Update app with SM-colored centroid map and sensor layout

app copy.py

@@ -0,0 +1,1205 @@
+# app.py
+# ============================================================
+# Soil Moisture Sensor Optimization – Gradio App
+# ------------------------------------------------------------
+# - Upload field GeoJSON (or draw AOI on map & export)
+# - For a given date:
+#       • Build grids at various cell sizes (GeoPandas + UTM)
+#       • Predict SM (ExtraTrees) at centroids via Earth Engine
+#       • Compute CV (%)
+#       • Return optimal N sensors (min CV, with tolerance rule)
+# - UI:
+#       • Left: inputs + AOI drawer (folium Draw + search + geolocation)
+#       • Right tabs:
+#           - Optimization (CV vs N + table)
+#           - Sensor layout (SM basemap + sensor locations)
+# ============================================================
+
+import os
+import json
+import math
+from pathlib import Path
+
+import requests
+import ee
+import joblib
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import gradio as gr
+
+from huggingface_hub import hf_hub_download
+
+import geopandas as gpd
+from shapely.geometry import box
+
+import folium
+from folium.plugins import Draw, LocateControl
+from branca.colormap import linear
+
+# ------------------------------------------------------------
+# Paths / config for model + example AOI
+# ------------------------------------------------------------
+
+HF_MODEL_REPO = os.environ.get(
+    "HF_MODEL_REPO",
+    "IWMIHQ/soil-moisture-sensor-optimizer-model",
+)
+
+HF_MODEL_FILE = os.environ.get(
+    "HF_MODEL_FILE",
+    "extratrees_s1_soil_moisture_points.pkl",
+)
+
+HF_FEATURES_FILE = os.environ.get(
+    "HF_FEATURES_FILE",
+    "extratrees_s1_soil_moisture_features.txt",
+)
+
+EXAMPLE_AOI_PATH = "examples/example_field.geojson"
+
+
+def load_model_and_features():
+    """
+    Download the ExtraTrees model + feature list from a Hugging Face
+    model repo, then load them into memory.
+    """
+    try:
+        model_path = hf_hub_download(
+            repo_id=HF_MODEL_REPO,
+            filename=HF_MODEL_FILE,
+            repo_type="model",
+        )
+        features_path = hf_hub_download(
+            repo_id=HF_MODEL_REPO,
+            filename=HF_FEATURES_FILE,
+            repo_type="model",
+        )
+    except Exception as e:
+        raise RuntimeError(
+            "Could not download model files from HF Hub.\n"
+            f"Repo: {HF_MODEL_REPO}\n"
+            f"Model file: {HF_MODEL_FILE}\n"
+            f"Features file: {HF_FEATURES_FILE}\n"
+            f"Original error: {e}"
+        )
+
+    model = joblib.load(model_path)
+    with open(features_path, "r") as f:
+        feature_cols = [ln.strip() for ln in f.readlines() if ln.strip()]
+
+    print(f"✅ Loaded model from {HF_MODEL_REPO}/{HF_MODEL_FILE}")
+    print(f"✅ Loaded {len(feature_cols)} feature names.")
+    return model, feature_cols
+
+
+# Load once at import time
+MODEL, FEATURE_COLS = load_model_and_features()
+
+
+# ------------------------------------------------------------
+# AOI drawer map (folium) – Draw + Search + Geolocation
+# ------------------------------------------------------------
+
+def make_drawer_map_html(center_lat: float = -23.0,
+                         center_lon: float = 30.0,
+                         zoom: int = 7) -> str:
+    """
+    Returns a folium map HTML string with:
+      - OSM basemap (default)
+      - Esri World Imagery (satellite) basemap
+      - Draw control (polygon only) with export to GeoJSON
+      - Geolocation button ("locate me") that auto-switches to satellite
+        when the location is found.
+    """
+    m = folium.Map(
+        location=[center_lat, center_lon],
+        zoom_start=zoom,
+        tiles=None,
+        control_scale=True,
+    )
+
+    # Base layers
+    osm_layer = folium.TileLayer(
+        "OpenStreetMap",
+        name="OpenStreetMap",
+        control=True,
+        show=True,
+    ).add_to(m)
+
+    sat_layer = folium.TileLayer(
+        tiles=(
+            "https://services.arcgisonline.com/ArcGIS/rest/services/"
+            "World_Imagery/MapServer/tile/{z}/{y}/{x}"
+        ),
+        attr="Esri, Maxar, Earthstar Geographics",
+        name="Esri World Imagery",
+        control=True,
+        show=False,
+    ).add_to(m)
+
+    # Draw tools (polygon only)
+    Draw(
+        export=True,
+        filename="aoi.geojson",
+        position="topleft",
+        draw_options={
+            "polyline": False,
+            "rectangle": False,
+            "circle": False,
+            "circlemarker": False,
+            "marker": False,
+            "polygon": True,
+        },
+        edit_options={
+            "edit": True,
+            "remove": True,
+        },
+    ).add_to(m)
+
+    # Geolocation ("locate me") button – auto start, top-right
+    LocateControl(
+        auto_start=True,
+        position="topright",
+        strings={"title": "My location"},
+        flyTo=True,
+        keepCurrentZoomLevel=False,
+        drawCircle=True,
+        drawMarker=True,
+    ).add_to(m)
+
+    folium.LayerControl().add_to(m)
+
+    # JS hook: when location is found, switch to satellite
+    map_js_name = m.get_name()
+    osm_js_name = osm_layer.get_name()
+    sat_js_name = sat_layer.get_name()
+
+    switch_script = f"""
+    <script>
+    {map_js_name}.on('locationfound', function(e) {{
+        try {{
+            {map_js_name}.removeLayer({osm_js_name});
+        }} catch (err) {{
+            console.log('OSM layer remove error:', err);
+        }}
+        {map_js_name}.addLayer({sat_js_name});
+    }});
+    </script>
+    """
+
+    m.get_root().html.add_child(folium.Element(switch_script))
+
+    return m._repr_html_()
+
+
+def geocode_place(query: str):
+    """
+    Use OpenStreetMap Nominatim to get (lat, lon) from a place name.
+    """
+    url = "https://nominatim.openstreetmap.org/search"
+    params = {"q": query, "format": "json", "limit": 1}
+    headers = {"User-Agent": "giims-sm-sensor-app/1.0"}
+
+    r = requests.get(url, params=params, headers=headers, timeout=15)
+    r.raise_for_status()
+    data = r.json()
+    if not data:
+        raise ValueError(f"No results for '{query}'.")
+    lat = float(data[0]["lat"])
+    lon = float(data[0]["lon"])
+    return lat, lon
+
+
+def update_drawer_map(search_query: str) -> str:
+    """
+    Gradio callback to refresh the AOI drawer map.
+    """
+    if not search_query or not search_query.strip():
+        return make_drawer_map_html()
+
+    try:
+        lat, lon = geocode_place(search_query.strip())
+        html = make_drawer_map_html(center_lat=lat, center_lon=lon, zoom=13)
+        return html
+    except Exception as e:
+        base = make_drawer_map_html()
+        msg = (
+            f"<div style='color:#b91c1c;font-size:13px;margin-bottom:4px;'>"
+            f"Could not find '{search_query}': {e}</div>"
+        )
+        return msg + base
+
+
+# ------------------------------------------------------------
+# Earth Engine AUTH via environment variables
+# ------------------------------------------------------------
+
+SA_EMAIL = os.environ.get(
+    "EE_SERVICE_ACCOUNT",
+    "zolokiala@tethys-app-1.iam.gserviceaccount.com",
+)
+PROJECT_ID = os.environ.get("EE_PROJECT_ID", "tethys-app-1")
+EE_KEY_JSON = os.environ.get("EE_SERVICE_ACCOUNT_KEY")  # full JSON as string
+
+
+def init_earth_engine():
+    if EE_KEY_JSON is None:
+        raise RuntimeError(
+            "EE_SERVICE_ACCOUNT_KEY env var is not set.\n"
+            "Add EE_SERVICE_ACCOUNT_KEY with the full service-account JSON."
+        )
+
+    key_path = "/tmp/ee-service-account.json"
+    if not os.path.exists(key_path):
+        with open(key_path, "w") as f:
+            f.write(EE_KEY_JSON)
+
+    from ee import ServiceAccountCredentials
+
+    credentials = ServiceAccountCredentials(SA_EMAIL, key_path)
+    ee.Initialize(credentials, project=PROJECT_ID)
+    print(f"✅ EE initialized: {SA_EMAIL} | project={PROJECT_ID}")
+
+
+init_earth_engine()
+
+# ---------------------------
+# USER SETTINGS (constants)
+# ---------------------------
+MAX_DAYS_DIFF = 6
+STEP_DAYS = 6
+AOI_BUFFER_M = 15000
+SCALE = 20
+
+# ---------------------------
+# DEM-based predictors (elev, slope)
+# ---------------------------
+DEM_COLL = ee.ImageCollection("COPERNICUS/DEM/GLO30")
+DEM = DEM_COLL.mosaic()
+DEM_ELEV = DEM.select("DEM").rename("elev")
+DEM_SLOPE = ee.Terrain.slope(DEM).rename("slope")
+
+# ---------------------------
+# Sentinel-1 collection
+# ---------------------------
+S1_ORBIT_PASS = None  # or "ASCENDING"/"DESCENDING"
+
+
+def get_s1_collection(aoi, orbit_pass=None):
+    col = (
+        ee.ImageCollection("COPERNICUS/S1_GRD")
+        .filterBounds(aoi)
+        .filter(ee.Filter.eq("instrumentMode", "IW"))
+        .filter(
+            ee.Filter.listContains("transmitterReceiverPolarisation", "VV")
+        )
+        .filter(
+            ee.Filter.listContains("transmitterReceiverPolarisation", "VH")
+        )
+    )
+    if orbit_pass:
+        col = col.filter(ee.Filter.eq("orbitProperties_pass", orbit_pass))
+    return col
+
+
+# ---------------------------
+# Step-day composites
+# ---------------------------
+
+def make_s1_composites(s1_col, start_date, end_date, step_days=6):
+    start = ee.Date(start_date)
+    end = ee.Date(end_date)
+    n = end.difference(start, "day").divide(step_days).ceil().int()
+
+    empty = (
+        ee.Image.constant([0, 0, 0])
+        .rename(["VV", "VH", "angle"])
+        .updateMask(ee.Image.constant(0))
+    )
+
+    def make_one(i):
+        i = ee.Number(i)
+        d0 = start.advance(i.multiply(step_days), "day")
+        d1 = d0.advance(step_days, "day")
+        win = s1_col.filterDate(d0, d1)
+
+        comp = ee.Image(
+            ee.Algorithms.If(
+                win.size().gt(0),
+                win.median().select(["VV", "VH", "angle"]),
+                empty,
+            )
+        )
+
+        mid = d0.advance(ee.Number(step_days).divide(2), "day")
+
+        comp = comp.set(
+            {
+                "system:time_start": mid.millis(),
+                "date": mid.format("YYYY-MM-dd"),
+                "n_images": win.size(),
+            }
+        )
+        return comp
+
+    comps = ee.ImageCollection(
+        ee.List.sequence(0, n.subtract(1)).map(make_one)
+    )
+    comps = comps.filter(ee.Filter.gt("n_images", 0))
+    return comps
+
+
+# ---------------------------
+# FC -> pandas
+# ---------------------------
+
+def fc_to_pandas(fc, force_columns=None):
+    d = fc.getInfo()
+    rows = [f.get("properties", {}) for f in d.get("features", [])]
+    df = pd.DataFrame(rows)
+    print("Downloaded rows   :", len(df))
+    print("Downloaded columns:", df.columns.tolist())
+
+    if force_columns:
+        for c in force_columns:
+            if c not in df.columns:
+                df[c] = np.nan
+                print(f"⚠️ Added missing column '{c}' with NaNs.")
+    return df
+
+
+# ============================================================
+# 1) Past-only join: composite_date <= date
+# ============================================================
+
+def attach_s1_nearest_composite_past(fc_obs, s1_comps, max_days_diff=6):
+    def add_t(f):
+        return f.set("t", ee.Date(f.get("date")).millis())
+
+    fc = fc_obs.map(add_t)
+
+    max_diff_ms = max_days_diff * 24 * 60 * 60 * 1000
+
+    diff_filter = ee.Filter.maxDifference(
+        difference=max_diff_ms,
+        leftField="t",
+        rightField="system:time_start",
+    )
+
+    past_filter = ee.Filter.greaterThanOrEquals(
+        leftField="t", rightField="system:time_start"
+    )
+
+    filt = ee.Filter.And(diff_filter, past_filter)
+
+    join = ee.Join.saveBest(matchKey="best_img", measureKey="time_diff")
+    joined = ee.FeatureCollection(join.apply(fc, s1_comps, filt))
+
+    matched = joined.filter(ee.Filter.notNull(["best_img"]))
+    unmatched = joined.size().subtract(matched.size())
+    print(
+        "🔎 Join matched (server-side):",
+        matched.size().getInfo(),
+        "/",
+        joined.size().getInfo(),
+    )
+    print(
+        "    Unmatched (no composite within -MAX_DAYS_DIFF BEFORE date):",
+        unmatched.getInfo(),
+    )
+
+    def sample_one(feat):
+        img = ee.Image(feat.get("best_img"))
+        full_img = img.addBands(DEM_ELEV).addBands(DEM_SLOPE)
+
+        vals = full_img.reduceRegion(
+            reducer=ee.Reducer.first(),
+            geometry=feat.geometry(),
+            scale=SCALE,
+            maxPixels=1e7,
+        )
+        return feat.set(
+            {
+                "VV": vals.get("VV"),
+                "VH": vals.get("VH"),
+                "angle": vals.get("angle"),
+                "elev": vals.get("elev"),
+                "slope": vals.get("slope"),
+                "comp_date": img.get("date"),
+                "time_diff_ms": feat.get("time_diff"),
+                "n_images": img.get("n_images"),
+            }
+        )
+
+    sampled = matched.map(sample_one)
+    got_vv = sampled.filter(ee.Filter.notNull(["VV"])).size()
+    tot = sampled.size()
+    print(
+        "🧪 Sampled non-null VV (server-side):",
+        got_vv.getInfo(),
+        "/",
+        tot.getInfo(),
+    )
+    return sampled
+
+
+# ============================================================
+# 2) Build grid centroids (GeoPandas + UTM → EE)
+# ============================================================
+
+def build_plot_grid_centroids(date_str, plot_geojson_path, cell_size_m):
+    """
+    Build a regular grid over the AOI using a local UTM CRS
+    (meter-based), clip it to the AOI, compute centroids, then
+    return as an Earth Engine FeatureCollection (WGS84) plus
+    the AOI geometry as ee.Geometry.
+    """
+    plot_geojson_path = Path(plot_geojson_path)
+    if not plot_geojson_path.exists():
+        raise FileNotFoundError(
+            f"Plot GeoJSON not found at {plot_geojson_path}."
+        )
+
+    print(f"[READ] {plot_geojson_path}")
+    aoi = gpd.read_file(plot_geojson_path)
+
+    if aoi.empty:
+        raise RuntimeError("AOI file has no features.")
+
+    aoi = aoi.dissolve().reset_index(drop=True)
+
+    print("[CRS] Estimating local UTM CRS...")
+    utm_crs = aoi.estimate_utm_crs()
+    aoi_utm = aoi.to_crs(utm_crs)
+
+    minx, miny, maxx, maxy = aoi_utm.total_bounds
+    print(f"[BOUNDS] {minx:.2f}, {miny:.2f}, {maxx:.2f}, {maxy:.2f}")
+
+    n_cols = math.ceil((maxx - minx) / cell_size_m)
+    n_rows = math.ceil((maxy - miny) / cell_size_m)
+    print(f"[GRID] rows={n_rows} cols={n_cols} cell_size={cell_size_m} m")
+
+    grid_polys = []
+    for i in range(n_cols):
+        x0 = minx + i * cell_size_m
+        x1 = x0 + cell_size_m
+        for j in range(n_rows):
+            y0 = miny + j * cell_size_m
+            y1 = y0 + cell_size_m
+            cell = box(x0, y0, x1, y1)
+            grid_polys.append(cell)
+
+    grid = gpd.GeoDataFrame({"geometry": grid_polys}, crs=utm_crs)
+
+    print("[CLIP] Clipping grid to AOI...")
+    grid_clip = gpd.overlay(grid, aoi_utm, how="intersection")
+
+    if grid_clip.empty:
+        raise RuntimeError(
+            f"Clipped grid is empty for cell_size_m={cell_size_m}. "
+            "Try a larger cell size or check your AOI geometry."
+        )
+
+    print("[CENTROIDS] Computing centroids in UTM...")
+    centroids = grid_clip.copy()
+    centroids["geometry"] = centroids.centroid
+
+    print("[CRS] Reprojecting AOI & centroids to EPSG:4326 ...")
+    aoi_4326 = aoi_utm.to_crs(epsg=4326)
+    centroids_4326 = centroids.to_crs(epsg=4326)
+
+    aoi_union = aoi_4326.geometry.unary_union
+    aoi_geojson = aoi_union.__geo_interface__
+    geom = ee.Geometry(aoi_geojson)
+
+    features = []
+    for _, row in centroids_4326.iterrows():
+        pt = row.geometry
+        lon = float(pt.x)
+        lat = float(pt.y)
+        feat = ee.Feature(
+            ee.Geometry.Point([lon, lat]),
+            {
+                "lon": lon,
+                "lat": lat,
+                "date": date_str,
+                "Sheet": "plot_grid",
+            },
+        )
+        features.append(feat)
+
+    fc_pts = ee.FeatureCollection(features)
+    print(f"[EE] Built {len(features)} centroids in EE FeatureCollection.")
+
+    return fc_pts, geom
+
+
+# ============================================================
+# 3) Predict SM on grid & compute CV
+# ============================================================
+
+def predict_sm_on_grid(date_target, plot_geojson_path, cell_size_m):
+    fc_pts, geom = build_plot_grid_centroids(
+        date_target, plot_geojson_path, cell_size_m
+    )
+    n_pts = fc_pts.size().getInfo()
+    print(f"✅ Grid centroids inside plot (cell size {cell_size_m} m): {n_pts}")
+    if n_pts == 0:
+        raise RuntimeError(
+            f"No grid centroids inside plot for cell_size_m={cell_size_m}.\n"
+            "Check GeoJSON coordinates and/or reduce cell_size_m."
+        )
+
+    aoi = geom.buffer(AOI_BUFFER_M)
+    s1 = get_s1_collection(aoi, S1_ORBIT_PASS)
+
+    start_wide = (
+        ee.Date(date_target)
+        .advance(-MAX_DAYS_DIFF, "day")
+        .format("YYYY-MM-dd")
+        .getInfo()
+    )
+    end_wide = ee.Date(date_target).format("YYYY-MM-dd").getInfo()
+    print("📅 Wide S1 date range (map):", start_wide, "to", end_wide)
+
+    s1_period = s1.filterDate(start_wide, end_wide)
+    n_s1 = s1_period.size().getInfo()
+    print("🛰️ S1 images in WIDE range (map):", n_s1)
+    if n_s1 == 0:
+        raise RuntimeError(
+            f"No S1 images in map period for this AOI (cell_size_m={cell_size_m}). "
+            "Try another date or expand range."
+        )
+
+    comps = make_s1_composites(s1_period, start_wide, end_wide, STEP_DAYS)
+    n_comps = comps.size().getInfo()
+    print("🧱 Composites kept (non-empty, map):", n_comps)
+    if n_comps == 0:
+        raise RuntimeError(
+            f"No non-empty composites for map inference (cell_size_m={cell_size_m}). "
+            "Try a larger STEP_DAYS or date window."
+        )
+
+    fc_pts_s1 = attach_s1_nearest_composite_past(
+        fc_pts, comps, MAX_DAYS_DIFF
+    )
+    n_pts_s1 = fc_pts_s1.size().getInfo()
+    print(f"✅ Grid centroids with S1 match: {n_pts_s1} / {n_pts}")
+    if n_pts_s1 == 0:
+        raise RuntimeError(
+            "No grid centroids could be matched to a Sentinel-1 composite in the past-only join."
+        )
+
+    df = fc_to_pandas(
+        fc_pts_s1,
+        force_columns=["VV", "VH", "angle", "elev", "slope", "lon", "lat"],
+    )
+
+    if len(df) == 0:
+        raise RuntimeError("Joined dataframe is empty (no rows).")
+
+    for col in ["VV", "VH", "angle"]:
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+
+    df["VV_VH_ratio"] = df["VV"] / df["VH"]
+    df["VV_minus_VH"] = df["VV"] - df["VH"]
+    df["VV_plus_VH"] = df["VV"] + df["VH"]
+    df["VV_dB"] = 10.0 * np.log10(df["VV"] + 1e-6)
+    df["VH_dB"] = 10.0 * np.log10(df["VH"] + 1e-6)
+
+    if "time_diff_ms" in df.columns:
+        df["time_diff_days"] = pd.to_numeric(
+            df["time_diff_ms"], errors="coerce"
+        ) / (1000.0 * 60.0 * 60.0 * 24.0)
+    if "n_images" in df.columns:
+        df["n_images"] = pd.to_numeric(df["n_images"], errors="coerce")
+
+    for col in ["elev", "slope"]:
+        if col in df.columns:
+            df[col] = pd.to_numeric(df[col], errors="coerce")
+
+    model = MODEL
+    feature_cols = FEATURE_COLS
+
+    for col in feature_cols:
+        if col not in df.columns:
+            df[col] = np.nan
+            print(
+                f"⚠️ Added missing feature column '{col}' with NaNs for map inference."
+            )
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+        med = df[col].median()
+        df[col] = df[col].fillna(med)
+
+    X = df[feature_cols].values
+    if X.shape[0] == 0:
+        raise RuntimeError("No samples available for prediction (X has 0 rows).")
+
+    df["sm_pred"] = model.predict(X)
+
+    mean_sm = df["sm_pred"].mean()
+    std_sm = df["sm_pred"].std(ddof=1)
+    cv_pct = (std_sm / mean_sm) * 100 if mean_sm != 0 else np.nan
+
+    print("\n=== SOIL MOISTURE UNIFORMITY (GRID CENTROIDS) ===")
+    print(f"Date        : {date_target}")
+    print(f"Cell size   : {cell_size_m} m")
+    print(f"Mean SM     : {mean_sm:.2f}")
+    print(f"Std  SM     : {std_sm:.2f}")
+    print(f"CV (percent): {cv_pct:.1f}%")
+    print(f"N centroids : {len(df)}")
+
+    map_csv = f"sm_map_{date_target}_grid_{cell_size_m}m.csv"
+    keep_cols = []
+    for col in [
+        "date",
+        "lat",
+        "lon",
+        "elev",
+        "slope",
+        "VV",
+        "VH",
+        "angle",
+        "sm_pred",
+        "comp_date",
+        "time_diff_days",
+        "n_images",
+    ]:
+        if col in df.columns and col not in keep_cols:
+            keep_cols.append(col)
+
+    out = df[keep_cols].copy()
+    out.to_csv(map_csv, index=False)
+    print("💾 Saved grid-centroid map CSV:", map_csv)
+    print("   Rows (grid cells / centroids):", len(out))
+
+    return cv_pct, out, geom
+
+
+# ============================================================
+# 4) Gradio core: run multiple grid sizes (CV tolerance rule)
+# ============================================================
+
+def run_sensor_optimization(date_target, geojson_file, cell_sizes_str):
+    if geojson_file is None:
+        msg = (
+            "<b>Provide a field AOI.</b> Upload a Polygon/MultiPolygon GeoJSON (EPSG:4326), "
+            "or use the AOI drawer to draw, export & upload."
+        )
+        raise gr.Error(msg)
+
+    plot_geojson_path = str(geojson_file)
+
+    try:
+        cell_sizes = [int(s.strip()) for s in cell_sizes_str.split(",") if s.strip()]
+    except Exception:
+        raise gr.Error(
+            "Could not parse grid sizes. Use a comma-separated list, e.g. '5,10,20,30'."
+        )
+
+    cvs = []
+    n_sensors = []
+    used_cell_sizes = []
+
+    for cell_size in cell_sizes:
+        print("\n" + "=" * 60)
+        print(f"🔧 Running grid size {cell_size} m ...")
+        try:
+            cv_pct, df_grid, _geom = predict_sm_on_grid(
+                date_target, plot_geojson_path, cell_size
+            )
+            cvs.append(cv_pct)
+            n_sensors.append(len(df_grid))
+            used_cell_sizes.append(cell_size)
+        except Exception as e:
+            print(f"⚠️ Skipping cell_size={cell_size} due to error: {e}")
+
+    if len(cvs) == 0:
+        raise gr.Error(
+            "All grid sizes failed. Check date, GeoJSON, or model availability."
+        )
+
+    summary_df = (
+        pd.DataFrame(
+            {
+                "cell_size_m": used_cell_sizes,
+                "n_sensors": n_sensors,
+                "cv_percent": cvs,
+            }
+        )
+        .sort_values("n_sensors")
+        .reset_index(drop=True)
+    )
+
+    # ----- Optimal choice: "statistically similar" CV -> fewer sensors -----
+    CV_TOLERANCE = 0.5  # CV percentage points
+
+    min_cv = float(summary_df["cv_percent"].min())
+    candidates = summary_df[summary_df["cv_percent"] <= min_cv + CV_TOLERANCE].copy()
+
+    best_row = candidates.sort_values("n_sensors").iloc[0]
+
+    opt_n = int(best_row["n_sensors"])
+    opt_cv = float(best_row["cv_percent"])
+    opt_cell = int(best_row["cell_size_m"])
+
+    print("\n[OPTIMAL GRID SELECTION]")
+    print(f"  Min CV overall        : {min_cv:.3f} %")
+    print(f"  CV tolerance          : ±{CV_TOLERANCE:.3f} %")
+    print("  Candidate grids (within tolerance):")
+    print(candidates)
+    print(
+        f"  → Chosen grid: cell_size={opt_cell} m, "
+        f"n_sensors={opt_n}, cv={opt_cv:.3f} %"
+    )
+
+    # Plot CV vs N, highlight optimal configuration
+    fig, ax = plt.subplots(figsize=(6, 4))
+    ax.plot(summary_df["n_sensors"], summary_df["cv_percent"], marker="o")
+    ax.set_xlabel("Number of sensors (N centroids)")
+    ax.set_ylabel("CV of soil moisture (%)")
+    ax.set_title(f"CV vs Number of Sensors – {date_target}")
+    ax.grid(True, alpha=0.3)
+
+    ax.scatter([opt_n], [opt_cv], s=120, marker="*", edgecolor="black")
+    ax.annotate(
+        f"Optimal\nN={opt_n}\nCV={opt_cv:.1f}%",
+        xy=(opt_n, opt_cv),
+        xytext=(5, 5),
+        textcoords="offset points",
+        fontsize=9,
+        bbox=dict(boxstyle="round,pad=0.3", fc="white", alpha=0.8),
+    )
+
+    ax.text(
+        0.99,
+        0.01,
+        f"Optimal grid ≈ {opt_cell} m",
+        transform=ax.transAxes,
+        ha="right",
+        va="bottom",
+        fontsize=8,
+        bbox=dict(boxstyle="round,pad=0.3", fc="white", alpha=0.6),
+    )
+
+    plt.tight_layout()
+
+    return fig, summary_df
+
+
+# ============================================================
+# 5) Centroid map (folium) + SM basemap + coordinates table
+# ============================================================
+
+def show_centroid_map(date_target, geojson_file, cell_size_m):
+    """
+    Build grid centroids for a single cell size, run the model
+    to predict soil moisture, and render:
+        - SM basemap (colored rectangles)
+        - Red sensor locations on top
+        - Table of coordinates + predicted SM
+    """
+    empty = pd.DataFrame(
+        columns=["sensor_id", "Longitude (°E)", "Latitude (°S)", "sm_pred"]
+    )
+
+    if geojson_file is None:
+        msg = (
+            "<i>Please upload a field GeoJSON first, then click "
+            "<b>Show centroid map</b>.</i>"
+        )
+        return msg, empty
+
+    plot_geojson_path = str(geojson_file)
+
+    try:
+        cell_size_m = int(cell_size_m)
+    except Exception:
+        msg = "<i>Cell size must be a single integer (e.g. 10, 20, 30).</i>"
+        return msg, empty
+
+    # Run inference for this grid size
+    try:
+        cv_pct, df_sm, geom = predict_sm_on_grid(
+            date_target, plot_geojson_path, cell_size_m
+        )
+    except Exception as e:
+        msg = (
+            f"<i>Could not build SM map for this configuration: {e}</i>"
+        )
+        return msg, empty
+
+    n_pts = len(df_sm)
+    if n_pts == 0:
+        msg = (
+            f"<i>No grid centroids inside the plot for cell_size_m={cell_size_m} m. "
+            "Try a smaller cell size or check your GeoJSON.</i>"
+        )
+        return msg, empty
+
+    print(f"🗺️ Preview map: {n_pts} centroids for cell size {cell_size_m} m")
+
+    centroid = geom.centroid().coordinates().getInfo()
+    lon_c, lat_c = centroid[0], centroid[1]
+
+    m = folium.Map(
+        location=[lat_c, lon_c],
+        zoom_start=16,
+        tiles=None,
+        control_scale=True,
+    )
+    folium.TileLayer(
+        tiles="https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}",
+        attr="Esri, Maxar, Earthstar Geographics",
+        name="Esri World Imagery",
+        show=True,
+    ).add_to(m)
+    folium.TileLayer("OpenStreetMap", name="OpenStreetMap", show=False).add_to(m)
+
+    # Field polygon
+    try:
+        with open(plot_geojson_path, "r") as f:
+            gj = json.load(f)
+        folium.GeoJson(
+            gj,
+            name="Field polygon",
+            style_function=lambda x: {
+                "color": "#10b981",
+                "weight": 2,
+                "fillOpacity": 0.05,
+            },
+        ).add_to(m)
+    except Exception as e:
+        print("⚠️ Could not add field polygon to map:", e)
+
+    # SM basemap (rectangles)
+    df_sm = df_sm.copy()
+    df_sm["lon"] = pd.to_numeric(df_sm["lon"], errors="coerce")
+    df_sm["lat"] = pd.to_numeric(df_sm["lat"], errors="coerce")
+    df_sm["sm_pred"] = pd.to_numeric(df_sm["sm_pred"], errors="coerce")
+
+    sm_min = float(df_sm["sm_pred"].min())
+    sm_max = float(df_sm["sm_pred"].max())
+    if sm_min == sm_max:
+        sm_min -= 0.5
+        sm_max += 0.5
+
+    colormap = linear.viridis.scale(sm_min, sm_max)
+    colormap.caption = "Predicted soil moisture (%)"
+    colormap.add_to(m)
+
+    rect_group = folium.FeatureGroup(name="SM basemap")
+    for _, row in df_sm.iterrows():
+        lat = row["lat"]
+        lon = row["lon"]
+        sm = row["sm_pred"]
+
+        if np.isnan(lat) or np.isnan(lon) or np.isnan(sm):
+            continue
+
+        cell_deg_lat = cell_size_m / 111_320.0
+        lat_rad = math.radians(lat)
+        cell_deg_lon = (cell_size_m / 111_320.0) / max(math.cos(lat_rad), 1e-6)
+
+        half_lat = cell_deg_lat / 2.0
+        half_lon = cell_deg_lon / 2.0
+
+        bounds = [
+            [lat - half_lat, lon - half_lon],
+            [lat + half_lat, lon + half_lon],
+        ]
+
+        folium.Rectangle(
+            bounds=bounds,
+            fill=True,
+            fill_color=colormap(sm),
+            fill_opacity=0.8,
+            stroke=False,
+        ).add_to(rect_group)
+
+    rect_group.add_to(m)
+
+    # Centroids / sensors – added AFTER rectangles so they are on top
+    df_coords = df_sm[["lon", "lat", "sm_pred"]].copy()
+    df_coords["lon"] = df_coords["lon"].round(6)
+    df_coords["lat"] = df_coords["lat"].round(6)
+    df_coords.insert(0, "sensor_id", np.arange(1, len(df_coords) + 1))
+    df_coords.rename(
+        columns={"lon": "Longitude (°E)", "lat": "Latitude (°S)"}, inplace=True
+    )
+
+    points_group = folium.FeatureGroup(name=f"Centroids ({n_pts} sensors)")
+    for _, row in df_coords.iterrows():
+        folium.CircleMarker(
+            location=[row["Latitude (°S)"], row["Longitude (°E)"]],
+            radius=5,               # a bit larger so clearly visible
+            color="#ef4444",
+            weight=1,
+            fill=True,
+            fill_color="#ef4444",
+            fill_opacity=0.95,
+            popup=(
+                f"id={int(row['sensor_id'])}<br>"
+                f"SM={row['sm_pred']:.2f} %<br>"
+                f"lon={row['Longitude (°E)']}, lat={row['Latitude (°S)']}"
+            ),
+        ).add_to(points_group)
+
+    points_group.add_to(m)  # LAST → draws on top of rectangles
+
+    legend_html = """
+    <div style="
+        position: fixed;
+        bottom: 20px;
+        left: 20px;
+        z-index: 9999;
+        background: rgba(15,23,42,0.85);
+        color: #f9fafb;
+        padding: 8px 12px;
+        border-radius: 8px;
+        font-size: 12px;
+        box-shadow: 0 2px 6px rgba(0,0,0,0.3);
+    ">
+      <b>Map features</b><br>
+      <span style="display:inline-block;width:10px;height:10px;
+                   border-radius:50%;background:#ef4444;margin-right:4px;"></span>
+      Soil moisture sensors (grid centroids)
+    </div>
+    """
+    m.get_root().html.add_child(folium.Element(legend_html))
+
+    folium.LayerControl().add_to(m)
+
+    map_html = m._repr_html_()
+    return map_html, df_coords
+
+
+# ============================================================
+# 6) Helper: load example AOI (for demo button)
+# ============================================================
+
+def load_example_aoi():
+    if not os.path.exists(EXAMPLE_AOI_PATH):
+        raise gr.Error(
+            f"Example AOI not found at '{EXAMPLE_AOI_PATH}'. "
+            "Make sure the file exists in your repo."
+        )
+    return EXAMPLE_AOI_PATH
+
+
+# ============================================================
+# 7) Gradio UI – with search + Load example AOI button
+# ============================================================
+
+theme = gr.themes.Soft(
+    primary_hue="teal", secondary_hue="cyan", neutral_hue="slate"
+)
+
+with gr.Blocks(
+    theme=theme,
+    css="""
+.gradio-container {
+    max-width: 1080px !important;
+    margin: 0 auto !important;
+}
+#sm-header h1 {
+    text-align: center;
+}
+#sm-header p {
+    text-align: center;
+    font-size: 0.95rem;
+}
+.small-note {
+    font-size: 0.78rem;
+    opacity: 0.8;
+}
+""",
+) as demo:
+
+    with gr.Column(elem_id="sm-header"):
+        gr.Markdown(
+            """
+            # 🌱 Soil Moisture Sensor Optimization
+            **Sentinel-1 + ExtraTrees – Field-scale sensor planning**
+
+            Upload or draw a field polygon, explore different grid sizes, and find the number of
+            soil moisture sensors that minimises spatial variability (CV%).
+            """
+        )
+
+    with gr.Row():
+        # Left column
+        with gr.Column(scale=1):
+            gr.Markdown("### 📥 Inputs")
+
+            date_input = gr.Textbox(
+                label="Target date (YYYY-MM-DD)",
+                value="2025-10-17",
+                info=(
+                    "Date of interest for soil moisture mapping "
+                    "(must overlap Sentinel-1 coverage)."
+                ),
+                placeholder="e.g. 2025-10-17",
+            )
+
+            cell_sizes_input = gr.Textbox(
+                label="Grid cell sizes for optimization (m, comma-separated)",
+                value="5,10,20,30",
+                info="Each value defines a regular grid (cell size in metres) over your field.",
+                placeholder="5,10,20,30",
+            )
+
+            geojson_input = gr.File(
+                label="Field polygon (GeoJSON; EPSG:4326, Polygon/MultiPolygon)",
+                file_types=[".geojson"],
+                file_count="single",
+                type="filepath",
+            )
+
+            example_button = gr.Button(
+                "📂 Load example AOI",
+                variant="secondary",
+            )
+
+            with gr.Accordion(
+                "Draw / Search AOI (folium Draw) — export & upload here", open=False
+            ):
+                search_box = gr.Textbox(
+                    label="Search place (optional)",
+                    placeholder="e.g. Groblersdal, South Africa",
+                    info=(
+                        "Type a place name and click 'Search & update AOI map' "
+                        "to centre the AOI drawer."
+                    ),
+                )
+                search_button = gr.Button("🔍 Search & update AOI map")
+
+                drawer_map_html = gr.HTML(
+                    value=make_drawer_map_html(), label="AOI drawer map"
+                )
+
+                gr.Markdown(
+                    """
+                    <div class="small-note">
+                    1. Use the search box above or just pan/zoom on the map.<br>
+                    2. Let the geolocation button find you, or navigate manually.<br>
+                    3. Draw a polygon with the draw tools (top-left).<br>
+                    4. Use the <b>Export</b> button in the draw toolbar to download <code>aoi.geojson</code>.<br>
+                    5. Upload that file in the <b>Field polygon</b> input above — or click <b>Load example AOI</b>.
+                    </div>
+                    """,
+                    elem_classes=["small-note"],
+                )
+
+            run_button = gr.Button("▶ Run sensor optimization", variant="primary")
+
+            gr.Markdown(
+                """
+                <div class="small-note">
+                💡 <b>Quick start:</b> Click <b>Load example AOI</b> → run optimization.  
+                Or: Search/draw your own field → export GeoJSON → upload it → run optimization.
+                </div>
+                """,
+                elem_classes=["small-note"],
+            )
+
+        # Right column
+        with gr.Column(scale=1.2):
+            with gr.Tabs():
+                with gr.Tab("Optimization"):
+                    gr.Markdown("### 📊 CV vs Number of Sensors")
+
+                    plot_output = gr.Plot(label="CV vs Number of Sensors")
+
+                    table_output = gr.Dataframe(
+                        label="Summary by grid size",
+                        headers=["cell_size_m", "n_sensors", "cv_percent"],
+                        interactive=False,
+                    )
+
+                    gr.Markdown(
+                        """
+                        <div class="small-note">
+                        The optimal configuration is marked with a star ⭐ on the graph, and corresponds to the
+                        lowest coefficient of variation (CV%) in predicted soil moisture, subject to the rule
+                        that if CVs are similar we prefer fewer sensors.
+                        </div>
+                        """,
+                        elem_classes=["small-note"],
+                    )
+
+                with gr.Tab("Sensor layout preview"):
+                    gr.Markdown("### 🗺️ SM basemap and sensor locations")
+
+                    map_cell_size_input = gr.Dropdown(
+                        label="Grid cell size for map (m)",
+                        choices=[5, 10, 20, 30, 50],
+                        value=10,
+                        interactive=True,
+                        info="Choose one grid size to preview SM map and centroid locations.",
+                    )
+
+                    map_button = gr.Button(
+                        "Show centroid map", variant="secondary"
+                    )
+
+                    map_html_output = gr.HTML(
+                        label="Field SM map and sensor centroids"
+                    )
+
+                    centroid_table_output = gr.Dataframe(
+                        label=(
+                            "Centroid coordinates "
+                            "(sensor_id, Longitude (°E), Latitude (°S), sm_pred)"
+                        ),
+                        interactive=False,
+                    )
+
+                    gr.Markdown(
+                        """
+                        <div class="small-note">
+                        The coloured grid shows predicted soil moisture (%) from the model.  
+                        Red points (layers added on top) mark sensor locations with their coordinates and SM values.
+                        </div>
+                        """,
+                        elem_classes=["small-note"],
+                    )
+
+    gr.Markdown(
+        """
+        ---
+        <div class="small-note">
+        Prototype developed around GIIMS soil-moisture workflow. Exported CSVs (per grid size)
+        can be used for further analysis or for designing field experiments.
+        </div>
+        """,
+        elem_classes=["small-note"],
+    )
+
+    run_button.click(
+        fn=run_sensor_optimization,
+        inputs=[date_input, geojson_input, cell_sizes_input],
+        outputs=[plot_output, table_output],
+    )
+
+    map_button.click(
+        fn=show_centroid_map,
+        inputs=[date_input, geojson_input, map_cell_size_input],
+        outputs=[map_html_output, centroid_table_output],
+    )
+
+    search_button.click(
+        fn=update_drawer_map,
+        inputs=[search_box],
+        outputs=[drawer_map_html],
+    )
+
+    example_button.click(
+        fn=load_example_aoi,
+        inputs=None,
+        outputs=[geojson_input],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

sm_map_2025-10-17_grid_10m.csv

@@ -1,52 +1,71 @@
 date,lat,lon,elev,slope,VV,VH,angle,sm_pred,comp_date,n_images
-2025-10-17,-25.16734793821706,29.41730020845953,889.5120849609375,0.2736734743049231,-12.639859695444295,-19.963452857293976,39.46442413330078,14.467744408275315,2025-10-14,1
-2025-10-17,-25.16734793821706,29.417399461584484,889.4650268554688,0.2736734743049231,-15.227510966029502,-21.22593001929396,39.46347427368164,12.882178583196119,2025-10-14,1
-2025-10-17,-25.16725810709956,29.416803942834765,889.200439453125,0.2736734743049231,-11.653781307912025,-22.934843306817307,39.4663200378418,17.952386165609024,2025-10-14,1
-2025-10-17,-25.16725810709956,29.41690319595972,889.200439453125,0.2736734743049231,-11.653781307912025,-22.934843306817307,39.4663200378418,17.952386165609024,2025-10-14,1
-2025-10-17,-25.16725810709956,29.41700244908467,889.200439453125,0.2736734743049231,-12.639859695444295,-19.963452857293976,39.465370178222656,14.583095984113323,2025-10-14,1
-2025-10-17,-25.16725810709956,29.417101702209624,889.200439453125,0.2736734743049231,-12.639859695444295,-19.963452857293976,39.465370178222656,14.583095984113323,2025-10-14,1
-2025-10-17,-25.16725810709956,29.41720095533458,889.5120849609375,0.2736734743049231,-12.639859695444295,-19.963452857293976,39.46442413330078,14.467744408275315,2025-10-14,1
-2025-10-17,-25.16725810709956,29.41730020845953,889.5120849609375,0.2736734743049231,-12.639859695444295,-19.963452857293976,39.46442413330078,14.467744408275315,2025-10-14,1
-2025-10-17,-25.16725810709956,29.417399461584484,889.4650268554688,0.2736734743049231,-15.227510966029502,-21.22593001929396,39.46347427368164,12.882178583196119,2025-10-14,1
-2025-10-17,-25.16725810709956,29.41749871470944,889.4650268554688,0.2736734743049231,-11.466343625686308,-23.857049277340188,39.462528228759766,14.777984522665802,2025-10-14,1
-2025-10-17,-25.16716827598206,29.41630767721,889.0532836914062,0.2736734743049231,-10.552644273439329,-20.740964119707908,39.469482421875,16.31034755269654,2025-10-14,1
-2025-10-17,-25.16716827598206,29.41640693033495,889.0532836914062,0.2736734743049231,-10.552644273439329,-20.740964119707908,39.469482421875,16.31034755269654,2025-10-14,1
-2025-10-17,-25.16716827598206,29.416506183459905,889.0532836914062,0.2736734743049231,-9.989968098575709,-20.81114542490097,39.468536376953125,16.164618385994498,2025-10-14,1
-2025-10-17,-25.16716827598206,29.41660543658486,889.19482421875,0.2736734743049231,-12.046903142328162,-22.343096498691782,39.46759033203125,15.68414617480545,2025-10-14,1
-2025-10-17,-25.16716827598206,29.41670468970981,889.19482421875,0.2736734743049231,-12.046903142328162,-22.343096498691782,39.46759033203125,15.68414617480545,2025-10-14,1
-2025-10-17,-25.16716827598206,29.416803942834765,889.200439453125,0.2736734743049231,-15.203315371168966,-18.957470473846158,39.466644287109375,13.453680248564567,2025-10-14,1
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sm_map_2025-10-17_grid_20m.csv

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sm_map_2025-10-17_grid_30m.csv

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sm_map_2025-10-17_grid_50m.csv

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sm_map_2025-10-17_grid_5m.csv

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