Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes  
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1 Image Processing Laboratory (IFL), University of Valencia, C/Catedratico [PERSON] 2, Paterna, 46980 Valencia, Spain; [EMAIL_ADDRESS]. [PERSON]  
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for proper plant growth, nutrient uptake, and photosynthesis. Vegetation water content (VWC) is a useful indicator of the water status of crops and can help in monitoring the water needs and ensuring optimal management [4; 5; 6]. Climate change has become more pronounced over the past few decades and has caused water scarcity in many regions of the world. Consequently, water stress manifested as among the most critical abiotic stressors, negatively affecting plant growth and crop yield worldwide [7]. Real-time monitoring of the plant water status is urgently required to enable effective management of irrigation scheduling and to prevent waste of water and crop stress. Leaf water content (LWC) is a key variable for vital physiological processes, such as stomatal conductance and transpiration [8]. However, decoupling the spectral contributions of LWC and vegetation's canopy is challenging. Instead, the estimation of VWC, described by LWC \(\times\) LAI, is more feasible and often preferred [9]. Today, an unprecedented amount of Earth observation (EO) opportunities emerged from the advent of the European Space Agency's Copernicus programme [10]. The Sentinels satellites guarantee long-term observational commitment and operate a variety of sensors with different spectral configurations and spatial resolutions offering global coverage and synergistic data exploiting possibilities [10]. The flagship land optical operational EO system is the Sentinel-2 (S2) constellation consisting of two polar-orbiting satellites (S2-A and S2-B) [11]. The multi-spectral instrument (MSI) on board S2, combines a relatively high spatial resolution (10-20 m) with a good spectral resolution (13 bands), and the combination of the two satellites ensures a high revisit time (5-day). A remarkable amount of research applying S2 data for crop traits mapping in a quantitative way has been successfully conducted [12; 13; 14; 15; 16]. Beyond the well-known benefits that optical RS provides for cropland monitoring, the retrieval of complete vegetation traits time series along the phenological cycle is sometimes hampered by weather conditions in high-latitude areas. In this regard, radar-based RS brings the technology to mitigate this limitation. Sentinel-1 (S1) as a synthetic aperture radar (SAR) sensor permits C-band image acquisition in all-weather conditions during day or night time [17]. The S1 constellation, comprising of S1-A and S1-B satellites, predefines the interferometric wide swath (IW) mode over land consisting of dual-polarized (VV & VH) images, at 10 m of spatial resolution every 6 days in a single pass. Although there is consensus that the vegetation-radar backscatter mechanisms are complex, there is no doubt that the radar signal at C-band is altered by the vegetation's three-dimensional structure and biomass [18; 19]. Unlike optical satellites, the observation geometry plays a weighty role in radar acquisitions [20; 21]. Multiple studies exploited radar imagery for vegetation biophysical variables monitoring [22; 23; 24; 25]. For instance, [PERSON] et al. [25] presented a novel approach based on S1 radar observations at different local incidence angles for winter wheat LAI monitoring. They achieved satisfactory validation results (\(R^{2}\) = 0.67 and RMSE = 0.88 m\({}^{2}\) m\({}^{-2}\)), and proved that S1-based LAI predictions can support cropland monitoring in cloud-prone areas where the optical vegetation traits retrieval models cannot be applied for quantitative crop mapping purposes.