diff --git a/pixi.lock b/pixi.lock
index 6ff6967..6692d6c 100644
--- a/pixi.lock
+++ b/pixi.lock
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+  purls:
+  - pkg:pypi/toolz?source=hash-mapping
+  size: 52623
+  timestamp: 1728059623353
 - kind: conda
   name: tornado
   version: 6.4.1
diff --git a/pyproject.toml b/pyproject.toml
index 3879fdb..2b1f8f5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -44,6 +44,7 @@ dependencies = [
   "stac-asset>=0.4.3,<0.5",
   "stac-geoparquet>=0.6.0,<0.7",
   #"s3fs>=2024.9.0,<2025",
+  "odc-stac>=0.3.10,<0.4"
 ]
 
 [project.optional-dependencies]
@@ -222,6 +223,7 @@ rioxarray = "*"
 stac-geoparquet = "*"
 pyarrow = "*"
 jsonschema = ">=4.23.0,<5"
+odc-stac = "*"
 #nbconvert = ">=7.16.4,<8"
 #cloudpathlib-s3 = ">=0.20.0,<0.21"
 #matplotlib-base = ">=3.9.2,<4"
diff --git a/src/coincident/datasets/planetary_computer.py b/src/coincident/datasets/planetary_computer.py
index f4fcbbd..3115f4c 100644
--- a/src/coincident/datasets/planetary_computer.py
+++ b/src/coincident/datasets/planetary_computer.py
@@ -18,15 +18,15 @@
 
 @dataclass
 class COP30(Dataset):
-    """Essential metadata for Copernicus DEM"""
+    """Essential metadata and data access for Copernicus DEM"""
 
     alias: str = "cop30"
     has_stac_api: bool = True
     collections: list[str] = field(default_factory=lambda: ["cop-dem-glo-30"])
     search: str = STACAPI
-    start: str | None = None  # NOTE: has 'representative' datetime of 2021-04-22
+    start: str | None = None  # Copernicus DEM has 'representative' datetime: 2021-04-22
     end: str | None = None
-    type: str = "sar"
+    type: str = "dem"
     provider: str = "microsoft"
 
 
diff --git a/src/coincident/search/main.py b/src/coincident/search/main.py
index b3c030a..e2bfed6 100644
--- a/src/coincident/search/main.py
+++ b/src/coincident/search/main.py
@@ -4,10 +4,11 @@
 from typing import Any
 
 import geopandas as gpd
-from shapely.validation import make_valid
+import odc.stac
 
 # Used to access formatters
 from pystac_client.item_search import ItemSearch as _ItemSearch
+from shapely.validation import make_valid
 
 from coincident.datasets import _alias_to_Dataset
 from coincident.datasets.general import Dataset
@@ -41,9 +42,12 @@ def search(
 
     Returns
     -------
-    gpd.GeoDataFrame
+    gpd.GeoDataFrame (for datasets excluding cop30 and worldcover)
         A GeoDataFrame containing the search results.
 
+    xarray.core.dataset.Dataset (only for cop30 and worldcover datasets)
+        An Xarray dataset containing the search results.
+
     Raises
     ------
     ValueError
@@ -139,6 +143,34 @@ def search(
 
             gf = stac.to_geopandas(item_collection)
 
+            if dataset.alias in ["cop30", "worldcover"]:
+                warnings.warn(
+                    "WARNING: searching large areas for COP30 and/or ESA WorldCover may take a while...",
+                    UserWarning,
+                    stacklevel=2,
+                )
+                # TODO: 1) improve loading speed
+                # tricky when no dates defined for ESA WorldCover as items returns both 2020 and 2021 data
+                # 2) is there a better way of getting search GDF/GeoSeries coordinates?
+                # given aoi = _pystac_client._format_intersects(shapely_geometry)  # to JSON geometry
+                coordinates = aoi["coordinates"][0]
+                total_bounds = (
+                    min(x for x, y in coordinates),
+                    min(y for x, y in coordinates),
+                    max(x for x, y in coordinates),
+                    max(y for x, y in coordinates),
+                )
+                items = stac.to_pystac_items(gf)
+                ds = odc.stac.load(
+                    items,
+                    bbox=total_bounds,
+                )
+                return ds.rename(
+                    {"data": "elevation"}
+                    if dataset.alias == "cop30"
+                    else {"map": "landcover"}
+                )
+
     # Non-STAC Searches
     elif dataset.alias == "3dep":
         gf = wesm.search_bboxes(
@@ -255,8 +287,8 @@ def cascading_search(
         A list of GeoDataFrames containing the search results for each secondary dataset.
     """
     # Do searches on simple geometry, but intersect results with original geometry
-    search_geometry = primary_dataset.simplify(0.01).apply(make_valid) 
-    #search_geometry = primary_dataset.geometry.apply(lambda geom: make_valid(geom.simplify(0.01)))
+    search_geometry = primary_dataset.simplify(0.01).apply(make_valid)
+    # search_geometry = primary_dataset.geometry.apply(lambda geom: make_valid(geom.simplify(0.01)))
     detailed_geometry = primary_dataset[["geometry"]]
 
     if "end_datetime" in primary_dataset.columns: