@inproceedings{4061f79933594a2faf5d9c0e77fcf75f,
title = "Stratified machine learning models for wheat yield estimation using remote sensing data",
abstract = "Field-Level cereal yield estimation using Machine Learning (ML) models poses a significant challenge especially when applied across large areas. A large sample size is required to represent the high yield variability caused by varying topographic and climatic conditions. To enhance ML-based prediction accuracy, we propose to decompose the complexity of agricultural landscape using landforms and agro-ecological zones and use these classes as spatially explicit constraints to partition field samples. We trained three ML models using remote sensing data to estimate wheat yield. When training ML models without the mentioned spatial constraints, we achieved an R2=0.58 and RMSE=840kg/ha. Training ML separately across various landform classes increase the accuracy. For instance, wheat yield cultivated in plain areas was predicted with R2=0.72, and RMSE=809kg/ha. These results emphasized the potential of training ML separately across main landform classes for improving the accuracy of yield predictions across diverse geographical contexts.",
keywords = "global agroecological zones, Landform classification, Sentinel-2, stratification, yield, 2024 OA procedure",
author = "Keltoum Khechba and Mariana Belgiu and Ahmed Laamrani and Qi Dong and Alfred Stein and Abdelghani Chehbouni",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024, IGARSS ; Conference date: 07-07-2024 Through 12-07-2024",
year = "2024",
month = sep,
day = "5",
doi = "10.1109/IGARSS53475.2024.10641044",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "IEEE",
pages = "1946--1949",
booktitle = "IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "United States",
}