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I am trying to use Python to convert a directory of 4 band PlanetScope Surface Reflectance TIFF tiles to RGB JPEGs so I can upload the smaller, easy to visualize JPEG files to LabelBox for manually labeling Arctic lakes as either ice covered or ice free.I have thrown together a script (relevant functions attached below) which reads in the bands from each of my downloaded TIFF files (I have around 100 right now) and normalizes each of them to 0-255 for conversion to JPEG. I have tried many different normalization algorithms, including histogram stretching, but all methods so far leave almost all my images with significant red, green or blue tint, which makes identifying which lakes are ice covered vs. ice free much more difficult (example of blue tint below.)Is there a way to automatically process RGB files for each of my scenes which will appear without these tints, more like the rgb preview images shown on Planet Explorer?  (Or is there a way to directly download pre-processed RGB dir

I would like to contact you regarding my account, which I am currently using as an Education and Research Standard account, but it has been inactive since September 15, 2024. Could you please inform me of the steps I need to take to renew my account?I appreciate your assistance and look forward to your guidance.

Hi, I am using Planet Explorer for an educational research program.The other day, around 8 pm (CST), Explorer stopped retrieving images.Of course, I did not exceed the usage limit, nor did I change any settings, I was logged in and using it as usual.I can't find a solution whether this behavior is a bug or a problem on our end.If you could help us, we would appreciate your response. 

Hi, According to the Planet UDM2 documentation, UDM2.1 is better than UDM2.I downloaded an image (20240810_090129_25_2460), and the bundle contains two TIFF files: SR.tif and udm2.tif.How can I verify whether the udm2 file corresponds to UDM2.0 or UDM2.1 Best,Haiyan  

Hi,I am trying to import Planet Images into Google Earth Engine. I am following all the steps required which includes creating a Cloud Project, enabling the Google Earth Engine API, giving access to Planet account and Earth Engine writer in IAM section. After following the steps, when I am ordering the images, my delivery fails and I am getting following error - "Error while calling the GEE image import API: failed to trigger GEE ingestion: googleapi: Error 403: Google Earth Engine API has not been enabled in project: y.".I am not sure why I am getting the following error. If anyone can help me with this, it will be great.Thank You.

Am currently doing my project with E& R quota credits data,am wondering if am allowed to share the images when am submitting my work to a journal

Hello, I want to generate a cloud mask using a single band from a PlanetScope image of the NCFI monthly mosaics. Which band would you recommend and why? I want to clarify that I'm not interested in combining bands, but rather in working with just one band to create the cloud mask.

What is the importance of band 5 of the yellow nanosatellite Super Dove? Do you have any articles or reports showing the importance of this band for agricultural productivity and vegetation health?

I’m very new to Planet and have been using Planet Explorer to to search for higher resolution imagery than Sentinel 2 data. Planet Scope scenes appear to cover my AOI. In the explorer the Add to Cart button is greyed out. How can I purchase / download these scenes?

I have downloaded several planet PS Scenes, all of them are captured by PSB.SD. According to https://developers.planet.com/docs/data/planetscope/, the image size should be 32.5 * 19.6 km2. However, when I use python to load the tiff file and print the numpy array shape, I get following results. All images have different size, and some are significantly smaller than others, like (547, 13063). Why is it like this? I can provide the file name if it is necessary. Thanks. (8519, 11745) (9018, 13084) (7716, 13091) (9424, 12945) (5661, 4270) (9410, 13041) (8524, 11748) (9439, 12959) (9407, 13028) (8515, 11734) (9419, 13039) (3742, 13094) (547, 13063) (8570, 5336) (9390, 13051) (8578, 8503) (1973, 12990) (7370, 12967) (8577, 6384) (3999, 9560) (5910, 13025) (2293, 13087) (5041, 13054) (9432, 12959) (8583, 7444) (7560, 11803) (7586, 11785) (2799, 11786) (6345, 13019)

I have placed several orders on the Planet website, which are now available for download to my PC.However, I would like to deliver these orders as assets to my GEE project (in Jupyter Notebooks), without going through the download then upload step.Is this possible? Or do I have to list all the individual item ID’s and create a new order? 

Hi Sir,I am facing one issue with GEE App . i am able to run the code in GEE editor and able to load the planet data but as i publish the GEE app it is not showing me the planet data . kindly help me to resolve this .  ee.ImageCollection('projects/planet-nicfi/assets/basemaps/asia' 

I am downloading clipped ortho_analytic_sr image and its udm for the list of geojson geometries. I need to apply several filters (date, clear_percent, and asset) before downloading. I also need to clip the images to reduce my storage cost. However, I cannot find a way to clip the image first and apply the clear_percent filter without downloading the whole image first.By filtering first and clipping results, since my geometry is very small, I am getting clipped images with noise despite selecting a high clear_percent.I need in clipping the image first and select the clipped image with high clear_percent value. Thanks in advance!This is the code I am using,sfilter = data_filter.and_filter([        data_filter.permission_filter(),        data_filter.date_range_filter('acquired', gte=start_date, lte=end_date),        data_filter.geometry_filter(geojson_geometry),        data_filter.asset_filter('ortho_analytic_8b_sr'),        data_filter.range_filter('clear_percent', gte=80)    ])     ids

Hello!I am new here,Do we need to apply any corrections to BGR NIR images obtained from analytic radiance (TOAR) 4-band images to estimate spectral indices like NDVI and NDWI, especially when comparing them with Landsat 8 spectral images?

When you deliver Planet data to Sentinel Hub, you can use the Processing, Statistical, and OGC APIs to visualize and analyze data. To use these APIs, you need to provide an evalscript, or custom script, which defines how the satellite data should be processed.  With the evalscripts, you can perform multi-temporal analysis, data fusion, apply custom symbology, band math, and much more on your Planet data.To help you get started, you can find example custom scripts in our Custom Scripts Repository.  We’ve just added three new custom scripts for PlanetScope for Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Red Edge Index (NDRE), and a cloudless mosaic.What custom scripts would you like to see added to Planet Insights Platform? Drop a comment or post on the Ideas forum!Note: to use these scripts, you need to first order your Planet data to Sentinel Hub, learn more here:  Normalized Difference Chlorophyll Index (NDCI)An index for PlanetScope that uses the Red Edge an

Hello, I need to separate the Planet 8 band surface reflectance composite into separate bands using QGIS 3.36.  Any advice will be highly appreciated. Thanks in advance.Ahmed   

Hello,I am study the topography change of mining site in Vietnam and wonder is there anyone experienced the DSM/DEM generation from multiple PlanetScope images? And comparison between stereo and multiple-images result for DEM/DSM. Although there are several scientific papers, there is lack of guideline to retrieve the high accuracy of DEM/DSM.  Thank you.

Hello, I was wondering if anyone knows any good indices to use in urban landscape for picking out buildingactivity and not vegetation?

Hi folks!From the available documentationhttps://assets.planet.com/docs/Planet_PSScene_Imagery_Product_Spec_letter_screen.pdfI understand that the PlanetScope data is distributed with a CubicConvolution type resampling.I was wondering if a different choice, nearest neighbor resampling, was possible in order to preserve the original value of the image pixels.Thank

Hi! Has anyone insights into the quality of the UDM mask (clouds especially) for snow-covered regions?Below you can see an image (2017-08-01, 45.97N, 7.35E) with some glaciers & snow and the corresponding cloud mask from the UDM layer.You can notice: a lot of snow/ice areas are miss-classified as clouds  there is a line that separates the cloud mask into two sub-regions (the data comes from two different stripes); what's strange is that one sub-region has many more FPs than the other; looks like the algorithm behind is very sensitive to the underlying sensorI’m wondering if it is common that snow & ice are frequently classified as clouds. I’m more familiar with the Sentinel-2 data and there the masks usually manage to capture the difference between snow and clouds.Has anyone experience in this area?Thanks!

Hi, I am new to Planet and am currently going through the tutorials, etc., but I have a quick question on data. I downloaded some imagery yesterday, but it wasn't quite what I was looking for. It came clipped and also complete as an RGB image. I was hoping for something more like you would get in Earth Explorer with a Tile covering the area and individual bands of imagery so I can do ratios and data fusion. Is this possible with Planet imagery? Thanks for any assistance or advice.  

Hi, I'm currently working on estimating chlorophyll-a (chl-a) concentrations in large water bodies using PlanetScope raw data, enhanced with acolite atmospheric correction. However, I've encountered a consistent issue where some scenes exhibit unusual lines or stripes, which I suspect to be noise. This has happened across multiple data sets and seems to disrupt the accuracy of my chl-a concentration readings. Upon reviewing the PlanetScope data documentation (https://developers.planet.com/docs/apis/data/sensors/#the-psbsd-instrument), I understand that each frame consists of eight stripes. I believe these are causing the discrepancies in chl-a concentrations, as values significantly differ from one side of a stripe to the other. This variance is impacting the trustworthiness of my output. Here's an image for reference 

Hello,Recently I have started using Planet Imagery through a CSDA license for the first time in about a year. Last year we were using PlanetScope Ortho Tiles through the same license. Upon recently browsing Planet Explorer I do not see an option for PlanetScope Ortho Tiles anymore. Are these still available to CSDA users? If so how can we go about accessing them? Thanks,Andrew

Dear Community,I would like to find out how to deal with cloud masking using the UDM2 data from the PlanetScope.I already acquired an image that is cloud-free (from metadata cloud is 0.16). Do I need to do any cloud processing or masking? since this cloud percentage is basically insignificant.Again, I used a shape-file to acquire the image of my location since I want the image to span a period of data range; how do I go about the image mosaicing process?Thank you for the help 

Hi everyone, I am trying to decide whether I should perform my own atmospheric corrections to develop SuperDove surface reflectance products (especially over waterbodies).What best practices do you have for atmospheric correction over terrestrial and aquatic environments? Are there any workflows/code repositories that you have found helpful? As MODIS is decommissioned, what options exist to replace the water vapor and ozone inputs currently used in the process for generating surface reflectance products? What level of success have people had using these other inputs?