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

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 (S2) satellites constellation offers the possibility to estimate brown LAI (LAI G ) 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 S2 images, generating a multiband LAI product at 20 m spatial resolution. The models were adequately validated with in-situ data from various European study sites (LAI G : R2 = 0.7, RMSE = 0.67 m2/m2; LAI B : R2 = 0.62, RMSE = 0.43 m2/m2). Thanks to the S2 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, S2 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 S2 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.

Remote sensing of cyanobacterial blooms in a hypertrophic lagoon (Albufera of València, Eastern Iberian Peninsula) using multitemporal Sentinel-2 images.

Eutrophy in Albufera of Valencia (Eastern Iberian Peninsula) is a quite old problem since after the intense eutrophication processes throughout the 1960s. The system shifted to a turbid stable state consolidated by the virtual disappearance of macrophytes by the early 1970s. The lagoon has been studied extensively since the 1980s, but efforts to revert the system to a clear state have not yielded the expected results because cultural eutrophication due to the growth of population in its area of influence and the effects of climate change, decreasing rainfall and increasing evaporation. This has driven to an increase in water retention times in the lagoon in recent years, resulting in a phytoplanktonic shift towards potentially toxic cyanobacteria species, often forming blooms. Cyanobacterial blooms severely affect water quality for human use, ranging from recreation and fishing to drinking water resources, as indicated in the surveillance protocol of World Health Organization (WHO). The current state of the lake requires constant monitoring and remote sensing is an optimal tool for the continuous monitoring of the whole water mass. This work is included in the ESAQS project (Ecological Status of AQuatic systems with Sentinel satellites), to establish a protocol for regular and frequent monitoring of the ecological status of reservoirs, lakes and lagoons. Algorithms are developed using the images provided by the Sentinel-2 (A and B), provided with a spatial resolution of 10 m and a temporal frequency of 5 days. In this work we demonstrate that using this new earth observation satellite is possible to develop a consistent and suitable algorithm to estimate the phycocyanin concentration [PC] and establish a protocol for regular and frequent monitoring. Calibrating (R2 = 0.841; n = 21; p < 0.001) and validating (R2 = 0.775; n = 55; p < 0.001; RMSE% = 40) the algorithm with field data are also demonstrated.