diff --git a/presto/inference.py b/presto/inference.py
index 7f31f36..122ece9 100644
--- a/presto/inference.py
+++ b/presto/inference.py
@@ -1,3 +1,4 @@
+import logging
 from typing import Tuple, Union
 
 import numpy as np
@@ -20,6 +21,9 @@
 from .presto import Presto
 from .utils import device, prep_dataframe
 
+logger = logging.getLogger(__name__)
+
+
 # Index to band groups mapping
 IDX_TO_BAND_GROUPS = {
     NORMED_BANDS[idx]: band_group_idx
@@ -59,6 +63,8 @@ def __init__(self, model: Presto, batch_size: int = 8192):
         "temperature-mean": "temperature_2m",
     }
 
+    STATIC_BAND_MAPPING = {"DEM-alt-20m": "elevation", "DEM-slo-20m": "slope"}
+
     @classmethod
     def _preprocess_band_values(cls, values: np.ndarray, presto_band: str) -> np.ndarray:
         """
@@ -106,8 +112,25 @@ def _extract_eo_data(cls, inarr: xr.DataArray) -> Tuple[np.ndarray, np.ndarray]:
                 )
                 idx_valid = values != cls._NODATAVALUE
                 values = cls._preprocess_band_values(values, presto_band)
-                eo_data[:, :, BANDS.index(presto_band)] = values
+                eo_data[:, :, BANDS.index(presto_band)] = values * idx_valid
                 mask[:, :, IDX_TO_BAND_GROUPS[presto_band]] += ~idx_valid
+            else:
+                logger.warning(f"Band {org_band} not found in input data.")
+                eo_data[:, :, BANDS.index(presto_band)] = 0
+                mask[:, :, IDX_TO_BAND_GROUPS[presto_band]] = 1
+
+        for org_band, presto_band in cls.STATIC_BAND_MAPPING.items():
+            if org_band in inarr.coords["bands"]:
+                values = np.swapaxes(
+                    inarr.sel(bands=org_band).values.reshape((num_timesteps, -1)), 0, 1
+                )
+                idx_valid = values != cls._NODATAVALUE
+                eo_data[:, :, BANDS.index(presto_band)] = values * idx_valid
+                mask[:, IDX_TO_BAND_GROUPS[presto_band]] += ~idx_valid
+            else:
+                logger.warning(f"Band {org_band} not found in input data.")
+                eo_data[:, :, BANDS.index(presto_band)] = 0
+                mask[:, :, IDX_TO_BAND_GROUPS[presto_band]] = 1
 
         return eo_data, mask
 
@@ -369,18 +392,25 @@ def process_parquet(df: pd.DataFrame) -> pd.DataFrame:
     ]
     bands100m = ["METEO-precipitation_flux", "METEO-temperature_mean"]
 
+    # ----------------------------------------------------------------------------
     # PLACEHOLDER for substituting start_date with one derived from crop calendars
     # df['start_date'] = seasons.get_season_start(df[['lat','lon']])
 
+    # For now, in absence of a relevant start_date, we get time difference with respect
+    # to end_date so we can take 12 months counted back from end_date
     df["valid_date_ind"] = (
         (((df["timestamp"] - df["end_date"]).dt.days + 365) / 30).round().astype(int)
     )
 
-    # once the start date is settled, we take 12 months from that as input to Presto
+    # Now reassign start_date to the actual subset counted back from end_date
+    df["start_date"] = df["end_date"] - pd.DateOffset(years=1) + pd.DateOffset(days=1)
+
     df_pivot = df[(df["valid_date_ind"] >= 0) & (df["valid_date_ind"] < 12)].pivot(
         index=index_columns, columns="valid_date_ind", values=feature_columns
     )
 
+    # ----------------------------------------------------------------------------
+
     if df_pivot.empty:
         raise ValueError("Left with an empty DataFrame!")