Added plot of timeseries with sentinel-2 and fusion.

ndvi.py

@@ -7,6 +7,8 @@ from datetime import datetime
 
 RED_BAND = 3
 NIR_BAND = 4
+BLUE_BAND = 1
+GREEN_BAND = 2
 
 
 def _calculate_and_write_ndvi(input_file, output_file):
@@ -62,21 +64,53 @@ def _get_ndvi_value(ndvi_file, site_position):
     return None
 
 
-def _create_timeseries_for_dir(output_dir, site_position, source_name):
+def _get_ndvi_from_original(input_file, site_position):
+    """Calculate NDVI directly from original file without creating GeoTIFF."""
+    try:
+        with rasterio.open(input_file) as src:
+            if src.count < 4:
+                return None
+            
+            red = src.read(RED_BAND).astype(np.float32)
+            nir = src.read(NIR_BAND).astype(np.float32)
+            
+            lon, lat = site_position[1], site_position[0]
+            x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
+            
+            if not (
+                src.bounds.left <= x[0] <= src.bounds.right
+                and src.bounds.bottom <= y[0] <= src.bounds.top
+            ):
+                return None
+            
+            row, col = src.index(x[0], y[0])
+            if row < 0 or row >= src.height or col < 0 or col >= src.width:
+                return None
+            
+            r_val = float(red[row, col])
+            n_val = float(nir[row, col])
+            
+            if r_val <= 0 or n_val <= 0 or np.isnan(r_val) or np.isnan(n_val):
+                return None
+            
+            ndvi = (n_val - r_val) / (n_val + r_val)
+            return ndvi if not np.isnan(ndvi) else None
+    except Exception as e:
+        return None
+
+
+def _create_timeseries_for_dir(input_dir, output_dir, site_position, source_name, pattern="*.geotiff"):
     print(f"[NDVI-{source_name}] Creating timeseries.json...")
     timeseries = []
 
-    for ndvi_file in sorted(output_dir.glob("*.geotiff")):
+    for input_file in sorted(input_dir.glob(pattern)):
-        filename = ndvi_file.name
+        if "DIST_CLOUD" in input_file.name:
-        # Extract date from filename
+            continue
-        # Format examples:
+        
-        # - YYYYMMDD_ndvi.geotiff -> date is at [0]
+        filename = input_file.name
-        # - YYYYMMDD_0.geotiff -> date is at [0]
-        # - composite_YYYYMMDD.geotiff -> date is at [1]
         parts = filename.replace(".geotiff", "").split("_")
         date_str = None
 
-        # Try to find a date pattern (8 digits)
         for part in parts:
             if len(part) == 8 and part.isdigit():
                 date_str = part
@@ -88,21 +122,17 @@ def _create_timeseries_for_dir(output_dir, site_position, source_name):
             except ValueError:
                 date = date_str
         else:
-            # Fallback: use first part (for old MSIL2A_ndvi.geotiff files)
             date_str = parts[0]
             date = date_str
             print(
                 f"[NDVI-{source_name}] Warning: Could not extract date from {filename}, using '{date_str}'"
             )
 
-        ndvi_value = _get_ndvi_value(ndvi_file, site_position)
+        ndvi_value = _get_ndvi_from_original(input_file, site_position)
         if ndvi_value is None:
             print(
                 f"[NDVI-{source_name}] Warning: Could not sample {filename} (outside bounds or nodata)"
             )
-        # Note: 0 is a valid NDVI value (no vegetation), so we keep it
-        # The _get_ndvi_value function now properly distinguishes between
-        # valid 0 values and nodata values
 
         timeseries.append({"date": date, "filename": filename, "ndvi": ndvi_value})
 
@@ -154,16 +184,15 @@ def _process_ndvi_files(
 
 
 def generate_ndvi_raw(season, site_position, site_name):
-    for source in ["s2", "s3"]:
+    # No longer creating NDVI GeoTIFF files, only timeseries
-        input_dir = Path(f"data/{site_name}/{season}/raw/{source}/")
+    pass
-        output_dir = Path(f"data/{site_name}/{season}/raw/ndvi/{source}/")
-        _process_ndvi_files(input_dir, output_dir, source.upper())
 
 
 def create_ndvi_timeseries_raw(season, site_position, site_name):
     for source in ["s2", "s3"]:
+        input_dir = Path(f"data/{site_name}/{season}/raw/{source}/")
         output_dir = Path(f"data/{site_name}/{season}/raw/ndvi/{source}/")
-        _create_timeseries_for_dir(output_dir, site_position, source.upper())
+        _create_timeseries_for_dir(input_dir, output_dir, site_position, source.upper())
 
 
 def _get_output_name_prepared(geotiff_file):
@@ -192,34 +221,208 @@ def _fusion_namer(f):
 
 
 def generate_ndvi_post_process(season, site_position, site_name):
-    for source in ["s2", "s3"]:
+    # No longer creating NDVI GeoTIFF files, only timeseries
-        input_dir = Path(f"data/{site_name}/{season}/processed/{source}/")
+    pass
-        output_dir = Path(f"data/{site_name}/{season}/processed/ndvi/{source}/")
-        _process_ndvi_files(
-            input_dir,
-            output_dir,
-            f"POST-PROCESS-{source.upper()}",
-            pattern="*.geotiff",
-            output_namer=lambda f: f.name.replace(".geotiff", "_ndvi.geotiff"),
-        )
-
-    input_dir = Path(f"data/{site_name}/{season}/processed/fusion/")
-    output_dir = Path(f"data/{site_name}/{season}/processed/ndvi/fusion/")
-    _process_ndvi_files(
-        input_dir,
-        output_dir,
-        "POST-PROCESS-FUSION",
-        pattern="*.geotiff",
-        output_namer=lambda f: f.name.replace(".geotiff", "_ndvi.geotiff"),
-    )
 
 
 def create_ndvi_timeseries_post_process(season, site_position, site_name):
     for source in ["s2", "s3"]:
+        input_dir = Path(f"data/{site_name}/{season}/processed/{source}/")
         output_dir = Path(f"data/{site_name}/{season}/processed/ndvi/{source}/")
         _create_timeseries_for_dir(
-            output_dir, site_position, f"POST-PROCESS-{source.upper()}"
+            input_dir, output_dir, site_position, f"POST-PROCESS-{source.upper()}"
         )
 
+    input_dir = Path(f"data/{site_name}/{season}/processed/fusion/")
     output_dir = Path(f"data/{site_name}/{season}/processed/ndvi/fusion/")
-    _create_timeseries_for_dir(output_dir, site_position, "POST-PROCESS-FUSION")
+    _create_timeseries_for_dir(input_dir, output_dir, site_position, "POST-PROCESS-FUSION")
+
+
+def _calculate_and_write_gcc(input_file, output_file):
+    with rasterio.open(input_file) as src:
+        blue = src.read(BLUE_BAND).astype(np.float32)
+        green = src.read(GREEN_BAND).astype(np.float32)
+        red = src.read(RED_BAND).astype(np.float32)
+
+        total = red + green + blue
+        mask = total > 0
+        gcc = np.zeros_like(green, dtype=np.float32)
+        gcc[mask] = green[mask] / total[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(gcc, 1)
+            dst.set_band_description(1, "GCC")
+
+
+def _get_gcc_value(gcc_file, site_position):
+    try:
+        with rasterio.open(gcc_file) as src:
+            lon, lat = site_position[1], site_position[0]
+            x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
+
+            if not (
+                src.bounds.left <= x[0] <= src.bounds.right
+                and src.bounds.bottom <= y[0] <= src.bounds.top
+            ):
+                return None
+
+            samples = list(src.sample([(x[0], y[0])]))
+            if samples:
+                value = float(samples[0][0])
+                if src.nodata is not None and value == src.nodata:
+                    return None
+                if np.isnan(value):
+                    return None
+                return value
+    except Exception as e:
+        print(f"Error sampling {gcc_file.name}: {e}")
+        pass
+    return None
+
+
+def _get_gcc_from_original(input_file, site_position):
+    """Calculate GCC directly from original file without creating GeoTIFF."""
+    try:
+        with rasterio.open(input_file) as src:
+            if src.count < 3:
+                return None
+            
+            blue = src.read(BLUE_BAND).astype(np.float32)
+            green = src.read(GREEN_BAND).astype(np.float32)
+            red = src.read(RED_BAND).astype(np.float32)
+            
+            lon, lat = site_position[1], site_position[0]
+            x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
+            
+            if not (
+                src.bounds.left <= x[0] <= src.bounds.right
+                and src.bounds.bottom <= y[0] <= src.bounds.top
+            ):
+                return None
+            
+            row, col = src.index(x[0], y[0])
+            if row < 0 or row >= src.height or col < 0 or col >= src.width:
+                return None
+            
+            b_val = float(blue[row, col])
+            g_val = float(green[row, col])
+            r_val = float(red[row, col])
+            
+            total = r_val + g_val + b_val
+            if total <= 0 or np.isnan(total):
+                return None
+            
+            gcc = g_val / total
+            return gcc if not np.isnan(gcc) else None
+    except Exception as e:
+        return None
+
+
+def _create_gcc_timeseries_for_dir(input_dir, output_dir, site_position, source_name, pattern="*.geotiff"):
+    print(f"[GCC-{source_name}] Creating timeseries.json...")
+    timeseries = []
+
+    for input_file in sorted(input_dir.glob(pattern)):
+        if "DIST_CLOUD" in input_file.name:
+            continue
+        
+        filename = input_file.name
+        parts = filename.replace(".geotiff", "").split("_")
+        date_str = None
+
+        for part in parts:
+            if len(part) == 8 and part.isdigit():
+                date_str = part
+                break
+
+        if date_str:
+            try:
+                date = datetime.strptime(date_str, "%Y%m%d").isoformat()
+            except ValueError:
+                date = date_str
+        else:
+            date_str = parts[0]
+            date = date_str
+            print(
+                f"[GCC-{source_name}] Warning: Could not extract date from {filename}, using '{date_str}'"
+            )
+
+        gcc_value = _get_gcc_from_original(input_file, site_position)
+        if gcc_value is None:
+            print(
+                f"[GCC-{source_name}] Warning: Could not sample {filename} (outside bounds or nodata)"
+            )
+
+        timeseries.append({"date": date, "filename": filename, "greenness_index": gcc_value})
+
+    timeseries.sort(key=lambda x: x["date"])
+    output_dir.mkdir(parents=True, exist_ok=True)
+    timeseries_file = output_dir / "timeseries.json"
+    with open(timeseries_file, "w") as f:
+        json.dump(timeseries, f, indent=2)
+
+    print(f"[GCC-{source_name}] Saved: {timeseries_file} ({len(timeseries)} entries)")
+
+
+def _process_gcc_files(
+    input_dir, output_dir, source_name, pattern="*.geotiff", output_namer=None
+):
+    output_dir.mkdir(parents=True, exist_ok=True)
+    print(f"[GCC-{source_name}] Processing {input_dir}...")
+
+    geotiff_files = sorted(input_dir.glob(pattern))
+    if not geotiff_files:
+        print(f"[GCC-{source_name}] No files found")
+        return
+
+    for geotiff_file in geotiff_files:
+        if "DIST_CLOUD" in geotiff_file.name:
+            continue
+
+        try:
+            with rasterio.open(geotiff_file) as src:
+                if src.count < 3:
+                    print(
+                        f"[GCC-{source_name}] Skipping {geotiff_file.name} (only {src.count} band(s), need 3+)"
+                    )
+                    continue
+        except Exception as e:
+            print(
+                f"[GCC-{source_name}] Skipping {geotiff_file.name} (error reading: {e})"
+            )
+            continue
+
+        output_file = output_dir / (
+            output_namer(geotiff_file) if output_namer else geotiff_file.name
+        )
+
+        _calculate_and_write_gcc(geotiff_file, output_file)
+        print(f"[GCC-{source_name}] Saved: {output_file}")
+
+
+def generate_gcc_post_process(season, site_position, site_name):
+    # No longer creating GCC GeoTIFF files, only timeseries
+    pass
+
+
+def create_gcc_timeseries_post_process(season, site_position, site_name):
+    for source in ["s2", "s3"]:
+        input_dir = Path(f"data/{site_name}/{season}/processed/{source}/")
+        output_dir = Path(f"data/{site_name}/{season}/processed/gcc/{source}/")
+        _create_gcc_timeseries_for_dir(
+            input_dir, output_dir, site_position, f"POST-PROCESS-{source.upper()}"
+        )
+
+    input_dir = Path(f"data/{site_name}/{season}/processed/fusion/")
+    output_dir = Path(f"data/{site_name}/{season}/processed/gcc/fusion/")
+    _create_gcc_timeseries_for_dir(input_dir, output_dir, site_position, "POST-PROCESS-FUSION")

run.py

@@ -5,6 +5,8 @@ from ndvi import (
     create_ndvi_timeseries_raw,
     generate_ndvi_post_process,
     create_ndvi_timeseries_post_process,
+    generate_gcc_post_process,
+    create_gcc_timeseries_post_process,
 )
 from download_s2 import download_s2
 from download_s3 import download_s3
@@ -18,7 +20,7 @@ def run_pipeline(season, site_position, site_name):
         # download_s2(season, site_position, site_name)
         # download_s3(season, site_position, site_name)
         # download_phenocam(season, site_position, site_name)
-        download_phenocam_greenness(season, site_position, site_name)
+        #download_phenocam_greenness(season, site_position, site_name)
 
         # print(f"Generating NDVI for raw data: {site_name}, {season}")
         # generate_ndvi_raw(season, site_position, site_name)
@@ -39,6 +41,9 @@ def run_pipeline(season, site_position, site_name):
         #print(f"Generating NDVI for final outputs: {site_name}, {season}")
         #generate_ndvi_post_process(season, site_position, site_name)
         #create_ndvi_timeseries_post_process(season, site_position, site_name)
+        #print(f"Generating GCC for final outputs: {site_name}, {season}")
+        generate_gcc_post_process(season, site_position, site_name)
+        create_gcc_timeseries_post_process(season, site_position, site_name)
 
     except Exception as e:
         print(f"Error: {e}")

webapp/index.html

@@ -29,6 +29,9 @@
         .map-label { font-size: 12px; margin-bottom: 5px; color: #666; }
         .map-date { font-size: 11px; margin-top: 5px; color: #999; }
         .map { height: 500px; border: 1px solid #ccc; }
+        .combined-plot { margin-top: 20px; }
+        .combined-plot-label { font-size: 12px; margin-bottom: 5px; color: #666; }
+        .combined-plot-canvas { width: 100%; height: 120px; border: 1px solid #ccc; }
         .leaflet-image-layer { image-rendering: pixelated; }
         .leaflet-control-attribution { display: none; }
     </style>
@@ -60,36 +63,37 @@
                 <h3>S2</h3>
                 <div class="timeseries-label">NDVI Timeseries</div>
                 <canvas id="s2timeseries" class="timeseries"></canvas>
+                <div class="timeseries-label">Greenness Index Timeseries</div>
+                <canvas id="s2gcctimeseries" class="timeseries"></canvas>
                 <div class="map-label">RGB Composite</div>
                 <div id="s2rgbdate" class="map-date"></div>
                 <div id="s2map" class="map"></div>
-                <div class="map-label">NDVI</div>
-                <div id="s2ndvidate" class="map-date"></div>
-                <div id="s2ndvimap" class="map"></div>
             </div>
             <div class="map-container">
                 <h3>Fusion</h3>
                 <div class="timeseries-label">NDVI Timeseries</div>
                 <canvas id="fusiontimeseries" class="timeseries"></canvas>
+                <div class="timeseries-label">Greenness Index Timeseries</div>
+                <canvas id="fusiongcctimeseries" class="timeseries"></canvas>
                 <div class="map-label">RGB Composite</div>
                 <div id="fusionrgbdate" class="map-date"></div>
                 <div id="fusionmap" class="map"></div>
-                <div class="map-label">NDVI</div>
-                <div id="fusionndvidate" class="map-date"></div>
-                <div id="fusionndvimap" class="map"></div>
             </div>
             <div class="map-container">
                 <h3>S3</h3>
                 <div class="timeseries-label">NDVI Timeseries</div>
                 <canvas id="s3timeseries" class="timeseries"></canvas>
+                <div class="timeseries-label">Greenness Index Timeseries</div>
+                <canvas id="s3gcctimeseries" class="timeseries"></canvas>
                 <div class="map-label">RGB Composite</div>
                 <div id="s3rgbdate" class="map-date"></div>
                 <div id="s3map" class="map"></div>
-                <div class="map-label">NDVI</div>
-                <div id="s3ndvidate" class="map-date"></div>
-                <div id="s3ndvimap" class="map"></div>
             </div>
         </div>
+        <div class="combined-plot">
+            <div class="combined-plot-label">Greenness Index Timeseries (S2 & Fusion)</div>
+            <canvas id="combinedgcctimeseries" class="combined-plot-canvas"></canvas>
+        </div>
     </div>
     <script>
         proj4.defs("EPSG:32632", "+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs");
@@ -109,26 +113,19 @@
             fusion: L.map("fusionmap", mapOpts).setView(sitePosition, 12).addLayer(L.tileLayer(osmUrl, osmOpts)),
             s3: L.map("s3map", mapOpts).setView(sitePosition, 12).addLayer(L.tileLayer(osmUrl, osmOpts))
         };
-        const ndvimaps = {
-            s2: L.map("s2ndvimap", mapOpts).setView(sitePosition, 12).addLayer(L.tileLayer(osmUrl, osmOpts)),
-            fusion: L.map("fusionndvimap", mapOpts).setView(sitePosition, 12).addLayer(L.tileLayer(osmUrl, osmOpts)),
-            s3: L.map("s3ndvimap", mapOpts).setView(sitePosition, 12).addLayer(L.tileLayer(osmUrl, osmOpts))
-        };
         const overlays = { s2: null, fusion: null, s3: null };
-        const ndviOverlays = { s2: null, fusion: null, s3: null };
         const markers = { s2: null, fusion: null, s3: null };
-        const ndviMarkers = { s2: null, fusion: null, s3: null };
         let timeseries = { s2: [], fusion: [], s3: [] };
-        let greennessTimeseries = [];
+        let greennessTimeseries = { s2: [], fusion: [], s3: [] };
+        let phenocamGreennessTimeseries = [];
         
         // Add site marker to all maps
         for (const source of ["s2", "fusion", "s3"]) {
             markers[source] = L.marker(sitePosition, { icon: L.divIcon({ className: "site-marker", html: "<div style='width:5px;height:5px;background:red;border:1px solid white;border-radius:50%;box-shadow:0 0 1px rgba(0,0,0,0.5);'></div>", iconSize: [5, 5] }) }).addTo(maps[source]);
-            ndviMarkers[source] = L.marker(sitePosition, { icon: L.divIcon({ className: "site-marker", html: "<div style='width:5px;height:5px;background:red;border:1px solid white;border-radius:50%;box-shadow:0 0 1px rgba(0,0,0,0.5);'></div>", iconSize: [5, 5] }) }).addTo(ndvimaps[source]);
         }
         
         let syncing = false;
-        const allMaps = Object.values(maps).concat(Object.values(ndvimaps));
+        const allMaps = Object.values(maps);
         const syncMaps = (sourceMap) => {
             if (syncing) return;
             syncing = true;
@@ -144,19 +141,95 @@
         });
         
         async function loadTimeseries() {
-            const [s2, fusion, s3, greenness] = await Promise.all([
+            const [s2, fusion, s3, s2gcc, fusiongcc, s3gcc, phenocam] = await Promise.all([
                 fetch("../data/innsbruck/2024/processed/ndvi/s2/timeseries.json").then(r => r.json()),
                 fetch("../data/innsbruck/2024/processed/ndvi/fusion/timeseries.json").then(r => r.json()).catch(() => []),
                 fetch("../data/innsbruck/2024/processed/ndvi/s3/timeseries.json").then(r => r.json()),
+                fetch("../data/innsbruck/2024/processed/gcc/s2/timeseries.json").then(r => r.json()).catch(() => []),
+                fetch("../data/innsbruck/2024/processed/gcc/fusion/timeseries.json").then(r => r.json()).catch(() => []),
+                fetch("../data/innsbruck/2024/processed/gcc/s3/timeseries.json").then(r => r.json()).catch(() => []),
                 fetch("../data/innsbruck/2024/raw/phenocam/timeseries.json").then(r => r.json()).catch(() => [])
             ]);
             timeseries = { s2, fusion, s3 };
-            greennessTimeseries = greenness;
+            greennessTimeseries = { s2: s2gcc, fusion: fusiongcc, s3: s3gcc };
+            phenocamGreennessTimeseries = phenocam;
             drawTimeseries();
             drawGreennessTimeseries();
+            drawPhenocamGreennessTimeseries();
+            drawCombinedGreennessTimeseries();
         }
         
         function drawGreennessTimeseries() {
+            const currentDate = dateFromDays(parseInt(slider.value));
+            for (const source of ["s2", "fusion", "s3"]) {
+                const canvas = document.getElementById(`${source}gcctimeseries`);
+                const ctx = canvas.getContext("2d");
+                canvas.width = canvas.offsetWidth;
+                canvas.height = 120;
+                const w = canvas.width, h = canvas.height;
+                const pad = 30;
+                const plotW = w - pad * 2, plotH = h - pad * 2;
+                
+                ctx.clearRect(0, 0, w, h);
+                let data = greennessTimeseries[source].filter(t => t.date && t.greenness_index !== null && t.greenness_index !== undefined);
+                if (!data.length) continue;
+                
+                const dates = data.map(t => new Date(t.date));
+                const minDate = new Date(Math.min(...dates));
+                const maxDate = new Date(Math.max(...dates));
+                const dateRange = maxDate - minDate || 1;
+                
+                const values = data.map(t => t.greenness_index);
+                const minVal = Math.min(...values);
+                const maxVal = Math.max(...values);
+                const valRange = maxVal - minVal || 1;
+                
+                const x = (d) => pad + ((new Date(d) - minDate) / dateRange) * plotW;
+                const y = (v) => pad + plotH - ((v - minVal) / valRange) * plotH;
+                
+                ctx.strokeStyle = "#ccc";
+                ctx.beginPath();
+                ctx.moveTo(pad, pad);
+                ctx.lineTo(pad, pad + plotH);
+                ctx.lineTo(pad + plotW, pad + plotH);
+                ctx.stroke();
+                
+                ctx.fillStyle = "#000";
+                ctx.font = "9px sans-serif";
+                ctx.fillText(minVal.toFixed(3), 2, pad + plotH + 10);
+                ctx.fillText(maxVal.toFixed(3), 2, pad + 3);
+                
+                ctx.strokeStyle = "#00aa00";
+                ctx.beginPath();
+                let first = true;
+                for (const t of data) {
+                    const px = x(t.date), py = y(t.greenness_index);
+                    if (first) { ctx.moveTo(px, py); first = false; }
+                    else ctx.lineTo(px, py);
+                }
+                ctx.stroke();
+                
+                const xPos = x(currentDate);
+                ctx.strokeStyle = "#f00";
+                ctx.lineWidth = 2;
+                ctx.beginPath();
+                ctx.moveTo(xPos, pad);
+                ctx.lineTo(xPos, pad + plotH);
+                ctx.stroke();
+                
+                const closest = data.reduce((c, t) => 
+                    Math.abs(new Date(t.date) - new Date(currentDate)) < Math.abs(new Date(c.date) - new Date(currentDate)) ? t : c
+                );
+                if (closest && closest.greenness_index !== null) {
+                    const yPos = y(closest.greenness_index);
+                    ctx.fillStyle = "#f00";
+                    ctx.font = "bold 10px sans-serif";
+                    ctx.fillText(closest.greenness_index.toFixed(3), xPos + 5, yPos - 5);
+                }
+            }
+        }
+        
+        function drawPhenocamGreennessTimeseries() {
             const canvas = document.getElementById("greennesstimeseries");
             const ctx = canvas.getContext("2d");
             canvas.width = canvas.offsetWidth;
@@ -166,7 +239,7 @@
             const plotW = w - pad * 2, plotH = h - pad * 2;
             
             ctx.clearRect(0, 0, w, h);
-            const data = greennessTimeseries.filter(t => t.date && t.greenness_index !== null && t.greenness_index !== undefined);
+            const data = phenocamGreennessTimeseries.filter(t => t.date && t.greenness_index !== null && t.greenness_index !== undefined);
             if (!data.length) return;
             
             const dates = data.map(t => new Date(t.date));
@@ -224,6 +297,79 @@
             }
         }
         
+        function drawCombinedGreennessTimeseries() {
+            const canvas = document.getElementById("combinedgcctimeseries");
+            const ctx = canvas.getContext("2d");
+            canvas.width = canvas.offsetWidth;
+            canvas.height = 120;
+            const w = canvas.width, h = canvas.height;
+            const pad = 30;
+            const plotW = w - pad * 2, plotH = h - pad * 2;
+            
+            ctx.clearRect(0, 0, w, h);
+            const s2data = greennessTimeseries.s2.filter(t => t.date && t.greenness_index !== null && t.greenness_index !== undefined);
+            const fusiondata = greennessTimeseries.fusion.filter(t => t.date && t.greenness_index !== null && t.greenness_index !== undefined);
+            if (!s2data.length && !fusiondata.length) return;
+            
+            const allDates = [...s2data.map(t => new Date(t.date)), ...fusiondata.map(t => new Date(t.date))];
+            const minDate = new Date(Math.min(...allDates));
+            const maxDate = new Date(Math.max(...allDates));
+            const dateRange = maxDate - minDate || 1;
+            
+            const allValues = [...s2data.map(t => t.greenness_index), ...fusiondata.map(t => t.greenness_index)];
+            const minVal = Math.min(...allValues);
+            const maxVal = Math.max(...allValues);
+            const valRange = maxVal - minVal || 1;
+            
+            const x = (d) => pad + ((new Date(d) - minDate) / dateRange) * plotW;
+            const y = (v) => pad + plotH - ((v - minVal) / valRange) * plotH;
+            
+            ctx.strokeStyle = "#ccc";
+            ctx.beginPath();
+            ctx.moveTo(pad, pad);
+            ctx.lineTo(pad, pad + plotH);
+            ctx.lineTo(pad + plotW, pad + plotH);
+            ctx.stroke();
+            
+            ctx.fillStyle = "#000";
+            ctx.font = "9px sans-serif";
+            ctx.fillText(minVal.toFixed(3), 2, pad + plotH + 10);
+            ctx.fillText(maxVal.toFixed(3), 2, pad + 3);
+            
+            if (s2data.length) {
+                ctx.strokeStyle = "#ff6600";
+                ctx.beginPath();
+                let first = true;
+                for (const t of s2data) {
+                    const px = x(t.date), py = y(t.greenness_index);
+                    if (first) { ctx.moveTo(px, py); first = false; }
+                    else ctx.lineTo(px, py);
+                }
+                ctx.stroke();
+            }
+            
+            if (fusiondata.length) {
+                ctx.strokeStyle = "#9900cc";
+                ctx.beginPath();
+                let first = true;
+                for (const t of fusiondata) {
+                    const px = x(t.date), py = y(t.greenness_index);
+                    if (first) { ctx.moveTo(px, py); first = false; }
+                    else ctx.lineTo(px, py);
+                }
+                ctx.stroke();
+            }
+            
+            const currentDate = dateFromDays(parseInt(slider.value));
+            const xPos = x(currentDate);
+            ctx.strokeStyle = "#f00";
+            ctx.lineWidth = 2;
+            ctx.beginPath();
+            ctx.moveTo(xPos, pad);
+            ctx.lineTo(xPos, pad + plotH);
+            ctx.stroke();
+        }
+        
         function drawTimeseries() {
             const currentDate = dateFromDays(parseInt(slider.value));
             for (const source of ["s2", "fusion", "s3"]) {
@@ -275,7 +421,7 @@
                 ctx.fillText(minNdvi.toFixed(2), 2, pad + plotH + 10);
                 ctx.fillText(maxNdvi.toFixed(2), 2, pad + 3);
                 
-                ctx.strokeStyle = source === "s2" ? "#0066ff" : source === "fusion" ? "#00aa00" : "#ff6600";
+                ctx.strokeStyle = "#0066ff";
                 ctx.beginPath();
                 let first = true;
                 for (const t of data) {
@@ -395,49 +541,6 @@
             document.getElementById(`${source}rgbdate`).textContent = date;
         }
         
-        async function loadNDVI(source, filename, dateStr) {
-            const tiff = await GeoTIFF.fromArrayBuffer(await (await fetch(`../data/innsbruck/2024/processed/ndvi/${source}/${filename}`)).arrayBuffer());
-            const image = await tiff.getImage();
-            const data = Array.from((await image.readRasters())[0]);
-            const width = image.getWidth();
-            const height = image.getHeight();
-            const bbox = image.getBoundingBox();
-            const geoKeys = image.getGeoKeys();
-            const crsCode = geoKeys.ProjectedCSTypeGeoKey ? `EPSG:${geoKeys.ProjectedCSTypeGeoKey}` : 
-                          geoKeys.GeographicTypeGeoKey !== 4326 ? `EPSG:${geoKeys.GeographicTypeGeoKey}` : "EPSG:4326";
-            
-            let minVal = Infinity, maxVal = -Infinity;
-            for (const v of data) if (!isNaN(v) && v !== 0) { minVal = Math.min(minVal, v); maxVal = Math.max(maxVal, v); }
-            
-            const canvas = Object.assign(document.createElement("canvas"), { width, height });
-            const ctx = canvas.getContext("2d");
-            ctx.imageSmoothingEnabled = false;
-            const imgData = ctx.createImageData(width, height);
-            
-            for (let i = 0; i < data.length; i++) {
-                const val = data[i];
-                const idx = i * 4;
-                if (val === 0 || isNaN(val)) {
-                    imgData.data[idx + 3] = 0;
-                } else {
-                    const n = ((val - minVal) / (maxVal - minVal || 1)) * 255;
-                    imgData.data[idx] = imgData.data[idx + 1] = imgData.data[idx + 2] = n;
-                    imgData.data[idx + 3] = 255;
-                }
-            }
-            ctx.putImageData(imgData, 0, 0);
-            
-            const bounds = crsCode === "EPSG:4326" ? [[bbox[1], bbox[0]], [bbox[3], bbox[2]]] : transformBounds(bbox, crsCode);
-            if (ndviOverlays[source]) ndvimaps[source].removeLayer(ndviOverlays[source]);
-            ndviOverlays[source] = L.imageOverlay(canvas.toDataURL(), bounds, { opacity: 0.95 }).addTo(ndvimaps[source]);
-            ndvimaps[source].fitBounds(bounds);
-            
-            // Extract date from filename to show the actual date of the file found (closest available date)
-            const extractedDateStr = filename.split("_")[0];
-            const date = `${extractedDateStr.slice(0,4)}-${extractedDateStr.slice(4,6)}-${extractedDateStr.slice(6,8)}`;
-            document.getElementById(`${source}ndvidate`).textContent = date;
-        }
-        
         const fmtDate = (d) => `${d.getFullYear()}-${String(d.getMonth() + 1).padStart(2, "0")}-${String(d.getDate()).padStart(2, "0")}`;
         const dateFromDays = (days) => fmtDate(new Date(start.getTime() + days * 86400000));
         const daysFromDate = (dateStr) => {
@@ -445,37 +548,6 @@
             return Math.floor((new Date(y, m - 1, d) - start) / 86400000);
         };
         
-        async function findNDVIFile(dateStr, source) {
-            const target = new Date(dateStr);
-            // Search outward from target date (0, ±1, ±2, ±3, ...) until we find the closest file
-            // Check dates in order: exact, then -1, +1, then -2, +2, etc.
-            // Limit to ±365 days to avoid infinite search
-            for (let offset = 0; offset <= 365; offset++) {
-                // Check exact date first (offset=0)
-                if (offset === 0) {
-                    const date = target.toISOString().split("T")[0].replace(/-/g, "");
-                    const filename = `${date}_0_ndvi.geotiff`;
-                    try {
-                        const res = await fetch(`../data/innsbruck/2024/processed/ndvi/${source}/${filename}`, { method: 'HEAD' });
-                        if (res.ok) return filename;
-                    } catch {}
-                } else {
-                    // Check -offset and +offset days
-                    for (const dir of [-1, 1]) {
-                        const d = new Date(target);
-                        d.setDate(d.getDate() + offset * dir);
-                        const date = d.toISOString().split("T")[0].replace(/-/g, "");
-                        const filename = `${date}_0_ndvi.geotiff`;
-                        try {
-                            const res = await fetch(`../data/innsbruck/2024/processed/ndvi/${source}/${filename}`, { method: 'HEAD' });
-                            if (res.ok) return filename;
-                        } catch {}
-                    }
-                }
-            }
-            return null;
-        }
-        
         async function loadPhenoCam(dateStr) {
             const target = new Date(dateStr);
             for (let offset = 0; offset < 15; offset++) {
@@ -506,6 +578,8 @@
             history.replaceState({}, "", `?${params}`);
             drawTimeseries();
             drawGreennessTimeseries();
+            drawPhenocamGreennessTimeseries();
+            drawCombinedGreennessTimeseries();
             for (const source of ["s2", "fusion", "s3"]) {
                 const filename = await findFile(date, source);
                 if (filename) {
@@ -515,14 +589,6 @@
                         console.error(`Error loading ${source}:`, e);
                     }
                 }
-                const ndviFilename = await findNDVIFile(date, source);
-                if (ndviFilename) {
-                    try {
-                        await loadNDVI(source, ndviFilename, date);
-                    } catch (e) {
-                        console.error(`Error loading NDVI ${source}:`, e);
-                    }
-                }
             }
             await loadPhenoCam(date);
         }