached the image below.
How to encounter this error?
I need a suggestion to get rid of this sort of error. Waiting for the response from any expertise persons.
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Dear Suraj,
it means all your input files do not have same extent
solution:
Go to ArcGis and with the help of "Extract by Mask", mask all the input files by your study area shape file
then use them in Terrset for rest of the processing
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Emissivity is a crucial parameter for calculating Land Surface Temperature (LST). One of the algorithms to calculate LST using single thermal band of Landsat 8 (Band 10) or Landsat 7 (Band 6) or Landsat 5 (Band 6) is based on the simplified Radiative Transfer Model (RTM) equation as documented in Barsi et al.(2003). The RTM equation in question is the following :
Ltoa = τεLt + Lu + (1-ε) Ld
where, τ is the atmospheric transmission, ε is the emissivity of the surface, Lt is the radiance of a blackbody target of kinetic temperature t, Lu is the upwelling or atmospheric path radiance, Ld is the downwelling or sky radiance, Ltoa is the Top Of Atmosphere (TOA) radiance measured by the sensor.
For this question, I'm only interested in estimating ε. Generally, I've been using the NDVI Threshold Method as described in Sobrino et al. (2001), which probably is the most commonly used method for estimating emissivity. This works more or less okay for Landsat scenes that have been acquired during the day. However, for night-time Landsat acquisitions, estimating emissivity using the NDVI method is illogical because the NIR and Red bands of Landsat mostly register only noise. This could be due to the absence of reflected energy from the sun at night and radiometric sensitivity of the sensor. With that said, I'm aware of alternate ways to estimate emissivity, e.g., (1) by Image Classification method which assumes emissivity for each class, or (2) by using emissivity values from spectral libraries such as the ASTER spectral library (http://asterweb.jpl.nasa.gov), or (3) by taking into account the seasonal ASTER Global Emissivity Database (GED) provided by JPL or (4) In-situ emissivity measurements. Except the last method, that is rarely available, the other methods may not be close enough in regard to the temporal resolution of the night-time Landsat image acquired for which the Land Surface Temperature is to be determined. In such a case, what would be the best way to accurately obtain a Land Surface Emissivity image for the corresponding night time Landsat thermal image?
Does anybody have any other ideas? You're encouraged to add relevant references in addition to your answers/comments.
PS: USGS has developed a Landsat Surface Temperature product that is available for US Region and rest of the world, but I'm not sure whether it includes night time surface temperature product.
References:
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Eneche Patrick Samson Udama, glad to know this discussion page helped. Appreciate your comments.
I was recently attending some NASA Applied Remote Sensing Training (ARSET) on Land Surface Temperature and Urban Heat Island (UHI) and Night Time Satellite Imagery Applications. If someone is interested they can follow the links:
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My CNN model input requires around 30,000 square-shaped satellite images, the corner coordinates(lat-long) of which I have already compiled. However, I am not able to figure out how to collectively obtain the images for these specific co-ordinates using the Google Earth Engine.
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Hi Arka,
If you have the coordinates of the area which you need, you may use the following code to extract the images combined together for long/short time periods.
However, you have to make sure about the size of the image/ area as sometimes, it may give error from the server-side in GEE, if there are a huge number of images in one go. In that case, you may try to take it in small sets.
Hope this helps you.
Cheers
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.
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More Precisely,
Let's, Sensor, (Sw x Sh) = 13.2 x 8.8 mm
Focal length, F = 8.8 mm (rear focal length, not 35 mm equivalent)
The flight height, H = 100 m
Video resolution, (imW x imH) = 4096 x 2160 (in pixels)
Then,
Ground Sampling Distance, (GSD) = (H * Sw) / (F * imW) (in cm)
Also, Image width footprint on the ground = (H * Sw) / (F ) (in meters)
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I have a Corine 2000 land use data set and i want to update it using a Worldview Satellite Image with 8 channels.. WHat would be the best way you think?
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Cartosat-3 is an advanced Indian Earth Observation satellite built and developed by ISRO, which will replace the IRS series. It has a panchromatic resolution of 0.25 metres making it the imaging satellite with highest resolution in the world and Mx of 1 metre with a high quality resolution which is a major improvement from the previous payloads in the Cartosat series. Potential uses include weather mapping, cartography or army defence, and strategic applications.
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i want to prospect ground water in rocky terrain using satellite imagery. i need to know the most suitable data to be use, the method of extraction and the procedure for achieving it. Thank you in anticipation of your useful contribution.
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For automatic lineament extraction, you can achieve this by applying a combination of the following softwares: Arcmap 10.0+, ENVI 5.1, PCI GEOMATICA 2016, ROCKWORKS 16.
STEP1: Use ENVI software to perform principal component analysis (PCA) on the spectral image band of Landsat8.
STEP2: GEOMATICA is used to perform an automatic lineament delineation from the PC1.tiff generated from ENVI. This is done with the aid of lineament extraction algorithm in the Tool from toolbar
STEP3: ARCMAP is used for handling extracted lineament (splitting compound line into simple lines, editing lineament attribute, exporting lineament as cad file
STEP4: ROCKWORKS is used to process the exported lineament Cad file to determine the trend of the lineament and hence generating rose diagram.
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What are the procedures to make lineament map using satellite image in ArcGIS or ERDAS IMAGINE? Is there any video tutorial? Or Step by step procedures.
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For automatic lineament extraction, you can achieve this by applying a combination of the following softwares: Arcmap 10.0+, ENVI 5.1, PCI GEOMATICA 2016, ROCKWORKS 16.
STEP1: Use ENVI software to perform principal component analysis (PCA) on the spectral image.
STEP2: GEOMATICA is used to perform an automatic lineament delineation from the PC1.tiff generated from ENVI. This is done with the aid of lineament extraction algorithm in the Tool from toolbar
STEP3: ARCMAP is used for handling extracted lineament (splitting compound line into simple lines, editing lineament attribute, exporting lineament as cad file
STEP4: ROCKWORKS is used to process the exported lineament Cad file to determine the trend of the lineament and hence generating rose diagram.
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Dear esteemed researchers,
I have developed an algorithm for contrast enhancement of satellite images. I need some recent methods for comparison. I have managed to get 6 methods so far, but I need more. If you have a Matlab code (even if its a Matlab p-code) and you would like to share it, please do. I will use it for comparison purposes only.
Thank you in advance, your help is much appreciated.
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Does the Wavelet transformation has been used at any stage of your research?
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Though Anderson's 1976 classification is the most conventional LULC classification system, and at the same time NLCD 2011 being a subset of the earlier one, there is confusion with barren and water class level-2 classification. The major confusion is with class 7. Barren ( 71 Dry Salt Flats. 72 Beaches. 73 Sandy Areas other than Beaches....) What does the level-2 73 class mean? Is it the whole coastal line other than beaches (for instance, the yellow highlighted areas in the image attached)? What if the sand holds moisture content? Can't we classify it under the water class? If the latter case is acceptable, then it will be like violating Anderson's classification scheme. I have found that NLCD 2011 classification is a brief categorization whereas Anderson's is not. Can anyone suggest how to proceed with this confusion/issue?
Thank you very much in advance.
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You could classify the images using Object-based classification by eCognition, or ArcGIS Pro, then do an LCLU analysis using TerrSet. Good luck.
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Hi,
I am trying to plot daily LST(Land Surface temerature) data for a region using MODIS and Landsat data. I have used ESTARFM model to fuse MODIS and Landsat data to produce daily LST data at landsat resolution. However MODIS data (Acqua-MYD11A1) is only available after a few days of lag. Can somebody please let me know how many days of lag is there between acquisition date and the date on which the data is made available and whether it is possible to produce the LST data at near real time if we compute the LST ourselves?
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It takes about 15 days for the data to be available , this is due to some pre-processing techniques and steps.
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Does anyone have a fuzzy method to solve mixed pixel in crop (sugarcane/rice) classification for district level area?
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yes, first you have to check your comailed database.
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What is the accuracy of these images regarding their geo-referencing and how accurate are they for their use of known sites with control points for geo-referencing?
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Dear Leônidas Luiz Volcato Descovi Filho ,
Yes, I agree with you but it is not always possible to get reference geodetic bases of the first order.
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What satellite images/bands should I use for this purpose?
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Hi Mannan,
Are the qanat's track superficial or underground? if they are superficial, you can use thermal bands. If the qanat are underground, I think you can use Ground Penetrating Radar for this purpose.
Good Luck,
Reda
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I have no knowledge as to how images are prepared for supervised classification in machine learning. I would like to ask help on this matter. I will be working with satellite images and my variable of interest would be the roofing material of houses. How do I prepare my training sample? What software is needed and what would be the program? Your inputs and help will be greatly appreciated.
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For RS and GIS applications such as yours, ERDAS IMAGINE, ENVI, ARCMAP, IDRISI may be of great help. They are dedicated remote sensing and GIS tools that provide platforms for a great variety of applications such as mapping, and change analysis. Since your study is mapping roofing materials, all of the above mentioned softwares provide good platforms for your work. There are thousands of videos available online for learning and understanding supervised classification. Data preparation is equally important as well. Refer "Introductory digital image processing" by Jenson for more detailed information on both classification and data preparation.
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I want to implement image registration for satellite images using mutual information and intensity based. And I want to calculate mutual information between two images, translation and rotation.
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and also in c++
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Hi!
Currently we are investigating about dendrometric variables of some tree species in Bogotá-Colombia, and we need to calculate the tree crown diameter per tree (in an automatic or semi-automatic), but we only have access to satellital images and a old inventory of tree heighs.
We are searching for a method that can be used in Python or R or with an open-source software.
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Dear D. Object oriented segmentation with sub-meter resolution satellite imagery and using SW like eCognition sounds very promising! Good Luck!
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In spite of several campaigns and infinite awareness activities, water wastage is still an avid problem. In countries like India especially in its rural areas, this problem imposes a great threat.
Is it possible to retrieve information relevant to this from the satellite image data and use it to build a systematic surveillance system for this?
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If you are willing to pay the costs for high resolution imagery like Worldview or Ikonos repeatedly, it certainly is possible at an excruciating amount. With opensource image its really difficult because of the coarse resolution. You can try using Sentinel-2 which is 10m to may be monitor the drainage systems. But the problem is in the rural areas these are very narrow and would not even comprise a single pixel on certain occasions. The best would be to use UAVs for monitoring, it wouldn't require a very sophisticated sensor so you could manage your budget also.
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If I have thee successive satellite images (A, B, C) with almost 80% overlap, I can generate for example two disparity maps for each image ( AB disparity & and AC disparity maps). The question is how to merge these two disparity maps to fill the gaps/ holes and produce more complete map even with few occluded areas?
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Dear
there are a large number of softwares can do that such as envi, arc map
regards
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What scaling factor to use to convert floating point data into integers to perform the unsupervised classification?
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you can use Envi software and then u can use unsupervised classification
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Can compression (before classification) increased the classification acuracy?
How can we improve the classification accuracy of remote sensing image(RSI) by the help of compression?
Dear respective researchers can you please help me by providing the related articles link or your valuable opinion for these issue.
Regards
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Hello!
Yes! the careful examination/interpretation of remote sensing images by using various false color composites will help us to collect the signature of land use/land cover classes effectively.
You may refer following article for information about, how to achieve better classification accuracy.
All the best
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I am doing analysis of MODIS data in order to build yield model from the Vegetation Indices.
Recently, I used R since I have found it's been used widely by many research regarding time series Remote Sensing.
I found several packages, but it was still confusing me, "what should I do first with my Raster data downloaded from USGS?" before I use the packages to analysis my stack of data.
If you have some best practice tutorial, I will be glad if you can share it to me!
Many thanks!
Regards.
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You need to know the seasonal transition parameters in order to extract phenology data out of remote sensing time-series data. I recommend you yo check TIMESAT (http://web.nateko.lu.se/timesat/timesat.asp) for the extraction of these parameters which are mainly: beginning of season, end of season,length of season, time of middle of season, maximum value, amplitude etc. You can model the same approach or try different fittings for your time series data with R but the main idea is explained briefly in the software manual.
After extracting phenological data you can build your own model between yield and vegetation indices.
You can also check our study, performed with Landsat data, which is not totally aiming yield prediction but stating that the use of phenological data for agricultural applications helps to improve accuracy:
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I am working on the paper " An Adaptive Pansharpening Method by Using Weighted Least Squares Filter". I downloaded the WLS filter code and when i am applying satellite image, it is not working. please send me some more information regarding that.
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I think you are better to visit this website .I hope it is useful for you
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Dear Experts,
I am going to estimate land surface temperature in a cloudy city and prepare its LST map for further analysis. I searched among Landsat images of the case study , but, the cloud coverage is very high ! (Since I prefer to use high resolution images, i searched among Landsat images).
I would be thankful if you provide me some solutions and suggestions.
Best regards,
Majid
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SENTINEL-1 IW Level-1 products are Single Look Complex (SLC) and Ground Range Detected (GRD).
Which of them is better in the aim of soil moisture retrieval? Is there any difference to choose one of them?
(amount of data and being free is important for me)
Below is the difference between these two products.
Level-1 GRD consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at the cost of reduced geometric resolution. Ground range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected magnitude. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle, but with reduced resolution.
Level-1 SLC products consist of focused SAR data geo-referenced using orbit and attitude data from the satellite and provided in Zero-Doppler slant-range geometry and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in Zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track. The products include a single look in each dimension using the full TX signal bandwidth and consist of complex samples preserving the phase information.
SLC images are distributed as a GeoTIFF file per polarisation with pixel interleaved I and Q. Each I and Q value is 16 bits per pixel.
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Dear @Muhammad Budi
You need to work with SLC not GRD when you study about displacement and deformation because only SLC have phase and amplitude while GRD have only amplitude.
My Greeting
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Does anyone know how frequently sand storms and dust storms that arise from middle east or north africa travel to Pakistan and North India? I was wondering, in view of the already worsening air pollution levels in North India, events such as dust and sand storms reaching the subcontinent may exacerbate the situation. How rare or common are such sand and dust storms being carried from their place of origin (usually middle east and north africa) and intermix with fog or haze intensified by smoke or other atmospheric pollutants in another far off location? Has there been any similar, possible mixing of phenomena (dust storm and smog) reported/documented/studied anywhere around the globe at any time, preferably that was also caught by polar or geostationary satellites?
I was looking at a true-color or natural color satellite image acquired on 29th Oct. 2017 by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the joint NASA/NOAA Suomi-National Polar orbiting Partnership (S-NPP) satellite around early afternoon. I've attached a screenshot of the image as well as provided the full link to access the satellite imagery. These satellite images have been stitched together to create a global mosaic. Unlike MODIS, VIIRS do not show any data gaps (except sun glints!). I found this satellite image particularly compelling because it clearly shows the sand storm picking up over northern Saudi Arabia and moving around Iraq, Iran, Caspian Sea towards Afghanistan with the movement of wind. I also think the Earth's rotation from west to east has a role to play in the movement and direction of the wind laden with sand and dust. But it seems difficult to understand their dynamics. The smog over North India and parts of Pakistan can be differentiated from the sand storm over middle east in this satellite image. In North India this is the time of the year when there are intentional crop fires due to the traditional slash-and-burn agriculture practice.
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Hello,
Does anyone knows how to work with the sentinel-2 .jp2 files in R?
The earthexplorer has .jp2 files for sentinel-2 and I'm having some trouble in simply open it in R (I'm using R Studio in Windows)
Thank you!
---- Answer Update ----
What I was looking for was this:
"
Masita D. M. Manessa Added an answer
require(rgdal)
require(gdalUtils)
gdal_chooseInstallation('JP2OpenJPEG') its works"
Thank you Manessa! =)
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You could write a batch (.bat) file with something like:
for %%N in (path/to/your/sentinel2/*.jp2) DO C:/Program Files/QGIS 2.14.5/bin/gdal_translate.exe -of GTiff C:/temp/sentinel2/%%~nN.tif
@ECHO OFF
SETLOCAL EnableDelayedExpansion
SET mypath=C:\temp\sentinel2\
FOR /F %%i IN ('DIR /B %mypath%*.jp2') DO (
SET infile=%%i
SET outfile=!infile:.jp2=.tif!
C:/Program Files/QGIS 2.14.5/bin/gdal_translate.exe -of "GTiff" %mypath%!infile! %mypath%!outfile!
)
You'll need to adjust it and redirect to your gdal_translate.exe and jp2 folders.
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(Machine learning & computer vision)I am finding a public satellite image dataset with road & building masks. I already know the SpaceNet (NVIDIA, AWS) and TorontoCity dataset (Wang et al.), but downloading SpaceNet by using the AWS cli is too inconvenient and TorontoCity dataset is not available yet.
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I was reading some literature on forest canopy density change detection using Landsat image. And I found on some literature that they normalize the Landsat image bands before using the formula of different indices to determine the forest canopy density map. But I am not sure what does normalization of bands mean. Does it mean converting the band DN values into Top of Atmosphere reflectance values?
I am including 3 literature I have studied
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Normalization in the context of remote sensing refers to a process to make something comparable with something else.
For example, if you are doing a change detection study, you may be obviously looking at two or multiple Landsat images of a particular location. So how can you make sure that the changes that you are observing in these different landsat images are caused by the land surfaces or are the affects of atmosphere? Therefore, in order to compare two images from two different time periods for a land cover change detection study, you would first remove the effects of atmosphere by 'atmospheric correction'. This is a kind of normalization. To give you another example, if you are analyzing two different satellite images with different spatial resolutions, you would employ some kind of pixel resampling technique in order to make the two images comparable. Yet another example, lets say, two satellite images were acquired with different sun elevation and azimuth. In this case, you may try out some sun angle corrections, before comparing the two images. This is also a kind of normalization. There are several other normalizations but the aim is the same.
Q. How can you make sure that the target (e.g., vegetation) which you are studying has indeed changed over time or it looks changed only because of the effect of atmosphere or sun/sensor geometry or something else?
Ans. Normalization!
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Hello everyone!
I try to select nighttime images of Landsat 8 through the option "night" in the browser: https://earthexplorer.usgs.gov/ (Additional Criteria tab) and the answer is always "No Results Found". Anyone knows if it's possible to download nighttime images of Landsat (in general)? by google engine/lv.eosda.com/usgs... I thought that some time ago it was possible to download Landsat nightime images.
Thank you in advance!
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Joan, just wanted to share an update. All the Landsat 8 night time scenes that were acquired in the past are back online available for download on the Earthexplorer database. Out of the 17607 night time scenes acquired with Landsat 8 globally, there are around 5 scenes covering Barcelona for the time period July-Sept. 2015.
Cheers!
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How to reproject Kompsat 2 Lever 1R to UTM zone 36N?
There is an option in ENVI 5.3 to open the bundle as Kompsat, it recognises bands, but in strange coordinate system. So, is it possible to use somehow metadata for geocoding image?
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Dear researchers,
i am working on disparity estimation methods. I am using sum of squared difference methods.The method is applied on  two images with different views.I got the disparity map. I got a disparity map with zeros ,ones and two's.My asumption is that region covered with 0's in map shows similarity and other regions with 1's and 2 shows dissimilarity.This is my assumption.I would like to measure the similarity in the map in terms of number of 1's covering the regions.
My point is that my methodology is correct or not? please advice me in this regard?
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Dear Venkata,
Your question is too vague to be able to assemble a meaningful answer. Please provide more details on your problem and in particular address the following points:
- What is the origin of your stereo images (ground-based, airborne or space-borne) and what is their spatial resolution?
- What is the nature of your target, and is it flat or rough (i.e., if it is a land surface, does it include topography)?
- Have these images been co-registered so that pixel (i, j) in both images correspond to the same point on the target?
- What are the format and mathematical nature of your stereo images (Black and White, RGB or multi-spectral; binary, integer, floating point)?
- What method did you use to generate the 0/1/2 values included in your 'similarity.xlsx' file? Specifically, the sum of squared differences between the values retrieved from two images would yield a single number, so I don't understand how you could map that...
- Also, what is the intended meaning of the scores 0, 1, and 2?
Once your problem and your approach are better understood, it may be possible to provide some guidance about how to proceed. Cheers, Michel.
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Can anyone tell me the detailed procedure to estimate the paddy crop acreage using SAR data. I also want know that which SAR data will be better for this which is freely available.
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Crop estimation or monitoring is done best with high-resolution polarimetric SAR data. It allows you to decompose the backscattered signal into different scattering mechanisms (see reference 1). PolSARpro is a great open software for that, but currently, there are no free full-polarimetric scenes available. Sensors which support it are TerraSAR-X, Radarsat-2 and ALOS PALSAR-2. For the first two, you can apply for academic access to a limited number of scenes (see references 2 and 3).
Generally speaking, there is no universal workflow which can be applied. But having field samples of crop types and volume estimates would surely be a good point to start with. Then, findinig some way to link the backscatter to the expected information is the next step.
There is a great tutorial by SAR EDU on crop type classification (see reference 4). You can access it after free registration.
If you rely on freely available data, Sentinel-1 would be the best alternative due to its high repeat frequency in many regions and at least dual-polarization (VV/VH). Unfortunately, c-band is not optimal for vegetation and crop types, but if your fields are large (spatial resolution is 10m) you can make time-series analyses on the backscatter of different plots and correlate it to your field samples in order to estimate the crop types.
Besides that, I can recommend going through some review papers on the topic which often show the pros and cons and present 0possible applications of all kinds of sensors (see references 5-6). 
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Im doing a research on mapping air pollution and its spatial distribution using a set of multi-date Landsat data. Is there anybody who can help provide me a step wise methodological activities? I hope this can help me a lots.  Thanks in advance.    
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Hassan, there are appropriate missions and sensors to study AOD like Aqua, Aura, CALIPSO, MISR, MODIS, NPP, Terra, VIIRS to name a few. I agree with Rangarajan, Landsat is not the best for AOD studies. I would strongly recommend you to follow some basic training on application of remote sensing in air quality studies. I have attached some links which you will find helpful. 
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Hi everyone! I just want to ask this question since it is necessary especially in obtaining multi temporal related studies. As based from experience, there's an instance that utilizing different kinds of Landsat images would cause the images not properly aligned. 
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I have noticed that USGS has started reporting 'Combined RMSE of the geometric residuals (meters) in both across-track and along-track directions measured on the GCPs used in geometric precision correction' in the metadata (MTL.txt) file. This is very useful information. As you will read in the attached link, the geometric accuracy is influenced by the kind of Level-1 (L1-TP, L1-GT, L1-GS) landsat data that you are working with, which in turn is affected by 1) clouds 2) GCPs 3) ephemeris info. Another useful link is the 'GCP search link' provided by USGS. You can use the 'GCP search link' to extract ground control point for your area of interest and geo-reference Landsat Level-1 data. Links are attached.
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I'm a researcher currently working on Land surface temperature over a particular area and i want to know the difference between LST and Skin temperature to help me get the various satellite data set that would be used for the research. 
Thanks.
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Can I use this tool for images from Landsat 7 and 8? And Sentinel 2?... I should use surface reflectance images?
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You can use the Semi-Automatic Classification Plugin for QGIS. It allows user to download, pre-process and work with Landsat, Aster and Sentinel data.
It has a very good documentation and a number of tutorial are available at:  https://fromgistors.blogspot.com/p/semi-automatic-classification-plugin.html
Moreover, a few month ago, NASA had a webinar showing how to use this plugin: https://arset.gsfc.nasa.gov/land/webinars/advanced-land-classification
Finally, as it is developed in qgis, you can easely use it toghether with other algorithms, such as the already mensioned i.topo.corr or i.smap by grassgis.
Federico
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I have ship-borne gravity data along some profiles of a region, as well as satellite gravity gridded data of the region. I want to combine both the data and prepare a gridded gravity data of that region.
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Hi 
Obviously you have data from two different sources, then you have to adjust these data for altitude in order to obtained the adjusted combined data for gravity of that region.  
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I am giving up, I cannot find a way reading EUMETSAT NWC SAF products, like Cloud Analysis, Cloud top heights, Cloud masking etc. The products are provided in a "space view perspective" grid in grib2 format and seems that this is not a supported projection by any famous software for handling satellite products or by NCL which I am mostly using. 
Has anyone managed to handle this kind of data?
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Hi Stavros,
Obviously the space view perspective grid is natural choice for products derived from geostationary platforms and thus standard in EUMETSAT. I have the same problem with the H05 product of H-SAF. I use very frequently cdo, but in this case you can not do very much with it. I wrote to the H-SAF with this question and I received the attached file.
 Best wishes, Hr.
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i want to derive Aerosol Optical Thickness(AOT) from the bands of Landsat 8 or Sentinel 2 images.....or any other datasets can be used for AOT mapping which is freely available??
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Dear Akash,
If you need reliable quantitative information on the aerosol optical thickness (AOT), look at the atmospheric products generated by the MISR Science Team at NASA JPL. MISR is a multi-angle, multi-spectral instrument on-board the Terra platform. It has been operational since February 2000 and is still generating data today. The main purpose of that mission is precisely to characterize clouds and aerosols.
For more information on the MISR instrument itself, and for an exhaustive list of publications using MISR, visit
If you want to download data products, you can get them for free from the NASA Atmospheric Science Data Center (ASDC), which is accessible here:
Good luck in your investigations. Michel.
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I need to perform DN to Reflectance conversion on subset of image in specific band not multi-spectral image. I am able to do it on multispectral image.
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I think Abdelaziz gave good advice. The data in the L8 image are scaled to 16b integer to keep the size of the files down.
Reacaling coefficients are in the metadata files.
Info on how to do that here:
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It will perform in Landsat data to calculate AOT and TOA.
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Respected all,
Kindly let me know that:
What should be the Incidence angle of RADARSAT or any SAR data for flood mapping?
what could be the best combination of polarization (HH,VV,HV etc.) and incidence angle for flood mapping and related studies?
Thanks and regards
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Nice Firoz ji...
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Satellite (like TRMM) provides Total Column Water Vapor over the sea. But over land, the albedo interferes. My objective is to measure water vapor into a cumulonimbus over a tropical andean area.
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Mohamed, Very thanks MODIS was a great suggestion!
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I'm studying remote sensing.
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As many others have stated, the blue wavelength penetrates the water the best which is why Landsat 8  band 1 (ultraviolet/blue) is called the coastal/aerosol band.  This band is affected most by atmospheric scatter (Rayleight scatter) so it also has a great deal of atmospheric 'noise'. 
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I applied the FLAASH atmospheric correction tool in ENVI for Landsat images and got the result as given below. This area is located in high mountain areas with significant amount of snow and glacier. My question is about the how can I validate this result, whether it is correct or not. Also, I can see the only green color, where the area is covered by snow (mostly) in false color combination and pink in true color. I thought it is a noise but, when I tried to remove the noise, it is still same. What might be the reason for this. Does anybody can help me on this regards.
Thanking you in advance.
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Hi Mohan,
(1) You can use band math in ENVI to eliminate the negative values:
-This is the post-FLAASH  equation:
(B1 le 0)*0+(B1 ge 10000)*1+(B1 gt 0 and B1 lt 10000)*float (b1)/10000
- The post-DOS equation is similar but use 1 instead of 10.000 like this
(B1 le 0)*0+(B1 ge 1)*1+(B1 gt 0 and B1 lt 1)*float (b1)/1
(2) You need to collect spectra profiles in-situ at the same time that the pass of Landsat to make comparisons...
good luck!
Santiago 
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Hi, I want to check if the De Grandi speckle filter works on 2 temporal SAR images or multi temporal SAR images are required to get the best results from it?
Thanks a million!
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Hi Vikas, thanks for your answer. Could you please give me some suggestions on books/papers to refer for spatio-temporal filtering of SAR images?
Thanks!
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I need to know if is it scientifically valid to fuse/pan-sharpen Landsat 8 surface reflectance products with pan band of that respective scene? Landsat reflactance product details can be found at below link. It needs to be mentioned that one needs to order surface reflectance product separately to get this product. This product contains only 7 band (30m) not IR and Pan band. So, again, my question is, is it valid to fuse 7 bands(30m) of surface reflectance product with normal(not surface reflactance) pan band(15m)?
N.B. It would be a great help if someone comes up with some reference.
Thanks
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I would recommend using the panchromatic band (i.e. one single channel that covers almost the entire visible spectrum) in the process of pansharpening solely for image interpretation and vizualization purposes. As PS will modify the radiometric information contained in the bands, you should not use these in quantitative spectral analyses (e.g. calculation of a vegetation index) thereafter.
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Hi,
Anybody who can point me in the right direction cause I can't apply multi-temporal speckle filtering on my 2007, 2008, 2009, 2010 and 2015 ALOS PALSAR mosaic datasets. It just gives me an erros saying that the bands need to have a unit.
Does this tool only work on standard products (L1.1 and L1.5)?
Thanks!
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You may update SNAP and try again. 
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I would like work on analysis of SAR images and optical/multispectral images. Hence I am in need of freely available database of these images. Images of same location should be captured during same time (may be same week or same month).   
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You can get Sentinel-1 data from scihub.esa. Requires only registration (And most likely, non-commercial use). As Sentinel-1 has just become operational the archive is not very extensive but should grow quite quickly.
You can set request data-access propospal on Alaska Satellite Facility. Some data open access. For ALOS-PALSAR you must be a resident of the United States to receive approval for data access.
You can get data from UNAVCO SAR data archive.
And you can get data from earth.esa.int. To access the data, you must provide a project. To preview scenes, use EOLi esa. ENVISAT ASAR the best option.
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I am undertaking a project into the identification of tree species using satellite data, namely the hyper spectral data generated by Sentinel 2. Is there a way to use this data to identify trees to a species level or failing that identify whether a tree is broadleaved/coniferous etc?
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Although tree species level identification has always been challenging if your are working at 10 m spatial resolution.
1. So Before going for individual tree species level analysis using Sentinel-2. You must go for some preliminary analysis using Sentinel-2 data. Sentinel -2 provides 10 m spatial resolution for RGB and NIR bands while it provides 20 m spatial resolution for band 5, 6, 7 and 8A in Vegetation RedEdge category. 
2. Since RedEdge bands has capability to discriminate different tree species based upon their spectral variability. Therefore, Sentinel-2 can be a powerful opensource platform for these kind of analysis. 
3. If you do re-sampling of RedEdge bands to 10 m and create the Layer Stack of Red, Green, Blue, NIR and RedEdge Bands and then you can check the spectral signature of different trees species (it could be some homogeneous regions) for different band combinations. 
4. For this you must have some ground data. If you have a then you can cross check these spectral signatures and then you can go ahead for either object based or pixel based supervised classification procedures. 
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Hi there,
I am trying to convert the DN values of WorldView-3 multi-spectral to  surface reflectance  using the ENVI  WV-3 Radiometric Calibration and FLAASH tools.
I can easily convert the DN values to radiance, but the calculated radiance values for the WV-3 coastal band seem not to be right (large spike values, please see the attachment for an example of vegetation spectra).  
As a result, after conducting the FLAASH atmospheric correction, the reflectance in coastal and blue bands (Right) is also abnormal (vegetation spectra in blue band even has negative values, please see the attachment). My best guess is that the atmospheric scattering has huge impacts on the short-wavelength bands of WV-3 on this imagery, and it is too "wrong" to be corrected by WV-3 Radiometric Calibrations and FLAASH.  My study site is in tropical region (Brazil) and the WV-3 acquisition time is in June. Any suggestions? Thanks a lot!
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 Hi Ran,
While ordering WV-3, did u unselect the DRA (Dynamic Range Adjustment) option?
If u received the data with DRA enhancement applied on it, then pre-processing cannot be performed. Even if radiometric correction is performed, it will not produce correct values
So, my suggestion is kindly check this.
Please check my communication with Longbotham, DigitalGlobe.
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I am undertaking a project looking at possible ways to identify tree species using satellite data and am considering using near infared imagery provided by Sentinel 2. However to do this I will need a list of individual trees spectral signatures and was wondering if a complete database existed and was accessible? 
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Dear Daniel,
Firoz is right: all (or most) trees look 'green' from space when the leaves are out, and it is not clear that their spectral response in the near-infrared is much different from species to species. You may find a somewhat higher discriminating power in the mid-infrared, where there may be more subtle variations due to the presence of components in variable concentrations such as lignin and cellulose, which exhibit specific vibrational spectroscopic signatures. Of course, this may not be applicable to Sentinel-2: that instrument is great to investigate environmental issues at fairly high spatial resolution, but it is not a hyperspectral sensor.
Instruments like WorldView-3 and others provide a better spatial resolution than Sentinel-2, but feature only a limited number of bands too. It may be worth exploring to what extent these instruments may be useful for your purpose, but don't expect miracles: you may very well reach the conclusion that tree species can't be distinguished this way.
On the other hand, if you want to pursue hyperspectral investigations, consider these leads:
Databases of spectroscopic signatures do exist, but they are often held by projects rather than widely available. One worth mentioning is the 1985 NASA study by Bowker et al., which is available here:
Check also the NASA Jet Propulsion Laboratory site concerning the airborne AVIRIS instrument:
Dr. Robert O. Green, the Principal Investigator for AVIRIS (http://scienceandtechnology.jpl.nasa.gov/people/r_green) and his colleagues have accumulated lots of hyperspectral measurements and generated a number of publications over the years.
Another researcher you may want to follow is Prof. Susan Ustin (http://lawr.ucdavis.edu/people/faculty/ustin-susan), as she has also worked extensively with hyperspectral data for ecological applications.
Other projects that may hold spectral signatures of different vegetation types (including trees) might be the LTER or FluxNet sites, for instance.
Check also the data generated by the Hyperion mission, which acquired over 200 spectral bands from space. In that case, you will need to assess to what extent the small signatures that may exist at the surface may still be distinguishable after the solar radiation has been transmitted through the atmosphere, before reaching the instrument:
Lastly, if you want to (or have to) use Sentinel-2 data, then you may be restricted to differentiate between evergreen and deciduous groups of species (using time series of observations), for example. Good luck, Michel.
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HI please I have classified my satellite images, done change detection and produced my Land Surface Temperature maps, but I have no access the landuse data and the national topomaps of the study area.
How do I carry out my accuracy assessment for the classification and change detection?
How can I validate the LST?
Thanks
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keep accuracy  90 to 95%
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How to generate bathymetric map using landsat-8 data?
reference is required?
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Dear Dr. Firoz,
Please check the attached file which is an article about how to extract bathymetric map from Landsat 8.
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Sometimes we prefer to use satellite data in different resolution. So while performing resampling, aggregating pixel by simple averaging or resizing data set into different resolution what should I do fast.
A) should I apply the mask to negatives values & cloud pixels, and shadow pixels 1st and the I do resampling?
or
B) should I perform the resampling or aggregation etc 1st then I resample?
which one is correct sequence please mention with an explanation.
thanks in advance
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Dear Sumit,
As Artem indicated earlier, the answer to your question depends mostly on the ultimate purpose of your investigation, as well as on the required accuracy of the information generated. From a purely logical point of view, however, it makes much more sense to first mask the clouds and cloud shadows at the high spatial resolution (30 m) of the Landsat data, than after aggregation into a low resolution product. First of all, it is much easier to detect and classify clouds and cloud shadows at high resolution. Second, the area contaminated by clouds and cloud shadows in the low resolution product will be larger than in the original Landsat data.
It is not clear why you are concerned by completing this task 'fast': the execution time largely depends on the performance of the hardware available, and somewhat on the software efficiency. In any case, new faster hardware will become available sooner or later. Except for a small number of time-critical applications, you should generally be much more concerned by the quality of your outputs instead. Another way to state this is that 'fast' is generally incompatible with 'safe' (or 'good quality').
Lastly, remember that there are various satellite-based instruments of Earth Observation that generate data at spatial resolutions around 250 m, including MODIS, MISR, MERIS, or Sentinel-3, and that those instruments provide a much more frequent coverage of your area of interest than Landsat. In other words, you may want to evaluate whether downgrading the spatial resolution of Landsat data by a factor 69 or so is really the best approach.
Cheers, Michel.
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what are the use of these products "num_observations","250m Reflectance Band Quality",obs_cov, in the MODIS data set. What does the bit no and bit combination mean in table 1 below? how could I use them to mask the cloud?
these are collected from the MODIS product description link attached below.
Thanks in advance.
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see this attachment as pdf file CMUSERSGUIDE.pdf
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During mosaicing of liss 3 images of nearby region  , there is a shift in the river path.
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I am agree with Firoz Ahmad ...good luck
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research shows that remotely sensed images from satellites can be used to study water and air quality. In low income countries it is expensive to obtain high resolution satellite images for such studies. So I am wondering if one can buy a phantom drone and use it images taken to do similar studies.
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Great discussion. We have found the use of drones to often add unnecessary complexity and make the analytics challenging to impossible. While drones may be useful to you,  as detailed above in other answers, weigh your options carefully and align your research needs and applications with the available technologies. We have found often renting a pilot and airplane for 1 hour can produce far more reliable high resolution nadir or oblique images even with handheld Nikon D700-D812 camera's shot out of windows of small airplanes. Even mosiaced and georeferenced  oblique imagery when compared to drone imagery can produce significantly better imagery, more useful and comparable to high resolution airborne multispectral magery from stable airborne plateforms. Drone imagery has been challenging to mosaic, scale, georeference, control for platform instability, color normalize from adjacent flight lines, and for these reasons has not been particularly useful for measurements. We've opted for small aircraft very high resolution multispectral imaging, and are moving to very high resolution, full spectrum hypersphaetrael imagery complimented with Nikon 36 mp oblique imagery.
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I need to use multi spectral images like IKONOS, Worldview 3 , Quickbird, SPOT5 to monitor nitrogen in oil palm canopy.
Which companies purchase the images.
The area of my study is in Pahang, Malaysia 
Thanks in advance 
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For SPOT 5 images, you could use the Airbus DS GeoStore directly:
But Airbus already has a few partners within Malaysia, maybe you find a way to cooperate with any of them for your needs:
Depending on your exact need for resolution and repeat cycles, you could also give the freely available Sentinel-2 or Landsat-8 data a chance to fulfill your needs:
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I know the orbital difference between both satellites. But for Chlorophyll a estimation, which is best MODIS TERRA AND MODIS AQUA. Because most of research studies i have come across utilizing MODIS AQUA OVER MODIS TERRA . WHY? 
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Aloha all,
Please look at these documents:
Ocean Color - MODIS-Terra Ocean Color Reprocessing 2014.0 - Global mean deep-water
Compare multiple sensors between coastal and open ocean:
Comparative Analysis of Chlorophyll-a Distribution from SeaWiFS, MODIS-Aqua, MODIS-Terra and MERIS in the Arabian Sea
Hope this help,
Mahalo,
Roberto
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What makes the satellite-based SIF–GPP relationship unclear
in a deciduous broad-leaved forest?
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Probably due to differences in color is not too sharp (or the area is too large) so that misperception gradations becomes larger.
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I am looking for downloading hyperspectral imageries. From where I could be able to download hyperspectral imageries free of cost (if it is freely available)?
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I am looking for a software to download Google Earth Imagery.
I tried Google Map Downloader (Trial Version) {annd Stitch Map} but I am not sure about the resolution of image downloaded from its licensed version.
I will appreciate if anyone can suggest something in this regard.
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Use Sas planet software to download bulk of data including iconos and quickbird even tiles from different dates.