Stratified machine learning models for wheat yield estimation using remote sensing data

Keltoum Khechba*, Mariana Belgiu, Ahmed Laamrani, Qi Dong, Alfred Stein, Abdelghani Chehbouni

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherIEEE
Pages1946-1949
Number of pages4
ISBN (Electronic)9798350360325
DOIs
Publication statusPublished - 5 Sept 2024
Event2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, Greece
Duration: 7 Jul 202412 Jul 2024

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Abbreviated titleIGARSS
Country/TerritoryGreece
CityAthens
Period7/07/2412/07/24

Keywords

  • global agroecological zones
  • Landform classification
  • Sentinel-2
  • stratification
  • yield
  • 2024 OA procedure

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