Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring
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| Publicado en: | Remote Sensing vol. 17, no. 15 (2025), p. 2666-2685 |
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| Autor Principal: | |
| Outros autores: | , , , , , , , |
| Publicado: |
MDPI AG
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| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17152666 |2 doi | |
| 035 | |a 3239079530 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Chen Xiaokai |u College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China; xiaokaichen@nwafu.edu.cn (X.C.); fenlingli@nwafu.edu.cn (F.L.); 2022060387@nwafu.edu.cn (B.S.) | |
| 245 | 1 | |a Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Wheat | ||
| 653 | |a Accuracy | ||
| 653 | |a Nitrogen | ||
| 653 | |a Regression analysis | ||
| 653 | |a Satellite imagery | ||
| 653 | |a Least squares method | ||
| 653 | |a Remote sensing | ||
| 653 | |a Data processing | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Winter | ||
| 653 | |a Data integration | ||
| 653 | |a Monitoring | ||
| 653 | |a Machine learning | ||
| 653 | |a Spectral reflectance | ||
| 653 | |a Prediction models | ||
| 653 | |a Vegetation | ||
| 653 | |a Agricultural economics | ||
| 653 | |a Regression | ||
| 653 | |a Support vector machines | ||
| 653 | |a Precision agriculture | ||
| 653 | |a Sensors | ||
| 653 | |a Fertilizers | ||
| 653 | |a Remote sensing systems | ||
| 653 | |a Data collection | ||
| 653 | |a Crops | ||
| 653 | |a Winter wheat | ||
| 653 | |a Emission standards | ||
| 700 | 1 | |a Miao Yuxin |u Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN 55108, USA; ymiao@umn.edu | |
| 700 | 1 | |a Kusnierek Krzysztof |u Center for Precision Agriculture, Norwegian Institute of Bioeconomy Research (NIBIO), Nylinna 226, 2849 Kapp, Norway; krzysztof.kusnierek@nibio.no | |
| 700 | 1 | |a Li Fenling |u College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China; xiaokaichen@nwafu.edu.cn (X.C.); fenlingli@nwafu.edu.cn (F.L.); 2022060387@nwafu.edu.cn (B.S.) | |
| 700 | 1 | |a Wang, Chao |u College of Agronomy, Shanxi Agriculture University, Taigu 030801, China; wangc1988@sxau.edu.cn | |
| 700 | 1 | |a Shi Botai |u College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China; xiaokaichen@nwafu.edu.cn (X.C.); fenlingli@nwafu.edu.cn (F.L.); 2022060387@nwafu.edu.cn (B.S.) | |
| 700 | 1 | |a Wu, Fei |u Precision Agriculture Lab, Department Life Science Engineering, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany; feiy.wu@tum.de (F.W.); kang.yu@tum.de (K.Y.) | |
| 700 | 1 | |a Chang Qingrui |u College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China; xiaokaichen@nwafu.edu.cn (X.C.); fenlingli@nwafu.edu.cn (F.L.); 2022060387@nwafu.edu.cn (B.S.) | |
| 700 | 1 | |a Kang, Yu |u Precision Agriculture Lab, Department Life Science Engineering, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany; feiy.wu@tum.de (F.W.); kang.yu@tum.de (K.Y.) | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 15 (2025), p. 2666-2685 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3239079530/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3239079530/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3239079530/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |