diff --git a/torchgeo/datasets/biomassters.py b/torchgeo/datasets/biomassters.py
index 70a53a4220..d2f4d35e87 100644
--- a/torchgeo/datasets/biomassters.py
+++ b/torchgeo/datasets/biomassters.py
@@ -53,7 +53,7 @@ class BioMassters(NonGeoDataset):
     valid_splits = ('train', 'test')
     valid_sensors = ('S1', 'S2')
 
-    metadata_filename = 'The_BioMassters_-_features_metadata.csv.csv'
+    default_metadata_filename = 'The_BioMassters_-_features_metadata.csv.csv'
 
     def __init__(
         self,
@@ -61,6 +61,10 @@ def __init__(
         split: str = 'train',
         sensors: Sequence[str] = ['S1', 'S2'],
         as_time_series: bool = False,
+        metadata_filename: str = default_metadata_filename,
+        max_cloud_percentage: float | None = None,
+        max_red_mean: float | None = None,
+        include_corrupt: bool = True,
     ) -> None:
         """Initialize a new instance of BioMassters dataset.
 
@@ -74,6 +78,10 @@ def __init__(
                 Sentinel 2 ('S1', 'S2')
             as_time_series: whether or not to return all available
                 time-steps or just a single one for a given target location
+            metadata_filename: metadata file to be used
+            max_cloud_percentage: maximum allowed cloud percentage for images
+            max_red_mean: maximum allowed red_mean value for images
+            include_corrupt: whether to include images marked as corrupted
 
         Raises:
             AssertionError: if ``split`` or ``sensors`` is invalid
@@ -91,6 +99,10 @@ def __init__(
         ), f'Please choose a subset of valid sensors: {self.valid_sensors}.'
         self.sensors = sensors
         self.as_time_series = as_time_series
+        self.metadata_filename = metadata_filename
+        self.max_cloud_percentage = max_cloud_percentage
+        self.max_red_mean = max_red_mean
+        self.include_corrupt = include_corrupt
 
         self._verify()
 
@@ -103,6 +115,16 @@ def __init__(
         # filter split
         self.df = self.df[self.df['split'] == self.split]
 
+        # additional filtering based on metadata
+        if self.max_cloud_percentage is not None and 'cloud_percentage' in self.df.columns:
+            self.df = self.df[self.df['cloud_percentage'] <= self.max_cloud_percentage]
+
+        if self.max_red_mean is not None and 'red_mean' in self.df.columns:
+            self.df = self.df[self.df['red_mean'] <= self.max_red_mean]
+
+        if not self.include_corrupt and 'corrupt_values' in self.df.columns:
+            self.df = self.df[self.df['corrupt_values'] is False]
+
         # generate numerical month from filename since first month is September
         # and has numerical index of 0
         self.df['num_month'] = (