Assessment of the Effectiveness of Spectral Indices Derived from EnMAP Hyperspectral Imageries Using Machine Learning and Deep Learning Models for Winter Wheat Yield Prediction

Assessment of the Effectiveness of Spectral Indices Derived from EnMAP Hyperspectral Imageries Using Machine Learning and Deep Learning Models for Winter Wheat Yield Prediction

Accurate and timely crop yield estimation is essential for effective agricultural management and global food security, particularly for winter wheat. This study aimed to assess the effectiveness of EnMAP hyperspectral imagery in combination with machine learning and deep learning models for winter w...

Teljes leírás

Elmentve itt :
Bibliográfiai részletek
Szerzők: Mucsi László
Litkey-Kovács Dorottya
Bonus Krisztián
Farmonov Nizom
Elgendy Ali
Aji Lutfi
Sóti Márkó
Dokumentumtípus: Cikk
Megjelent: 2025
Sorozat:REMOTE SENSING 17 No. 20
Tárgyszavak:
Föld- és kapcsolódó környezettudományok
doi:10.3390/rs17203426

mtmt:36379317
Online Access:http://publicatio.bibl.u-szeged.hu/38068
Leíró adatok
Tartalmi kivonat:Accurate and timely crop yield estimation is essential for effective agricultural management and global food security, particularly for winter wheat. This study aimed to assess the effectiveness of EnMAP hyperspectral imagery in combination with machine learning and deep learning models for winter wheat yield prediction in Hungary. Using EnMAP images from February and May 2023, along with ground truth yield data from four fields, we derived 10 distinct vegetation indices. Random Forest, Gradient Boosting, and Multilayer Perceptron algorithms were employed, and model performance was evaluated using Mean Absolute Error (MAE) and Coefficient of Determination (R2) values. The results consistently demonstrated that integrating multi-temporal data significantly enhanced predictive accuracy, with the MLP model achieving an R2 of 0.79 and an MAE of 0.27, notably outperforming single-date predictions. Shortwave infrared (SWIR) indices were particularly critical for early-season yield estimations. This research highlights the substantial potential of hyperspectral data and advanced machine learning techniques in precision agriculture, emphasizing the promising role of future missions such as CHIME in further refining and expanding yield estimation capabilities.
Terjedelem/Fizikai jellemzők:24
ISSN:2072-4292

Hasonló tételek