Kingdom of Saudi Arabia · National Land Evaluation · Database Application

Data Acquisition

For every one of the 64 elements: which dataset, which exact layer or field inside it, in what native unit, and the steps that turn it into the stored column value. The Data Sources module names the dataset; this one tells an engineer what to open and what to do.

Elements64
Datasets referenced34
Acquirable now32
Awaiting a register21
How to read a sheet

Each element expands to the dataset, the exact layer, band or field name, its native unit and range, the numbered extraction steps, and the target column. Where a step is easy to get wrong the reason is stated rather than implied.

Layer names should be verified against current product documentation before use. Band and variable naming changes between product versions, and a name that was correct at one release is silently wrong at the next.

Capability — 17 elements

LQ-SP-01
Available water capacity
SoilGrids 2.0 · 250 m
SOILGRIDS_250awc_mm_m▾
Dataset
SoilGrids 2.0
Layer / field
wv0033 and wv1500
Native unit
cm³/cm³ ×10
Native range
0–1000
Target column
awc_mm_m
Target unit
mm/m
Normalising to the 1 ha cell
Source pixel250 m
→
OperationCopy-down
→
Statisticcentroid pixel
→
Cell width 107.46 mres score 0.6215
One source pixel covers 6 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 6 cells the appearance of independent observations.
Extraction
1Download wv0033 (field capacity, 33 kPa) and wv1500 (wilting point, 1500 kPa) for the six standard depth intervals: 0–5, 5–15, 15–30, 30–60, 60–100, 100–200 cm.
2Subtract: AWC_layer = wv0033 − wv1500, still in cm³/cm³ ×10.
3Divide by 10 to get volumetric fraction.
4Depth-weight over 0–100 cm: multiply each interval by its thickness in mm and sum.
5Truncate the profile at rooting_depth_cm where that is shallower than 100 cm — water below a root barrier is not available.
6Write mm per metre of profile.
Watch this. SoilGrids publishes values ×10 as integers. Failing to divide gives a result ten times too large that still looks plausible.
LQ-SP-02
Soil workability
SoilGrids 2.0 · 250 m
SOILGRIDS_250workability_idx▾
Dataset
SoilGrids 2.0
Target column
workability_idx
Target unit
index
Normalising to the 1 ha cell
Source pixel250 m
→
OperationCopy-down
→
Statistic—
→
Cell width 107.46 mres score 0.6215
One source pixel covers 6 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 6 cells the appearance of independent observations.
Extraction
1The inputs exist — texture, bulk density, organic carbon, coarse fragments are all published layers.
2The function that combines them into a 0–100 index has never been written down.
3Until it is, no acquisition sequence can be specified: there is no target quantity to acquire.
Watch this. Blocked by definition, not by data. This is one of three remaining. Confidence cannot be scored either, because component 1 asks how a value was acquired and there is no acquisition path to name.
LQ-SP-03
Rooting conditions
SoilGrids 2.0 · 250 m
SOILGRIDS_250rooting_depth_cm▾
Dataset
SoilGrids 2.0
Layer / field
bdricm, constrained by cfvo
Native unit
cm
Native range
0–200
Target column
rooting_depth_cm
Target unit
cm
Normalising to the 1 ha cell
Source pixel250 m
→
OperationCopy-down
→
Statisticcentroid pixel
→
Cell width 107.46 mres score 0.6215
One source pixel covers 6 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 6 cells the appearance of independent observations.
Extraction
1Take bdricm, absolute depth to bedrock, in cm.
2Take cfvo, coarse fragment volume, per depth interval, in cm³/dm³ ×10.
3Find the shallowest interval where cfvo/10 > 40 % — a stone content above that is root-restricting in practice.
4Effective rooting depth = min(bdricm, top of that interval).
5Cap at 200 cm; deeper is not distinguished by the source.
Watch this. Verify the layer name against the current SoilGrids release. Depth-to-bedrock was BDRICM_M in SoilGrids v1 and is not part of the standard v2 property set; it may need to be sourced separately.
LQ-SP-04
Surface sealing and crusting
SoilGrids 2.0 · 250 m
SOILGRIDS_250sealing_idx▾
Dataset
SoilGrids 2.0
Target column
sealing_idx
Target unit
index
Normalising to the 1 ha cell
Source pixel250 m
→
OperationCopy-down
→
Statistic—
→
Cell width 107.46 mres score 0.6215
One source pixel covers 6 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 6 cells the appearance of independent observations.
Extraction
1The inputs exist — texture, bulk density, organic carbon, coarse fragments are all published layers.
2The function that combines them into a 0–100 index has never been written down.
3Until it is, no acquisition sequence can be specified: there is no target quantity to acquire.
Watch this. Blocked by definition, not by data. This is one of three remaining. Confidence cannot be scored either, because component 1 asks how a value was acquired and there is no acquisition path to name.
LQ-SC-01
Salinity (ECe)
GSASMAP + SENTINEL2_L2A
GSASMAP + SENTINEL2_L2Aece_ds_m▾
Dataset
GSASMAP + SENTINEL2_L2A
Layer / field
salt-affected class; B2 B3 B4 B8 B11
Native unit
class; reflectance
Native range
0–4; 0–10000
Target column
ece_ds_m
Target unit
dS/m
Source  —
Normalising to the 1 ha cell
Copy-down. Centroid pixel, refined by area-weighted mean of the s2 index.
Extraction
1GSASMAP gives a salt-affected class, not a continuous ECe. Take the class as the prior.
2Build a bare-soil composite from Sentinel-2: median of cloud-free scenes where NDVI < 0.15.
3Compute a salinity index — SI = √(B2 × B4) is the common form; several exist and the choice must be registered.
4Regress the index against the GSASMAP class midpoints to refine within class.
5Write dS/m.
Watch this. Two problems. The chain measures the surface crust, not root-zone ECe, and the two diverge sharply after irrigation. And the index-to-ECe regression has no Saudi calibration points, so the refinement is currently notional.
LQ-SC-02
Sodicity (ESP)
Global Map of Salt-Affected Soils · 1 km
GSASMAPesp_pct▾
Dataset
Global Map of Salt-Affected Soils
Layer / field
sodic class
Native unit
class
Native range
0–4
Target column
esp_pct
Target unit
%
Normalising to the 1 ha cell
Source pixel1000 m
→
OperationCopy-down
→
Statisticcentroid pixel
→
Cell width 107.46 mres score 0.3833
One source pixel covers 100 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 100 cells the appearance of independent observations.
Extraction
1Take the sodic-soil class.
2Map each class to its ESP midpoint.
3Write per cent.
Watch this. Class midpoints are an assumption. A sodic class spans a wide ESP range and the midpoint is not the expected value within it.
LQ-SC-03
Nutrient availability
SoilGrids 2.0 · 250 m
SOILGRIDS_250nutrient_idx▾
Dataset
SoilGrids 2.0
Target column
nutrient_idx
Target unit
index
Normalising to the 1 ha cell
Source pixel250 m
→
OperationCopy-down
→
Statistic—
→
Cell width 107.46 mres score 0.6215
One source pixel covers 6 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 6 cells the appearance of independent observations.
Extraction
1The inputs exist — texture, bulk density, organic carbon, coarse fragments are all published layers.
2The function that combines them into a 0–100 index has never been written down.
3Until it is, no acquisition sequence can be specified: there is no target quantity to acquire.
Watch this. Blocked by definition, not by data. This is one of three remaining. Confidence cannot be scored either, because component 1 asks how a value was acquired and there is no acquisition path to name.
LQ-SC-04
Toxicity (boron)
Global Lithological Map v1.0 · Vector
GLIMboron_mg_l▾
Dataset
Global Lithological Map v1.0
Layer / field
xx first-level lithology
Native unit
16 classes
Native range
su, sm, sc, mt, pa, va, vi, vb, pi, py, pb, ev, ig, wb, nd
Target column
boron_mg_l
Target unit
mg/L
Normalising to the 1 ha cell
Vector overlay. Majority by area.
Extraction
1Take the first-level lithology class polygon.
2Assign an expected boron concentration per class from published parent-material chemistry.
3Majority by area where a cell spans two units.
4Write mg/L.
Watch this. This is inference, not measurement — confidence component 1 scores it 0.55. Evaporite (ev) and some sedimentary classes carry high boron; the per-class values must be sourced and registered.
LQ-W-01
Drainage condition
Harmonized World Soil Database v2.0 · ~1 km
HWSD2drainage_class▾
Dataset
Harmonized World Soil Database v2.0
Layer / field
DRAINAGE field on HWSD2_LAYERS
Native unit
ordinal class
Native range
1–7
Target column
drainage_class
Target unit
class
Normalising to the 1 ha cell
Source pixel1000 m
→
OperationCopy-down
→
Statisticmajority by area
→
Cell width 107.46 mres score 0.3833
One source pixel covers 100 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 100 cells the appearance of independent observations.
Extraction
1Join the HWSD2 raster mapping-unit ID to the HWSD2_LAYERS attribute table.
2Read the DRAINAGE field: 1 excessively, 2 somewhat excessively, 3 well, 4 moderately well, 5 imperfectly, 6 poorly, 7 very poorly.
3Take the dominant soil unit where the mapping unit is composite.
4Majority by area for a cell spanning two units.
5Write the class code directly — no index construction.
Watch this. Resolved at the drainage-class decision. HWSD2 mapping units are coarse and composite; within-unit variation is lost and cannot be recovered from this source.
LQ-W-02
Flood hazard
JRC Global Surface Water · 30 m
JRC_GSWflood_ev_10y▾
Dataset
JRC Global Surface Water
Layer / field
occurrence band
Native unit
% of months
Native range
0–100
Target column
flood_ev_10y
Target unit
events/10 yr
Normalising to the 1 ha cell
Source pixel30 m
→
OperationAggregate
→
Statisticmaximum
→
Cell width 107.46 mres score 1.00
11 pixels fall inside each cell. The cell value is a statistic over real observations of that hectare, so no resolution discount applies.
80.5 % of the cell lies within one pixel-width of its boundary, so exact area weighting is required — a centroid test is not sufficient.
Extraction
1Take the occurrence band, percentage of months in 1984–2021 with observed surface water.
2Convert to an event rate: occurrence > 5 % of months indicates recurrent inundation.
3Cross-check against DEM-derived flow accumulation to exclude permanent water bodies, which are an exclusion class rather than a flood hazard.
4Express as events per decade.
Watch this. Optical water detection misses short flash-flood events entirely — the mechanism that matters most in a wadi system. This chain understates flash-flood exposure.
LQ-W-04
Waterlogging risk
JRC Global Surface Water · 30 m
JRC_GSWwaterlog_d_y▾
Dataset
JRC Global Surface Water
Layer / field
seasonality band
Native unit
months/yr
Native range
0–12
Target column
waterlog_d_y
Target unit
days/yr
Normalising to the 1 ha cell
Source pixel30 m
→
OperationAggregate
→
Statisticarea-weighted mean
→
Cell width 107.46 mres score 1.00
11 pixels fall inside each cell. The cell value is a statistic over real observations of that hectare, so no resolution discount applies.
80.5 % of the cell lies within one pixel-width of its boundary, so exact area weighting is required — a centroid test is not sufficient.
Extraction
1Take the seasonality band, number of months per year with water present.
2Multiply by 30 for days per year.
3Exclude cells classed as permanent water.
Watch this. Same optical limitation. Sub-monthly waterlogging is invisible.
LQ-T-01
Terrain (slope)
Copernicus DEM GLO-30 · 30 m
COP_DEM_GLO30slope_pct▾
Dataset
Copernicus DEM GLO-30
Layer / field
DEM elevation band
Native unit
m, EGM2008
Native range
−100 to 3600
Target column
slope_pct
Target unit
%
Normalising to the 1 ha cell
Source pixel30 m
→
OperationAggregate
→
Statisticarea-weighted mean
→
Cell width 107.46 mres score 1.00
11 pixels fall inside each cell. The cell value is a statistic over real observations of that hectare, so no resolution discount applies.
80.5 % of the cell lies within one pixel-width of its boundary, so exact area weighting is required — a centroid test is not sufficient.
Extraction
1Reproject the DEM once, deliberately, to UTM 37N or 38N. Slope in degrees requires equal horizontal and vertical units.
2Compute slope with a 3×3 Horn kernel.
3Convert to per cent: slope_pct = tan(slope_rad) × 100.
4Area-weighted mean over the cell — exact intersection, not centroid. At 30 m, 80.5 % of a 1 ha cell lies within one pixel of its boundary.
Watch this. Slope is one of the few chains where reprojecting the raster is correct rather than an error. Doing it on the fly per tile introduces edge artefacts; reproject the mosaic once.
LQ-T-02
Water erosion
COP_DEM_GLO30 + SOILGRIDS_250 + CHIRPS
COP_DEM_GLO30 + SOILGRIDS_250 + CHIRPSerosion_w_t_ha▾
Dataset
COP_DEM_GLO30 + SOILGRIDS_250 + CHIRPS
Layer / field
R, K, LS, C, P factors
Native unit
t/ha/yr
Native range
0–200
Target column
erosion_w_t_ha
Target unit
t/ha/yr
Source  —
Normalising to the 1 ha cell
Aggregate. Area-weighted mean of the per-pixel rusle result.
Extraction
1R from CHIRPS: annual rainfall erosivity, computed from the daily series, not from the annual total.
2K from SoilGrids: sand, silt, clay, soc, structure and permeability through the Wischmeier nomograph.
3LS from the DEM: slope length and steepness.
4C from ESA WorldCover class → published cover-management values.
5P = 1. No conservation-practice layer exists for the Kingdom.
6Multiply per pixel, then aggregate. Aggregating the factors first and multiplying the means gives a different and wrong answer.
Watch this. P = 1 systematically overstates erosion wherever terracing or contour working already exists. CHIRPS at 5 km sets the resolution score for the whole chain at the 0.35 floor.
LQ-T-03
Wind erosion
ERA5_LAND + SOILGRIDS_250
ERA5_LAND + SOILGRIDS_250erosion_wind_t_ha▾
Dataset
ERA5_LAND + SOILGRIDS_250
Layer / field
10m wind components; sand fraction
Native unit
m/s; g/kg
Native range
—
Target column
erosion_wind_t_ha
Target unit
t/ha/yr
Source  —
Normalising to the 1 ha cell
Aggregate. Area-weighted mean of the per-pixel rweq result.
Extraction
1Wind: 10m_u_component_of_wind and 10m_v_component_of_wind → speed, then the RWEQ weather factor from the speed distribution, not the mean.
2Soil erodible fraction from sand and clay.
3Surface crust factor from soc and clay.
4Cover factor from ESA WorldCover class.
5Combine per RWEQ, per pixel, then aggregate.
6Write t/ha/yr — the same unit as water erosion, so the two are comparable and summable.
Watch this. Resolved at the RWEQ decision. ERA5-Land at 9 km cannot resolve the local wind acceleration around dune fields that drives actual erosion.
LQ-T-04
Sand encroachment
Sentinel-2 MSI Level-2A · 10–20 m
SENTINEL2_L2Asand_encr_m_y▾
Dataset
Sentinel-2 MSI Level-2A
Layer / field
B8, B11, B12 bare-soil composite
Native unit
reflectance
Native range
0–10000
Target column
sand_encr_m_y
Target unit
m/yr
Normalising to the 1 ha cell
Source pixel10 m
→
OperationAggregate
→
Statistic95th percentile
→
Cell width 107.46 mres score 1.00
100 pixels fall inside each cell. The cell value is a statistic over real observations of that hectare, so no resolution discount applies.
33.8 % of the cell lies within one pixel-width of its boundary, so exact area weighting is required — a centroid test is not sufficient.
Extraction
1Build an annual bare-soil composite: median of scenes with NDVI < 0.15, cloud and dust masked.
2Classify sand versus non-sand on the SWIR/NIR ratio.
3Repeat for consecutive years across a five-year window.
4Measure dune-front displacement between years along the prevailing wind vector.
5Take the 95th percentile of displacement within the cell — not the maximum, which a single mis-registered pixel sets.
Watch this. Requires accurate co-registration between years. Sentinel-2 geolocation is good but a 10 m error over a 2 m/yr signal is fatal; use the same relative orbit throughout.
LQ-C-02
Thermal regime
ERA5-Land reanalysis · 9 km
ERA5_LANDgdd▾
Dataset
ERA5-Land reanalysis
Layer / field
2m_temperature
Native unit
K
Native range
230–330
Target column
gdd
Target unit
°C·day
Normalising to the 1 ha cell
Source pixel9000 m
→
OperationCopy-down
→
Statisticcentroid pixel
→
Cell width 107.46 mres score 0.3500
One source pixel covers 8,100 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 8,100 cells the appearance of independent observations.
Extraction
1Take hourly 2m_temperature, convert K to °C.
2Aggregate to daily mean.
3Accumulate growing degree days above the base temperature: Σ max(0, T_mean − T_base).
4Store per base temperature. A crop with a 10 °C base and one with 5 °C give different totals from the same series.
Watch this. This is why the wx schema exists. Without the retained daily series, every new crop base temperature means reprocessing the entire national archive.
LQ-C-03
Radiation
ERA5-Land reanalysis · 9 km
ERA5_LANDradiation_mj▾
Dataset
ERA5-Land reanalysis
Layer / field
surface_solar_radiation_downwards
Native unit
J/m² accumulated
Native range
—
Target column
radiation_mj
Target unit
MJ/m²/day
Normalising to the 1 ha cell
Source pixel9000 m
→
OperationCopy-down
→
Statisticcentroid pixel
→
Cell width 107.46 mres score 0.3500
One source pixel covers 8,100 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 8,100 cells the appearance of independent observations.
Extraction
1Take surface_solar_radiation_downwards, accumulated J/m² per hour.
2Sum to daily total, divide by 1,000,000 for MJ/m²/day.
3Multi-year mean.
4Cross-check against CM SAF SARAH-3 at 5 km, which is the better product for radiation specifically.
Watch this. No class ranges are published for this element. The value can be produced; it cannot yet be classed.

Water — 15 parameters

None of the fifteen can be acquired yet. Thirteen need the SWA well register, monitoring network and water quality records; two need MEWA basin determinations. No request has been sent. Each sheet below states the field that must be asked for, which is what the request should specify.
WP-A-01
Source type
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSsource_type▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
Well type / source classification
Native unit
per the register
Target column
source_type
Normalising to the 1 ha cell
Point interpolation. Nearest well within the service radius.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Well type / source classification.
3Categorical: renewable, fossil, managed recharge, treated wastewater, desalinated, surface.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-A-02
Available volume
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSavail_vol_m3_ha▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
Licensed annual abstraction, m³/yr, and the served area in ha
Native unit
per the register
Target column
avail_vol_m3_ha
Normalising to the 1 ha cell
Point interpolation. Licensed volume of the serving well ÷ its service area.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Licensed annual abstraction, m³/yr, and the served area in ha.
3Divide one by the other.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-A-03
Distance to source
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSdist_source_km▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
Well coordinates
Native unit
per the register
Target column
dist_source_km
Normalising to the 1 ha cell
Point interpolation. Euclidean distance to the nearest well.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Well coordinates.
3Euclidean distance from the cell centroid to the nearest serving well.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-B-01
Water-level trend
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSlevel_trend_m_y▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
Monitoring well time series, static water level by date
Native unit
per the register
Target column
level_trend_m_y
Normalising to the 1 ha cell
Point interpolation. idw or kriging over the monitoring network.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Monitoring well time series, static water level by date.
3Linear regression over the record; slope in m/yr.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-B-02
Aquifer type
Hydrogeology and aquifer mapping · Vector
SGS_HYDROaquifer_type▾
Dataset
Hydrogeology and aquifer mapping
Layer / field
SGS hydrogeological unit polygon and its recharge classification
Native unit
per the register
Target column
aquifer_type
Normalising to the 1 ha cell
Vector overlay. Majority by area.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: SGS hydrogeological unit polygon and its recharge classification.
3Majority by area.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-B-03
Remaining supply horizon
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSsupply_horizon_y▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
Saturated thickness and abstraction rate
Native unit
per the register
Target column
supply_horizon_y
Normalising to the 1 ha cell
Point interpolation. Computed per cell from interpolated level and saturated thickness.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Saturated thickness and abstraction rate.
3Thickness divided by the decline rate.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-C-01
Irrigation water salinity
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSecw_ds_m▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
Water quality analysis, EC at 25 °C
Native unit
per the register
Target column
ecw_ds_m
Normalising to the 1 ha cell
Point interpolation. Value of the serving well.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Water quality analysis, EC at 25 °C.
3Value of the serving well; unit conversion µS/cm ÷ 1000.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-C-02
Sodium adsorption ratio
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSsar▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
Na, Ca, Mg in meq/L
Native unit
per the register
Target column
sar
Normalising to the 1 ha cell
Point interpolation. Value of the serving well.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Na, Ca, Mg in meq/L.
3SAR = Na / √((Ca + Mg)/2). Classed jointly with ECw.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-C-03
Chloride
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSchloride_mg_l▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
Cl in mg/L
Native unit
per the register
Target column
chloride_mg_l
Normalising to the 1 ha cell
Point interpolation. Value of the serving well.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Cl in mg/L.
3Value of the serving well.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-C-04
Boron in irrigation water
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSboron_w_mg_l▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
B in mg/L
Native unit
per the register
Target column
boron_w_mg_l
Normalising to the 1 ha cell
Point interpolation. Value of the serving well.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: B in mg/L.
3Value of the serving well.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-C-05
Treated wastewater tier
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSww_tier▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
Treatment plant register, process level
Native unit
per the register
Target column
ww_tier
Normalising to the 1 ha cell
Point interpolation. Tier of the serving plant, null where the source is not treated wastewater.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Treatment plant register, process level.
3Tier of the serving plant; NULL where the source is not treated wastewater.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-D-01
Pumping lift
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSpump_lift_m▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
Static water level
Native unit
per the register
Target column
pump_lift_m
Normalising to the 1 ha cell
Point interpolation. Interpolated water level subtracted from the dem cell mean.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Static water level.
3DEM surface elevation minus interpolated water level.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-D-02
Conveyance distance
Well register, monitoring network and abstraction licences · Points · records
SWA_WELLSconveyance_km▾
Dataset
Well register, monitoring network and abstraction licences
Layer / field
Canal and pipeline network geometry
Native unit
per the register
Target column
conveyance_km
Normalising to the 1 ha cell
Vector overlay. Network distance to the nearest offtake.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Canal and pipeline network geometry.
3Network distance to the nearest offtake — not Euclidean.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-E-01
Basin sustainable yield
Basin allocation and water policy determinations · Records
MEWA_BASINbasin_yield_mm3▾
Dataset
Basin allocation and water policy determinations
Layer / field
MEWA basin determination, Mm³/yr
Native unit
per the register
Target column
basin_yield_mm3
Normalising to the 1 ha cell
Vector overlay. Value of the containing basin polygon.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: MEWA basin determination, Mm³/yr.
3Value of the containing basin polygon.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.
WP-E-02
Allocation status
Basin allocation and water policy determinations · Records
MEWA_BASINalloc_pct▾
Dataset
Basin allocation and water policy determinations
Layer / field
Sum of licensed abstraction in the basin
Native unit
per the register
Target column
alloc_pct
Normalising to the 1 ha cell
Vector overlay. Value of the containing basin polygon.
Extraction
1The register has not been requested. No acquisition can begin.
2Field required: Sum of licensed abstraction in the basin.
3Divided by the sustainable yield.
4Assigned from the serving source, not interpolated — except WP-B-01, which is a genuine continuous field.
Watch this. Thirteen of the fifteen water parameters need SWA records. See the Ministry Data module, requests MD-02 and MD-03.

Land rights — 6 legal checks

No satellite proxy is admissible for any of the six. A legal determination cannot be inferred from imagery at any resolution. Each sheet states the register field required and the rule that differs from every other module: any intersection, not majority by area.
EP
Protected area
Protected area polygons · Vector
NCW_PROTECTEDep_protected▾
Dataset
Protected area polygons
Layer / field
Protected area polygon, designation category and permitted-use attribute
Native unit
categorical
Native range
per the register
Target column
ep_protected
Target unit
E0–E3
Normalising to the 1 ha cell
Vector overlay. any intersection.
Extraction
1The register has not been requested.
2Field required: Protected area polygon, designation category and permitted-use attribute.
3Any intersection, not majority by area. If any part of the hectare falls inside a designated polygon, the hectare is constrained.
4Where a cell spans several designations, the most restrictive state wins.
5The category-to-class mapping cannot be written until the category list arrives with the register.
Watch this. No satellite proxy is admissible. A legal determination cannot be inferred from imagery at any resolution. Until delivery every hectare is E0 — unknown, never clear.
EV
Rangeland, forest and afforestation
Rangeland, forest and afforestation layer · Vector
NCVC_RANGEev_rangeland▾
Dataset
Rangeland, forest and afforestation layer
Layer / field
Rangeland, forest and afforestation polygon with its designation class
Native unit
categorical
Native range
per the register
Target column
ev_rangeland
Target unit
E0–E3
Normalising to the 1 ha cell
Vector overlay. Any intersection.
Extraction
1The register has not been requested.
2Field required: Rangeland, forest and afforestation polygon with its designation class.
3Any intersection, not majority by area. If any part of the hectare falls inside a designated polygon, the hectare is constrained.
4Where a cell spans several designations, the most restrictive state wins.
5The category-to-class mapping cannot be written until the category list arrives with the register.
Watch this. No satellite proxy is admissible. A legal determination cannot be inferred from imagery at any resolution. Until delivery every hectare is E0 — unknown, never clear.
ET
Tenure
Watheeq cadastral records · Records
MOJ_WATHEEQet_tenure▾
Dataset
Watheeq cadastral records
Layer / field
Parcel geometry, title status, dispute and injunction flags
Native unit
categorical
Native range
per the register
Target column
et_tenure
Target unit
E0–E3
Normalising to the 1 ha cell
Vector overlay. Any intersection, worst state wins.
Extraction
1The register has not been requested.
2Field required: Parcel geometry, title status, dispute and injunction flags.
3Any intersection, not majority by area. If any part of the hectare falls inside a designated polygon, the hectare is constrained.
4Where a cell spans several designations, the most restrictive state wins.
5The category-to-class mapping cannot be written until the category list arrives with the register.
Watch this. No satellite proxy is admissible. A legal determination cannot be inferred from imagery at any resolution. Until delivery every hectare is E0 — unknown, never clear.
EZ
Zoning designation
Governorate boundaries and zoning schemes · Vector
MOMAH_ADMINez_zoning▾
Dataset
Governorate boundaries and zoning schemes
Layer / field
Zoning scheme polygon and its land-use class
Native unit
categorical
Native range
per the register
Target column
ez_zoning
Target unit
E0–E3
Normalising to the 1 ha cell
Vector overlay. Any intersection, worst state wins.
Extraction
1The register has not been requested.
2Field required: Zoning scheme polygon and its land-use class.
3Any intersection, not majority by area. If any part of the hectare falls inside a designated polygon, the hectare is constrained.
4Where a cell spans several designations, the most restrictive state wins.
5The category-to-class mapping cannot be written until the category list arrives with the register.
Watch this. No satellite proxy is admissible. A legal determination cannot be inferred from imagery at any resolution. Until delivery every hectare is E0 — unknown, never clear.
EX
Conflicting use
Hazard designation and environmental constraint mapping · Vector
NCEC_HAZARDex_conflicting▾
Dataset
Hazard designation and environmental constraint mapping
Layer / field
Petroleum, mining, military and industrial designation polygons
Native unit
categorical
Native range
per the register
Target column
ex_conflicting
Target unit
E0–E3
Normalising to the 1 ha cell
Vector overlay. Any intersection.
Extraction
1The register has not been requested.
2Field required: Petroleum, mining, military and industrial designation polygons.
3Any intersection, not majority by area. If any part of the hectare falls inside a designated polygon, the hectare is constrained.
4Where a cell spans several designations, the most restrictive state wins.
5The category-to-class mapping cannot be written until the category list arrives with the register.
Watch this. No satellite proxy is admissible. A legal determination cannot be inferred from imagery at any resolution. Until delivery every hectare is E0 — unknown, never clear.
EH
Hazard designation
Hazard designation and environmental constraint mapping · Vector
NCEC_HAZARDeh_hazard▾
Dataset
Hazard designation and environmental constraint mapping
Layer / field
Hazard designation polygon with type and severity
Native unit
categorical
Native range
per the register
Target column
eh_hazard
Target unit
E0–E3
Normalising to the 1 ha cell
Vector overlay. Any intersection, highest severity wins.
Extraction
1The register has not been requested.
2Field required: Hazard designation polygon with type and severity.
3Any intersection, not majority by area. If any part of the hectare falls inside a designated polygon, the hectare is constrained.
4Where a cell spans several designations, the most restrictive state wins.
5The category-to-class mapping cannot be written until the category list arrives with the register.
Watch this. No satellite proxy is admissible. A legal determination cannot be inferred from imagery at any resolution. Until delivery every hectare is E0 — unknown, never clear.

Suitability — 26 factors

SQ-A-01
Available water capacity
—
—awc_mm_m▾
Layer / field
same as LQ-SP-01
Target column
awc_mm_m
Target unit
as LQ-SP-01
Source  —
Normalising to the 1 ha cell
Copy-down. Centroid pixel.
Extraction
1The same measurement as LQ-SP-01. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-A-02
Soil workability
—
—workability_idx▾
Layer / field
same as LQ-SP-02
Target column
workability_idx
Target unit
as LQ-SP-02
Source  —
Normalising to the 1 ha cell
Blocked. —.
Extraction
1The same measurement as LQ-SP-02. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-A-03
Rooting conditions
—
—rooting_depth_cm▾
Layer / field
same as LQ-SP-03
Target column
rooting_depth_cm
Target unit
as LQ-SP-03
Source  —
Normalising to the 1 ha cell
Copy-down. Centroid pixel.
Extraction
1The same measurement as LQ-SP-03. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-A-04
Surface sealing and crusting
—
—sealing_idx▾
Layer / field
same as LQ-SP-04
Target column
sealing_idx
Target unit
as LQ-SP-04
Source  —
Normalising to the 1 ha cell
Blocked. —.
Extraction
1The same measurement as LQ-SP-04. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-B-01
Salinity (ECe)
—
—ece_ds_m▾
Layer / field
same as LQ-SC-01
Target column
ece_ds_m
Target unit
as LQ-SC-01
Source  —
Normalising to the 1 ha cell
Copy-down. Centroid pixel, refined by area-weighted mean of the s2 index.
Extraction
1The same measurement as LQ-SC-01. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-B-02
Sodicity (ESP / SAR)
—
—esp_pct▾
Layer / field
same as LQ-SC-02
Target column
esp_pct
Target unit
as LQ-SC-02
Source  —
Normalising to the 1 ha cell
Copy-down. Centroid pixel.
Extraction
1The same measurement as LQ-SC-02. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-B-04
Toxicity risk (boron)
—
—boron_mg_l▾
Layer / field
same as LQ-SC-04
Target column
boron_mg_l
Target unit
as LQ-SC-04
Source  —
Normalising to the 1 ha cell
Vector overlay. Majority by area.
Extraction
1The same measurement as LQ-SC-04. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-C-01
Drainage condition
—
—drainage_class▾
Layer / field
same as LQ-W-01
Target column
drainage_class
Target unit
as LQ-W-01
Source  —
Normalising to the 1 ha cell
Vector overlay. Majority by area.
Extraction
1The same measurement as LQ-W-01. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-C-02
Flood hazard
—
—flood_ev_10y▾
Layer / field
same as LQ-W-02
Target column
flood_ev_10y
Target unit
as LQ-W-02
Source  —
Normalising to the 1 ha cell
Aggregate. maximum.
Extraction
1The same measurement as LQ-W-02. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-C-03
Waterlogging risk
—
—waterlog_d_y▾
Layer / field
same as LQ-W-04
Target column
waterlog_d_y
Target unit
as LQ-W-04
Source  —
Normalising to the 1 ha cell
Aggregate. Area-weighted mean.
Extraction
1The same measurement as LQ-W-04. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-D-01
Terrain workability
—
—slope_pct▾
Layer / field
same as LQ-T-01
Target column
slope_pct
Target unit
as LQ-T-01
Source  —
Normalising to the 1 ha cell
Aggregate. Area-weighted mean.
Extraction
1The same measurement as LQ-T-01. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-D-02
Water erosion hazard
—
—erosion_w_t_ha▾
Layer / field
same as LQ-T-02
Target column
erosion_w_t_ha
Target unit
as LQ-T-02
Source  —
Normalising to the 1 ha cell
Aggregate. Area-weighted mean of the per-pixel rusle result.
Extraction
1The same measurement as LQ-T-02. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-D-03
Wind erosion hazard
—
—erosion_wind_t_ha▾
Layer / field
same as LQ-T-03
Target column
erosion_wind_t_ha
Target unit
as LQ-T-03
Source  —
Normalising to the 1 ha cell
Aggregate. Area-weighted mean of the per-pixel rweq result.
Extraction
1The same measurement as LQ-T-03. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-D-04
Sand encroachment hazard
—
—sand_encr_m_y▾
Layer / field
same as LQ-T-04
Target column
sand_encr_m_y
Target unit
as LQ-T-04
Source  —
Normalising to the 1 ha cell
Aggregate. 95th percentile.
Extraction
1The same measurement as LQ-T-04. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-E-02
Thermal suitability
—
—gdd▾
Layer / field
same as LQ-C-02
Target column
gdd
Target unit
as LQ-C-02
Source  —
Normalising to the 1 ha cell
Copy-down. Centroid pixel.
Extraction
1The same measurement as LQ-C-02. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-E-03
Radiation and solar energy
—
—radiation_mj▾
Layer / field
same as LQ-C-03
Target column
radiation_mj
Target unit
as LQ-C-03
Source  —
Normalising to the 1 ha cell
Copy-down. Centroid pixel.
Extraction
1The same measurement as LQ-C-03. Acquired once, stored once, read twice.
2The difference is the rating, not the acquisition: capability compares it against thr.class_range, suitability against thr.crop_requirement keyed on crop and system.
3Do not acquire it twice. A second extraction would produce two values that drift apart.
SQ-E-01
Moisture deficit (aridity)
Global Aridity Index and PET v3 · 1 km
GLOBAL_AI_PETaridity_idx▾
Dataset
Global Aridity Index and PET v3
Layer / field
ai_v3_yr
Native unit
ratio ×10000
Native range
0–30000
Target column
aridity_idx
Target unit
P/PET
Normalising to the 1 ha cell
Source pixel1000 m
→
OperationCopy-down
→
Statisticcentroid pixel
→
Cell width 107.46 mres score 0.3833
One source pixel covers 100 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 100 cells the appearance of independent observations.
Extraction
1Take the annual aridity index, P/PET.
2Divide by 10,000.
3Rated against this crop's water demand, not as a land property.
4Under irrigation it becomes a cost driver rather than a limit.
Watch this. Primary since methodology version 10.1, when moisture deficit moved out of capability.
SQ-E-04
Length of growing period
ERA5-Land reanalysis · 9 km
ERA5_LANDlgp_days▾
Dataset
ERA5-Land reanalysis
Layer / field
2m_temperature
Native unit
K
Native range
230–330
Target column
lgp_days
Target unit
days
Normalising to the 1 ha cell
Source pixel9000 m
→
OperationCopy-down
→
Statisticcentroid pixel
→
Cell width 107.46 mres score 0.3500
One source pixel covers 8,100 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 8,100 cells the appearance of independent observations.
Extraction
1Take daily mean 2m_temperature.
2Count days above the crop base temperature — the thermally possible season.
3Not the rainfed season, which reads zero across almost the whole Kingdom.
Watch this. Primary since version 10.1, and redefined at the same time.
SQ-A-05
Soil texture
SoilGrids 2.0 · 250 m
SOILGRIDS_250texture_class▾
Dataset
SoilGrids 2.0
Layer / field
sand, silt, clay
Native unit
g/kg
Native range
0–1000
Target column
texture_class
Target unit
category
Normalising to the 1 ha cell
Source pixel250 m
→
OperationCopy-down
→
Statisticcentroid pixel, classified after depth weighting
→
Cell width 107.46 mres score 0.6215
One source pixel covers 6 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 6 cells the appearance of independent observations.
Extraction
1Take sand, silt and clay for the six depth intervals.
2Depth-weight the fractions first, over 0–100 cm.
3Then apply the USDA texture triangle once.
4Classifying each horizon and taking a majority gives a different class — the two orders are not equivalent.
SQ-A-06
Coarse fragments
SoilGrids 2.0 · 250 m
SOILGRIDS_250coarse_frag_pct▾
Dataset
SoilGrids 2.0
Layer / field
cfvo
Native unit
cm³/dm³ ×10
Native range
0–1000
Target column
coarse_frag_pct
Target unit
%
Normalising to the 1 ha cell
Source pixel250 m
→
OperationCopy-down
→
Statisticcentroid pixel
→
Cell width 107.46 mres score 0.6215
One source pixel covers 6 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 6 cells the appearance of independent observations.
Extraction
1Take cfvo, coarse fragment volume.
2Divide by 10 for per cent.
3Depth-weight over 0–100 cm.
SQ-B-05
Calcium carbonate
Harmonized World Soil Database v2.0 · ~1 km
HWSD2caco3_pct▾
Dataset
Harmonized World Soil Database v2.0
Layer / field
CACO3 on HWSD2_LAYERS
Native unit
% weight
Native range
0–100
Target column
caco3_pct
Target unit
%
Normalising to the 1 ha cell
Source pixel1000 m
→
OperationCopy-down
→
Statisticarea-weighted mean
→
Cell width 107.46 mres score 0.3833
One source pixel covers 100 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 100 cells the appearance of independent observations.
Extraction
1Join the mapping-unit ID to HWSD2_LAYERS.
2Read CACO3.
3Take the topsoil layer.
4Area-weighted mean where a cell spans two units.
Watch this. SoilGrids carries no carbonate layer, which is why this comes from HWSD2 at a coarser and older mapping.
SQ-B-06
Gypsum content
Harmonized World Soil Database v2.0 · ~1 km
HWSD2gypsum_pct▾
Dataset
Harmonized World Soil Database v2.0
Layer / field
GYPSUM on HWSD2_LAYERS
Native unit
% weight
Native range
0–100
Target column
gypsum_pct
Target unit
%
Normalising to the 1 ha cell
Source pixel1000 m
→
OperationCopy-down
→
Statisticarea-weighted mean
→
Cell width 107.46 mres score 0.3833
One source pixel covers 100 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 100 cells the appearance of independent observations.
Extraction
1As for carbonate, reading GYPSUM.
2Write NULL where the attribute is absent — never zero.
Watch this. High null rate expected. National gypsum mapping is incomplete, and an absent attribute is unknown, not zero.
SQ-B-07
Soil pH
SoilGrids 2.0 · 250 m
SOILGRIDS_250ph_h2o▾
Dataset
SoilGrids 2.0
Layer / field
phh2o
Native unit
pH ×10
Native range
30–110
Target column
ph_h2o
Target unit
pH
Normalising to the 1 ha cell
Source pixel250 m
→
OperationCopy-down
→
Statisticcentroid pixel
→
Cell width 107.46 mres score 0.6215
One source pixel covers 6 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 6 cells the appearance of independent observations.
Extraction
1Take phh2o.
2Divide by 10.
3pH is logarithmic. Where any averaging occurs it must be on hydrogen-ion concentration: convert, average, convert back.
Watch this. Depth-weighting pH units directly is a common and invisible error.
SQ-E-05
Frost risk
ERA5-Land reanalysis · 9 km
ERA5_LANDfrost_days▾
Dataset
ERA5-Land reanalysis
Layer / field
2m_temperature minimum
Native unit
K
Native range
230–330
Target column
frost_days
Target unit
days/yr
Normalising to the 1 ha cell
Source pixel9000 m
→
OperationCopy-down
→
Statisticcentroid pixel
→
Cell width 107.46 mres score 0.3500
One source pixel covers 8,100 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 8,100 cells the appearance of independent observations.
Extraction
1Take daily minimum 2m_temperature.
2Count days below the crop killing temperature.
Watch this. ERA5-Land at 9 km cannot resolve cold-air drainage, which is how frost actually forms in the Asir and Al Bahah highlands. NCM station records would fix both the method and the resolution term — request MD-10.
SQ-F-01
Irrigation demand (ETc)
ERA5-Land reanalysis · 9 km
ERA5_LANDetc_m3_ha▾
Dataset
ERA5-Land reanalysis
Layer / field
ET₀ drivers
Native unit
various
Native range
—
Target column
etc_m3_ha
Target unit
mm/season
Normalising to the 1 ha cell
Source pixel9000 m
→
OperationCopy-down
→
Statisticcentroid pixel, × crop K₋
→
Cell width 107.46 mres score 0.3500
One source pixel covers 8,100 cells. No aggregation is possible; the cell takes the value of the pixel containing its centroid.
No smoothing, no blending. Resampling the source to a finer grid first would invent detail that is not in the data, and give 8,100 cells the appearance of independent observations.
Extraction
1Compute ET₀ per pixel by FAO-56 Penman-Monteith from temperature, dewpoint, wind and radiation.
2Apply the crop coefficient Kc per growth stage.
3ETc is a property of the crop, not of the land. It is informational and does not rate the hectare.
SQ-F-02
Crop nutrient requirement
ALUES 0.2.1 threshold database · Tabular
ALUESnutrient_req_idx▾
Dataset
ALUES 0.2.1 threshold database
Layer / field
crop nutrient requirement
Native unit
kg/ha
Native range
—
Target column
nutrient_req_idx
Target unit
kg/ha
Normalising to the 1 ha cell
Crop table. Lookup by crop.
Extraction
1Read from the crop table. Not a spatial variable at all.
2Joined at classification time from crop.crop.