TY - GEN
T1 - High Spatial Resolution for Crop Yield Prediction in Large Farming Systems
T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
AU - Ofori-Ampofo, Stella
AU - Kuzu, Ridvan Salih
AU - Xiang Zhu, Xiao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The availability of open-access satellite data and advancements in machine learning techniques has exhibited significant potential in crop yield prediction. In the context of large farming systems and county-level predictions, it is customary to rely on coarse-resolution satellite images. However, these images often lack the sufficient textural detail to accurately summarise spatial information. This research aims to evaluate the advantages of enhanced spatial resolution by conducting a comparative analysis between coarse-resolution, high-temporal-frequency MODIS data and relatively high-resolution, low-temporal-frequency Landsat data for predicting corn yield in the USA. We benchmark this comparison against several models in a spatial versus non-spatial input data context. Our results suggest that, the use of high-spatial resolution for county-level yield prediction in large farming systems is not beneficial and the models explored are unable to generalize well to drought-struck years.
AB - The availability of open-access satellite data and advancements in machine learning techniques has exhibited significant potential in crop yield prediction. In the context of large farming systems and county-level predictions, it is customary to rely on coarse-resolution satellite images. However, these images often lack the sufficient textural detail to accurately summarise spatial information. This research aims to evaluate the advantages of enhanced spatial resolution by conducting a comparative analysis between coarse-resolution, high-temporal-frequency MODIS data and relatively high-resolution, low-temporal-frequency Landsat data for predicting corn yield in the USA. We benchmark this comparison against several models in a spatial versus non-spatial input data context. Our results suggest that, the use of high-spatial resolution for county-level yield prediction in large farming systems is not beneficial and the models explored are unable to generalize well to drought-struck years.
KW - convolutional neural network
KW - crop yield prediction
KW - food security
KW - machine learning
KW - recurrent neural network
KW - remote sensing
KW - spatio-temporal resolution
UR - http://www.scopus.com/inward/record.url?scp=85178367781&partnerID=8YFLogxK
U2 - 10.1109/IGARSS52108.2023.10282610
DO - 10.1109/IGARSS52108.2023.10282610
M3 - Conference contribution
AN - SCOPUS:85178367781
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3534
EP - 3537
BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 July 2023 through 21 July 2023
ER -
