Aki298/AML-Johor-Tangkak-Sagil-GEE-dataset

Sagil Monthly GEE Environmental Risk Dataset

This dataset contains monthly Google Earth Engine environmental observations and deterministic time-series features for agricultural grid cells in Sagil, Tangkak, Johor, Malaysia.

It supports a prototype task:

monthly environmental features at month t
-> one-month-ahead signed flood/drought proxy-risk score at month t+1

The learned score used by the project is an environmental anomaly and directional risk proxy. It is not a verified flood event label, crop-loss label, or calibrated flood/drought probability.

Dataset Summary

• Study area: Sagil, Tangkak, Johor, Malaysia
• Spatial unit: 1 km x 1 km grid-cell centroid
• Current valid grid cells: 49
• Monthly period: 2021-01-01 through 2026-06-01
• Months per valid grid cell: 66
• Processed rows: 3,234
• Data type: tabular monthly spatiotemporal environmental data
• Primary file for modeling: processed/processed_data.csv

Repository Structure

input/
  coordinates.csv

metadata/
  sample_index.csv
  extraction_manifest.json

raw/
  monthly/
    sagil_1.csv
    sagil_2.csv
    ...

processed/
  processed_data.csv

File Descriptions

input/coordinates.csv

Manual coordinate source for the Sagil grid cells.

Important columns:

sample_id
grid_row
grid_col
latitude
longitude
study_area
cell_size_m

Each row represents the centroid of a 1 km x 1 km grid cell. grid_row = 0 is the northernmost row and grid_col = 0 is the westernmost column.

metadata/sample_index.csv

Extraction lookup table for raw monthly files.

Important columns:

sample_id
grid_row
grid_col
latitude
longitude
cell_area_m2
first_month
last_month
row_count
raw_csv
extraction_status

Use rows with extraction_status = ok for modeling.

metadata/extraction_manifest.json

Extraction run metadata, including:

extraction timestamp
study area
first and last month
month count
source datasets
thresholds
failed samples

raw/monthly/<sample_id>.csv

One raw monthly observation file per valid grid cell. Each file contains 66 monthly rows.

Raw observation groups include:

• Identifiers: sample_id, grid_row, grid_col, latitude, longitude, month
• Sentinel-2 optical indices: ndvi_mean, evi_mean, ndmi_mean, ndwi_mean
• Sentinel-2 quality: s2_image_count, s2_valid_pixel_fraction
• Sentinel-1 SAR: vv_mean_db, vh_mean_db, vv_minus_vh_db, vv_vh_ratio_linear, s1_image_count
• CHIRPS rainfall: rainfall_total_mm, rainfall_mean_daily_mm, rainfall_max_1day_mm, dry_days_count, heavy_rain_days_count
• ERA5-Land climate and water variables: temperature_2m_mean_c, relative_humidity_mean_pct, soil_water_layer1_mean, surface_runoff_total_m, evaporation_total_m
• Dynamic World probabilities: water_probability_mean, flooded_vegetation_probability_mean, built_probability_mean, dynamicworld_image_count
• Terrain and historical extent: elevation_mean_m, elevation_min_m, elevation_max_m, slope_mean_deg, lowland_fraction, max_water_extent_fraction
• Missingness flags: optical_missing_flag, sar_missing_flag, climate_missing_flag, any_missing_flag

processed/processed_data.csv

Combined model-ready deterministic feature table.

It contains:

• all raw monthly observation columns
• calendar features: year, month_number, month_sin, month_cos
• lag features for selected hydrology, vegetation, SAR, and water variables
• rolling mean and rolling sum features
• month-to-month difference features

It intentionally does not contain:

• learned anomaly scores
• learned risk scores
• one-month-ahead target columns
• model predictions

Those values are generated in the modeling pipeline so that training-only preprocessing and target construction remain leakage-controlled.

Source Datasets

The dataset is derived through Google Earth Engine using:

• Sentinel-2 Surface Reflectance Harmonized
• Sentinel-1 GRD
• CHIRPS Daily Rainfall
• ERA5-Land Daily Aggregates
• Dynamic World V1
• SRTM elevation
• JRC Global Surface Water maximum extent

Please consult the original providers' terms for downstream use and redistribution requirements.

Intended Use

This dataset is intended for:

• academic machine learning experiments
• environmental time-series forecasting prototypes
• flood/drought proxy-risk modeling
• remote-sensing feature engineering demonstrations
• chronological train/test/holdout evaluation

The matching project code constructs a learned signed risk proxy and trains:

• persistence baseline
• Ridge regression
• Random Forest regressor
• LSTM sequence model
• Transformer encoder sequence model

Not Intended For

This dataset should not be used as:

• an official flood warning dataset
• a calibrated disaster-probability dataset
• verified crop-loss or damage labels
• a substitute for field survey, hydrological model output, river gauge data, or official disaster records

Risk Proxy Method

The project builds the target outside processed_data.csv.

For each grid cell i and month t, selected environmental variables form:

x_i,t

Training-fitted preprocessing:

z_i,t = StandardScaler(MedianImputer(x_i,t))

Anomaly magnitude:

anomaly_magnitude = mean((z_i,t - MLP_reconstruct(z_i,t))^2)

Flood/drought direction is estimated from standardized environmental indicators:

flood_direction_score = average of positive wetness and flood indicators
drought_direction_score = average of dry and vegetation-stress indicators

Signed proxy score:

signed_risk_score = anomaly_magnitude * direction_sign

where:

direction_sign = +1 for flood-directed anomaly
direction_sign = -1 for drought-directed anomaly
direction_sign = 0 for neutral or ambiguous anomaly

Forecasting target:

target_risk_score_t_plus_1 = signed_risk_score at month t+1

Temporal Split Used In The Project

The project code uses target-month splitting:

train:   target_month <= 2025-05-01
test:    2025-06-01 <= target_month <= 2026-05-01
holdout: target_month == 2026-06-01

The June 2026 source row has no July 2026 target and is excluded from supervised evaluation.

Loading Example

from huggingface_hub import snapshot_download
import pandas as pd

local_dir = snapshot_download(
    repo_id="Aki298/AML-Johor-Tangkak-Sagil-GEE-dataset",
    repo_type="dataset",
    local_dir="data",
)

processed = pd.read_csv("data/processed/processed_data.csv")
coordinates = pd.read_csv("data/input/coordinates.csv")

Limitations

• The target risk score is self-supervised and proxy-based, not field-verified.
• The current release covers one agricultural study area in Sagil, Johor.
• The effective independent sample size is smaller than the row count because neighboring grid cells and adjacent months are correlated.
• Next-month flood/drought behavior may depend on future rainfall and hydrological conditions that are not known at month t.
• Model results should be interpreted as prototype environmental-risk forecasting, not operational disaster forecasting.

Citation

If using this dataset, cite the dataset repository and acknowledge the original Earth observation and climate data providers used through Google Earth Engine.