samapriya · santanuremote · Dec 26, 2025
diff --git a/docs/tutorials/examples/Idaho_CropWise_VegDRI_Drought_2024.md b/docs/tutorials/examples/Idaho_CropWise_VegDRI_Drought_2024.md
@@ -0,0 +1,259 @@
+# Crop-Wise Drought Sensitivity Mapping in Idaho Using the Vegetation Drought Response Index (VegDRI), 2024
+
+## 1. What is VegDRI?
+
+The **Vegetation Drought Response Index (VegDRI)** is a weekly drought-monitoring product for the United States that captures how vegetation actually responds to drought stress, rather than only describing climate dryness.
+
+VegDRI was jointly developed by:
+- USGS EROS Center  
+- National Drought Mitigation Center (NDMC), University of Nebraska  
+- High Plains Regional Climate Center (HPRCC)
+
+VegDRI integrates three categories of information:
+1. Satellite-based vegetation condition
+2. Climate-based drought indicators
+3. Biophysical and environmental controls
+
+Satellite-based vegetation condition is derived from MODIS (Terra) NDVI data at 1 km spatial resolution using:
+- **PASG** (Percent Annual Seasonal Greenness)
+- **SOSA** (Start of Season Anomaly)
+
+Climate-based drought indicators include:
+- **PDSI** (Palmer Drought Severity Index)
+- **SPI** (Standardized Precipitation Index)
+
+Additional biophysical inputs include land use/land cover, soil available water capacity, elevation, irrigation status, and ecological region. Non-drought disturbances such as wildfire, pests, flooding, or land-use change can also influence VegDRI values.
+
+## Why VegDRI is useful for agriculture
+
+VegDRI reflects **actual crop stress** rather than potential drought conditions. For Idaho croplands such as alfalfa, potatoes, barley, and wheat, VegDRI enables:
+- Crop-wise drought sensitivity assessment  
+- Identification of spatial variability within the same crop  
+- Comparison of drought response across crop types  
+- Evaluation of drought response patterns for a single year (2024)
+
+## Citation
+
+Brown, J. F., Wardlow, B. D., Tadesse, T., Hayes, M. J., & Reed, B. C. (2008).  
+**The Vegetation Drought Response Index (VegDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation.**  
+*GIScience & Remote Sensing*, 45(1), 16–46.
+
+## What you will learn
+
+This workflow demonstrates how to:
+- Load VegDRI data in Google Earth Engine (GEE)
+- Extract VegDRI for Idaho croplands
+- Generate crop-wise drought sensitivity maps
+- Compare drought response among major crops in Idaho
+
+GEE Code Link: https://code.earthengine.google.com/902d5e23281c379a6a3e4ba15173a650
+
+---
+
+## Idaho VegDRI 2024 Analysis — Complete Workflow with Explanation
+
+```javascript
+// ======================================================================
+// STEP 1: DEFINE THE REGION OF INTEREST (IDAHO)
+// ----------------------------------------------------------------------
+// The TIGER/2018/States dataset is used to extract the boundary of Idaho.
+// This limits all subsequent analysis and visualization to Idaho only.
+// ======================================================================
+var idaho = ee.FeatureCollection('TIGER/2018/States')
+  .filter(ee.Filter.eq('NAME', 'Idaho'));
+
+// Center the map view over Idaho for better visualization
+Map.centerObject(idaho, 6);
+
+// ======================================================================
+// STEP 2: LOAD AND SCALE VEGDRI DATA FOR 2024
+// ----------------------------------------------------------------------
+// VegDRI values are stored as integer values.
+// They must be converted to scaled VegDRI values using:
+// VegDRI_scaled = (PixelValue - 128) / 16
+// This rescales the data to approximately -4 (dry) to +4 (moist).
+// ======================================================================
+var vegdri_raw = ee.ImageCollection('projects/climate-engine-pro/assets/ce-veg-dri')
+  .filterBounds(idaho)
+  .filterDate('2024-01-01', '2025-01-01')
+  .sort('system:time_start');
+
+// Function to apply VegDRI scaling to each image
+function scaleVeg(img){
+  return img.addBands(
+    img.select("vegdri")
+      .subtract(128)
+      .divide(16)
+      .rename("vegdri_scale")
+  );
+}
+
+// Apply scaling to all 2024 VegDRI images
+var veg2024 = vegdri_raw.map(scaleVeg);
+
+// ======================================================================
+// STEP 3: DEFINE VEGDRI VISUALIZATION PALETTE
+// ----------------------------------------------------------------------
+// Red tones represent dry conditions.
+// White represents near-normal vegetation conditions.
+// Green tones represent moist conditions.
+// ======================================================================
+var vegPaletteSimple = [
+  "#720206", "#cb3121", "#e36b09", "#fee301", // Dry
+  "#ffffff",                                // Normal
+  "#88f9c7", "#53c285", "#2b8032"           // Moist
+];
+
+// ======================================================================
+// STEP 4: LOAD USDA CROPLAND DATA LAYER (CDL)
+// ----------------------------------------------------------------------
+// The Cropland Data Layer (CDL) is used to identify crop-specific pixels.
+// Individual crops are selected using their CDL class codes.
+// ======================================================================
+var cdl = ee.ImageCollection("USDA/NASS/CDL")
+  .filterBounds(idaho)
+  .sort("system:time_start", false)
+  .first()
+  .select("cropland")
+  .clip(idaho);
+
+// List of major Idaho crops and their CDL codes
+var crops = [
+  {name: 'Corn', value: 1},
+  {name: 'Barley', value: 21},
+  {name: 'Spring Wheat', value: 23},
+  {name: 'Winter Wheat', value: 24},
+  {name: 'Alfalfa', value: 36},
+  {name: 'Potatoes', value: 43},
+  {name: 'Sugarbeets', value: 41}
+];
+
+// ======================================================================
+// STEP 5: CREATE ANNUAL MEAN VEGDRI COMPOSITE
+// ----------------------------------------------------------------------
+// All weekly VegDRI images from 2024 are averaged to produce
+// a single annual VegDRI map representing overall drought response.
+// ======================================================================
+var veg_annual = veg2024
+  .select("vegdri_scale")
+  .mean()
+  .rename("veg_annual");
+
+// Display the statewide annual VegDRI map
+Map.addLayer(
+  veg_annual.clip(idaho),
+  {min:-5, max:5, palette: vegPaletteSimple},
+  "VegDRI 2024 – Annual Mean"
+);
+
+// ======================================================================
+// STEP 6: GENERATE CROP-SPECIFIC VEGDRI MAPS
+// ----------------------------------------------------------------------
+// For each crop, the CDL is used to mask VegDRI pixels belonging
+// only to that crop, allowing crop-wise drought visualization.
+// ======================================================================
+function addCropAnnual(crop){
+  var mask = cdl.eq(crop.value).selfMask();
+  var layer = veg_annual.updateMask(mask);
+
+  Map.addLayer(
+    layer,
+    {min:-5, max:5, palette: vegPaletteSimple},
+    crop.name + " – VegDRI 2024",
+    false
+  );
+}
+
+// Apply the crop-specific mapping function
+crops.forEach(addCropAnnual);
+
+// ======================================================================
+// STEP 7: CALCULATE CROP-WISE STATISTICS
+// ----------------------------------------------------------------------
+// Mean and standard deviation of VegDRI are calculated for each crop
+// across the entire state of Idaho.
+// ======================================================================
+function cropStats(crop){
+  var mask = cdl.eq(crop.value).selfMask();
+
+  var stats = veg_annual.updateMask(mask)
+    .reduceRegion({
+      reducer: ee.Reducer.mean().combine({
+        reducer2: ee.Reducer.stdDev(),
+        sharedInputs: true
+      }),
+      geometry: idaho,
+      scale: 500,
+      maxPixels: 1e13
+    });
+
+  return ee.Feature(null, {
+    crop: crop.name,
+    mean: stats.get("veg_annual_mean"),
+    std: stats.get("veg_annual_stdDev")
+  });
+}
+
+// Convert statistics into a FeatureCollection
+var stats_fc = ee.FeatureCollection(crops.map(cropStats));
+
+// ======================================================================
+// STEP 8: VISUALIZE CROP-WISE MEAN VEGDRI
+// ----------------------------------------------------------------------
+// A bar chart is created to compare annual mean VegDRI
+// across different crop types.
+// ======================================================================
+var chart = ui.Chart.feature.byFeature(stats_fc, 'crop', ['mean'])
+  .setChartType('ColumnChart')
+  .setOptions({
+    title: "VegDRI 2024 – Annual Mean by Crop",
+    vAxis: {title: "VegDRI (scaled)"},
+    hAxis: {title: "Crop"},
+    legend: "none"
+  });
+
+// Display the chart in the console
+print(chart);
+
+// ======================================================================
+// STEP 9: ADD VEGDRI LEGEND
+// ----------------------------------------------------------------------
+// A custom legend is added to explain drought and moisture classes.
+// ======================================================================
+var legend = ui.Panel({
+  style: {position: 'bottom-left', padding: '8px'}
+});
+
+legend.add(ui.Label({
+  value: 'VegDRI Drought Scale',
+  style: {fontWeight:'bold', fontSize:'14px'}
+}));
+
+var labels = [
+  "Severe","Moderate–Severe","Moderate","Mild Dry",
+  "Normal","Mild Moist","Moist","Very Moist"
+];
+
+for (var i = 0; i < vegPaletteSimple.length; i++){
+  var row = ui.Panel({layout: ui.Panel.Layout.Flow('horizontal')});
+
+  row.add(ui.Label({
+    style:{
+      backgroundColor: vegPaletteSimple[i],
+      padding:'8px',
+      border:'1px solid black'
+    }
+  }));
+
+  row.add(ui.Label(labels[i], {margin:'6px 0 0 6px'}));
+  legend.add(row);
+}
+
+// Add the legend to the map
+Map.add(legend);
+
+
+
+
+
+