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README.md

@@ -66,4 +66,19 @@ It is important to acknowledge that the CDL (Crop Data Layer) labels employed in
 
 
 ### Inference
-The github repo includes an inference script an inference script that allows to run the hls-cdl crop classification model for inference on HLS images. These input have to be geotiff format, including 18 bands for 3 time-step, and each time-step includes the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification-demo)**.
+The github repo includes an inference script an inference script that allows to run the hls-cdl crop classification model for inference on HLS images. These input have to be geotiff format, including 18 bands for 3 time-step, and each time-step includes the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification-demo)**.
+
+## Citation
+
+If this model helped your research, please cite `HLS Multi Temporal Crop Classification Model` in your publications. Here is an example BibTeX entry:
+
+```
+@misc{hls-multi-temporal-crop-classification-model,
+    author = {Cecil, Michael and Kordi, Fatemehand Li, Hanxi (Steve) and Khallaghi, Sam and Alemohammad, Hamed and Ramachandran, Rahul},
+    doi    = {https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification},
+    month  = aug,
+    title  = {{HLS Multi Temporal Crop Classification Model}},
+    url    = {https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification},
+    year   = {2023}
+}
+```