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SoilGrids Africa GYGA_Results_1km Aggregated_overERZD root_zone: Effective Root Zone Depth data
SoilGrids Africa GYGA_Results_1km Aggregated_overERZD Effective Root Zone Depth: Effective Root Zone Depth (cm) for Maize.Independent Variables (Grids)
- Longitude
- grid: /X (km) ordered (-3978.0 km) to (3397.0 km) by 1.0 N= 7376 pts :grid
- Latitude
- grid: /Y (km) ordered (-4322.0 km) to (3554.0 km) by 1.0 N= 7877 pts :grid
Other Info
- bufferwordsize
- 1
- datatype
- stringtype
- missing_value
- 255
- SpatialReferenceSystemDims
- X
Y - SpatialReferenceSystemWKT
- null
- units
- cm
- standard units*
- 0.01 meter
Last updated: Tue, 15 Aug 2017 14:04:01 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| X | Y | |
 SoilGrids Africa GYGA_Results_1km Aggregated_overERZD[moist_cont_wilt
wat_cap_fine
Textural_Class
wat_cap_whole
moist_cont_sat
root_zone
soil_qual
coarse_frag_cont
wat_cap_abs
] | ||
| grid: /X (km) ordered (-3978.0 km) to (3397.0 km) by 1.0 N= 7376 pts :grid | M | |
|---|---|---|
| grid: /Y (km) ordered (-4322.0 km) to (3554.0 km) by 1.0 N= 7877 pts :grid | M | |
 SoilGrids Africa GYGA_Results_1km Aggregated_overERZD root_zone[ X Y |] | M | M |
Filters
Here are some filters that are useful for manipulating data. There are actually many more available, but they have to be entered manually. See Ingrid Function Documentation for more information.- Monthly Climatology calculates a monthly climatology by averaging over all years.
- anomalies calculates the difference between the (above) monthly climatology and the original data.
- Integrate along X Y
- Differentiate along X Y
- Take differences along X Y
RMS (root mean square with mean *not* removed) over X Y | X Y |
RMSA (root mean square with mean removed) over X Y | X Y |
Maximum over X Y | X Y |
Minimum over X Y | X Y |
Detrend (best-fit-line) over X Y | X Y |
Note on units