diff --git a/src/dolphin/timeseries.py b/src/dolphin/timeseries.py
index dd269f02..03007602 100644
--- a/src/dolphin/timeseries.py
+++ b/src/dolphin/timeseries.py
@@ -524,7 +524,9 @@ def invert_stack(
 
 
 def get_incidence_matrix(
-    ifg_pairs: Sequence[tuple[T, T]], sar_idxs: Sequence[T] | None = None
+    ifg_pairs: Sequence[tuple[T, T]],
+    sar_idxs: Sequence[T] | None = None,
+    delete_first_date_column: bool = True,
 ) -> np.ndarray:
     """Build the indicator matrix from a list of ifg pairs (index 1, index 2).
 
@@ -538,6 +540,10 @@ def get_incidence_matrix(
         were formed from.
         Otherwise, created from the unique entries in `ifg_pairs`.
         Only provide if there are some dates which are not present in `ifg_pairs`.
+    delete_first_date_column : bool
+        If True, removes the first column of the matrix to make it full column rank.
+        Size will be `n_sar_dates - 1` columns.
+        Otherwise, the matrix will have `n_sar_dates`, but rank `n_sar_dates - 1`.
 
     Returns
     -------
@@ -553,13 +559,13 @@ def get_incidence_matrix(
         sar_idxs = sorted(set(flatten(ifg_pairs)))
 
     M = len(ifg_pairs)
-    N = len(sar_idxs) - 1
+    col_iter = sar_idxs[1:] if delete_first_date_column else sar_idxs
+    N = len(col_iter)
     A = np.zeros((M, N))
 
     # Create a dictionary mapping sar dates to matrix columns
     # We take the first SAR acquisition to be time 0, leave out of matrix
-    date_to_col = {date: i for i, date in enumerate(sar_idxs[1:])}
-    # Populate the matrix
+    date_to_col = {date: i for i, date in enumerate(col_iter)}
     for i, (early, later) in enumerate(ifg_pairs):
         if early in date_to_col:
             A[i, date_to_col[early]] = -1
@@ -1316,3 +1322,109 @@ def invert_stack_l1(A: ArrayLike, dphi: ArrayLike) -> Array:
     # residuals = jnp.sum(residual_vecs, axis=0)
 
     return phase, residuals
+
+
+def create_nonzero_conncomp_counts(
+    conncomp_file_list: Sequence[PathOrStr],
+    output_dir: PathOrStr,
+    ifg_date_pairs: Sequence[Sequence[DateOrDatetime]] | None = None,
+    block_shape: tuple[int, int] = (256, 256),
+    num_threads: int = 4,
+) -> list[Path]:
+    """Count the number of valid interferograms per date.
+
+    Parameters
+    ----------
+    conncomp_file_list : Sequence[PathOrStr]
+        List of connected component files
+    output_dir : PathOrStr
+        The directory to save the output files
+    ifg_date_pairs : Sequence[Sequence[DateOrDatetime]], optional
+        List of date pairs corresponding to the interferograms.
+        If not provided, will be parsed from filenames.
+    block_shape : tuple[int, int], optional
+        The shape of the blocks to process in parallel.
+    num_threads : int
+        The number of parallel blocks to process at once.
+
+    Returns
+    -------
+    out_paths : list[Path]
+        List of output files, one per unique date
+
+    """
+    output_dir = Path(output_dir)
+    output_dir.mkdir(exist_ok=True, parents=True)
+
+    if ifg_date_pairs is None:
+        ifg_date_pairs = [get_dates(str(f))[:2] for f in conncomp_file_list]
+    try:
+        # Ensure it's a list of pairs
+        ifg_tuples = [(ref, sec) for (ref, sec) in ifg_date_pairs]  # noqa: C416
+    except ValueError as e:
+        raise ValueError(
+            "Each item in `ifg_date_pairs` must be a sequence of length 2"
+        ) from e
+
+    # Get unique dates and create the counting matrix
+    sar_dates: list[DateOrDatetime] = sorted(set(utils.flatten(ifg_date_pairs)))
+
+    date_counting_matrix = np.abs(
+        get_incidence_matrix(ifg_tuples, sar_dates, delete_first_date_column=False)
+    )
+
+    # Create output paths for each date
+    suffix = "_valid_count.tif"
+    out_paths = [output_dir / f"{d.strftime('%Y%m%d')}{suffix}" for d in sar_dates]
+
+    if all(p.exists() for p in out_paths):
+        logger.info("All output files exist, skipping counting")
+        return out_paths
+
+    logger.info("Counting valid interferograms per date")
+
+    # Create VRT stack for reading
+    vrt_name = Path(output_dir) / "conncomp_network.vrt"
+    conncomp_reader = io.VRTStack(
+        file_list=conncomp_file_list,
+        outfile=vrt_name,
+        skip_size_check=True,
+        read_masked=True,
+    )
+
+    def count_by_date(
+        readers: Sequence[io.StackReader], rows: slice, cols: slice
+    ) -> tuple[np.ndarray, slice, slice]:
+        """Process each block by counting valid interferograms per date."""
+        stack = readers[0][:, rows, cols]
+        valid_mask = stack.filled(0) != 0  # Shape: (n_ifgs, block_rows, block_cols)
+
+        # Use the counting matrix to map from interferograms to dates
+        # For each pixel, multiply the valid_mask to get counts per date
+        # Reshape valid_mask to (n_ifgs, -1) to handle all pixels at once
+        valid_flat = valid_mask.reshape(valid_mask.shape[0], -1)
+        # Matrix multiply to get counts per date
+        # (date_counting_matrix.T) is shape (n_sar_dates, n_ifgs), and each row
+        # has a number of 1s equal to the nonzero conncomps for that date.
+        date_count_cols = date_counting_matrix.T @ valid_flat
+        date_counts = date_count_cols.reshape(-1, *valid_mask.shape[1:])
+
+        return date_counts, rows, cols
+
+    # Setup writer for all output files
+    writer = io.BackgroundStackWriter(
+        out_paths, like_filename=conncomp_file_list[0], dtype=np.uint16, units="count"
+    )
+
+    # Process the blocks
+    io.process_blocks(
+        readers=[conncomp_reader],
+        writer=writer,
+        func=count_by_date,
+        block_shape=block_shape,
+        num_threads=num_threads,
+    )
+    writer.notify_finished()
+
+    logger.info("Completed counting valid interferograms per date")
+    return out_paths