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Remote Sensing
Volume 17
Issue 15
10.3390/rs17152666
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring

by
Xiaokai Chen
1,
Yuxin Miao
2,
Krzysztof Kusnierek
3,
Fenling Li
1,
Chao Wang
4,
Botai Shi
1,
Fei Wu
5,
Qingrui Chang
1,* and
Kang Yu
5
1
College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
2
Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul., MN 55108, USA
3
Center for Precision Agriculture, Norwegian Institute of Bioeconomy Research (NIBIO), Nylinna 226, 2849 Kapp, Norway
4
College of Agronomy, Shanxi Agriculture University, Taigu 030801, China
5
Precision Agriculture Lab, Department Life Science Engineering, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(15), 2666; https://doi.org/10.3390/rs17152666 (registering DOI)
Submission received: 27 June 2025 / Revised: 29 July 2025 / Accepted: 30 July 2025 / Published: 1 August 2025
(This article belongs to the Special Issue Perspectives of Remote Sensing for Precision Agriculture)
Versions Notes

Abstract

Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms.
Keywords: plant nitrogen concentration; satellite remote sensing; machine learning; UAV remote sensing plant nitrogen concentration; satellite remote sensing; machine learning; UAV remote sensing

Share and Cite

MDPI and ACS Style

Chen, X.; Miao, Y.; Kusnierek, K.; Li, F.; Wang, C.; Shi, B.; Wu, F.; Chang, Q.; Yu, K. Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring. Remote Sens. 2025, 17, 2666. https://doi.org/10.3390/rs17152666

AMA Style

Chen X, Miao Y, Kusnierek K, Li F, Wang C, Shi B, Wu F, Chang Q, Yu K. Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring. Remote Sensing. 2025; 17(15):2666. https://doi.org/10.3390/rs17152666

Chicago/Turabian Style

Chen, Xiaokai, Yuxin Miao, Krzysztof Kusnierek, Fenling Li, Chao Wang, Botai Shi, Fei Wu, Qingrui Chang, and Kang Yu. 2025. "Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring" Remote Sensing 17, no. 15: 2666. https://doi.org/10.3390/rs17152666

APA Style

Chen, X., Miao, Y., Kusnierek, K., Li, F., Wang, C., Shi, B., Wu, F., Chang, Q., & Yu, K. (2025). Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring. Remote Sensing, 17(15), 2666. https://doi.org/10.3390/rs17152666

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