NON-DESTRUCTIVE MONITORING OF RICE BY HYPERSPECTRAL IN-FIELD SPECTROMETRY AND UAV-BASED REMOTE SENSING: CASE STUDY OF FIELD-GROWN RICE IN NORTH RHINE-WESTPHALIA, GERMANY

13 citations as recorded by crossref.

1. A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems L. G. Poley & G. J. McDermid https://doi.org/10.3390/rs12071052
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3. Very high resolution aerial data to support multi-temporal precision agriculture information management L. Pádua et al. https://doi.org/10.1016/j.procs.2017.11.055
4. Performance and Accuracy Comparisons of Classification Methods and Perspective Solutions for UAV-Based Near-Real-Time “Out of the Lab” Data Processing Z. Varga et al. https://doi.org/10.3390/s22228629
5. From Application-Driven Growth to Paradigm Shift: Scientific Evolution and Core Bottleneck Analysis in the Field of UAV Remote Sensing D. Huang et al. https://doi.org/10.3390/app15158304
6. Urban Growth Patterns and Forest Carbon Dynamics in the Metropolitan Twin Cities of Islamabad and Rawalpindi, Pakistan A. Mannan et al. https://doi.org/10.3390/su132212842
7. Flight Altitude and Sensor Angle Affect Unmanned Aerial System Cotton Plant Height Assessments O. Adedeji et al. https://doi.org/10.3390/drones8120746
8. Monitoring the Structure of Regenerating Vegetation Using Drone-Based Digital Aerial Photogrammetry R. Nuijten et al. https://doi.org/10.3390/rs13101942
9. Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages Y. Dai et al. https://doi.org/10.3389/fpls.2024.1328834
10. Remote sensing monitoring of rice growth under Cnaphalocrocis medinalis (Guenée) damage by integrating satellite and UAV remote sensing data C. Chen et al. https://doi.org/10.1080/01431161.2024.2302350
11. A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV C. Bazzo et al. https://doi.org/10.3390/rs15030639
12. Spatio-Temporal Estimation of Biomass Growth in Rice Using Canopy Surface Model from Unmanned Aerial Vehicle Images C. Peprah et al. https://doi.org/10.3390/rs13122388
13. Estimation of Agronomic Characters of Wheat Based on Variable Selection and  Machine Learning Algorithms D. Wang et al. https://doi.org/10.3390/agronomy13112808