diff --git a/odc/stats/plugins/lc_level3.py b/odc/stats/plugins/lc_level3.py
index 0f7242e8..1a5c68c8 100644
--- a/odc/stats/plugins/lc_level3.py
+++ b/odc/stats/plugins/lc_level3.py
@@ -4,6 +4,7 @@
 
 from typing import Tuple
 import xarray as xr
+from odc.stats._algebra import expr_eval
 from ._registry import StatsPluginInterface, register
 
 NODATA = 255
@@ -22,30 +23,35 @@ def measurements(self) -> Tuple[str, ...]:
 
     def reduce(self, xx: xr.Dataset) -> xr.Dataset:
 
-        l34_dss = xx.classes_l3_l4
-        urban_dss = xx.urban_classes
-        cultivated_dss = xx.cultivated_class
-
         # Cultivated pipeline applies a mask which feeds only terrestrial veg (110) to the model
         # Just exclude no data (255) and apply the cultivated results
-        cultivated_mask = cultivated_dss != int(NODATA)
-        l34_cultivated_masked = xr.where(cultivated_mask, cultivated_dss, l34_dss)
+        res = expr_eval(
+            "where(a<nodata, a, b)",
+            {"a": xx.cultivated_class.data, "b": xx.classes_l3_l4.data},
+            name="mask_cultivated",
+            dtype="float32",
+            **{"nodata": NODATA},
+        )
 
-        # Urban is classified on l3/4 surface output (210)
-        urban_mask = l34_dss == 210
-        l34_urban_cultivated_masked = xr.where(
-            urban_mask, urban_dss, l34_cultivated_masked
+        # Mask urban results with bare sfc (210)
+
+        res = expr_eval(
+            "where(a==_u, b, a)",
+            {
+                "a": res,
+                "b": xx.urban_classes.data,
+            },
+            name="mark_urban",
+            dtype="uint8",
+            **{"_u": 210},
         )
 
         attrs = xx.attrs.copy()
         attrs["nodata"] = NODATA
-        l34_urban_cultivated_masked = l34_urban_cultivated_masked.squeeze(dim=["spec"])
-        dims = l34_urban_cultivated_masked.dims
+        dims = xx.classes_l3_l4.dims[1:]
 
         data_vars = {
-            "level3_class": xr.DataArray(
-                l34_urban_cultivated_masked.data, dims=dims, attrs=attrs
-            )
+            "level3_class": xr.DataArray(res.squeeze(), dims=dims, attrs=attrs)
         }
 
         coords = dict((dim, xx.coords[dim]) for dim in dims)
diff --git a/tests/test_lc_level3.py b/tests/test_lc_level3.py
index 3064d41d..524d4912 100644
--- a/tests/test_lc_level3.py
+++ b/tests/test_lc_level3.py
@@ -1,6 +1,7 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
+import dask.array as da
 
 from odc.stats.plugins.lc_level3 import StatsLccsLevel3
 import pytest
@@ -24,7 +25,7 @@ def image_groups():
                 [223, 255, 223],
             ]
         ],
-        dtype="int",
+        dtype="uint8",
     )
 
     urban = np.array(
@@ -36,7 +37,7 @@ def image_groups():
                 [216, 216, 216],
             ]
         ],
-        dtype="int",
+        dtype="uint8",
     )
 
     cultivated = np.array(
@@ -48,7 +49,7 @@ def image_groups():
                 [255, 255, 255],
             ]
         ],
-        dtype="int",
+        dtype="uint8",
     )
 
     tuples = [
@@ -63,13 +64,19 @@ def image_groups():
 
     data_vars = {
         "classes_l3_l4": xr.DataArray(
-            l34, dims=("spec", "y", "x"), attrs={"nodata": 255}
+            da.from_array(l34, chunks=(1, -1, -1)),
+            dims=("spec", "y", "x"),
+            attrs={"nodata": 255},
         ),
         "urban_classes": xr.DataArray(
-            urban, dims=("spec", "y", "x"), attrs={"nodata": 255}
+            da.from_array(urban, chunks=(1, -1, -1)),
+            dims=("spec", "y", "x"),
+            attrs={"nodata": 255},
         ),
         "cultivated_class": xr.DataArray(
-            cultivated, dims=("spec", "y", "x"), attrs={"nodata": 255}
+            da.from_array(cultivated, chunks=(1, -1, -1)),
+            dims=("spec", "y", "x"),
+            attrs={"nodata": 255},
         ),
     }
     xx = xr.Dataset(data_vars=data_vars, coords=coords)