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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+
+# On exploring spatio-temporal encoding strategies for county-level yield prediction
+
+Crop yield information plays a pivotal role in ensuring food security. Advances in Earth Observation technology and the availability of historical yield records have promoted the use of machine learning for yield prediction. 
+Significant research efforts have been made in this direction, encompassing varying choices of yield determinants and particularly how spatial and temporal information are encoded. 
+However, these efforts are often conducted under diverse experimental setups, complicating their inter-comparisons. 
+
+The dataset ```SpatioTemporalYield``` is the data used in our comparative studies. 
+
+
+# Data coverage
+- The United States of America (USA) is the world’s largest producer of corn, accounting for approximately one-third of global production. 
+- SpatioTemporalYield covers the USA’s top five corn-producing states: Iowa, Illinois, Indiana, Nebraska, and Minnesota.  
+- Altogether, they accounted for over one-half of the USA’s corn(grain) production in 2021.
+
+
+# Structure of the data
+The folder contains numpy arrays (over 8000) in the form ```YYYY_GEOID.npy``` and a single json file ```labels.json``` corresponding to their labels.
+- YYYY  - is the year of acquisition (from 2003 to 2021)
+- GEOID - a five-character code representing a stateid (first two characters) and a county id (next three characters)
+- Each ```.npy``` file has the structure ```T (time) x C (channel) x S (number of pixels)```
+- There are 46 sequences (observed from January to December) and 12 channels in each array.
+
+The channels/features are in the following order 
+
+| Order     | Band     | Source               |
+|---------- |----------|----------------------|
+| Index 0   | red      | MOD9A1.061           |
+| Index 1   | nir      | MOD9A1.061           |
+| Index 2   | blue     | MOD9A1.061           |
+| Index 3   | green    | MOD9A1.061           |
+| Index 4   | nir2     | MOD9A1.061           |
+| Index 5   | swir1    | MOD9A1.061           |
+| Index 6   | swir2    | MOD9A1.061           |
+| Index 7   | tmin     | Daymet               |
+| Index 8   | tmax     | Daymet               |
+| Index 9   | prcp     | Daymet               |
+| Index 10  | ndvi     | MOD9A1.061 (derived) |
+| Index 11  | ndwi     | MOD9A1.061 (derived) |
+
+
+# Citation
+If you use this data, please cite our work as:
+```
+TBD
+
+```
+
+
+# Notes
+The ```SpatioTemporalYield``` is a first version of our on-going initiative to create a multi-task and multi-sensory benchmark dataset for agricultural monitoring in the USA
+Please check back for updates.