I am trying to plot Leaf Area Index of two individual crop fields using EVI data from Sentinel2. Each field is less than 0.25 km^2. Does cloud masking in such a small area make sense or does it just lead me to lose data since there is a high likelihood that either the field is not covered by a cloud or fully covered by a cloud?

I have various satellite images, some with 4 bands and some with 8 bands. I’m working in python and ArcGIS but also trying a few things in R.

I want to explore regions within these images, regions like ocean, ice, rocks and large wildlife. To do this I want to create a spectral profile figure of each of these regions. The goal is to work out a way to detect wildlife from these images but these can be difficult to detect using classifiers. So I figured if I know each regions spectral profiles I can create an index (similar to water index or vegetation index) to detect the specific region im interested in. I’ve found ways to get spectral profiles of pixels or entire images, but not regions of interest

My current thinking is either to crop the regions by using shapefiles and get the profiles of the cropped image, but I’m wondering if there is an easier way.

Does anyone have any advice for websites or books or GitHub’s that could useful to get spectral profiles? Thanks!

Hello, I'm currently working on my grade project in order to get my degree. I'm focusing on multi-temporal analysis of glacier retreats. A way I found to do this is with satellite images from usgs or Nasa earth data with arcgis. Is there any other way to do this? Any software similar to ArcGis I can use? Any book or guide I can get to know more about this?

Hi there, I'm looking to purchase a phenocam to monitor seasonal change in greenness (or NDVI) at my study site. I would just use Sentinel-2 imagery but nearly every image is obscured by clouds, and I would prefer a higher temporal resolution.

I haven't been able to find a vendor for what I'm looking for anywhere - does anyone have any suggestions? Ideally it would be something I could strap to a tower and would take an image once a day. RGB is fine, but having an NIR band would be even better.

Thanks.

Hi everyone,

I'm interested in learning more about research applying point cloud understanding techniques (like classification and segmentation and etc.) to remote sensing data.

Are there any recent papers you'd recommend that explore this field?

any area: forestry, urban environments, disaster response....

EDIT*** EarthExlorer doesn't work

Upon attempting to log in, I received the following error message:

"An error occurred during execution; please try again later."

Despite several attempts, the issue persists. As this system is integral to my tasks, I am concerned about the potential impact of this error on my work.

Could you kindly provide some alternative sites from which I can download Landsat data?

How do we extract signatures from aerial photo graphs using drone?

Just to give y'all a context. I am trying to map benthic habitat using a drone. I gathered some underwater photos and manually classified it (e.g. coral, rock, sand, seagrass, etc.). Then i georeferenced its gps coordinates and matched it to the aerial imagery. Now, i am wondering how i can exteact the signatures to feed for the classification algorithm?

Hi,

I need some assistance working on a mining project.

Im using Aster data which is a lot older than the typical imagery i use and im having some teething problems getting it onto a usable state in arcgis.

Im using python to pre-process the L1T data.

Has anyone got any useful python processing tips for aster data?

Either that or i process manually in arcgis which i dont really want to do. Lol

Thanks in advance

Hello! I am a total newbie so please pardon me if this doesn't make a lot of sense or is really elementary.

I have downloaded GEDI L3 Gridded data from: https://webmap.ornl.gov/ogc/dataset.jsp?ds_id=1952 Specifically, I am interested in canopy height/SD (rh100_mean and rh100_stdev files). I have downloaded files for my region of interest (total of 5 files). I wanted to combine them into a single file so I can extract the canopy height values for a series of points. However, when I do so, I see there is streaking in the dataset. (not the gaps inherent to GEDI data, but streaks due to variation in measurement with each orbit.)

I am wondering what is the best way to 1) combine all the files into a single raster and 2) correct for the measurement variation between orbits.

I've tried creating a mosaic and using the average value (below), but the data still looks really streaky? I am not sure what best practices are for dealing with this, but I am hoping someone can help me out?

My area of interest, showing the streaks in the dataset (I am not concerned about the gaps but the streaks from measurement error with each orbit).

Hello people. I am new in remote sensing and I want to know methods to measure biodiversity using remote sensing. What type of images are used, at which scale....

Thanks for my silly question

Hello, I am recently starting to use satellite images.

I have a script where I am using lansat 5 and 8 collection 1 and it asks me to migrate

I have not been able to do it, that is why my question is, are the data in collection 1 invalid?

I was thinking of publishing my scientific article with the results of these scripts, but I am afraid that they will reject my article for using data from the landsat collection 1

could this happen?

I would greatly appreciate your response, thank you

How to do mapping of vegetation of any area through arcgis? Can anyone share any youtube video.. I have searched but didnt find any relevant.

As the title suggests, I am creating a training dataset for supervised semantic segmentation. I’m using surface reflectance scenes from both Landsat 5 and 9. I’ve accounted for the differences in band naming/order. Im only using the bands they share in common (R,G,B,NIR,SWIR1,SWIR2).

However I am concerned that Landsat 5 and Landsat 9’s different sensors may have differences in wavelength ranges for their bands. If that’s the case, can they still be used interchangeably (maybe the differences are negligible), or should they be somehow calibrated (or normalised if that’s the right term?) so they share similar ranges? If so, what method is typically used for this calibration?

Any answers appreciated 😊

Using Google Earth Engine, I am trying to plot EVI over time for a certain date range over an agricultural field. I am trying to use the approach to cloud masking with s2cloudless as shown [here]. As far as I understand, the s2_sr_median shown here should be the cloud-free composite image collection right?

However, when I try to get EVI from that by doing the following:

var S2_field1_EVI_cloudfree = s2_sr_cld_col.map(addEVI);

I get the error that s2_sr_median.map(addEVI) is not a function.

I am, however, able to use s2_sr_cld_col.map(addEVI) but I am not sure if that is the correct image collection that has the clouds masked.

I am brand new to GEE and GIS in general so I'm sorry if there is a huge knowledge gap here.

Dear SNAP Community,

I want to introduce you to 𝘿𝘼𝙑𝘼𝙇𝙄𝙀𝙉. The 𝘿𝘼ta 𝙑𝘼𝙇𝙄dation 𝙀𝙉vironment.
It provides an environment for validating EO data and is intended for quality assurance of EO data and data processing software. It is based on SNAP software and uses the GPF.

Maybe it is something for you to ensure the high quality of the data you generate.

You can define tests by specifying the SNAP GPT command line call and the expected product content. The test environment will then execute the command and compare the result with the result that was expected. In the end you will get 2 report files, one JSON and one HTML file.

https://preview.redd.it/tb84lph5r5yc1.png?width=1498&format=png&auto=webp&s=9255ebea133f6ff5b3403644ad5edff07976b075

The DAVALIEN plugin is available via the SNAP Plugin Manager. For details on how to use the environment see the Getting Started guide.

Your feedback and suggestions are very welcome and can be posted via the issue tracker or in the EOMasters Forum or directly in this thread.

Automated Polygon Splitting Using Voronoi Diagrams and Clustering

In this Blog the features of the new EOMasters Toolbox are introduced.

EOMasters Blog: EOMasters Toolbox v1.0 Released - An Introduction

Maybe you want to have look.

​

Can someone please explain correlation to me like I'm 5 years old?

I'm in an Intro to Remote Sensing class and I'm frustrated to the point of banging my head on my desk. We're using the ENVI software for our assignments, and this week, the topic is Multispectral Classification. The image we're using is a subset of a Landsat 5 TM scene, path 44, row 34, 6 bands, and was acquired August 30 2016.

The bands of the image were changed to 742 as RGB. One of the tasks was to create 2-D scatter plots for the image, as well as compute band statistics and interpret the bands using the correlation matrix that was generated. Then we have to answer 2 questions based on the results:

1. Which two (different) Landsat TM bands are most highly correlated (the bands that exhibit the least variance)?
2. What two Landsat TM bands are the least correlated (the bands that demonstrate the most variance)? 

My problem is that I have no idea how to answer these questions. The instructor doesn't really explain correlation or variance, beyond "This band and that band are highly correlated. The end." No explanation as to how or why, and he isn't returning my email. (This instructor really is the bane of my existence this semester, this is just one of many reasons why.) The course textbook isn't any help either, nor do any of the half dozen remote sensing books I have. Google is no longer my friend and I am at my wits' end.

Any help would be greatly appreciated. Thank you.

These are the scatter plots for the bands in the image:

https://preview.redd.it/kj62oftepkxc1.jpg?width=1253&format=pjpg&auto=webp&s=3203d11dedada9fcdf0b73ac3e9a4229d79f4d50

And this is the correlation matrix:

https://preview.redd.it/42n1z6ofpkxc1.jpg?width=1442&format=pjpg&auto=webp&s=dc5ab3f2920bf4be046bf4accf164a83272849f4

I'd like to ask the technical term for the type of satellite image that looks exactly from top of a city, i.e. a view precisely perpendicular to the ground surface (assuming it's flat), which means only the roofs of buildings, and slanted walls, are visible and all (vertical) building facades are invisible.

An example from Google Sunroof :

https://preview.redd.it/nndoz1w7fkxc1.png?width=2084&format=png&auto=webp&s=f64c686786415d6ca732384f68d8bee18abb22de

...as compared to normal satellite image on Google Maps which is taken from an angled view, and some part of the building facades are visible:

https://preview.redd.it/fvkpgrm6gkxc1.png?width=1731&format=png&auto=webp&s=9c55163eab51a7b989a84a6f51cd109ce0cb64fd

​

Hello everyone. For a school project we are designing a space mission. I’m using a telescope with an aperture area of 0.5 sq meters. The half cone angle is 30 degrees. How can I find out how big a pixel is? The spacecraft semi major axis is 8500 km.

Hi there, basically I am playing around with the XMP data of my raw .tif files collected with the DJI P4 Multispectral to understand why I obtain certain absolute reflectance values after creating my orthomosaics. I am incredibly confused by some of the values I am getting and I'm hoping someone here can help me out with some of the following questions:

1. Does anyone know what the unit of Irradiance (i.e. calibrated sunlight sensor value) is? I cannot find any information about the SI units anywhere, unsure how to interpret it.
2. Follow up on that - what is the unit of the Exposure time? According to the image processing guide it should be in microseconds, but I get numbers around 0.003 - I am more inclined to think this is in milliseconds.
3. What's the deal with the Blue band ISO and shutter speed values? I am looking into what values were used for some of the parameters that determine exposure (in this case shutter speed and ISO speed). For most bands the ISO varies between 200-800 but for the majority of images recoded in the Blue the ISO is 1600. Also the shutter speed for the other bands is around 0.003 (ms?) but for the blue band the value ranges between 0.003 to 0.007. I also noticed that the absolute reflection in this band is much lower when calculated for the final orthomosaics, which is counterintuitive to me given the high ISO and shutterspeed. Anyone has any info of what might be happening here?

Hello There,

I am a 30y/o graduate from a GIS program in Ontario, Canada. I've done a lot of work with Esri Software, Catalyst, eCognition Pro and Python. I'm trying to beef up my resume a bit and gain a bit more perspective into the world of Remote Sensing and was wondering if anyone had any insights into volunteering opportunities for Remote Sensing work?

Or am I better off looking into some free online courses to help boost my knowledge and add another line to my resume?

Any help would be greatly appreciated!

Hi! i'm participating in a university program for which we have to take a novel technology and apply it to a new use case in order to disrupt a market (we must keep a long term view, 20y, and we can slightly "fantasize" over the technology capabilities in question, but necessarily not "breaking" the physics behind it).

I was thinking about applying the technology in consideration to satellites, as it supposedly would allow to produce scalable, lightweight and dimension reduced small satellites, equipped with large arrays of MEMS sensor with enhanced optical capabilities. Also the possibility of low consuption equipment on satellite is a very interesting possibility.

Today's optical sensors, such as spectrometers or SAR, from what i've uderstood are bulky and overall may improve their capabilities with MEMS optical equipment or be eventually substituted with MEMS optic (in a very distant future?)

I know there are currently studies over the enlargement of the field of view of mems sensors, but I cannot grasp if it is something that could be developed enough to be fitted on a telescope optics for EO purposes (air quality monitoring, fire and oil spill detections ).

Studies papers that I suppose may reinforce what I'm trying to deliver:

Similar Examples:

https://www.researchgate.net/publication/226057314_Retrieval_of_aerosol_properties_over_land_using_MISR_observations

https://www.researchgate.net/publication/307753754_Remote_sensing_of_aerosols_by_using_polarized_directional_and_spectral_measurements_within_the_A-Train_the_PARASOL_mission

https://link.springer.com/article/10.1007/s12200-009-0061-5

https://www.academia.edu/10918610/Review_of_passive_imaging_polarimetry_for_remote_sensing_applications?sm=b

There are currently some systems developed by various companies, such as https://www.darpa.mil/news-events/2021-10-26
https://www.darpa.mil/program/extreme-optics-and-imaging

Widening the field of Metalens:

https://www.nature.com/articles/srep07511

http://web.mit.edu/hujuejun/www/My%20Papers/Journal%20Papers/Wide%20field-of-view%20metalens%20--%20a%20tutorial.pdf

Multi Dimensional Metalenses:

https://www.degruyter.com/document/doi/10.1515/nanoph-2022-0803/html

​

I don't have direct technical competencies on this field and therefore Iwould really benefit from the opinions of you people. If this stuff doesn't make sense, feel free to tell me wathever I'm missing, as it would really help.
Thanks!

Hello,

I'm looking for a georeferenced remote sensing dataset that specifically contains spacial resolution of all the images. Most of the common datasets that I've seen so far such as "UC Merced Land Use Dataset" and "AID" are very easy to work with but the former has a fixed resolution of 1 foot and the latter does not mention anything whatsoever about the resolution of the images. As of yet, the only dataset that I could find was "SEN12MS", which I can't wrap my head around working with it and it's very large in size (I need to be able to download it on google colab and do some processing). I'm a newbie in remote sensing in general but I'm interested in it and I want to learn more. I appreciate your help.

​

Thanks

Hi everyone,

I need to identify zones prone to store groundwater within an area of approximately 34.000 Km^2 (I mention this because of the spatial resolution needed), can someone give me advice on which satellite images I could use to do this, or if there is a combination of bands that would be useful (and from which satellite)?

I'm currently having an issue with ENVI and Band Math, and I was wondering/hoping someone could point out where I'm going wrong.

I'm taking Band 10 in envi and trying to convert it to LST in Fahrenheit. The equation I'm using is is ((((b1)*0.00341802+149)-273.15)*(9/5)+32). When I test out individual values and calculate them manually to check, I get reasonable and correct results. When I put it through Band Math, however, I'm getting nonsense. Post Band Math, its showing an extra 59.687 million values compared to the original source (59764761 vs 76876).

I know I'm doing something wrong here, but I have no clue where I'm going wrong.

https://preview.redd.it/svou2qzvi3wc1.png?width=206&format=png&auto=webp&s=a419fc7f2278c9d693efdd7e84fbb0f2ddc8d73b

https://preview.redd.it/9grdc0dwi3wc1.png?width=206&format=png&auto=webp&s=cf79adad04488818635c56d38200dfb334a96df4

https://preview.redd.it/s69joqpwi3wc1.png?width=1582&format=png&auto=webp&s=74a8a043eaf89bc957843d5dcf265eedba794c92