Krzysztof Kusnierek
Head of Department/Head of Research
(+47) 920 12 953
krzysztof.kusnierek@nibio.no
Place
Apelsvoll
Visiting address
Nylinna 226, 2849 Kapp
Authors
Jakob Geipel Therese With Berge Krzysztof Kusnierek Kristian Sæther Malin Larsen Græsdahl Ingeborg Hogne Kristian Rindal Thor Johannes RognebyAbstract
No abstract has been registered
Authors
Krzysztof KusnierekAbstract
No abstract has been registered
Authors
Xiaokai Chen Yuxin Miao Krzysztof Kusnierek Fenling Li Chao Wang Botai Shi Fei Wu Qingrui Chang Kang YuAbstract
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.
