diff --git a/ecoscope/analysis/seasons.py b/ecoscope/analysis/seasons.py
index d6c3a87e..73f87ab0 100644
--- a/ecoscope/analysis/seasons.py
+++ b/ecoscope/analysis/seasons.py
@@ -19,11 +19,11 @@ def _min_max_scaler(x):
     return x_std
 
 
-def std_ndvi_vals(aoi=None, img_coll=None, band=None, img_scale=1, start=None, end=None):
+def std_ndvi_vals(aoi=None, img_coll=None, nir_band=None, red_band=None, img_scale=1, start=None, end=None):
 
     coll = (
         ee.ImageCollection(img_coll)
-        .select(band)
+        .select([nir_band, red_band])
         .filterDate(start, end)
         .map(lambda x: x.multiply(ee.Image(img_scale)).set("system:time_start", x.get("system:time_start")))
     )
@@ -33,11 +33,14 @@ def std_ndvi_vals(aoi=None, img_coll=None, band=None, img_scale=1, start=None, e
     else:
         geo = None
 
+    img_dates = pandas.to_datetime(coll.aggregate_array("system:time_start").getInfo(), unit="ms", utc=True)
+
+    coll = coll.map(lambda x: x.normalizedDifference([nir_band, red_band]))
     ndvi_vals = coll.toBands().reduceRegion("mean", geo, bestEffort=True).values().getInfo()
 
     df = pandas.DataFrame(
         {
-            "img_date": pandas.to_datetime(coll.aggregate_array("system:time_start").getInfo(), unit="ms", utc=True),
+            "img_date": img_dates,
             "NDVI": ndvi_vals,
         }
     ).dropna(axis=0)
@@ -110,13 +113,13 @@ def seasonal_windows(ndvi_vals, cuts, season_labels):
 
 
 def add_seasonal_index(
-    df, index_name, start_date, end_date, aoi_geom_filter=None, seasons=2, season_labels=["dry", "wet"]
+    df, index_name, start_date, end_date, time_col, aoi_geom_filter=None, seasons=2, season_labels=["dry", "wet"]
 ):
 
     aoi_ = None
     try:
         aoi_ = aoi_geom_filter.dissolve().iloc[0]["geometry"]
-    except:
+    except AttributeError:
         aoi_ = aoi_geom_filter
 
     if len(season_labels) != seasons:
@@ -124,7 +127,7 @@ def add_seasonal_index(
             f"Parameter value 'seasons' ({seasons}) must match the number of 'season_labels' elements ({season_labels})"
         )
     # extract the standardized NDVI ndvi_vals within the AOI
-    ndvi_vals = std_ndvi_vals(aoi_, start=since_filter.isoformat(), end=until_filter.isoformat())
+    ndvi_vals = std_ndvi_vals(aoi_, start=start_date.isoformat(), end=end_date.isoformat())
 
     # calculate the seasonal transition point
     cuts = val_cuts(ndvi_vals, seasons)
diff --git a/notebooks/05. Environmental Analyses/Seasonal Calculation.ipynb b/notebooks/05. Environmental Analyses/Seasonal Calculation.ipynb
index 349585bc..a021513a 100644
--- a/notebooks/05. Environmental Analyses/Seasonal Calculation.ipynb	
+++ b/notebooks/05. Environmental Analyses/Seasonal Calculation.ipynb	
@@ -155,15 +155,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "img_coll = \"MODIS/061/MOD13A1\"\n",
+    "img_coll = \"MODIS/061/MCD43A4\"\n",
     "band = \"NDVI\"\n",
-    "img_scale = 0.0001\n",
-    "since_filter = \"2010-01-01\"\n",
+    "img_scale = 1\n",
+    "since_filter = \"2020-01-01\"\n",
     "until_filter = \"2022-06-18\"\n",
     "\n",
     "ndvi_vals = seasons.std_ndvi_vals(\n",
     "    img_coll=img_coll,\n",
-    "    band=band,\n",
+    "    nir_band=\"Nadir_Reflectance_Band2\",\n",
+    "    red_band=\"Nadir_Reflectance_Band1\",\n",
     "    img_scale=img_scale,\n",
     "    aoi=aoi,\n",
     "    start=since_filter,\n",
diff --git a/tests/test_seasons.py b/tests/test_seasons.py
index f2a547d9..a39c4051 100644
--- a/tests/test_seasons.py
+++ b/tests/test_seasons.py
@@ -10,14 +10,21 @@
     )
 
 
-@pytest.mark.skip(reason="this has been failing since May 2022; will be fixed in a follow-up pull")
+@pytest.mark.skipif(not pytest.earthengine, reason="No connection to EarthEngine.")
 def test_seasons():
     gdf = gpd.read_file("tests/sample_data/vector/AOI_sites.gpkg").to_crs(4326)
 
     aoi = gdf.geometry.iat[0]
 
     # Extract the standardized NDVI ndvi_vals within the AOI
-    ndvi_vals = ecoscope.analysis.seasons.std_ndvi_vals(aoi, start="2010-01-01", end="2021-01-01")
+    ndvi_vals = ecoscope.analysis.seasons.std_ndvi_vals(
+        aoi,
+        img_coll="MODIS/061/MCD43A4",
+        nir_band="Nadir_Reflectance_Band2",
+        red_band="Nadir_Reflectance_Band1",
+        start="2010-01-01",
+        end="2021-01-01",
+    )
 
     # Calculate the seasonal transition point
     cuts = ecoscope.analysis.seasons.val_cuts(ndvi_vals, 2)