diff --git a/changelog/649.feature.rst b/changelog/649.feature.rst
new file mode 100644
index 000000000..8c422ff2d
--- /dev/null
+++ b/changelog/649.feature.rst
@@ -0,0 +1,2 @@
+Provides `~ndcube.wcs.tools.unwrap_wcs_to_fitswcs`, a function to create a `astropy.wcs.WCS` instance equivalent to a sliced and/or resampled WCS instance.
+Only valid if the underlying implementation of the wrapped WCS instance is also an `astropy.wcs.WCS` instance.
diff --git a/docs/reference/wcs.rst b/docs/reference/wcs.rst
index 80e9f8393..f2e7b094d 100644
--- a/docs/reference/wcs.rst
+++ b/docs/reference/wcs.rst
@@ -5,3 +5,5 @@ wcs (`ndcube.wcs`)
 .. automodapi:: ndcube.wcs
 
 .. automodapi:: ndcube.wcs.wrappers
+
+.. automodapi:: ndcube.wcs.tools
diff --git a/ndcube/wcs/tests/test_tools.py b/ndcube/wcs/tests/test_tools.py
new file mode 100644
index 000000000..ed9c99661
--- /dev/null
+++ b/ndcube/wcs/tests/test_tools.py
@@ -0,0 +1,41 @@
+import numpy as np
+from astropy.time import Time
+from astropy.wcs import WCS
+from astropy.wcs.wcsapi import SlicedLowLevelWCS
+from numpy.testing import assert_array_almost_equal, assert_array_equal
+
+from ndcube.wcs.tools import unwrap_wcs_to_fitswcs
+from ndcube.wcs.wrappers import ResampledLowLevelWCS
+
+
+def test_unwrap_wcs_to_fitswcs():
+    # Build FITS-WCS and wrap it in different operations.
+    time_ref = Time("2000-01-01T00:00:00", scale="utc", format="fits")
+    header = {
+        "CTYPE1": "TIME", "CTYPE2": "WAVE", "CTYPE3": "HPLT-TAN", "CTYPE4": "HPLN-TAN",
+        "CUNIT1": "s", "CUNIT2": "Angstrom", "CUNIT3": "deg", "CUNIT4": "deg",
+        "CDELT1": 600, "CDELT2": 0.2, "CDELT3": 0.5, "CDELT4": 0.4,
+        "CRPIX1": 0, "CRPIX2": 0, "CRPIX3": 2, "CRPIX4": 2,
+        "CRVAL1": 0, "CRVAL2": 10, "CRVAL3": 0.5, "CRVAL4": 1,
+        "CNAME1": "time", "CNAME2": "wavelength", "CNAME3": "HPC lat", "CNAME4": "HPC lon",
+        "NAXIS1": 5, "NAXIS2": 9, "NAXIS3": 4, "NAXIS4": 4,
+        "DATEREF": time_ref.fits}
+    orig_wcs = WCS(header)
+    # Slice WCS
+    wcs1 = SlicedLowLevelWCS(orig_wcs, (0, 0, slice(None), slice(1, None)))  # numpy order
+    # Resample WCS
+    wcs2 = ResampledLowLevelWCS(wcs1, [2, 3], offset=[0.5, 1])  # WCS order
+    # Slice WCS again
+    wcs3 = SlicedLowLevelWCS(wcs2, (slice(0, 2), slice(1, 2)))  # numpy order
+    # Reconstruct fitswcs
+    output_wcs, dropped_data_dimensions = unwrap_wcs_to_fitswcs(wcs3)
+    # Assert output_wcs is correct
+    assert_array_equal(dropped_data_dimensions, np.array([True, True, False, False]))
+    assert isinstance(output_wcs, WCS)
+    assert output_wcs._naxis == [1, 2, 1, 1]
+    assert list(output_wcs.wcs.ctype) == ['TIME', 'WAVE', 'HPLT-TAN', 'HPLN-TAN']
+    world_values = output_wcs.array_index_to_world_values([0], [0], [0, 1], [0])
+    assert_array_almost_equal(world_values[0][0], np.array([2700]))
+    assert_array_almost_equal(world_values[1], np.array([1.04e-09, 1.10e-09]))
+    assert_array_almost_equal(world_values[2][0], np.array([1.26915033e-05]))
+    assert_array_almost_equal(world_values[3][0], np.array([0.60002173]))
diff --git a/ndcube/wcs/tools.py b/ndcube/wcs/tools.py
new file mode 100644
index 000000000..78793cebf
--- /dev/null
+++ b/ndcube/wcs/tools.py
@@ -0,0 +1,187 @@
+from numbers import Integral
+
+import numpy as np
+from astropy.wcs import WCS
+from astropy.wcs.wcsapi import SlicedLowLevelWCS
+from astropy.wcs.wcsapi.wrappers.base import BaseWCSWrapper
+
+from ndcube.wcs.wrappers import ResampledLowLevelWCS
+
+__all__ = ["unwrap_wcs_to_fitswcs"]
+
+
+def unwrap_wcs_to_fitswcs(wcs):
+    """
+    Create FITS-WCS equivalent to (nested) WCS wrapper object.
+
+    Underlying WCS must be FITS-WCS.
+    No axes are dropped from original FITS-WCS, even if sliced by an integer.
+    Instead, integer-sliced axes is sliced to length-1 and marked True in the
+    ``dropped_data_axes`` output.
+    Currently supported wrapper classes include `astropy.wcs.wcsapi.SlicedLowLevelWCS`
+    and `ndcube.wcs.wrappers.ResampledLowLevelWCS`.
+
+    Parameters
+    ----------
+    wcs: `~astropy.wcs.wcsapi.BaseWCSWrapper`
+        The WCS Wrapper object.
+        Base level WCS implementation must be FITS-WCS.
+
+    Returns
+    -------
+    fitswcs: `astropy.wcs.WCS`
+        The equivalent FITS-WCS object.
+    dropped_data_axes: 1-D `numpy.ndarray`
+        Denotes which axes must have been dropped from the data array by slicing wrappers.
+        Axes are in array/numpy order, reversed compared to WCS.
+    """
+    # If wcs is already a FITS-WCS, return it.
+    low_level_wrapper = wcs.low_level_wcs if hasattr(wcs, "low_level_wcs") else wcs
+    if isinstance(low_level_wrapper, WCS):
+        return low_level_wrapper, np.zeros(low_level_wrapper.naxis, dtype=bool)
+    # Determine chain of wrappers down to the FITS-WCS.
+    wrapper_chain = []
+    while isinstance(low_level_wrapper, BaseWCSWrapper):
+        wrapper_chain.append(low_level_wrapper)
+        low_level_wrapper = low_level_wrapper._wcs
+        if hasattr(low_level_wrapper, "low_level_wcs"):
+            low_level_wrapper = low_level_wrapper.low_level_wcs
+    if not isinstance(low_level_wrapper, WCS):
+        raise TypeError(f"Base-level WCS must be type {type(WCS)}. Found: {type(low_level_wcs)}")
+    fitswcs = low_level_wrapper
+    dropped_data_axes = np.zeros(fitswcs.naxis, dtype=bool)
+    # Unwrap each wrapper in reverse order and edit fitswcs.
+    for low_level_wrapper in wrapper_chain[::-1]:
+        if isinstance(low_level_wrapper, SlicedLowLevelWCS):
+            slice_items = np.array([slice(None)] * fitswcs.naxis)
+            slice_items[dropped_data_axes == False] = low_level_wrapper._slices_array  # numpy order
+            fitswcs, dda = _slice_fitswcs(fitswcs, slice_items, numpy_order=True)
+            dropped_data_axes[dda] = True
+        elif isinstance(low_level_wrapper, ResampledLowLevelWCS):
+            factor = np.ones(fitswcs.naxis)
+            offset = np.zeros(fitswcs.naxis)
+            kept_wcs_axes = dropped_data_axes[::-1] == False  # WCS-order
+            factor[kept_wcs_axes] = low_level_wrapper._factor
+            offset[kept_wcs_axes] = low_level_wrapper._offset
+            fitswcs = _resample_fitswcs(fitswcs, factor, offset)
+        else:
+            raise TypeError("Unrecognized/unsupported WCS Wrapper type: {type(low_level_wrapper)}")
+    return fitswcs, dropped_data_axes
+
+
+def _slice_fitswcs(fitswcs, slice_items, numpy_order=True, shape=None):
+    """
+    Slice a FITS-WCS.
+
+    If an `int` is given in ``slice_items``, the corresponding axis is not dropped.
+    But the new 0th pixel will correspond the index given by the `int` in the
+    original WCS.
+
+    Parameters
+    ----------
+    fitswcs: `astropy.wcs.WCS`
+        The FITS-WCS object to be sliced.
+    slice_items: iterable of `slice` objects or `int`
+        The slices to by applied to each axis. If an `int` is provided, the axis
+        is sliced to length-1, but not dropped. However, its corresponding entry
+        in the ``dropped_data_axes`` output is marked True.
+    numpy_order: `bool`
+        If True, slices in ``slice_items`` are in array/numpy order, which is
+        reversed compared to the WCS order.
+    shape: sequence of `int`, optional
+        The length of each axis.  Only used if negative indices are supplied
+        in ``slice_items``.  If not supplied, set to ``fitswcs._naxis``.
+        Order defined by numpy_order kwarg.
+
+    Returns
+    -------
+    sliced_wcs: `astropy.wcs.WCS`
+        The sliced FITS-WCS.
+    dropped_data_axes: 1-D `numpy.ndarray`
+        Denotes which axes must have been dropped from the data array by slicing wrappers.
+        Order of axes (numpy or WCS) is dictated by ``numpy_order`` kwarg.
+    """
+    def negative_index_error_msg(x): return (
+        f"Negative indexing not supported as {x}th axis length is 0 in "
+        "underlying FITS-WCS. Supply axes lengths via shape kwarg.")
+    naxis = fitswcs.naxis
+    dropped_data_axes = np.zeros(naxis, dtype=bool)
+    # Sanitize inputs
+    if shape is None:
+        shape = fitswcs._naxis
+        if numpy_order:
+            shape = shape[::-1]
+    else:
+        if len(shape) != naxis:
+            raise ValueError("shape kwarg must be same length as number of pixel axes "
+                             f"in FITS-WCS, i.e. {naxis}")
+        if not all(isinstance(s, Integral) for s in shape):
+            raise TypeError("All elements of ``shape`` must be integers. "
+                            f"shapes types = {[type(s) for s in shape]}")
+    slice_items = list(slice_items)
+    for i, (item, len_axis) in enumerate(zip(slice_items, shape)):
+        if isinstance(item, Integral):
+            # Mark axis corresponding to int item as dropped from data array.
+            dropped_data_axes[i] = True
+            # Convert negative indices to positive equivalent.
+            if item < 0:
+                if len_axis == 0:
+                    raise ValueError(negative_index_error_msg(i))
+                item = len_axis + item
+            # Convert int item to slice so a FITS-WCS is returned after slicing.
+            slice_items[i] = slice(item, item + 1)
+        elif isinstance(item, slice):
+            # Convert negative indices inside slice item to positive equivalent.
+            start_neg = item.start is not None and item.start < 0
+            stop_neg = item.stop is not None and item.stop < 0
+            if start_neg or stop_neg:
+                if len_axis == 0:
+                    raise ValueError(negative_index_error_msg(i))
+                start = len_axis + item.start if start_neg else item.start
+                stop = len_axis + item.stop if stop_neg else item.stop
+                slice_items[i] = slice(start, stop, item.step)
+        else:
+            raise TypeError("All slice_items must be a slice or an int. "
+                            f"type(slice_items[{i}]) = {type(slice_items[i])}")
+    # Slice WCS
+    sliced_wcs = fitswcs.slice(slice_items, numpy_order=numpy_order)
+    return sliced_wcs, dropped_data_axes
+
+
+def _resample_fitswcs(fitswcs, factor, offset=0):
+    """
+    Resample the plate scale of a FITS-WCS by a given factor.
+
+    ``factor`` and ``offset`` inputs are in pixel order.
+
+    Parameters
+    ----------
+    fitswcs: `astropy.wcs.WCS`
+        The FITS-WCS object to be resampled.
+    factor: 1-D array-like or scalar
+        The factor by which the FITS-WCS is resampled.
+        Must be same length as number of axes in ``fitswcs``.
+        If scalar, the same factor is applied to all axes.
+        Factors must be given in WCS-order (opposite to data axes order).
+    offset: 1-D array-like or scalar
+        The location on the initial pixel grid which corresponds to zero on the
+        resampled pixel grid. If scalar, the same offset is applied to all axes.
+        Offsets must be given in WCS-order (opposite to data axes order).
+
+    Returns
+    -------
+    resampled_wcs: `astropy.wcs.WCS`
+        The resampled FITS-WCS.
+    """
+    # Sanitize inputs.
+    factor = np.asarray(factor)
+    if len(factor) != fitswcs.naxis:
+        raise ValueError(f"Length of factor must equal number of dimensions {fitswcs.naxis}.")
+    offset = np.asarray(offset)
+    if len(offset) != fitswcs.naxis:
+        raise ValueError(f"Length of offset must equal number of dimensions {fitswcs.naxis}.")
+    # Scale plate scale and shift by offset.
+    fitswcs.wcs.cdelt *= factor
+    fitswcs.wcs.crpix = (fitswcs.wcs.crpix + offset) / factor
+    fitswcs._naxis = list(np.round(np.array(fitswcs._naxis) / factor).astype(int))
+    return fitswcs