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Hi team, We have just started looking at the Statistics API and wanted to check if anyone has done something similar to help us jumpstart the learning. We have polygons of parcels and wanted to identify the best way to get the NDVI statistics for a year period so we can check trends. I did have a look at the EO Browser but i can see C0, C1, C2 returned and wasnt sure if i am doing this right. Any pointers would be much appreciated. Sincerely Darko

{“error”:{“status”:401,“reason”:“Unauthorized”,“message”:“You are not authorized - please provide header [Authorization: Bearer ] with your request.”,“code”:“COMMON_UNAUTHORIZED”}} This is an error that I get when I am trying to display an image preview in python. I use a code from here:docs.sentinel-hub.com Examples of Third Party Data Import Follow these examples to import commercial third-party data into Sentinel Hub. and I don’t see any place where I can put Bearer (I don’t have it. Only ID and key). I have an active package.

Hello everyone, I am a Network of Resources member. Yesterday and today I ordered some Pleiades satellite data and the orders were successful (checked already in the sentinel-hub “request builder” and on the “EO browser”). Afterwards I started downloading the data via the script (attached underneath) and everything worked well. One hour ago, using the same script (changing only the polygon coordinates) I started to receive a “400 Bad Request Response” (screenshot attached). Kindly I would like to know if there is an issue on the sentinel-hub side or if I have some limitations for downloading data. Thank you in advance. Kind regards

After generating images for SENTINEL1_IW , the images generated has zigzag at the end of each pixel (Edges are not smoothened). Could anyone provide me a solution to the problem . Related image for the current problem is been attached Thanks in advance

Introduction: Hello everyone, I’m a newcomer to working with satellite imagery and SentinelHub. I may need a straightforward explanation or solution, so I appreciate your patience. Despite searching extensively on Google and the forum, I haven’t found a solution to my problem. Context: I successfully followed this Jupyter notebook for migrating from the old API to the new one. However, when attempting this Jupyter notebook for large areas, I encountered an HTTPError 401 Client Error: “You are not authorized,” despite having created the necessary instance ID token. Problem Code Blocks: The code blocks causing the issue are found in cellblocks 15 & 16 and are look as follows in my slighlty modified version: from sentinelhub import SHConfig from decouple import config as credentials config = SHConfig() config.instance_id = credentials("POETRY_SENTINEL_INSTANCE_ID") config.sh_client_id = credentials("POETRY_SENTINEL_CLIENT_ID") config.sh_client_secret = credentials("POETRY_SENTINEL_C

Hey, This might be a bit of a silly question, but I’m querying Sentinel Hub for Sentinel-1 data over a specific AOI. Why is it that for Sentinel-1 we have to use a tuple (something like 20,20) for the resolution, and also how does the CRS affect the output? For instance, for the same AOI, requesting data with EPSG:4326 yields only one pixel, where as using the EPSG:3035 gives me about 17x14 pixels. Also in general, where do I find which CRS to use for a specific satellite? Additionally, if I understand correctly with Sentinel 1, this is also dependent on where the AOI is in the world? Correct? I guess in the EU Sentinel-1 offers higher resolution, where as in the rest of the world it offers lesser resolution? Would appreciate some input about how to wrap my head around the CRS when it comes to Sentinel Hub.

I am trying to do a moisture forecast using sentinel-2 for an area of interest and I’d like to get all observations from a certain data range. Here is my current approach: I define mosaicking as TILE in my evalscript for each week, I get the image with max cloud cover less than 0.4 I get several no data images which are dark. I know this is quite expected, but is there alternate way to get all observations for the same AOI and flag the cloudy ones. This is quite important for monitoring a particular FOI. I took a look at the area monitoring article, but the method to retrieve images are not clear to me. It would be great if you can offer my any insights on this. If I am correct, the use a similar approach like mine as well.

I tried to increase the time window to up to 8 months and set least cloud cover to 15 percent, but I am getting images that are too bright or completely dark, although the area is just 74.5 km2 which is large enough to not be completely dominated by clouds I assume. Is there a work around to get decent images for time series analysis of this specific area. example image (11 months least CC mosaicking) example image (3 months least CC mosaicking) The code used: /VERSION=3 function setup() { return { input: [{ bands: ["B01", "B02", "B03", "B04", "B08", "B05", "B06", "B07", "B8A", "B11", "B12"], units: "DN" }], output: { bands: 4, sampleType: "INT16" } }; } function evaluatePixel(sample) { return [sample.B01, sample.B02, sample.B03, sample.B04, sample.B05, sampl

I have a relatively long script in which I calculate multiple Sentinel-2 indices, and then I get an error. Is it possible to get more information about the error, most importantly - which line caused the error? Otherwise, I need to kind of guess via trial and error, which takes a long time. Just to be clear - The question here is not the specific error in this script (which I found and fixed), rather more generally, if it is possible to get the line number or some specific indication of where is the error in long scripts. This is the script: evalscript =‘’’ //VERSION=3 function setup() { return { input: [“B01”,“B02”,“B03”,“B04”,“B05”,“B06”,“B07”,“B08”,“B8A”,“B09”,“B11”,“B12”,“SCL”,“dataMask”], output: [ {id:“coastal_blue”,bands:1,sampleType:“UINT16”}, {id:“blue”,bands:1,sampleType:“UINT16”}, {id:“green”,bands:1,sampleType:“UINT16”}, {id:“red”,bands:1,sampleType:“UINT16”}, {id:“rededge1”,bands:1,sampleType:“UINT16”}, {id:“rededge2”,bands:1,sampleType:“UINT16”}, {id:“rededge3”,bands:1,s

We want to programatically download the PlanetScope data for either specific dates or date range. So, is there any provision currently to download a tile which has the maximum overlapping area with our shape/polygon on same date. For example i have attached the screenshot of two Planet Images of the same day. We would want to download the second one rather than first one.

We are having problem with inconsistency with downloading scene tiles. It seems that imagery is available on ESA site but not on AWS very frequently. The current issue is scene tiles 17SKR and surrounding scenes on 6/6/18 and 6/11/18. They do show up in quicklooks but cannot access through our script. Lanny

Hello, Assume I have an image whcih its size is (10000,10000) and its boundin box is: geometry = [ [ [ 37.831375, 37.319936 ], [ 39.855892, 37.319936 ], [ 39.855892, 36.648589 ], [ 37.831375, 36.648589 ], [ 37.831375, 37.319936 ] ] ] What can I do if I want to find this polygon coordinates in the image ? [[[39.496605773761, 37.323714883356], [39.496514330921, 37.323732909052], [39.495225305793, 37.319562136161], [39.500846543296, 37.31845390628], [39.502573247157, 37.321674080317], [39.496605773761, 37.323714883356]]]

Hello! I have this from my teacher (I’m a student). I need to display the image in RGB format, using the channels 4, 3, 2. I did that, and I managed to view the images (I tried with 2 for starters, but I will visualize all 12 of them). The code I used is: import os import cv2 import numpy as np import matplotlib.pyplot as plt import rasterio from skimage import exposure, filters def load_rgb_image(folder_path, red_band, green_band, blue_band): # Lista de nume de fișiere TIF din folder file_names = os.listdir(folder_path) # Citirea datelor din benzile specifice bands_data = [] for band in [red_band, green_band, blue_band]: band_path = os.path.join(folder_path, band + '.tif') with rasterio.open(band_path) as src: band_data = src.read(1) bands_data.append(band_data) # Crearea imaginii RGB rgb_image = np.dstack(bands_data) return rgb_image Setare căi către foldere folder_path_1 = “/content/drive/MyDrive/sentinel/S2A_MSIL2A_20170426T091031_N0204_R050_T35TMK_20170

In this S3 bucket sentinel-s2-l2a/tiles/36/N/UH/2021/9/26 I see two directories (0/ and 1/) which according to the file naming documentation refer to two images for the same date. How is this possible? I’ve plotted the images, and they seem to contain the same spatial patterns, but with different value ranges. Were they processed differently?

Greetings, I am trying to implement in my Python Script the s2cloudless. I am using python 3.6.13 so I installed s2cloudless 1.5.0. I have some questions: Is it necessary to check for clouds after the initial scene classification? Why I cannot import the specific s2cloudless version? It gives an error about future annotations meaning it needs newer python version when in the documentation it says that 1.5.0 needs python >= 3.6. Thank you in advance.

Hello, I can’t use the Qgis plugin with a proxy. I’ve tried several proxy configurations: default, http… Has anyone solved this problem? 2023-12-11T17:47:54 WARNING Traceback (most recent call last): File “C:\PROGRA~1\QGIS32~1.0\apps\Python39\lib\site-packages\urllib3\connection.py”, line 159, in _new_conn conn = connection.create_connection( File “C:\PROGRA~1\QGIS32~1.0\apps\Python39\lib\site-packages\urllib3\util\connection.py”, line 84, in create_connection raise err File “C:\PROGRA~1\QGIS32~1.0\apps\Python39\lib\site-packages\urllib3\util\connection.py”, line 74, in create_connection sock.connect(sa) ConnectionRefusedError: [WinError 10061] Aucune connexion n’a pu être établie car l’ordinateur cible l’a expressément refusée During handling of the above exception, another exception occurred: Traceback (most recent call last): File "C:\PROGRA~1\QGIS32~1.0\apps\Python39\lib\site-packages\urllib3\connectionpool.py", line 670, in urlopen

I’m trying to retrieve sentinel1 images using evalscript. I have followed the instruction as appear here but I couldn’t understand the reason why I keep getting the 400 client error (bad request). This is my evalscript: evalscript = """ function setup() { return { input: {bands: ["VV","VH"]} output:{bands: 2, sampleType:"FLOAT32"}} } function evaluatePixel(sample) { return [10 * Math.log((sample.VV)+0.0001) / Math.LN10, 10 * Math.log((sample.VH)+0.0001) / Math.LN10] } """ and this is how I have tried to use it, when dates is list of dates strings (e.g [‘2020-07-01’,‘2020-07-12’]… or d in dates: request = SentinelHubRequest( evalscript=evalscript, input_data=[ SentinelHubRequest.input_data( data_collection=DataCollection.SENTINEL1_IW_DES, time_interval=(d,d), other_args = other_args = {"dataFilter":{"resolution":"HIGH","acquisitionMode":"IW"},"processin

Hello, that might be very simple question 🙂 what is the parameter for cloud coverage precentage in sentinel hub request (evalscript)? I saw examples in the documentation for how to rank it by cloud cover but not how to filter it by the cloud precentage from the evalutate script. I’m a bit confused with all the different api, if you could even tell me where I can find all the possibles parameters I can put in the evalscript it would be great. evalscript_sentinel =""" //VERSION=3 function setup() { return { input: ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07','B08','B8A','B09','B11','B12'], output: { bands: 12, sampleType: "FLOAT32" } } } function evaluatePixel(sample) { return [ sample.B01,sample.B02,sample.B03,sample.B04,sample.B05,sample.B06,sample.B07,sample.B08,sample.B08A,sample.B09,sample.B11,sample.B12]; } """ ...... sen_request = SentinelHubRequest( evalscript=evalscript_sentinel, input_data=[ SentinelHubReque

Hello, We are working on S2 L2A images collected via eo-learn SentinelHubInputTask function. This function is described in the documentation as following : “Process API input task that loads 16bit integer data and converts it to a 32bit float feature”. As we are not passing any evalscript argument that may be overriding the default generated one, according to this article the DN to reflectance conversion should be correctly done. However, we are observing distributions of band reflectance values that are significantly below 0 (down to -.5 in some cases). This doesn’t seem to be an isolated problem as this happens consistently on all images of a time-series. However this doesn’t affect all the bands equally. Here is the frequency histogram of reflectances values coming from all the pixels of a time-series. We are observing agricultural lands, on a 2km x 2km area. Band number is expressed as band index of eo-patch ([‘B01’, ‘B02’, ‘B03’, ‘B04’, ‘B05’, ‘B06’, ‘B07’, ‘B08’,‘B8A’, ‘B09’, ‘B1

I do not see the scroll bar that enables downward movement through the list below the Discovery and Visualize Tabs. I see it in Sentinel Hub YouTube videos. I am unable to scroll down the list and view the image thumbprints below those displayed at the top of the static menu. Also unable to access Visualize info below the bottom edge of my Browser window. I am using the free Trail Account to see what Sentinel Hub offers.Screen capture of EO Browser left menu

We need to access the orthorectified/surface reflectance processing level. In addition, we want to avoid any tasks that may lead to data resampling, aside from atmospheric correction and orthorectification. Therefore, if possible, we would like to access data in the same structure of tilling and resolutions as their original data source (Landsat8-USGS & Copernicus . Datasets/processing level/ bands/spatial resolution: DATASET BANDS RESOLUTION (m) landsat-ot-l2 B02, B03, B04, B05, B06, B07 30m sentinel-2-l2a B02, B03, B04, B05, B06, B07, B08, B08A, B11, B12 10, 20m Time range: · From = 2017-01-01 · To = 2021-10-08 Temporal Resolution: · We would like to access all the scenes within the predetermined time range, except those with 100% cloud coverage Cloud coverage filter: · Landsat 8 = ~ 80% · Sentinel2 = ~ 60% AOI - Area of interest: · Sentinel2 tile: 23KKQ · Landsat 8 tile: 220076 We are suggesting the AOI based on the original data source tilling, to avoid pixel resampling due mo

Hi, would it be possible to receive, when mosaicking an image within a certain BBox, per pixel metadata about the tile (date of aquisition, tile code) that is underlying that pixel? Looking for a solution using either the Sentinel-hub package (SentinelHubRequest) or eo-learn package. Thanks!

Hi, I am trying to filter my scenes according to month and year. like this return scenes.filter(function(scene) { return scene.date.getFullYear()>=2017 && scene.date.getMonth()==10; }); but when I try to filter scenes with month in evaluatePixel function I get an error Cannot read property ‘date’ of undefined let initMonth = scenes[0].date.getMonth(); but it is working smoothly when only use year filtering. I just want to get specific scenes how can I do that ? Thanks in advance,

Hi, I am new to this. I am trying to access planet scope data using planet Api key but it shows forbidden error. Guide me how to access planet scope data and add it in sentinel hub.

Hi, I used a list of timestamps to initialise an eopatch. But, when i used the SentinelHubInputTask() function to download bands data for those timestamps, i was not able to download it. I was getting 2D arrays where each value was 0 for all the bands. Essentially, no data is getting downloaded. Thanks, Sidharrth