ankurdesai
commented
7 years ago SAmple query to get MODIS GPP at a flux tower site (US-PFa): {"error": null, "svc_version": "1.7.1", "status": "processing", "web_version": "1.7.1", "task_id": "faf4c1d7-ab52-4db6-a8e2-0d039ace70b2", "params": {"dates": [{"endDate": "08-01-2016", "startDate": "06-01-2016", "yearRange": [2000, 2017]}], "layers": [{"layer": "Gpp_1km", "product": "MOD17A2.005"}], "coordinates": [{"latitude": "45.946", "id": "US-PFa", "longitude": "-90.272", "category": "DBF"}]}, "created": "2017-03-30T13:51:49.223000", "task_type": "sample", "task_name": "Park", "attempts": 1, "user_id": "desai@aos.wisc.edu", "updated": "2017-03-30T13:51:49.377000", "retry_at": null}
Sample CSV output (first 2 lines): Category,ID,Latitude,Longitude,Date,MODIS_Tile,MOD17A2_005_Line_Y_1km,MOD17A2_005_Sample_X_1km,MOD17A2_005_Gpp_1km,MOD17A2_005_Psn_QC_1km,MOD17A2_005_Psn_QC_1km_bitmask,MOD17A2_005_Psn_QC_1km_MODLAND,MOD17A2_005_Psn_QC_1km_MODLAND_Description,MOD17A2_005_Psn_QC_1km_Sensor,MOD17A2_005_Psn_QC_1km_Sensor_Description,MOD17A2_005_Psn_QC_1km_DeadDetector,MOD17A2_005_Psn_QC_1km_DeadDetector_Description,MOD17A2_005_Psn_QC_1km_CloudState,MOD17A2_005_Psn_QC_1km_CloudState_Description,MOD17A2_005_Psn_QC_1km_SCF_QC,MOD17A2_005_Psn_QC_1km_SCF_QC_Description DBF,US-PFa,45.946,-90.272,2016-06-01,h11v04,486.0,867.0,0.029199998825788498,8.0,0b00001000,0b0,Good quality,0b0,Terra,0b0,"Detectors apparently fine for up to 50% of channels 1,2",0b01,Significant clouds WERE present,0b000,Very best possible DBF,US-PFa,
mdietze
github-actions[bot]
AppEEARS is a JSON-based (and web-based) query API for a range of USGS/NASA data on the LP DAAC that allows for easy (and fast) time series generation at point or region https://lpdaac.usgs.gov/tools/data_access/appeears
Description
Right now, datasets are limited (MODIS Aqua/Terra, WELD, SRTM topography), but they have plans for more and the API looks like a nice candidate for quickly extracting time series from these products (and hopefully things like Landsat) for benchmarking, initial condition workflows.
This is not high priority, but putting it here to remember the poster at NACP17. Might be nice to write a small R package for interfacing with this, should we find more useful data. Might be a neat way to automatically populate SRTM elevation in database for sites.
Context
Addresses need to interface with DAACs and satellite remote sensing products. This appears to likely extend (and potentially supplant) the ORNL DAAC MODIS subset tool and API.
Possible Implementation
We have experience with JSON based query in our met workflow. something like: extract.appeears(product,lat,lon,start_date,end_date), returns either URL with link to download or data.frame with the actual data.