diff --git a/ndvi.py b/ndvi.py
index f12b62f..20fe2bc 100644
--- a/ndvi.py
+++ b/ndvi.py
@@ -94,3 +94,88 @@ def create_ndvi_timeseries(year, site_position, site_name):
         print(
             f"[NDVI-{source.upper()}] Saved: {timeseries_file} ({len(timeseries)} entries)"
         )
+
+
+def generate_ndvi_fusion(year, site_position, site_name):
+    input_dir = Path(f"data/{site_name}/{year}/efast/fusion/")
+    output_dir = Path(f"data/{site_name}/{year}/ndvi/fusion/")
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    print(f"[NDVI-FUSION] Processing {input_dir}...")
+
+    geotiff_files = sorted(input_dir.glob("REFL_*.tif"))
+    if not geotiff_files:
+        print(f"[NDVI-FUSION] No files found")
+        return
+
+    for geotiff_file in geotiff_files:
+        date_str = geotiff_file.stem.split("_")[1]
+        output_file = output_dir / f"{date_str}_ndvi.geotiff"
+
+        if output_file.exists():
+            print(f"[NDVI-FUSION] Skipping {geotiff_file.name} (exists)")
+            continue
+
+        with rasterio.open(geotiff_file) as src:
+            red = src.read(3).astype(np.float32)
+            nir = src.read(4).astype(np.float32)
+
+            mask = (red > 0) & (nir > 0)
+            ndvi = np.zeros_like(red, dtype=np.float32)
+            ndvi[mask] = (nir[mask] - red[mask]) / (nir[mask] + red[mask])
+
+            profile = src.profile.copy()
+            profile.update(
+                {
+                    "count": 1,
+                    "dtype": "float32",
+                    "nodata": 0,
+                    "compress": "lzw",
+                }
+            )
+
+            with rasterio.open(output_file, "w", **profile) as dst:
+                dst.write(ndvi, 1)
+                dst.set_band_description(1, "NDVI")
+
+        print(f"[NDVI-FUSION] Saved: {output_file}")
+
+    print(f"[NDVI-FUSION] Completed")
+
+
+def create_ndvi_timeseries_fusion(year, site_position, site_name):
+    output_dir = Path(f"data/{site_name}/{year}/ndvi/fusion/")
+
+    print(f"[NDVI-FUSION] Creating timeseries.json...")
+    timeseries = []
+
+    ndvi_files = sorted(output_dir.glob("*.geotiff"))
+    for ndvi_file in ndvi_files:
+        filename = ndvi_file.name
+        date_str = filename.split("_")[0]
+        try:
+            date = datetime.strptime(date_str, "%Y%m%d").isoformat()
+        except ValueError:
+            date = date_str
+
+        ndvi_value = None
+        try:
+            with rasterio.open(ndvi_file) as src:
+                lon, lat = site_position[1], site_position[0]
+                x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
+                samples = list(src.sample([(x[0], y[0])]))
+                if samples and len(samples) > 0:
+                    value = float(samples[0][0])
+                    if value != 0 and not np.isnan(value):
+                        ndvi_value = value
+        except Exception as e:
+            print(f"[NDVI-FUSION] Warning: Could not sample {filename}: {e}")
+
+        timeseries.append({"date": date, "filename": filename, "ndvi": ndvi_value})
+
+    timeseries.sort(key=lambda x: x["date"])
+    timeseries_file = output_dir / "timeseries.json"
+    with open(timeseries_file, "w") as f:
+        json.dump(timeseries, f, indent=2)
+
+    print(f"[NDVI-FUSION] Saved: {timeseries_file} ({len(timeseries)} entries)")
diff --git a/run.py b/run.py
index ae58b5e..653b053 100644
--- a/run.py
+++ b/run.py
@@ -1,4 +1,5 @@
 from efast import run_efast, prepare_s2, prepare_s3
+from ndvi import generate_ndvi_fusion, create_ndvi_timeseries_fusion
 
 year = 2024
 site_position = (47.116171, 11.320308)
@@ -18,11 +19,16 @@ site_name = "innsbruck"
 # detect_clouds(year, site_name)
 # print("Cloud detection completed")
 
-print(f"Preparing data for EFAST fusion for {site_name}, {year}")
-prepare_s2(year, site_position, site_name)
-prepare_s3(year, site_position, site_name)
-print("Data preparation completed")
+# print(f"Preparing data for EFAST fusion for {site_name}, {year}")
+# prepare_s2(year, site_position, site_name)
+# prepare_s3(year, site_position, site_name)
+# print("Data preparation completed")
 
-print(f"Running EFAST fusion for {site_name}, {year}")
-run_efast(year, site_position, site_name)
-print("EFAST fusion completed")
+# print(f"Running EFAST fusion for {site_name}, {year}")
+# run_efast(year, site_position, site_name)
+# print("EFAST fusion completed")
+
+print(f"Generating NDVI for fusion outputs: {site_name}, {year}")
+generate_ndvi_fusion(year, site_position, site_name)
+create_ndvi_timeseries_fusion(year, site_position, site_name)
+print("Fusion NDVI generation completed")