Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.3390/rs15143664 |
Lizenz  |
|
| Titel (primär) | MISPEL: A multi-crop spectral library for statistical crop trait retrieval and agricultural monitoring |
| Autor | Borrmann, P.; Brandt, P.; Gerighausen, H. |
| Quelle | Remote Sensing |
| Erscheinungsjahr | 2023 |
| Department | RS |
| Band/Volume | 15 |
| Heft | 14 |
| Seite von | art. 3664 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Keywords | MISPEL; spectral library; crop monitoring; crop traits; hyperspectral remote sensing; Sentinel-2; machine learning; leaf area index (LAI); SNAP; winter wheat |
| Abstract | Spatiotemporally accurate estimates of crop traits are essential for both scientific modeling and practical decision making in sustainable agricultural management. Besides efficient and concise methods to derive these traits, site- and crop-specific reference data are needed to develop and validate retrieval methods. To address this shortcoming, this study first includes the establishment of ’MISPEL’, a comprehensive spectral library (SpecLib) containing hyperspectral measurements and reference data for six key traits of ten widely grown crops. Secondly, crop-specific statistical leaf area index (LAI) models for winter wheat are developed based on a hyperspectral (MISPELFR) and a simulated Sentinel-2 (MISPELS2) SpecLib applying four nonparametric methods. Finally, an independent Sentinel-2 model evaluation at the DEMMIN test site in Germany is conducted, including a comparison with the commonly used SNAP-LAI product. To date, MISPEL comprises a set of 1411 spectra of ten crops and more than 6800 associated reference measurements. Cross-validations of winter wheat LAI models revealed that Elastic-net generalized linear model (GLMNET) and Gaussian process (GP) regressions outperformed partial least squares (PLS) and random forest (RF) regressions, showing RSQ values up to 0.86 and a minimal NRMSE of 0.21 using MISPELFR. GLMNET and GP models based on MISPELS2 further outperformed SNAP-based LAI estimates derived for the external validation site. Thus, it is concluded that the presented SpecLib ’MISPEL’ and applied methodology have a very high potential for deriving diverse crop traits of multiple crops in view of most recent and future multi-, super-, and hyperspectral satellite missions. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=27494 |
| Borrmann, P., Brandt, P., Gerighausen, H. (2023): MISPEL: A multi-crop spectral library for statistical crop trait retrieval and agricultural monitoring Remote Sens. 15 (14), art. 3664 10.3390/rs15143664 |
|