Questions related to Remote Sensing Applications
I am seeking recommendations for established methods that utilize multispectral and Synthetic Aperture Radar (SAR) data for "Remotely Sensing Surface Water Bodies"
These could cover areas such as,
- Reflectance measurements
- Radiative transfer modeling
- Feature extraction
- Water Body Detection (WBD)
- Normalized Difference Water Index (NDWI)
- Modified Normalized Difference Water Index (NDWI)
- Automatic Water Extraction Model in Complex Environment (AWECE)
I would greatly appreciate any guidance or references that could be provided on this topic. Thank you in advance for your assistance.
I face a problem in LULC classification, like as an industrial area showing as a water body.
Please help me.
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.
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?
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?
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
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.
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.
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?
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?
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.
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?
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
Thanks in advance
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?
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?
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.
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
if you use one of them , please respond with your review .
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?
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.
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.
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.
Android application that does not use mobile inbuild GPS and instead get the NMEA messages from an external USB gps device.
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.
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.
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!
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?
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?
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.
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.
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.
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?
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.
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?
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.
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?
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.
Until now we only rely on MODIS fire and landsat data for peatland fire management.
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 ?
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?
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.
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.
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.
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,
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?
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?
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.
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.
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?
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.
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.
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!
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?
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?
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.
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: