Access to Landsat 4/5

Hey all,

i want to use the collection Landsat4-5_TM_L2 for a time series analysis.
My process works (just calculating an index for all the time steps and extracting the values for some locations) with the collections L7 and L8-9.
Not sure if I miss something…

Here is my processing graph (not working) for L4-5: L4-5 graph -
Please find the error log here: error processing batch job Traceback (most recent call last): File " -

Here is my processing graph for L8-9 (works!): L8 graph -

Thanks and best regards,

still needs more investigation, but this is the relevant part of the error stack trace:

Sentinel Hub returned an error response: HTTP/1.1 500 Internal Server Error with body:
{“error”:{“status”:500,“reason”:“Internal Server Error”,
“message”:“Illegal request to s3://usgs-landsat/collection02/level-2/standard/tm/1986/196/023/LT05_L2SP_196023_19860226_20200917_02_T1/LT05_L2SP_196023_19860226_20200917_02_T1_SR_B4.TIF.
HTTP Status: ‘403’”,“code”:“RENDERER_EXCEPTION”}}
request: POST with body: { “input”: { “bounds”: { “bbox”: [488820.0, 5831640.0, 496500.0, 5839320.0], “properties”: { “crs”: “” } }, “data”: [ { “type”: “landsat-tm-l2”, “dataFilter”: {“timeRange”:{“from”:“1986-02-26T00:00:00Z”,“to”:“1986-02-27T00:00:00Z”}}, “processing”: {} } ] }, “output”: { “width”: 256, “height”: 256, “responses”: [ { “identifier”: “default”, “format”: { “type”: “image/tiff” } } ] }, “evalscript”: “//VERSION=3\nfunction setup() {\n return {\n input: [{\n "bands": ["B04", "B05"]\n }],\n output: {\n bands: 2,\n sampleType: "FLOAT32",\n }\n };\n}\n\nfunction evaluatePixel(sample) {\n return [sample.B04, sample.B05];\n}” } at

problem seems to be related to

pinging @daniel.thiex to shed some more light on this

Dear @mludwig2,

The problem is actually with USGS and some wrong setting on their side where the data is stored. We reported this problem to them and it usually gets fixed within a matter of weeks.

Looking at your pg I see that you are requesting a 29 time range. Within such a long time range it can always be that one of the hundreds of products available are broken. I would therefore recommend you to split your temporal extend into several small ones and for now exclude the falsely date on 26.02.1986.

In addition you could enable the “soft error” feature in python so that a single failing file doesn’t ruin the whole job (see this related topic for more more details).