diff --git a/pyresample/bucket/__init__.py b/pyresample/bucket/__init__.py
index 6ea45d768..532d2da27 100644
--- a/pyresample/bucket/__init__.py
+++ b/pyresample/bucket/__init__.py
@@ -200,13 +200,17 @@ def _get_indices(self):
         target_shape = self.target_area.shape
         self.idxs = self.y_idxs * target_shape[1] + self.x_idxs
 
-    def get_sum(self, data, skipna=True):
+    def get_sum(self, data, fill_value=np.nan, skipna=True):
         """Calculate sums for each bin with drop-in-a-bucket resampling.
 
         Parameters
         ----------
         data : Numpy or Dask array
             Data to be binned and summed.
+        fill_value : float
+            Fill value to mark missing/invalid values in the input data,
+            as well as in the binned and averaged output data.
+            Default: np.nan
         skipna : boolean (optional)
                 If True, skips NaN values for the sum calculation
                 (similarly to Numpy's `nansum`). Buckets containing only NaN are set to zero.
@@ -241,6 +245,10 @@ def get_sum(self, data, skipna=True):
         # TODO remove following line in favour of weights = data when dask histogram bug (issue #6935) is fixed
         sums = self._mask_bins_with_nan_if_not_skipna(skipna, data, out_size, sums)
 
+        # set bin without data to fill_value if fill_value exists
+        if ~np.isnan(fill_value):
+            sums = da.where(sums == 0, fill_value, sums)
+
         return sums.reshape(self.target_area.shape)
 
     def _mask_bins_with_nan_if_not_skipna(self, skipna, data, out_size, statistic):
@@ -251,7 +259,7 @@ def _mask_bins_with_nan_if_not_skipna(self, skipna, data, out_size, statistic):
             statistic = da.where(nan_bins > 0, np.nan, statistic)
         return statistic
 
-    def _call_bin_statistic(self, statistic_method, data, fill_value=None, skipna=None):
+    def _call_bin_statistic(self, statistic_method, data, fill_value=np.nan, skipna=None):
         """Calculate statistics (min/max) for each bin with drop-in-a-bucket resampling."""
         if isinstance(data, xr.DataArray):
             data = data.data
@@ -268,6 +276,8 @@ def _call_bin_statistic(self, statistic_method, data, fill_value=None, skipna=No
             shape=out_shape,
             dtype=np.float64)
 
+        # set bin without data to fill_value
+        statistics = da.where(np.isin(statistics, (0, fill_value)), fill_value, statistics)
         return statistics
 
     def get_min(self, data, fill_value=np.nan, skipna=True):