Remote Sensing Applications - Science topic

Some of the water indexing methods are,

Google Scholar is your friend ( see graphic )

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

This is a good question.

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.

The article:

best wishes,

Jasem

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.

Dear Pritiranjan Das

Here are some of the most prominent models for simulating urban growth. Besides, Jiří Kroc has provided a very good approach as well.

Thanks and regards

Gowhar Meraj

20

@Pir Mohammad

I will try. Thank you so much for your valuable comments and suggestions.

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.

Most GIS and RS software have the standard classification methods (ArcGIS, QGIS, ENVI, Eedas etc).

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

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:

Dear Eko,

You may used chlor-a and phytoplankton products as indicators for nutrients concentration with empirical modelling.

Best of luck.

Dear Victor,

Optical remote sensing may reach 30 meters under the sea surface in clear water.

You can download this link

then process the SEADAS or ARC GIS software

Hi Dulan,

Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Google Maps and nearly every map creating company use this technique to increase image quality. Pansharpening produces a high-resolution color image from three, four or more low-resolution multispectral satellite bands plus a corresponding high-resolution panchromatic band:

Low-res color bands + High-res grayscale band = Hi-res color image

Such band combinations are commonly bundled in satellite data sets, for example Landsat 7, which includes six 30 m resolution multispectral bands, a 60 m thermal infrared band plus a 15 m resolution panchromatic band. SPOT, GeoEye and DigitalGlobe commercial data packages also commonly include both lower-resolution multispectral bands and a single panchromatic band. One of the principal reasons for configuring satellite sensors this way is to keep satellite weight, cost, bandwidth and complexity down. Pan sharpening uses spatial information in the high-resolution grayscale band and color information in the multispectral bands to create a high-resolution color image, essentially increasing the resolution of the color information in the data set to match that of the panchromatic band.

One common class of algorithms for pansharpening is called “component substitution, which usually involves the following steps:

Common color-space transformation used for pan sharpening are HSI (hue-saturation-intensity), and YCbCr. The same steps can also be performed using wavelet decomposition or PCA and replacing the first component with the pan band.

Pan-sharpening techniques can result in spectral distortions when pan sharpening satellite images as a result of the nature of the panchromatic band. The Landsat panchromatic band for example is not sensitive to blue light. As a result, the spectral characteristics of the raw pansharpened color image may not exactly match those of the corresponding low-resolution RGB image, resulting in altered color tones. This has resulted in the development of many algorithms that attempt to reduce this spectral distortion and to produce 'true color' imagery.

I agree with Bambang H Trisasongko

Well,

Regards

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.

4km free phytoplankton in MODIS data sets

follow this paper

I think this may be useful

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.

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).

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.

https://www.youtube.com/watch?v=0098p61dQuM

Cheers.

Rajasekhar

You can use SAR data for the purpose. Sentinel data is one of the bests.

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

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 :)

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.

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

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

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.

no

use GBS to collect point and use program Arc map

you can use these method or time series NDVI layer stacking for crop classification using unsupervised classification

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

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.

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.

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);

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

You may find the link below:

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.

Thank you James

thank you!!

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.

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

I write this scrip long ago to read Modis Aqua files in matlab, i hope someone else could help.

Thank you. I understand the idea.

Kind regards,

I would recommend the answer of lining...VV is better than HH 

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. 

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.

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.

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.

Hi Enawgaw,

There is yet another method to measure GPP using sap-flow sensors, which are relatively cheap:

Best regards,

Tamir Klein

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.

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.

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.

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. 

Maybe the problem comes from the DEM quality.

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. 

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.

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.

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.

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:

https://earthexplorer.usgs.gov/...... search here

or contact ESA for CHRIS Proba-multi-angle data

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.

You can perform resampling method in ArcGIS software.

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:) 

Thank you for everyone's help! I am now studying the papers.

thanks for you earning dear Oluwafemi Adeyeri

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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. 

Mohamed, Very thanks MODIS was a great suggestion!

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'.