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Remote Sensing Applications - Science topic

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USGS provide two kind of major Data sets, which are collection 01- Level 01 and Level-02 data. In Level-02 data All the other visual bands are process to surface reflectance but why panchromatic band isn't process? My question is how to process panchromatic band to surface reflectance? Can you suggest the method for me?
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Great question... I wonder the same (looking the answer and didn´t find it) !
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How to measure water quality through Remote Sensing? What methods, data and concepts can be used for this?
-Thank you-
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Google Scholar is your friend ( see graphic )
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Hi,
Can I prove or model a reclamation in the past using GIS/RS? Can you provide me some discussion, readings, or techniques?
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Yes, you can via the satellite images of the same area using the 4D analysis, the time period between these images have to be equal, this can help you to predict the future effects. I hope the link below can help you a little https://doi.org/10.3390/app10072266
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This is a good question.
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What is the range of accuracies for airborne lidar measurements of land topography and water bathymetry? Assume no vegetation cover on land and low turbidity of water!
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Dr. Klemas,
bathymetry measurement depends on many factors such as water turbidity, amount of sediment, and phytoplankton and zooplankton concentartion in the water. In one of my research, I measured the depth of coastal waters using multi-spectral remote sensing, and we found a reasonable accuracy.
best wishes,
Jasem
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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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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 have already been read many articles and found some methods but those are not cleared to me.
Can you please suggest me how to generate different LST map of each land classes using ArcGIS?
Or if you have any other methods to generate the LST of each land class please do recommend?
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@Pir Mohammad
I will try. Thank you so much for your valuable comments and suggestions.
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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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As a prerequisite dimensions of all rasters must be identical i.e. rows and columns for MCE.
TerrSet facilitates image resampling. A simple approach can be to use 'Window' tool available therein for the purpose. Just provide reference image to serve as desired spatial extent and clip all the input rasters accordingly.
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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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see above
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Dear Eko,
You may used chlor-a and phytoplankton products as indicators for nutrients concentration with empirical modelling.
Best of luck.
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Deep ocean dynamics and thermal/salinity structures cannot be observed by satellite remote sensors directly, but can be estimated with the help of models using satellite data. Which processes and structures can be estimated?
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Dear Victor,
Optical remote sensing may reach 30 meters under the sea surface in clear water.
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Do you think that the SNAP software from ESA could be considered as an applicable software or similar to GAMMA remote sensing or, is it still an educational software?Why?
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I agree with Bambang H Trisasongko
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What are the most effective methods for remotely detecting and mapping coastal freshwater springs? The water temperature of these springs changes rapidly to an intermediate temperature between the temperatures of the background water and the air temperature.
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I have produced Digital Elevation Model from tandem pairs of ERS 1&2.
When I compared the results with ASTER and SRTM, the difference produced was very large.
For what reason this difference refer?
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All of those DEMs, including processed InSAR DEM, are reprojected into WGS84 UTM (Zone 16 North) with post spacing of 16 meters, and the vertical datum of WGS84 ellipsoidal height in meter.
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I want to know whether Sentilnel data can be processed using matlab. My area of interest is SAR Interferometry.
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Conversion soil water content from m3/m3 to mm.
Some satelite soil moisture (SM) data measured in m3/m3.
 How can I convert this SM data into mm?
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The term volumetric water content (VWC) refers to a given volume of water contained in given volume of substrate (soil or any other soiless media that can soak water). this makes the term have a unit of measurement m3/m3. that means a given m3 of water contained in a given m3 of soil or other media. In short the units (m3/m3) cancels out virtually. So if your device measure for instance 20 % VWC in a soil depth of say 40 cm, it means, of the 40 cm depth in the soil there contains 20 % water. Hence the water content in terms of depth (cm),i.e. 40 cm soil depth is 20% of 40 cm=20/100 * 40 cm= 8 cm. if you wanted to have your final answer in mm then you convert 8 cm to mm which you multiply by 10 as the conversion factor from cm to mm. Your final answer therefore = 80 mm. this 80 mm you have as your final answer means, when you dig 40 cm deep in the earth of soil you will have 80 mm of water in that soil. I hope it is clear.
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Hi all,
I'm a student who is starting in the remote sensing field, specially in ocean and coastal studies.
Can someone explain me how to get the Remote Sensing Reflectance (Rrs) from Sentinel 2 images?
Until now I was using the pixel value as Rrs, but looking at
and
I realized that this assumption was clearly bad.
Thanks in advance
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I stumbled upon this thread and I see some confusion in it, so I would try to comment for the sake of other readers looking for similar information, although the question is a little old. I see Alfonso Hernandez is aware of this, but some of those responding are not: He uses L2A product, which means he already has product with atmospherically corrected reflectance, no need to recompute from radiance or use SNAP. The only thing which may be needed to use the product with some software, which supposes reflectance in the 0-1 range directly in pixel values is to divide all the values by the quantification coefficient mentioned by Prashant H Pandit - but other software may do it for you automatically by reading the metadata.
To the original question: Remote Sensing Reflectance (Rrs) is indeed the pixel value of the product divided by 1000 (the quantification coefficient). The term Surface reflectance only stresses the fact it is reflectance as if measured on the surface (i.e. atmospherically corrected, as opposed to term TOA reflectance), and the term Remote sensing reflectance similarly only stresses the method by which the reflectance is obtained (as opposed to reflectance obtained by in-situ measurement with a spectrometer - which should be more precise, usually is bidirectional, has also different form - only individual values for measured sites, while Rrs contains raster of values whose pixels contain average of reflectance over area of the pixel).
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The Landuse data is given on the bhuvan website but I am mot able to download that data. If anyone knows about the procedure to download these data, please tell me?
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Dear Shonam Sharma,
Data may download through WMS/WMTS in ArcGIS and QGIS and you can follow this video link to get data from Bhuvan.
Cheers.
Rajasekhar
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Mapping surface soil moisture from the SAR images is a demonstrated procedure, but several factors can interfere with the interpretation and must be taken into account. The most important factors are surface roughness and the radar configuration (frequency, polarization and incidence angle).
How we can consider surface roughness in estimation of the moisture content without field measurement?
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You can use SAR data for the purpose. Sentinel data is one of the bests.
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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 like to do bathymetric mapping by using satellite image of landsat 8 sensor. particularly by using BAND blue (specification for bathymetric mapping). Is it possible. 
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Dear all,
I am looking for a model of waterborne bathymetric drone (equiped with single-beam or multi-beam echo sounder) for the survey of highly turbid rivers and lakes and others complex aquatic bodies (drained rivers, ponds, shallow water) in tropical regions. Is somebody can provide me internet address of manufacturer? (speed : 5 m/s, jet propulsion) Thank you very much Jérôme
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Unlike Optically thick clouds, Cirrus Clouds are thin, high altitude clouds formed in the upper troposphere layer of the Earth's Atmosphere. These Cirrus Clouds are not easily identifiable in the satellite images acquired with Passive Remote Sensing Sensors such as Landsat MSS, TM, ETM+, ASTER, SPOT, etc. Although there are different kinds of Cirrus Clouds, the sub-visible Cirrus Clouds are particularly of interest because they can be hiding in plain sight and affect the measurements. However, they can be detected within the Short-wave infrared (SWIR) portion of the electromagnetic spectrum, specifically at ~1.38 µm bouncing off of the ice-crystals in these clouds but are absorbed by water vapor in the lower part the atmosphere. Due to the benefits of this wavelength at 1.38 µm, MODIS (1999 onwards), VIIRS (2011 onwards), Landsat 8 (2013 onwards) and Sentinel 2 (2015 onwards) were introduced with their respective Cirrus Cloud detection bands.
However, in the absence of Cirrus detection bands in passive satellite sensors operating before 1999, is there anyway to pin-point the presence of Cirrus Clouds in historical satellite images? It may be possible to identify Cirrus Clouds in satellite images acquired without cirrus band by comparing it with contemporary/concurrent satellite images acquired with sensors having cirrus band. But otherwise, is there any other alternative way? Is anybody aware of any operational tool/algorithm/products that can identify cirrus clouds in past satellite imagery and provide means for their masking/correction?
This topic may be of particular relevance in time-series studies where historical satellite images are frequently compared with the present. For example, if cirrus scattering affects are not corrected, they can lead to incorrect interpretation in Vegetation Indices such as NDVI.
Sources:
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It really depends on the type of imaging. If, as mentioned before, there is spectrally resolved information, one can make use of the fact that the contribution functions of some channels peak at different altitudes and apply some kind of minimization technique (e.g. see our algorithm for IR sounders https://bit.ly/2VAG2NZ).
However, if the image was obtained only at one wavelength or the contribution functions are too broad or they do not "scan" the whole altitude range then it's more difficult. One can train neural network using this kind of images and a combination of active and passive sounders (e.g. AIRS + CALIOP) serving as a reference, but it's easier to say than to make :)
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I am on a task of evaluation the following software in term of its functionality , performance and training availability in the INTERNET
i am concerning on
1. ERDAS
2. ENVI
3.GEOMATICA- PCI
if you use one of them , please respond with your review .
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I use the ERDAS in classifier "supervised classification and unsupervised classification for prepare soil map , calculate spectral reflectivity of soil, calculate some spectral evidence ... a useful and wonderful program.
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To apply MCSST algorithm for retrieving sea surface temperature data from Landsat 8 I need a 'sensor zenith angle' value. Unfortunately in the Landsat 8 metadata file (.MTL) only available sun azimuth angle and sun elevation angle?
So, where I can get the sensor zenith angle of Landsat 8 data?
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Landsat 8 has a near NADIR view. Hence, the sensor zenith angle is round zero. There is information in the MTL file about camera ROLL_ANGLE = -0.001 ~ 0
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Hi,
I am looking for alternatives from ENVI. I am currently learning Remote Sensing. What other applications are reputable in analysing satellite images, preferable free access software. Thank you.
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You should also take R into account. It is not specific to remote sensing but almost any classification algorithm is available in R. Take a look to https://cran.r-project.org/web/views/ and the tools for Machine Learning & Statistical Learning
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Satellite Images provides a detailed aspect of the earth surface, through many wavelengths (spectral) and spatial data. High-resolution (HR) image contains more pixels than a low-resolution image for the same area, which mean there is a difference between low and high-resolution features. The traditional image classification techniques consist of two categories: unsupervised and supervised classification. The most known unsupervised method is k-means classifier.
Papers:
1) Y. T. Hsieh, C. T. Chen, and J. C. Chen, 2017 “Applying object based image analysis and knowledge-based classification to ADS-40 digital aerial photographs to facilitate complex forest land cover classification,” Journal of Applied Remote Sensing, vol. 11, no. 1, article 015001.
2) Rathore, M.M.U.; Paul, A.; Ahmad, A.; Chen, B.-W.; Huang, B.; Ji, W., 2016 “Real-Time Big Data Analytical Architecture for Remote Sensing Application”. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. Vol. 8, 4610 - 4621.
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Dear Professor,
I have read this paper before which may help you (if you have not taken a look at it yet).
"Hamida, A. Ben, Alexandre Benoit, Patrick Lambert, Louis Klein, C. Ben Amar, Nicolas Audebert, and Sébastien Lefèvre. "Deep learning for semantic segmentation of remote sensing images with rich spectral content." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2569-2572. IEEE, 2017."
It presents recent Deep Learning approaches for fine or coarse land cover semantic segmentation estimation. it's a bit different than what you want but it may help you.
Best Regards.
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Android application that does not use mobile inbuild GPS and instead get the NMEA messages from an external USB gps device.
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no
use GBS to collect point and use program Arc map
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I am doing crop system classification for paddy field.
I recently employed MODIS products (MOD13Q1) and used R software for building the algorithm.
I know some method such as Empirical Mode Decomposition and Linear Mixture Model, but I don't have any idea how to apply it into algorithm based on RASTER STACK TIME SERIES using R.
I will be glad if you can share your knowledge about that.
Great thanks!
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you can use these method or time series NDVI layer stacking for crop classification using 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 about to start a small project where I am trying to provide a single metric for every MODIS tile that describes what is the information density for it in relationship to adjacent tiles. In other words, how many times, in the full MODIS record that particular tile has been "photographed" by the sensor compared to other tiles. If each tile has been sensed (raw data) and archived evenly, one could argue that the quality of the derived products, based on the density of information is similar. Thank you for any insights!
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Dear Miguel,
The answers to your questions are "no" and "no": the spatial density of MODIS observations on the Earth's surface is strongly dependent on the scan angle of the instrument (highest density at and near nadir, lowest density near the edges of the swath), and pretty much each pixel is acquired at a different time. Satellite remote sensing data are very different from photographs.
The MODIS instrument implements a whisk-broom design, where a small number of detectors are combined with a rotating mirror to scan a wide area of the planet across-track (i.e., pretty much in a direction orthogonal to the direction on flight). Large images are generated by combining successive scans, acquired as the satellite progresses along its trajectory. As a result, the spatial resolution of the data varies strongly with the scan angle, and each set of neighboring pixels along that scan is actually acquired at a slightly different time. Of course, the rotating mirror turns very quickly, so the timing may not be very different along a given scan line, but the further apart the scan lines, the larger the time difference between the observations. Those satellites move at the speed of about 7 km per second, so data points in the MODIS images that are, say, 100 km apart in the direction of flight in a given scene have been acquired 14.3 seconds apart.
To simplify data access and exploitation by users, the MODIS Team may deliver scenes, data granules or tiles that provide coverage for a limited area, which look like photographs, but these are quite different from pictures you would take yourself if you were on the platform, looking down.
Note also that the MODIS instruments (whether on the Terra or Aqua platforms) follow a repeating sun synchronous orbit, and therefore acquire the same areas on the ground every 233 paths, or 16 days. Tiles covering your area of interest may be available much more frequently than that (e.g., every 2 or 3 days), but they were acquired from different overlapping paths, and therefore from different scan angles. This means that the raw data actually exhibits a variable spatial resolution in each tile. Now, it may be that the higher-level data you wish to use has been resampled to a standard resolution, and re-projected on a particular geographical map, in which case you may not "see" these differences, but you should be aware of the fact that the raw acquisitions exhibit those peculiar characteristics.
And of course, if you use multiple such granules or tiles, they have been acquired over a period of multiple days, so there is absolutely no concept of simultaneous observations in this case.
Lastly, you seem to ask a question about the "quality" of the data: that is an entirely different issue. All NASA instrument Teams, including MODIS, are required to issue statements about the performance, accuracy and reliability of the data, including information on instrument calibration procedures, product validation and inter-comparisons with other instruments. A large number of papers have been published in the literature about that particular topic.
Good luck in your investigations. Michel.
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We have been tracking the mobility of pastoralist sheep, cattle and camel herds in Sudan, using 'archival' GPS tagging devices (see web link).
We had some problems with the devices we have used, and are looking to upgrade. We are seeking a device with: a long battery life (more than 3 months);  that is robust and can withstand Sahelian conditions; is relatively discrete and can be attached to a leather collar or harness; and is reliable, tried and tested!  
Please let me know what your experience has been, and what you might recommend? 
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Am also interested in tracking cattle movement in west Pokot in Kenya to try and provide a solution to cattle rustling problem in the area.
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I have several layers in my overlay, but when I run it they show for the whole area they have data for (global), whereas I would like them to display their results only within the area of a polygon I have created. Does anyone have any ideas as to the code required for this?
Many thanks
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Besides filtering the image collection itself, you can also clip the output you are visualizing using the following (.clip(roi)), where the roi is the region of interest you have set:
Map.addLayer(your_data.clip(roi), {}, "Your_data_name", true);
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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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I am using arcGIS 10.3 and geotiff file from landsat 8.
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Loris, USGS provides a 'Spectral Characteristics Viewer' in order to figure out which Landsat 8 (and others) bands are most suited for the desired research application. If I were you, I would start with this tool first. In addition, I've added some other relevant links.
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Dear colleagues, I want to know how to buy ASTERs high resolution images, not from MADAS/Earthdata Platforms.
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Thank you James
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I am trying to create a TIN. For better interpolation i first converted DEM to point and then i tried to create TIN from point shp. I am using Create TIN tool in arc gis. But i am getting this error continuously. The drive i am saving in have 47 GB of space. Kindly do answer me as soon as possible if anyone here know the answer, i will be very thankful. 
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thank you!!
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hello
converting Landsat TM5 digital number to Reflectance is my aim. I have studied that reflectance range is between 0 and 1 and it is dimensionless. I downloaded a Landsat TM5 surface reflectance of USGS website and opened in ENVI. Pixel values (reflectance) of the image didn't confine between 0 and 1 and were about 1000. please help me. The surface reflectance image can be right?
what is a difference between the top of atmosphere radiance and surface radiance?
Thank you.
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I will try to put it simply: TOA radiance is every light which reflects off the planet as seen from space measured in radiance units.
If you look at the planet from space (or if an instrument sensor takes an image) you see what's on the surface, but what you see of the surface it is foggy and bluish. That's because you see (or the sensor registers) mix of light reflected off the surface and off the atmosphere. In fact in the blue region of visible light, greater part of the light comes from atmosphere that from the surface.
Atmospheric correction is then a method how to try to remove influence of just that portion of light reflected off atmosphere on the image and preserve the part reflected off the surface below (Simpified to get the idea, because atmosphere does more than just reflecting part of the incoming light).
This way you get BOA (bottom of atmosphere) or surface radiance. The same applies to TOA and BOA/surface reflectance.
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Is there a way to mask out non-ocean chlorophyll values (e.g., from lakes) in global SeaWiFS products using SeaDAS or python?
I would like to perform statistical analyzes on ocean-only chlorophyll values from global SeaWiFS mapped L3 climatological products.
Cheers, Nicolas
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As python supports FERRET, there is a possibility for use the ETOPO file which highest resolution and create a mask of land values. The SeaWifs file available in NetCDF file can be used for this purpose. Then ferret commands for checking the land values can be used and the chla values can be eliminated.
uday
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I processed, using SeaDAS, a complete year of L2 files (with hi-res - 500 m). Also using the seaDAS software, I created the L3 bin files with 8 days temporal average but the .hdf file is completely different from the .hdf of the level 2 and I can't understand how to open it and map the ocean color products using the matlab software.
Without using seadas, is it correct doing a weekly average just with gridded and interpolated L2 files with matlab software. If yes, how is the correct procedure to do it?
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I write this scrip long ago to read Modis Aqua files in matlab, i hope someone else could help.
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I want to measure the quantity of light coming from both the Sun and the Sky on the same image. I think about the cmos sensor with log scale response.
Any reference or contact would help.
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Thank you. I understand the idea.
Kind regards,
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The NASA Soil Moisture Active Passive (SMAP) Produce Soil Moisture Product in HH, VV and dual polarization.
Which of these products are the best one? Which of these products are suitable in rangelands?
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I would recommend the answer of lining...VV is better than HH 
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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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For mapping air pollution using remote sensing data, which season needs to be considered. Means, will I collect data from only summer or winter, or both to see the seasonal variations? 
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For a complete study would be better to analyze the variation over a year. Take in consideration also the climate of the region, in some areas the level of pollution is higher during the winter caused be the low  temperature and in other region pollution could be higher in the summer season.
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Until now we only rely on MODIS fire and landsat data for peatland fire management. 
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Optical remote sensing satellite derived soil moisture products may be problematic (especially in areas with strong vegetation formations) as they are mostly restricted to the few centimeters of the top soil.  However, NASA's satellite gravity mission such as the Gravity Recovery and Climate Experiment can provide water storage changes in catchment stores. You can have a look at the attachments.
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I want to transform the TIFF file(Landsat and MODIS image) to jpg,but I will lost the latitude and longitude information and Surface reflectance information,or to use the ENVI to do the sparse representation ?
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JPG format is an image compression that results losing data. This format is usually used for quick look or presentation rather than technical analyzes. Also, this fomat doesn't have the geographical data embedded in the file.
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GPP~ chlorophyll index * PAR
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Hi Enawgaw,
There is yet another method to measure GPP using sap-flow sensors, which are relatively cheap:
Best regards,
Tamir Klein
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How can phytoplankton detect in MODIS data sets
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1km, 4 km or 9km
anyway, Thanks to all of you.
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I would like to find vegetation area in every aerial image. I can detect it by color-segmentation method. However, it is not working well for conditions that seasons are changing. Can anyone guide what the solution would be?
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Hi,
Then why not you try to account for the different valid color shades plus the texture cues when doing the classification? Another approach is to use machine learning.
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suggest problems
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Dear Deepak,
Many of problems can include: Nutrient Deficiencies by Normalized Difference Vegetation Index; Pest Control; Geological aspects of other planets, and so on.
Best regards.
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Hello all. I am working on object-based classification using LANDSAT OLI images to provide a land use map. Do you think that the SEGCLASS module in the TERRSET tutorial (IDRISI tutorial), can be considerd as an object-based classification?
Thanks!
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Terrset has a hybric method (as Raed Ahmad says) that combines a Segment-based Classification (SEGMENTATION) and a majority rule classifier (SEGCLASS) based on the segmentation image and pixel-based classification. Really, the Terrset segmentation module produces a smoother map-like. I would not call "Object-based classification" like the Ecognition tools.
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My research is about forest cover change detection using landsat 7, landsat 7 ETM+ and Landsat 8 satelite data for remote sensing and analysing the data using Erdas Imagine 8.5 and Arc GIS.  
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Stating and testing hypotheses are a matter of choice in any research. However,  researchers are often encouraged to formulate and test hypotheses when they engage in quantitative research. Qualitative research generally does not require hypothesis testing. Research questions are preferable for qualitative studies. 
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Hello Everyone. 
I am working on Forest Above Ground Biomass Estimation using Pol-InSAR. I acquired ALOS-2 data for my study area which is north of Pakistan. It contain very challenging topography. In order to perform Terrain Correction, I acquired TanDEM-X 12m DEM. I try to perform Terrain Correction using ESA SNAP but results are not very good. ASF MapReady also provide Terrain Correction tools but unfortunately ASF MapReady don't support ALOS-2 data. 
I want to ask if anyone already have experience of correcting Terrain Distortions of ALOS-2? If yes kindly guide me accordingly.
Many thanks
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Maybe the problem comes from the DEM quality.
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I want to calculate Suspended Sediment Concentration from the LANDSAT-MSS, LANDSAT-TM, LANDSAT-OLI images. There is a variation in the distribution of SSC in my study area. I want to develop a gradient map of SSC from each of the LANDSAT images. Would you like to tell me the methods of estimating SSC from the LANDSAT images in ArcMap 10.3 version?
Thanks in advance.
Supriti 
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Hello Supriti,
Just for your information.
You may aware of the fact that atmospheric correction of satellite imagery is necessary before using them for ocean color remote sensing. This task can be achieved by commercially available software such as ENVI FLAASH (works for all Landsat sensors) or freely available softwares such as SeaDAS (for Landsat 8 OLI). Many researchers also use reflectance at the top-of-atmosphere which does not require atmospheric correction procedure (personally I would not recommend that).
Next requirement is a suitable relationship between Landsat bands and in situ SSC concentrations. Try combinations of Landsat green, red, and NIR bands. Once you have reasonable algorithm and Landsat imagery, you can generate nice maps of SSC using SeaDAS.
checkout this publication. Although it maps turbidity ( a proxy for SSC), similar procedure can also be used for SSC. 
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Dear Scholars,
I am using Landsat 7 and 8 for estimation Land surface Temperature (LST) for my research. my purpose is comparing LST in my case of study from 2000 to 2015. 
I see some scholars only have used band 10 of Landsat 8 and band 6.1 of Landsat 7 for estimation LST ; while some other scholars have used the mean value of bands 10&11 of landsat 8 and mean values of 6.1 and 6.2 of Landsat 7! 
which one is better for research ?
Thanks in advance,
Majid
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Majid, if you have read the notice of USGS they recommend not to use Band 11 for LST estimation due to the stray light effect in Landsat 8 TIRS. Although researchers are working on correcting the problem, until further notice, it is advised to use only Band 10 From Landsat 8 TIRS for LST. But be aware of the errors even with Band 10! Regarding Landsat 7 ETM+ Band 6 (6H & 6L) have you already looked at the Landsat 7 Handbook for more information?
The question of which one is better for research will depend on the specific application. For Landsat 8 TIRS you don't have a choice other than to use Band 10. For Landsat 7 ETM+, it would be beneficial to look at the high (6H) and low gain (6L) states of Band 6. If you specify your application the RG community will be able to help you better because LST has so many applications ranging from active lava temperature estimation to urban studies.
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Hello,
I am trying to detect coastline changes using Landsat images (L1T) from different dates (along 20 years) and different sensors (ETM+, TM, OLI).
Do I need image to image co-registration for images already geo-referenced?
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geolocation information of Landsat products is usually very good, especially images from newly released "Collection" (https://landsat.usgs.gov/landsat-collections).
Furthermore, at areas with low topography (unlike mountainous regions) georeferencing should be sufficient.
Co-registering for multi-temporal analyses is basically a good idea. In your case I would say that you could probably introduce erros because changes in sea level could alter the shoreline (which you are exactly interested in), but co-registration could wrongly interpret this as misregistered parts of the image and 'correct' these areas resulting from temporal changes. You would then lose the information on shoreline variation.
Manually setting single GCPs however at places of constant location could help getting higher accuracies.
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Hi, I am currently working with Sentinel 2 imagery to make maps of water bodies, using Modified Normalized Water Index. The bands I want to use have different spatial resolutions. The green band has a resolution of 10 m and the SWIR Band has a resolution of 20 m. Therefor I want to downscale the 20 m resolution SWIR band to 10 m by using pan scharpening algorithms like Principal component analysis, Intensity Hue Saturation, High pass filter or A Trous Wavelet Transform. Anybody knows how to do these in ArcGIS/ ArcMap?
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Downscaling of 20-m Sentinel-2 bands to 10 m is a very complex problem, and I am preparing an article on that topic.
Sentinel-2 does not have a single panchromatic band like Landsat. Therefore, your first issue is to determine a 10-m reference band for sharpening of 20-m bands. For traditional fusion methods (e.g. IHS, PCA), it is very important that the higher resolution bands has approximately the same spectral range like coarse resolution bands. From my experience and susgestion in Selva, M., Aiazzi, B., Butera, F., Chiarantini, L., Baronti, S. 2015. “Hyper-sharpening: A first approach on SIM-GA data.” IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing 8: 3008–3024. doi:10.1109/JSTARS.2015.2440092, you have two options: selection among existing 10-m bands (2, 3, 4, or 8) or synthesis (e.g. weighted average of 10-m bands).
After you select/synthesise a higher resolution band, you can perform fusion in ArcGIS, using traditional algorithms, like Brovey, IHS etc. (http://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/fundamentals-of-panchromatic-sharpening.htm). Other option is Matlab and FuseBox library (https://github.com/sjtrny/FuseBox), where you can also find usual algorithms like IHS, PCA, Wavelet, Brovey, etc. As I haven't performed fusion in ArcGIS, I can't tell if it has problems with input values of bands (e.g. reflectance which ranges 0-1 in double precision), but with Matlab you will not have such problems.
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What satellites are still in operation, with current data, and how to obtain this information?
Is there any way to validate soil moisture data with a reduced number of field points (e.g. four) in a watershed?
Thank you for your feedback right away.
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Yeah...currently available nearly operational soil moisture products are from:
SMOS, SMAP, ASCAT, AMSR2, and ESA CCI SM.
You can easily find the soil moisture products via Google, all freely available.
Note that the ASCAT soil moisture product is available in near real-time (http://hsaf.meteoam.it/)
Many studies validates satellite soil moisture products with a low number of in situ sensors. If the temporal dynamic is of interest, temporal stability of soil moisture helps in performing this analysis.
See our recent review paper:
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I will be thankful somebody that can attached my needed file hear or in my email adress:
Or Please guide me,  how can I request some free space born hyperspectral data of my needed area with attached coordinate ?
I attached KMZ  Google earth file of my needed coordinate area.
Thanks every body.
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or contact ESA for CHRIS Proba-multi-angle data
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can it be possible to identify wheat crops using a weigheted overlay of NDVI, EVI, SAVI, NDMI( normalized differential moisture index) and DEM along with ground-based GPS point?
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If you know the phenology of the crops in your area you can specify the crops which are dominant and in their highest index value or visa versa in order to distinguish between them. You can give different weights for different dates. But this should not work if the crops you want to classify have alike phenological phases like winter wheat and barley.
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I am working on satellite images ( ETM+) bands , and i want to work with thermal band that one with 60m resolution and other band that with 30m resolution , so please if any one have an idea to make calibration with thermal band and make it's resolution with 30m.
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You can perform resampling method in ArcGIS software.
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I am working on an assignment wherein  I have to establish a network of sensors to detect soil parameters such as nutrient level, pH, Moisture Content, EC etc. Please help me to understand the required number of sensors and their placement in the field. I have to cover app. 100 ha. of oil palm plantation.
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You can create a power curve which will tell you the optimal number of sensors. But 100 is a lot of hectares – could you select perhaps just 30 ha and place 30 sensors in each ha? Thirty seems to be the magic number by all accounts lol. Also, I don't always recommend random placement because you want to sample in the relevant parts of each plot. For example, what if random placement caused no sensors to be placed under canopies. You would then have no data for these areas. So instead you could place sensors in line transects, for example. I hope this helps:) 
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I intend to calculate the Doppler centroid anomaly of Sentinel-1 SLC images to further use the Doppler centroid anomaly for the estimation of the line-of-sight motion. There are three sub-images in one SLC image with 27 Doppler coefficients of 9 groups. I have no idea how to calculate the Doppler centroid anomaly using the 27 Doppler coefficients. Thank you!
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Thank you for everyone's help! I am now studying the papers.
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Can we use remote sensing in the study of volcanic palaeococystems?
Can we to Catch and fix the silica or other postvolcanic hydrothermal alteration using of remote sensing?
Can we to fix volcanic abiotic ecosystems with remote sensing?
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.
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thanks for you earning dear Oluwafemi Adeyeri
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I am working to estimate Above Ground Biomass (AGB) using ground sample data and then intending to work out its potential relationship between different vegetation indices like NDVI, RVI, DVI, SAVI, MSAVI etc. This relationship will be ascertained using regression equation. Resultantly, I will be able to predict AGB of earlier date after choosing suitable index to proceed with. I am using images from two different sensors, Landsat 8 OLI (January 2017) and ETM+ (January 2007) to derive above noted vegetation indices and to see change in AGB over the period.
All of the said indices involve RED and NIR band to scale vegetation density and intensity. I have noticed the difference in wavelengths of both sensors’ required bands as follows;
Wavelength (micrometers)
Landsat 7 ETM+
Band 3 – Red : 0.63-0.69
Band 4 – Near Infrared (NIR) : 0.77-0.90
Landsat 8 OLI
Band 4 – Red : 0.64-0.67
Band 5 – Near Infrared (NIR) : 0.85-0.88
Keeping in view this wavelength difference of both sensors' RED & NIR Bands, I am interested to know whether this difference will affect my intended results? As such I am estimating increase or decrease of AGB over time by comparing both images with suitable Vegetation Index involving RED & NIR bands of both sensors.
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Nasir,
I believe the Roy et al. paper only compared OLI with ETM because their study design called for overlapping images. Since there have not been any TM images since late 2011 (when the satellite began to fail) they were not able to compare OLI with TM. I would say that given the number of studies published (myself included - see https://www.researchgate.net/publication/294090457_Spatial_and_temporal_trends_of_drought_effects_in_a_heterogeneous_semi-arid_forest_ecosystem?_iepl%5BviewId%5D=waN3NgatecbzH3LVIV91YKC0&_iepl%5BprofilePublicationItemVariant%5D=default&_iepl%5Bcontexts%5D%5B0%5D=prfpi&_iepl%5BinteractionType%5D=publicationTitle) that use both TM and ETM, they are generally viewed as comparable with no correction applied to normalize (if you are using surface reflectance).  
If you use OLI, ETM and TM in the same study, I would suggest the following:
-obtain surface reflectance products for each of the above (via USGS EarthExplorer or another portal)
-apply the between sensor transformation to normalize OLI to ETM as suggested in the Roy paper. Theoretically the OLI images should now be conducive to both ETM and TM imagery.
-select a series of psuedo-invariant features (PIFs) that you expect to be consistent throughout all images such as bare soil, parking lots, deep water, etc. Ideally use a 2x2 or 3x3 set of pixels as opposed to individual pixels. Then run a check (simple linear regression) with the PIFs data regressing OLI vs. ETM, OLI vs. TM, and ETM vs. TM. Hopefully your check shows very high agreement between the images with Roy et al's transformation applied. See Section A4 of this paper:  https://www.researchgate.net/publication/266024594_Modeling_a_Historical_Mountain_Pine_Beetle_Outbreak_Using_Landsat_MSS_and_Multiple_Lines_of_Evidence
Good luck,
Tim
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I am a graduate student of crop and soil science, learning several algorithms used for radiometric correction for estimation of the atmospheric parameter. What are the algorithm you guys are using?
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PONDER - Presence on the Net of Deists for ... - Acronym Finder
PONDER is defined as Presence on the Net of Deists for Enlightenment and ... appears somewhat frequently and is found in the following Acronym Finder ...
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Ecological applications that need remote sensing technology,like end-user in computer science, usually sustained over long periods of time, and sometimes requires contrast, especially in studies like landcover change, climate change. However, scientists of remote sensing image interpretation algorithms research have been working on various algorithms for digging more information.
So, if it is meaningful to compare results under different interpretation algorithms?
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New algorithms are meaningful as the Earth itself is not static. It may help in discovering other  neglected ecological issues. But other methods should be considered to validate the result. 
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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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Hi all:
We are examining AR environments from Reanalysis products using object-based algorithms. We though that perhaps you were interested in trying out our AR retrievals. 
Some details in:
Cheers,
--John
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Thanks, I will check it out.
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Net primary productivity, although available through satellite images and are commonly employed, but the validation with few of the case studies lacking. There is a need to evolve simple models for NPP estimates. Although DNDC, Daycent and other models cover this aspect directly/indirectly, but interested if anyone developed simple relations for estimating NPP on the basis of prime biotic/abiotic stress inputs.May be some simple excel/access based tool
regards
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Not sure if this is what you're looking after but see these two options:
Del Grosso, S., Parton, W., Stohlgren, T., Zheng, D., Bachelet, D., Prince, S., ... & Olson, R. (2008). Global potential net primary production predicted from vegetation class, precipitation, and temperature. Ecology, 89(8), 2117-2126.
Sallaba, F., Lehsten, D., Seaquist, J., & Sykes, M. T. (2015). A rapid NPP meta-model for current and future climate and CO 2 scenarios in Europe. Ecological Modelling, 302, 29-41.
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I am trying to download this data from GLOVIS USGS and other similar websites but the data is not downloadable. Any suggestions and where I can get this or similar data?  
It would be best for me to be able to download data for a given time period to be able to find the average to reduce the effect of clouds. 
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metaphor
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I want to calculate Photosynthetically Active Radiation (PAR) from band1 values of MOD02QKM to be used in satellite model.
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Thanks Nasem and Abdegdir for the reply. MRT process works fine with MOD09 and MOD13. However it couldn't process MOD02 which is a Level 2 product that lacks sinusoidal coordinates (V##h##). I just did  research and found out that the recommended software to process MOD02 is MRTSwath. I want to know the steps to derived Band 1 for daily measurement using MRTSwath Tool. I want to get final values in text file or other format as long as it appears in numbers for calculation purpose.
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 i want to use this data of  daily temperature extracted (if possible) snow covered area for Snowmelt Runoff model. please guide in detail with procedure.
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Hi Ishtiaq,
Regarding your inquiry about the existing satellite images that provide a daily-basis temperature data, I would suggest to use MODIS data. It has half-day temporal resolution and is available at 250 m grid space worldwide. Through one of their product i.e. MOD11, you can directly acquire the land surface temperature. You can access the MODIS data on https://modis.gsfc.nasa.gov/data/dataprod/dataproducts.php?MOD_NUMBER=11
However, analyzing a day-by-day MODIS data can be tedious in particular if you are working with a long time epoch. For me, I prefer to use the results of climate change model that are available from trusted several climate change research groups. I suggest to use the climate change model from the group of Climatic Research Unit (University of East Anglia), since their results have been used in many published articles. You can access their datasets on https://crudata.uea.ac.uk/cru/data/temperature/
Hope it helps.
Regards,
Hero
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What can I do to fix this problem?
My interest is to use the scenes (Landsat8) to prepare land cover raster for suitable habitat modeling.
If there is any way to help with this, kindly let me know
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Thank you for the replies. I will use all the suggestions to the problem.
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The lake has a relatively low brightness temperature (BT) than the land surface. When the beam of the space-born microwave radiometer was pointing to the land near a large lake, the observed BT was supposed to affected by the lake due to the side lobe of the radiometer antenna. Any body could give an general estimation how much Kelvins does the lake affect the observed BT?
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Hi - I'm not sure you have to invoke a sidelobe to see the lake affecting a land observation.  Each sensor channel's footprint has a characteristic size, which is usually stated as the half-power point of the (generally Gaussian) response function.  This size can be tens of km, depending on the sensor and channel frequency.  The second issue is that the emissivity of the surface depends critically on the water content of the scene.  For open water Tb is perhaps under 100K, while dry, bare land is much more.  However, moisture in vegetation and the soil results in a highly variable emissivity in land areas.  Finally, the atmospheric contribution tends to increase for increasing frequency.  In some sense, the low frequency channels are much more open window channels.  Summarizing, you need to consider the channel frequency, the character of the land scene (particularly vegetation and soil moisture state), and how much the lake intrudes on the footprint (the size of which is frequency- and sensor-dependent) before you can get an answer.
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I am trying to calculate the aerosol optical thickness and PM10 from landsat. For this purpose, relative azimuth angle is required to run the algorithm.In metadata of landsat, solar azimuth is there.Is there any equation to determine tha relative azimuth angle?
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Hi,
Assuming you do have the long-lat of two eastern or western corners, I believe the problem is now "simply" how to determine the azimuth given a pair of geographic coordinates. This is often called the inverse geodetic problem (or reverse geodetic problem). For this, you could explore either the Vincenty's formulas or GeographicLib algorithms. First link could help.
Second link is a similar question with valid answers
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Hi everyone,
First of all I know that backscatter coefficient is related to soil moisture, can anyone help me:
1. What is the procedure of pre-processing sentinel-1 data?
2. and how to get backscatter coefficient from sentinel-1 data? step by step is better.
Thanks a lot .
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Dear all
We are working on river discharge calculation using optical remote sensing at Tarbela upstream. In this regard, could you guys guide us about the best practice and techniques to follow?
Thanking you in advance!
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Dear Dr. Sultan, 
If you have access to a series of images of your location of interest, from which a time series of river widths can be reasonably estimated, you could apply a method called at-many-stations to estimate discharge. This method, which is one of the methods evaluated in the article that Dr. Hunt attached, uses scaling laws between river width and discharge to estimate discharge.  The full description of the method is presented in the attached article entitled “Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry” authored by Prof. Gleason.
The at-many-hydraulic-stations method was developed to work for large areas taking advantage of satellite imagery.  If instead what you intend to use are video images of a particular location to estimate discharge, a technique that could be applied is based on large scale particle image velocimetry. This is not my area of expertise, but I remember a prototype of a LSPIV system that was built in the laboratory where I worked during my PhD work.  It is simple to build and operate and compared well with a collocated gauge during tests.  If that’s the avenue that you would like to pursue, you could find details on the Kim et al (2008): Stream discharge using mobile large-scale particle image velocimetry: a proof of concept (http://onlinelibrary.wiley.com/doi/10.1029/2006WR005441/pdf). If you’d like a copy, please message me, as I’m unsure if that article’s copyright allows open sharing.
Best regards,
Renato Frasson
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Dear friends
Hello,
We derived precipitation data using TRMM satellite images which covered the study area by 4 pixels. The study area elevation ranges vary from 1729 to 3760 meters. The main object of this study was to evaluation of the remote sensing efficiency in the calculation of precipitation for the areas which is not equipped any ground stations. According to the large pixel size of the TRMM images (25*25 Km) as well as being point wise of the ground station, there is a significant difference between the obtained satellite data and ground recorded data. So, in your opinion is it logical? Or no, it's possible that unknown error may be happened during process implementation? What are your recommended in this regards?
Warm Regards
Bagher
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Dear Bagher,
You are asking a very pertinent but quite difficult question: if your study area is not equipped with rain gages (or has only one), it will be difficult to properly evaluate the performance of TRMM measurements because precipitations are expected to be highly dependent on altitude in mountainous environments.
However, as you point out, even if such local observations were available, the main difficulty would be to deal with the major difference in the scale of representativity between the satellite-based TRMM instrument, which only measures average radiation signals for large areas (25 by 25 km, corresponding to individual instrument pixels), and rain gages that generate in situ, quite local measurements. Moreover, the reliability of observations from both instrumental approaches are constrained by the accuracy of the models used to interpret the space-based measurements, and by intrinsic limitations in the effectiveness and accuracy of rain gages, especially in mountainous environments, respectively.
In any case, since you also indicate that you only have one precipitation ground station in the area anyway, you may want to look at alternative approaches. One possibility would be to delimit the boundary of a large enough river basin (say, 50,000 square km or more) for which you do have hydrographic data (runoff) as a function of time. The main advantage of runoff is that it is also an integral estimator, since it accumulates water over the watershed.
You could then feed a model of the water balance for that watershed with inputs from TRMM and other sources to estimate runoff, infiltration, and evapotranspiration in order to compute the outflow predicted by this model and compare it to the actual river discharge. Of course, this approach involves multiple sources of input data, as well as a model which can have its own limitations, but it would at least give you a sound basis for establishing the coherency and consistency (or lack thereof) of your tools and data sets. It would also permit you to conduct sensitivity analyses to determine the relative importance of each input, and indeed the role of TRMM data in this context.
Such an approach would probably not be applicable (or reliable) at the scale of 4 TRMM pixels because the actual size and shape of your study area are unlikely to closely match the region observed by remote sensing. If you deal with a larger area, these differences in coverage tend to reduce in proportion to the ratio of the actual basin area over the area observed by the satellite.
Another independent approach might consist in not trying to "validate" the TRMM data itself, but to investigate to what extent using those results yields meaningful results. For instance, if the area is at least partially exploited for agricultural production, you might develop a model of crop productivity using generic climatological data to predict yields, and then establish whether using TRMM data instead of some measured or assumed precipitation climatology actually improves the forecasting skill of the model. If the answer is positive, you will have shown that TRMM data may not be actually validated, but still very useful for practical applications.
A less satisfactory but simpler approach might be to establish whether there is any correlation between TRMM-derived precipitation levels and these (or some other) precipitation-dependent measurable quantities, and to explore whether these variables vary synchronously over long periods of time. This exercise could perhaps serve as a starting point for the second approach, as it would generate preliminary results while imposing fewer and less demanding requirements on the inputs.
Best regards, Michel.
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ground measurement or satellite data
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Thanks for your answers
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I mean, the condition of some ground data, specifically rainfall data isn't complete because it is recorded manually. Is there any alternative solution for validating the result of satellite images processing?
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To Mr. Alejandro González-Ollauri:
I have tried to use the linear regression interpolation methods in my previous research, but the result wasn't significant. That's the reason why I asked if there's another alternative method.
Thanks so much for your advice, Sir. I really appreciate your concern.
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land cover mapping
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I think the best spectral bands to use for land cover mapping using TM is band 5,4 and 3.
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Or any research of choosing NDVI thresholds automatically? I currently use rapideye (5 bands:r,g,b,red-edge,nir) and worldview2 (4 bands: r,g,b,nir). I prefer to do some classification automatically, because I don't have any ground truth.
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first analyze the image....by opening it as FCC ( IR, R, G ) all vegetation will appears as red.
RUN THE ndvi. make the threshold by swiping ndvi image over fcc.
note down the threshold value....
go to model-maker...give the condition if ( ndvi images> threshold.value....) then 1 else 0.
so you will get a thematic layer having vegetation....
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Could you explain which statistical index is better for evaluation of satellite rainfall product for Himalayan catchment?
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I suppose you want to validate the satellite products. If so, collocate the satellite observations with measurements and carry out the simiple statistical analysis like finding the R^2, RMSE, scatter index.
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SMAP (Soil Moisture Active Passive)
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You should note that SMAP products are in HDF5 (or .h5) format which are accessible through HDFView to learn data structure and their content. SMAP data (h5 files) could also be read in MATLAB where you can plot and work with the data. Here is a sample MATLAB code that I use for reading SMAP soil moisture data:
SMAP_data_dir = 'D:\research\SMAP_ET\SMAP_data\';
fname = [SMAP_data_dir,'SMAP_L3_SM_P_20150506_R13080_001.h5'];
a = h5info(fname);
% 2 "Groups"
a.Groups(1) % Metadata
a.Groups(2) % /Soil_Moisture_Retrieval_Data
a.Groups(2).Datasets
a.Groups(2).Datasets.Name % Found the one I wanted... soil_moisture
SMAP_mois = h5read(fname,'/Soil_Moisture_Retrieval_Data/soil_moisture');
SMAP_mois(SMAP_mois <0 ) = NaN;
h = imagesc(SMAP_mois');
set(h,'alphadata',~isnan(SMAP_mois'));
colormap(flipud(colormap(('parula'))));
caxis([0 0.4])
colorbar
I hope this helps.
Best, Erfan
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Something simple but solid, for a multitemporal study. Thanks.
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Dear Daniella, I'm not an expert in remote sensing, so other colleagues might help you there. Nevertheless, be aware that quite a bunch of "bofedales-like" ecoystems in the Peruvian Andes are inundated only during some months (i.e. during the rainy season, between November and April), so, you should consider this fact when searching for the best available sattelite images. Best, Jan
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Thanks Bannari for the comment, that would be interesting to try. Best regards
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You are welcome
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Hi,
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Thanks!
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HS Srivastava, P Patel, Y Sharma, RR Navalgund - International Journal of Geoinformatics, Vol. 02, No. 03, pp. 21-28.