Discourse

Hello, after frying my machine I decided to try to run the Slovenia-cover-map notebook on Google Colab (eo-learn-examples/SI_LULC_pipeline.ipynb at main · sentinel-hub/eo-learn-examples · GitHub) but there is a problem in importing some packages. I did !pip install jpy==0.7 !pip install SentinelHub !pip install eo-learn-core !pip install eo-learn-coregistration !pip install eo-learn-features !pip install eo-learn-geometry !pip install eo-learn-io !pip install eo-learn-mask !pip install eo-learn-ml-tools !pip install eo-learn-visualization !pip install “opencv-python-headless<4.3” before the imports but i got this # Imports from eo-learn and sentinelhub-py from eolearn.core import EOTask, EOPatch, EOWorkflow, FeatureType, OverwritePermission, \ LoadTask, SaveTask, EOExecutor, ExtractBandsTask, MergeFeatureTask from eolearn.io import SentinelHubInputTask, VectorImportTask, ExportToTiffTask from eolearn.mask import AddValidDataMaskTask from eolearn.geometry import VectorToRasterTas

Hello, i’m interested in Landsat 8 level 2 images. I’m a bit confused regard the available bands as in USGS it mentioned to have 11 bands, while in sentinel hub configuration utility I see B1-B7 and B10, and it says that the thermal is based on B10. In addition to that I saw here some post regard extra band that relates to clouds but it doesn’t not appear in the bands in the configuration utility. Are these bands available? and if yes how can I acess them and where can I get information regard all the available bands? 🙂

I am currently, separately, using the S2 global mosaic hub, and accessing S2 imagery through the Evalscript V3 API. In some cases, I need to use my own compositing algorithm to develop monthly mosaics, so I use Evalscript. In some cases, the S2 global mosaic algorithm suites my needs more than perfectly! It works so well in cloudy tropical regions. I’d love to be able to access the S2 global mosaic outputs through the Evalscript API. I see that the script for generating the mosaic is available on the custom scripts repository here (https://custom-scripts.sentinel-hub.com/sentinel-2/s2gm) but with a note that it needs V2 to run. I’ve been trying to convert the script to V3 for awhile, but I can’t seem to get it to work. If I run the V2 script through V3, I either get “Output default requested but missing from function setup()” or “EvaluatePixel must return an array” as errors. I’ve been reading through the V3 documentation (https://docs.sentinel-hub.com/api/latest/evalscript/v3/) and c

I am trying to download Sentinel-2 L2A imagery from 6/19/2022 over the Chesapeake Bay at the 10m scale, including all 12 bands. When I put in this information, the download button is greyed out (see attached screenshot). I tried limited my extent to just Dorchester county, but the issue persisted. It seems that the imagery cannot be downloaded at the 10m resolution for this extent – is that correct? If so, how would you suggest access the data for my entire study area?

Hi everyone, I’m just launching into the development of a pipeline for Sentinel image processing, and I was wondering what this community considers best practice for storing/accessing geospatial data in DynamoDB. Most of my data will be a variety of polygons, multipolys, linestrings, and points… but I’m having difficulty seeing how DynamoDB can handle that efficiently for searches. (Also, I want to be able to quickly query by datetime as well, just to complicate things) Do any of you have any projects you’ve implemented thusly? Any recommendations or warnings? I intuitively understand relational databases better, and also that PostgreSQL is available via RDS, but nonetheless I’m curious what you do in the case where you aren’t using that. I’d love to hear your thoughts–I’m at a total loss right now!

Hello, I tried to order Maxar Satellite imagery but i keep getting the following error which I cannot solve: Request failed with status code 400 COMMON_BAD_PAYLOAD {“authenticated”:true,“error”:“Invalid JSON.”,“message”:“There was a problem with your request. Please contact ‘ordering@euspaceimaging.com’.”}

Hi, I’m having trouble in using one of the scripts on the custom-scripts repository, when I try to adapt it to the statistical api call. I’m trying it out in the requests builder, and it works well while using the process api. I just can’ really pinpoint how exactly I should adapt the script in order to fit the statistical api request. The script in use is [Vegetation Productivity Indicator | Sentinel-Hub custom scripts](https://Vegetation Productivity Indicator) My main doubt is on how to edit the evaluatePixel return, and also the time range, which in the Process API is part of the date filter, but in statistical api is part of aggregation (together with aggregation interval). Thank you in advance!

I am trying to download the following (S1 GRD AWS Gamma0 linear orthorectified VH):apps.sentinel-hub.com Sentinel-hub EO-Browser3 Sentinel-2 L2A imagery taken on August 18, 2018 With the following settings However, the file downloaded is only 571. KB (should be around 10-20MB), RGB 0-255 instead of single band 0-65535. Do you know what could be wrong? Also, I wanted to make sure that the image downloaded is Gamma0 orthorectified ? (see https://docs.sentinel-hub.com/api/latest/#/data/Sentinel-1-GRD) If so, where can I find the documentation of the processing steps applied to the raw S1 imagery available in Scihub-copernicus please?

Hello, Does anyone know what the geometric accuracy of Sentinel 2 level 2a products are? All I can find is information level 1 products which say the accuracy is 20 m without ground control points, and 12.5 with ground control points. Thanks

Hi, I am trying to download S2 images for a large area. I’ve splitted my boundig box in smaller patches and now I can download each one manually with the SentinelHubRequest object. I want to automate the process, but I cannot find a way to get the path where the tiff is saved from the get_data() or save_data() methods. Is there a way to this ? Thanks.

Hi, my code was working without problem downloading all bands in my old computer, now that i switched to a new one i get this error whenever i try to download all bands: DeflateError: libdeflate_zlib_decompress returned LIBDEFLATE_INSUFFICIENT_SPACE There is no issue when i try to download a regular natural true color image Versions: Python: 3.9.18 Sentinelhub: 3.9.1 Code example: all_bands_script = ‘return [B01,B02,B03,B04,B05,B06,B07,B08,B8A,B09,B10,B11,B12]’ config = SHConfig() config.instance_id = idcuarteles[c] wms_request = WmsRequest(layer='NATURAL-COLOR-TRUE-COLOR', custom_url_params={CustomUrlParam.EVALSCRIPT: all_bands_script}, bbox=BBox(bbox=mybbox, crs=CRS.WGS84), time=FECHADESCARGA, width=1000, height=856, maxcc=0.1, image

search_iterator in the code below finds two sentinel2 files as SAFE format (S2A_MSIL2A_20231114T110311_N0509_R094_T31UDQ_20231114T143858.SAFE and S2A_MSIL2A_20231114T110311_N0509_R094_T31UDR_20231114T143858.SAFE): import os import datetime import numpy as np import matplotlib.pyplot as plt import pandas as pd import requests import getpass from sentinelhub import ( SHConfig, DataCollection, SentinelHubCatalog, SentinelHubRequest, BBox, bbox_to_dimensions, CRS, MimeType, Geometry, MosaickingOrder, ) config = SHConfig("myprofile") aoi_coords_wgs84 = [2, 49, 2.5, 49.7] resolution = 10 aoi_bbox = BBox(bbox=aoi_coords_wgs84, crs=CRS.WGS84) aoi_size = bbox_to_dimensions(aoi_bbox, resolution=resolution) print(f"Image shape at {resolution} m resolution: {aoi_size} pixels") catalog = SentinelHubCatalog(config=config) time_interval = "2023-11-14", "2023-11-14" search_iterator = catalog.search( DataCollection.SENTINEL2_L2A, bbox=aoi_bbox, ti

Hi,I’m trying to run the script provided at https://github.com/sentinel-hub/eo-learn/blob/master/examples/land-cover-map/SI_LULC_pipeline.ipynbI am running Jupyter notebook and I am trying to import Eolearn after installing. I get this error:ModuleNotFoundError Traceback (most recent call last)in3132 # Imports from eo-learn and sentinelhub-py—> 33 from eolearn.core import EOTask, EOPatch, LinearWorkflow, FeatureType, OverwritePermission,34 LoadFromDisk, SaveToDisk, EOExecutor35 from eolearn.io import S2L1CWCSInput, ExportToTiffModuleNotFoundError: No module named ‘eolearn’Conda List# Name Version Build Channelappnope 0.1.0 py36hf537a9a_0attrs 19.1.0 py_0 conda-forgebackcall 0.1.0 py36_0blas 1.0 openblasbleach 3.1.0 py36_0boost-cpp 1.70.0 h75728bb_2 conda-forgebzip2 1.0.8 h01d97ff_0 conda-forgeca-certificates 2019.6.16 hecc5488_0 conda-forgecairo 1.16.0 he1c11cd_1002 conda-forgecertifi 2019.6.16 py36_1 conda-forgecfitsio 3.470 h389770f_2 conda-fo

Hi, I’m a bit confused about how to order PlanetScope data exploiting sentinel hub services. I need high resolution data for several AOI, I would like to use PlanetScope data through Subscription API. Currently I have a subscription for Sentinel Hub Exploration Processing Plan. The required data can be retrived withhttps://services.sentinel-hub.com/api/v1/process specifying geometry, timeRange, evalscript, collection_id etc etc in the json data and the SentinelHub token in the header. Collection_id can be obtained withhttps://services.sentinel-hub.com/api/v1/dataimport/orders and thenhttps://services.sentinel-hub.com/api/v1/dataimport/orders/order_id In the first url, it is required the planetApiKey. how i can obtain this value?

I’m trying to understand exactly the meaning of cloud, clear and cloudConfidence. from the docs: cloud number 0 for cloud confidence is not high, 1 for high confidence cloud clear number 0 if cloud or dilated cloud, or else 1 cloudConfidence number here is my logic:cloud is if there is a cloud in that location. but instead of 0 (no cloud) and 1 (yes cloud), it has values in between to account for "calculation/measurement anomalies " etc. I would expect to have clear as the opposite. but there is no confidence in the description. so it’s just a binary? or the doc is misleading? I have not downloaded and examined the data yet, I’m trying to understand it first. observation might be misleading 🙂 . if cloud is the confidence of a cloud, what is cloudConfidence and why do I need both values? the existence of cloudConfidence makes me doubt my reasoning for cloud. I can understand why someone will want clear, especially if it includes dilated cloud. does cloudconfidence include all

My bbox list is empty. I am unable to knwo why is it so. Create the splitter to obtain a list of bboxes bbox_splitter = UtmZoneSplitter([country_shape], country.crs, 5000) bbox_list = np.array(bbox_splitter.get_bbox_list()) info_list = np.array(bbox_splitter.get_info_list()) Prepare info of selected EOPatches geometry = [Polygon(bbox.get_polygon()) for bbox in bbox_list] idxs = [info[‘index’] for info in info_list] idxs_x = [info[‘index_x’] for info in info_list] idxs_y = [info[‘index_y’] for info in info_list] gdf = gpd.GeoDataFrame({‘index’: idxs, ‘index_x’: idxs_x, ‘index_y’: idxs_y}, crs=country.crs, geometry=geometry) select a 5x5 area (id of center patch) ID = 616 Obtain surrounding 5x5 patches patchIDs = [] for idx, [bbox, info] in enumerate(zip(bbox_list, info_list)): if (abs(info[‘index_x’] - info_list[ID][‘index_x’]) <= 2 and abs(info[‘index_y’] - info_list[ID][‘index_y’]) <= 2): patchIDs.append(idx) Check if final size is 5x5 if len(patchIDs) != 5*5: print(‘Warning! U

Good day, I have a collection of planetscope data/tiles sitting in Sentinelhub (i.e. the aws server you provide). What would be the easiest way for me to download all of the tiles on my local drive? Do you guys offer an option to download these as zarr or other array-based formats? FYI I am trying to run a NDVI time-series analysis and am having trouble doing it with your statistical API (via your request builder). Hence why I’m opting to just do this in python myself…feel free to suggest other options… Many thanks

Hi, It’s great to see HLS being available on SentinelHub! I’m interested in tracking NDVI for an AOI and I was wondering how comparable the results are with NDVI from Sentinel-2 L2A. The inputs to HLS are Sentinel-2 L1C and Landsat 8/9 (both of top-of-atmosphere reflectance), whereas Sentinel-2 L2A is of bottom-of-atmosphere reflectance. According to HLS documentation, atmospheric correlation is applied to both inputs: A series of radiometric and geometric corrections are applied to convert data to surface reflectance, adjust for BRDF differences, and adjust for spectral bandpass differences. “convert to surface reflectance” → Does it mean that HLS is in fact of bottom-of-atmosphere reflectance? Apologies in advance if this question is more suitable to HLS authors (USGS) but I thought I could get a quicker response here 🙂

Hello, currently I want to create NDVI and NDWI pictures with the python API for sentinelhub. My evalscript for NDVI looks like that: And for NDWI: It works fine for NDVI. In the case of NDWI, I only get a black picture. So, I want to understand how the NDWI picture is created. How the transformation of the values from -1 to 1 ( what we get, when we calculate let ndwi, let ndvi) to 0 - 255 works. Because when i add plus one: It works. I get an image, which looks good. But i don’t know if it is correct… Thanks for your help in advance!

Hi, I would like to use Sentinel-2 L1c band 8 data for research purpose. I work with pixel timeseries, therefore I have serious problems with the co-registration of Sentinel-2, since I found an error of a few pixels in many images. Is there any tutorial you can recommend to me to solve the issue? Thanks in advance

Hello, I wanted to ask if there is any option or plan to use the orthorectication an option to use SRTM (or ASTER DEM for areas greater than 60 degrees latitude as SRTM is not available)? It seems like using those files might give better results. Best, Reut

Hello, I have download sentinel1 image using sentinelhub: bbox_coords_wgs84=[-47.949734553694725,-8.746513680257486,-47.93459851294756,-8.717573061584929] #Time Interval for images time_interval = ('2020-12-01', '2021-01-10') evalscript = """ //VERSION=3 return [VV,VH, dataMask] """ time_interval = time_interval request = SentinelHubRequest( data_folder='sen1_test1', evalscript=evalscript, input_data=[ SentinelHubRequest.input_data( data_collection=DataCollection.SENTINEL1_IW, time_interval=time_interval, other_args = {"dataFilter":{"demInstance":"COPERNICUS_30"},"processing":{"orthorectify":True,"backCoeff":"SIGMA0_ELLIPSOID"}} ) ], responses=[ SentinelHubRequest.output_response('default', MimeType.TIFF) ], bbox=bbox, config=config ) image = request.get_data(save_data=True) plot_image(s1_data[0], factor=1) when I print len(s1_data) I get 6, so I believe it got two im

I am searching for proper code examples that can exactly perform the functionality mentioned in the following link:area-monitoring.sinergise.com Field delineation Field delineation The idea would be to take sentinel 2 image as an input and to get as an output either a mask for the boundaries or polygons. Thanks in advance. Cheers, SHERIF

Hello, I have a massive list (~1million) of locations at given date and need to find matchup raw spectral bands of MODIS, MERIS, sentinel-3 OLCI sensors at the given date/location if available. some example locations below: Name Lat Lon DateTime MB05SBS311 50.63333 -96.95000 2016-06-02T14:00:00 MB05SBS126 50.51692 -96.83340 2016-06-03T14:00:00 MB05SBS317 50.44290 -96.70935 2016-06-03T14:00:00 MB05SBS347 50.63838 -96.98022 2016-06-03T14:00:00 … I can do this task by downloading the imagery that covers the location/date, and then use ESA SNAP software, however, it is impossible with such a massive dataset and the download will take months or years, also I don’t have space to store that much image. I tried the CREODIAS platform, but it is very slow to access its eodata currently and don’t have access to MODIS data yet. After reading around I feel there maybe luck with SentinelHub, I probably need to use the Statistical API, but I did not find some example to start with. I am familiar

Hello, I’m getting {“code”:404,“description”:“Collection not found.”} returned when using the search api for landsat-tm-l1, directly hitting the rest endpoint or using the python module, e.g. curl -X GET -G https://services.sentinel-hub.com/api/v1/catalog/1.0.0/search \ --header 'Authorization: Bearer xxx' \ --data 'bbox=13,45,14,46' \ --data 'limit=5' \ --data 'collections=landsat-tm-l1' \ --data 'datetime=1990-12-10T00:00:00Z/2019-12-11T00:00:00Z' catalog.search( DataCollection.LANDSAT_TM_L1, bbox=bbox, time=(start, end), fields={"include": ["id", "properties.datetime", "properties.eo:cloud_cover"], "exclude": []} ) Looks like none of these are working: DataCollection.LANDSAT_MSS_L1 DataCollection.LANDSAT_TM_L1 DataCollection.LANDSAT_TM_L2 DataCollection.LANDSAT_ETM_L1 DataCollection.LANDSAT_ETM_L2 DataCollection.LANDSAT_OT_L1 DataCollection.LANDSAT_OT_L2