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