General discussion

Dear Terrascope-forum,

I have a few questions regarding the processing of LAI, NDVI, and FAPAR timeseries. I successfully generated timeseries for my own polygon using the notebooks.

However, I was wondering how clouds and shadows are handled in these timeseries. Are pixels with clouds or shadows automatically excluded from the calculations, or do they count as invalid pixels (from which you can select the percentage)? Additionally, does the timeseries for a polygon calculate the average of all pixels for which data is available, or how does this work?

Furthermore, I would like to know if it is possible to generate a 3D dataset for my polygon, where the NDVI values over time are displayed per pixel. Is this something that can be implemented in the notebooks?

Thank you in advance for your help!

Best regards

Emma

I am new to the OpenEO web editor and Copernicus in general, but it seems to be working great for me so far. My question is about the temporal composite function in the process wizard, which also seems to be working great. What does the temporal composite function in the wizard actually do? So for example, if I am doing an NDVI calculation for a one-month temporal coverage, what would the results be for the various temporal function options? I should mention that what I am ultimately interested in doing is finding the average NDVI for a region of about 1,000 acres of wooded terrain every few days over several summer months and over several years. Thanks for any advice you can offer.

Dear all,

I am using the python API. 

When downloading data like esa world cover, does the API always just give the whole tile where the area of interest lies in? (multiple tiles when the area of interest intersects with more tiles?) For my use case, I want to pass a geometry like the adiminstration boundary of a region and only get the data within this boundary or pass a bounding box and only get the data within the bounding box, but not the whole tile. I also opened an issue over on github (https://github.com/VITObelgium/terracatalogueclient/issues/6). Moreover, when the area of interest intersects with multiple tiles, I want to have the data already be processed and not have multiple files which I have to merge. Is this possible? :)  

Hi terrascope community,

     I noticed that VMs  had updated the SO2 SBR product, in the location "/data/MTDA/TERRASCOPE_Sentinel5P/L3_SO2CBR_TM_V2/". But it lacks  documents for explaing the tiff file, including the units and scale. So could you fill this on the docs in terrascope website(https://docs.terrascope.be/). many thanks!

Hello,

I'm creating weekly image mosaics for flanders of S1 GRD images, but for a specific week I always get an error. I tried playing around with the job_options to give more resources, even though other weeks worked fine in the past, but this doesn't seem to help either.

The last job options I tried:

job_options = {          "executor-memory": "5G",         "executor-memoryOverhead": "6G",         "driver-memoryOverhead": "5G"     }

I'm using the https://openeo.vito.be backend.

One of the job id's: j-2408089ae2154a3aa76f8bf12f5b4cf5

Hello, I noticed the layers of the Copernicus land cover product have different reading permissions for different years. Is there a reason for that? I include here, as example, the display of the files list for 2017 and 2018 retrieved from the Terminal in my VM. Note that the permissions differ for these years.  Thank you in advance for your help. ls 2017/20170101/ -lht total 38G -rw-rw----. 1 751 hspf_users 5.2G May 14 19:09 PROBAV_LC100_global_v3.0.1_2017-conso_Tree-CoverFraction-layer_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 107M May 14 19:01 PROBAV_LC100_global_v3.0.1_2017-conso_Snow-CoverFraction-layer_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 5.1G May 14 19:01 PROBAV_LC100_global_v3.0.1_2017-conso_Shrub-CoverFraction-layer_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 593M May 14 18:49 PROBAV_LC100_global_v3.0.1_2017-conso_SeasonalWater-CoverFraction-layer_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 474M May 14 18:48 PROBAV_LC100_global_v3.0.1_2017-conso_PermanentWater-CoverFraction-layer_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 257M May 14 18:47 PROBAV_LC100_global_v3.0.1_2017-conso_MossLichen-CoverFraction-layer_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 6.7G May 14 18:46 PROBAV_LC100_global_v3.0.1_2017-conso_Grass-CoverFraction-layer_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 974M May 14 18:30 PROBAV_LC100_global_v3.0.1_2017-conso_Forest-Type-layer_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 7.1G May 14 18:29 PROBAV_LC100_global_v3.0.1_2017-conso_Discrete-Classification-proba_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 1.6G May 14 18:17 PROBAV_LC100_global_v3.0.1_2017-conso_Discrete-Classification-map_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 3.6G May 14 18:15 PROBAV_LC100_global_v3.0.1_2017-conso_DataDensityIndicator_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 2.6G May 14 18:10 PROBAV_LC100_global_v3.0.1_2017-conso_Crops-CoverFraction-layer_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 175M May 14 18:03 PROBAV_LC100_global_v3.0.1_2017-conso_Change-Confidence-layer_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 541M May 14 18:02 PROBAV_LC100_global_v3.0.1_2017-conso_BuiltUp-CoverFraction-layer_EPSG-4326.tif -rw-rw----. 1 751 hspf_users 2.9G May 14 18:01 PROBAV_LC100_global_v3.0.1_2017-conso_Bare-CoverFraction-layer_EPSG-4326.tif [miguel@miguel lcc_100m_v1_yearly]$ ls 2018/20180101/ -lht total 38G -rw-rw-rw-. 1 754 750 5.1G May 14 21:44 PROBAV_LC100_global_v3.0.1_2018-conso_Shrub-CoverFraction-layer_EPSG-4326.tif -rw-rw-rw-. 1 754 750 5.2G May 14 21:44 PROBAV_LC100_global_v3.0.1_2018-conso_Tree-CoverFraction-layer_EPSG-4326.tif -rw-rw-rw-. 1 754 750 107M May 14 21:39 PROBAV_LC100_global_v3.0.1_2018-conso_Snow-CoverFraction-layer_EPSG-4326.tif -rw-rw-rw-. 1 754 750 578M May 14 21:39 PROBAV_LC100_global_v3.0.1_2018-conso_SeasonalWater-CoverFraction-layer_EPSG-4326.tif -rw-rw-rw-. 1 754 750 472M May 14 21:38 PROBAV_LC100_global_v3.0.1_2018-conso_PermanentWater-CoverFraction-layer_EPSG-4326.tif -rw-rw-rw-. 1 754 750 245M May 14 21:38 PROBAV_LC100_global_v3.0.1_2018-conso_MossLichen-CoverFraction-layer_EPSG-4326.tif -rw-rw-rw-. 1 754 750 6.7G May 14 21:38 PROBAV_LC100_global_v3.0.1_2018-conso_Grass-CoverFraction-layer_EPSG-4326.tif -rw-rw-rw-. 1 754 750 7.2G May 14 21:38 PROBAV_LC100_global_v3.0.1_2018-conso_Discrete-Classification-proba_EPSG-4326.tif -rw-rw-rw-. 1 754 750 986M May 14 21:32 PROBAV_LC100_global_v3.0.1_2018-conso_Forest-Type-layer_EPSG-4326.tif -rw-rw-rw-. 1 754 750 3.6G May 14 21:30 PROBAV_LC100_global_v3.0.1_2018-conso_DataDensityIndicator_EPSG-4326.tif -rw-rw-rw-. 1 754 750 1.6G May 14 21:30 PROBAV_LC100_global_v3.0.1_2018-conso_Discrete-Classification-map_EPSG-4326.tif -rw-rw-rw-. 1 754 750 2.9G May 14 21:27 PROBAV_LC100_global_v3.0.1_2018-conso_Bare-CoverFraction-layer_EPSG-4326.tif -rw-rw-rw-. 1 754 750 2.6G May 14 21:27 PROBAV_LC100_global_v3.0.1_2018-conso_Crops-CoverFraction-layer_EPSG-4326.tif -rw-rw-rw-. 1 754 750 169M May 14 21:25 PROBAV_LC100_global_v3.0.1_2018-conso_Change-Confidence-layer_EPSG-4326.tif -rw-rw-rw-. 1 754 750 551M May 14 21:25 PROBAV_LC100_global_v3.0.1_2018-conso_BuiltUp-CoverFraction-layer_EPSG-4326.tif  

When I use this code from the examples

# connect with the backend

eoconn = openeo.connect( "openeo.vito.be" ).authenticate_oidc()

How do I login with my terrascope account?

Hello,

I use the API Query Global Land product catalog to download hourly LST data; but every few moments I get a connection error  (ConnectionError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))) that interrupts my download, I don't know what is the problem. Please can someone help me fix this problem. Thank you for your help 

Hi everyone,

I've used terracatalogueclient so far and have never experienced any issues. I've used it in my dockerized app and my Dockerfile looks like this:

RUN pip install --extra-index-url https://artifactory.vgt.vito.be/api/pypi/python-packages/simple terracatalogueclient

as specified in the official Github documentation.

But for some reason this is no more working. Does anyone know why?

Thank you. 

Hello everyone,

I'm using Terrascope's jupyterlab in order to analyze the NDVI, NDBI and NDWI of a certain area over a time range. While analyzing the selected files, I noticed that while I selected the same AOI everywhere, I don't get the same image shape every time and the area observed in my image is not the same. To make my problem clearer, I have attached my code here: https://we.tl/t-jtpM5Is9W8

In the last cell, the different shapes of the considered files are printed. Do I have to isolate my AOI in another way, because I thought that using mask.msk only the aoi is considered?