Open any S2 MSI2LA product (S2B_MSIL2A_20190403T095029_N0211_R079_T34VFH_20190403T124934)
Open any band and zoom-in to any part of the image
Raster → Subset. Leave all parameters unchanged (it will use extent of displayed product view by default)
Optical → Geometric → S2 Resampling processor. Choose subset from previous step as an input. Do not change any other parameters and just hit Run.
Expected outcome:
A resampled version of S2 data subset.
Actual outcome:
„Invalid S2 source product“ error.
SNAP 7.0.3 with all mask loading enabled in S2 tbx settings.
Product Version = SNAP (Build 201609300101)
Operating System = Linux version 4.18.0-13-generic running on amd64
Java; VM; Vendor = 1.8.0_202; Java HotSpot™ 64-Bit Server VM 25.202-b08; Oracle Corporation
Runtime = Java™ SE Runtime Environment 1.8.0_202-b08
Is there any chance this could be added to a future development list?
Being able to subset before using S2 Resampling is quite useful as it can be a very intensive on computer resources compared to the original multi-sensor Resampling (at 10m I have exceeded 100gb ram). However the S2 Resampling does better for S2 compared to the original hence its addition to SNAP.
yes that’s the one .
Is this software issue ie that order of operation can be added at a later data or a technical issue that you can never perform these operations in this order?
It seems strange that one can’t subset the data before further processing like resampling and atmospheric correction. These are large datasets, where processing time would be improved by not having to use the whole dataset
Ana was most likely referring to the generic Resample operator.
The S2Resampling operator can indeed not resample subsetted products which would be a good feature what you have already said.
There’s also other issue generally in Resample operator, with *.xml, GUI, in case of resample the whole scene, the results is a cropped scene into a small part, it’s also applied for creating biophysics and resulted layer from BandMath, and sun_zenith as well, following is an example, However running same operators one by one leads to a correct output,
Actually not,
While I completed all the S2 preprocesses, and then reaching the Sharpen S3 LST, I fount the problem I already explained above, the goal is creating step by step easy way of SEN_ET, that’s why I didn’t run gpt, from command line,
Yes sorry for that, please find it here, the same thing is applied if an operator subset would be added up for sun_zenith!