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

Aug 6, 2026

Upload by Violet

Accurate, timely winter wheat yield estimation supports agricultural management and strengthens global food security. This study evaluates how EnMAP hyperspectral imagery combined with machine learning and deep learning models improves yield prediction in Hungary. Multi-temporal EnMAP observations from February and May 2023, together with ground-truth yields from four fields, generate 10 vegetation indices and train RF, Gradient Boosting, and MLP regressors. Results show multi-temporal integration markedly increases accuracy, reaching R2 up to 0.79 and MAE down to 0.27, with SWIR indices strongest early.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Highlights

Main findings

Implications

Abstract

Keywords

Introduction

Article Assessment of the Effectiveness of Spectral Indices Derived from EnMAP Hyperspectral Imageries Using Machine Learning and Deep Learning Models for Winter Wheat Yield Prediction László Mucsi 1, *, Dorottya Litkey-Kovács 2, Krisztián Bonus 3, Nizom Farmonov 4, Ali Elgendy 1,5, Lutfi Aji 1 and Márkó Sóti 6 Academic Editors: Xiaoyang Zhang and Ruyin Cao Received: 11 August 2025 Revised: 21 September 2025 Accepted: 8 October 2025 Published: 13 October 2025 Citation: Mucsi, L.; Litkey-Kovács, D.; Bonus, K.; Farmonov, N.; Elgendy, A.; Aji, L.; Sóti, M. Assessment of the Effectiveness of Spectral Indices Derived from EnMAP Hyperspectral Imageries Using Machine Learning and Deep Learning Models for Winter Wheat Yield Prediction. Remote Sens. 2025, 17, 3426. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/rs17203426](10.3390/rs17203426) Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 Department of Atmospheric and Geospatial Data Science, University of Szeged, Egyetem Str. 2, H-6722 Szeged, Hungary; [alielgendy@cu.edu.eg](alielgendy@cu.edu.eg) (A.E.); [aji.lutfi@stud.u-szeged.hu](aji.lutfi@stud.u-szeged.hu) (L.A.) 2 Lajtamag Ltd., Bereki Str. 1, H-9246 Mosonmagyaróvár, Hungary; [soproni.dorottya@lajtamag.hu](soproni.dorottya@lajtamag.hu) [3](3 Nemzeti M)[ Nemzeti M](3 Nemzeti M)é[nesbirtok](nesbirtok) é[s Tangazdas](s Tangazdas)á[g Zrt](g Zrt)., [Jung J](Jung J)ó[zsef Sq](zsef Sq). 1, H-5820 Mez˝ohegyes, Hungary; [bonus.krisztian@mezohegyesbirtok.hu](bonus.krisztian@mezohegyesbirtok.hu) 4 Laboratory for Climatology and Remote Sensing, Department of Geography, Philipps-Universität Marburg, Deutschhausstr. 12, 35032 Marburg, Germany; [farmonov@staff.uni-marburg.de](farmonov@staff.uni-marburg.de) [5](5 Department of Geology)[ Department of Geology](5 Department of Geology), [Faculty of Science](Faculty of Science), [Cairo University](Cairo University), [Giza P.O](Giza P.O). Box 12613, Egypt 6 Doctoral School of Geosciences, University of Szeged, Egyetem Str. 2, H-6722 Szeged, Hungary; [soti.marko@stud.u-szeged.hu](soti.marko@stud.u-szeged.hu) * [Correspondence: mucsi.laszlo@szte.hu](Correspondence: mucsi.laszlo@szte.hu) Highlights What are the main findings? • Multi-temporal EnMAP hyperspectral data combined with machine learning and deep learning models significantly improved the accuracy of winter wheat yield prediction (R2 up to 0.79) . • SWIR indices were particularly important for early-season estimation, whereas VNIR indices became dominant during later growth stages. What are the implications of the main findings? • Integrating hyperspectral observations across phenological stages enables more robust and reliable yield forecasts for precision agriculture. • Future missions, such as ESA’s CHIME, will further enhance large-scale, operational crop yield monitoring by providing frequent, high-resolution hyperspectral data. Abstract 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 signifi

FAQs

What dataset and indices were used for the winter wheat yield prediction?

EnMAP hyperspectral images from February and May 2023 were used, and 10 distinct vegetation indices were derived from the imagery.

Which models were evaluated and how was performance measured?

Random Forest, Gradient Boosting, and Multilayer Perceptron models were trained, and performance was assessed using MAE and R2 values.

How does using multi-temporal hyperspectral data affect prediction accuracy?

Integrating multi-temporal observations across phenological stages significantly improves predictive accuracy compared with single-date predictions.

Aug 6, 2026

Upload by Violet