Update README.md

README.md

@@ -61,8 +61,14 @@ The `opensr-test` package provides five datasets for benchmarking SR models. The
 - **`HR`**: High-resolution image (RGBNIR) without harmonization.
 - **`HRharm`**: Harmonized high-resolution image (RGBNIR). The HRharm image is **harmonized with respect to the Sentinel-2 L2A bands**.
 - **`metadata`**: A pandas DataFrame with the images' metadata.
-- **`gradient_threshold`**: Threshold estimated by human interpretation. All the pixels or patches with a gradient higher than this threshold are considered relevant and, therefore, used to estimate the percentage of hallucination, omission, and correctness by the SR model.
-
+    - **`lr_file:`** The low-resolution image file path.
+    - **`hr_file:`** The high-resolution image file path.
+    - **`roi:`** The spatial unique identifier.
+    - **`lr_gee_id:`** The low-resolution image Google Earth Engine id.
+    - **`reflectance:`** How SR affects the mean of reflectance values. It uses the L1 norm. The lower the value, the better the reflectance consistency.
+    - **`spectral:`** How the harmonization affects the spectral signature, when compared to the LR image. It uses the spectral angle distance. The lower the value, the better the spectral consistency. The values are in degrees.
+    - **`spatial:`** The spatial misalignment in term of LR pixels (10m). The lower the value, the better the spatial consistency.
+- **`gradient_threshold`**: Threshold estimated by human interpretation. All the pixels with a gradient higher than this threshold are considered as features, and therefore, used to estimate the percentage of hallucination, omission, and correctness by the SR model.
 We provide 12 bands for Sentinel-2 L2A (see table below) and 13 for Sentinel-2 L1C (see table below).
 
 | Band | Description | Resolution (m) | L2A Index | L1C index |