Update README to include Bibtex.

README.md

@@ -70,4 +70,19 @@ The 3,854 chips have been randomly split into training (80%) and validation (20%
 First, a set of 5,000 chips are defined based on samples from the USDA CDL to ensure a representative sampling across the CONUS. Next, for each chip the corresponding HLS S30 scenes between March and September 2022 are queried and scenes with low cloud cover are retrieved. Then, three scenes are selected among the low cloudy scenes to ensure we get a scene early in the season, one in the middle, and one toward the end. The three final scenes are then reprojected to CDL's projection grid (`EPSG:5070`) using bilinear interpolation. 
 
 ### Chip Generation
-In the final step, the three scenes for each chip are clipped to the bounding box of the chip, and 18 spectral bands are stacked together. In addition, a quality control is applied to each chip using the `Fmask` layer of HLS dataset. Any chip containing clouds, cloud shadow, adjacent to cloud or missing values is discarded. This resulted in 3,854 chips.
+In the final step, the three scenes for each chip are clipped to the bounding box of the chip, and 18 spectral bands are stacked together. In addition, a quality control is applied to each chip using the `Fmask` layer of HLS dataset. Any chip containing clouds, cloud shadow, adjacent to cloud or missing values is discarded. This resulted in 3,854 chips.
+
+### Citation
+
+If this dataset helped your research, please cite `hls-multi-temporal-crop-classification` in your publications. Here is an example BibTeX entry:
+
+```
+@misc{hls-multi-temporal-crop-classification,
+    author = {Cecil, Michael and Kordi, Fatemehand Li, Hanxi (Steve) and Khallaghi, Sam and Alemohammad, Hamed},
+    doi    = {https://huggingface.co/ibm-nasa-geospatial/multi-temporal-crop-classification},
+    month  = aug,
+    title  = {{HLS Multi Temporal Crop Classification}},
+    url    = {https://huggingface.co/ibm-nasa-geospatial/multi-temporal-crop-classification},
+    year   = {2023}
+}
+```