Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring

0562849 - ÚVGZ 2023 RIV US eng J - Článek v odborném periodiku
Amin, E. - Verrelst, J. - Rivera-Caicedo, J. P. - Pipia, L. - Ruiz-Verdu, A. - Moreno, J.
Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring.
Remote Sensing of Environment. Roč. 255, MAR 15 (2021), č. článku 112168. ISSN 0034-4257. E-ISSN 1879-0704
Výzkumná infrastruktura: CzeCOS III - 90123
Klíčová slova: Photosynthetic and non-photosynthetic vegetation * Gaussian processes regression (GPR) * Machine learning * Green LAI * Brown LAI * Sentinel-2
Obor OECD: Ecology
Impakt faktor: 13.850, rok: 2021
Způsob publikování: Omezený přístup
https://www.sciencedirect.com/science/article/pii/S0034425720305411?via%3Dihub

For agricultural applications, identification of non-photosynthetic above-ground vegetation is of great interest as it contributes to assess harvest practices, detecting crop residues or drought events, as well as to better predict the carbon, water and nutrients uptake. While the mapping of green Leaf Area Index (LAI) is well established, current operational retrieval models are not calibrated for LAI estimation over senescent, brown vegetation. This not only leads to an underestimation of LAI when crops are ripening, but is also a missed monitoring opportunity. The high spatial and temporal resolution of Sentinel-2 (52) satellites constellation offers the possibility to estimate brown LAI (LAI(B)) next to green LAI (LAI(G)). By using LAI ground measurements from multiple campaigns associated with airborne or satellite spectra, Gaussian processes regression (GPR) models were developed for both LAI(G) and LAI(B), providing alongside associated uncertainty estimates, which allows to mask out unreliable LAI retrievals with higher uncertainties. A processing chain was implemented to apply both models to 52 images, generating a multiband LAI product at 20 m spatial resolution. The models were adequately validated with insitu data from various European study sites (LAI(G): R-2 = 0.7, RMSE = 0.67 m(2)/m(2), LAI(B): R-2 = 0.62, RMSE = 0.43 m(2)/m(2)). Thanks to the 52 bands in the red edge (B5: 705 nm and B6: 740 nm) and in the shortwave infrared (B12: 2190 nm) a distinction between LAI(G) and LAI(B) can be achieved. To demonstrate the capability of LAI(B) to identify when crops start senescing, 52 time series were processed over multiple European study sites and seasonal maps were produced, which show the onset of crop senescence after the green vegetation peak. Particularly, the LAI(B) product permits the detection of harvest (i.e., sudden drop in LAI(B)) and the determination of crop residues (i.e., remaining LAI(B)), although a better spectral sampling in the shortwave infrared would have been desirable to disentangle brown LAI from soil variability and its perturbing effects. Finally, a single total LAI product was created by merging LAI(G) and LAI(B) estimates, and then compared to the LAI derived from 52 L2B biophysical processor integrated in SNAP. The spatiotemporal analysis results confirmed the improvement of the proposed descriptors with respect to the standard SNAP LAI product accounting only for photosynthetically active green vegetation.
Trvalý link: https://hdl.handle.net/11104/0335039