Prompt 1
Goal: create an integrated winterturf dataset for statistically downscaling soil moisture on golf course greens.
Steps:
Create files for each. (1) get locations of all WT v3 nodes (wt_v3_locations.csv) indexed by nodeID, (2) download all soil moisture and soil tempertaure data from v3 nodes, (3) use locations and min(time) and max(time) for each node to download corresponding SMAP values into SMAP_soiltemp_moisure.csv -- header: timestamp, node_id, , . 4) Create an integrated daily time step dataset (ml_ready_WT.csv)
Before execution: show me your planned outputs?
USER: verify and correct (no more than 1x)
Execute.
Prompt 2
Goal: for each winterturf location, download the corresponding Planet Scope data and calculate NDVI
Before execution: show me your plan.
USER: verify and correct (no more than 1x)
Prompt 1
Goal: create an integrated winterturf dataset for statistically downscaling soil moisture on golf course greens.
Steps:
Create files for each. (1) get locations of all WT v3 nodes (wt_v3_locations.csv) indexed by nodeID, (2) download all soil moisture and soil tempertaure data from v3 nodes, (3) use locations and min(time) and max(time) for each node to download corresponding SMAP values into SMAP_soiltemp_moisure.csv -- header: timestamp, node_id, , . 4) Create an integrated daily time step dataset (ml_ready_WT.csv)
Before execution: show me your planned outputs?
USER: verify and correct (no more than 1x)
Execute.
Prompt 2
Goal: for each winterturf location, download the corresponding Planet Scope data and calculate NDVI
Before execution: show me your plan.
USER: verify and correct (no more than 1x)