diff --git a/ci/doc_requirements.txt b/ci/doc_requirements.txt
index c933d75603..c51eb17063 100644
--- a/ci/doc_requirements.txt
+++ b/ci/doc_requirements.txt
@@ -4,5 +4,5 @@ sphinx-design==0.6.1
 sphinx-gallery==0.17.1
 myst-parser==4.0.0
 netCDF4==1.7.1
-geopandas==0.14.4
+geopandas==1.0.1
 rtree==1.3.0
diff --git a/examples/plots/nhc_wind_probabilities.py b/examples/plots/nhc_wind_probabilities.py
index 667ae87453..653d357adc 100644
--- a/examples/plots/nhc_wind_probabilities.py
+++ b/examples/plots/nhc_wind_probabilities.py
@@ -57,7 +57,7 @@
 # cities we selected above. Geopandas provides a spatial join method, which merges the two
 # GeoDataFrames and can tell us which wind speed probability polygon each of our city points
 # lies within. That information is stored in the 'PERCENTAGE' column below.
-cities = geopandas.sjoin(cities, wind_data, how='left', op='within')
+cities = geopandas.sjoin(cities, wind_data, how='left', predicate='within')
 cities
 
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