Time series profiles of CRNS derived soil moisture content compared to remote sensing and meteorological derived products: insights for up- and downscaling

Drought events and environmental disturbances related to water scarcity have become more severe and frequent, affecting food security and endangering vulnerable biomes. Reliable soil moisture content (SMC) estimations at the landscape scale are therefore essential to understand patterns in drought events and vegetation response to such occurrences. Accurate soil moisture predictions can support actions to mitigate water scarcity effects in vegetation, for instance, by precisely managing crops to avoid further depleting limited water resources. However, most available SM products derived from remote sensing (RS) or meteorological data are supplied at a coarse spatial scale and are unsuitable for heterogeneous landscapes in terms of topography and land cover. The gaps between significant changes in SMC levels at the root zone and the vegetation response during the dry and wet seasons are still unknown. Before defining whether up-scaling (or modelling) in situ data using RS or downscaling coarse images to a landscape scale would resolve this research gap, a better understanding of temporal and spatial contributions and uncertainties of different technologies to SMC products is needed. Despite the advance in sensors and processing capacity, a combination of spatial and temporal resolution required for SMC retrieval is unlikely to be available soon globally. Satellite sensors (e.g. microwave, optical, thermal) present different limitations and rely on proxies and assumptions to indirectly derive SMC at the root zone. Moreover, the relationships across time can be biased by weather conditions, masked by land cover type and clouds, or misled by spurious correlations between meteorological and plant trait variables (phenology). For instance, microwave signals can be affected over dense forests, snow cover, or steep topography. Furthermore, optical data are often unavailable due to cloud cover or have their reflectance drastically change from living vegetation to bare soil between two acquisitions in non-permanent crop fields. Therefore, multi-platform approaches, combining technologies and resolutions to derive a versatile and accurate SMC product, should be prioritized. As the model relies on indirect relationships with plant traits or moisture from the topsoil rather than the underlying hydrological processes, the spatial-temporal patterns (and autocorrelation) should not be neglected as they carry crucial information about water balance. In this study, we analyze 38 soil moisture probes installed in landscapes with different vegetation cover, topography, and soil type in Germany. The SMC measurements are provided by cosmic-ray neutron sensors (CRNSs), a non-invasive technology that provides measurements at a field scale (130 to 240m radius). The CRNS time-series measurements are compared to RS and meteorological products. Auxiliary variables such as precipitation, evapotranspiration, and vegetation parameters (e.g. LAI) are also aligned with the SMC and RS-derived products. The similarity and mismatching of the explanatory time-series patterns compared to the reference SMC for different vegetation cover (forest, grassland, and crops), season, regional characteristics (climate, soil properties, and topography), and resolutions (temporal and spatial) are presented. The results can support the development of a soil moisture retrieval approach at a medium to high spatial resolution based on a data cube combining different RS platforms and auxiliary variables.