Remote sensing of plant-water relations: An overview and future perspectives.

Vegetation is a highly dynamic component of the Earth surface and substantially alters the water cycle. Particularly the process of oxygenic plant photosynthesis determines vegetation connecting the water and carbon cycle and causing various interactions and feedbacks across Earth spheres. While vegetation impacts the water cycle, it reacts to changing water availability via functional, biochemical and structural responses. Unravelling the resulting complex feedbacks and interactions between the plant-water system and environmental change is essential for any modelling approaches and predictions, but still insufficiently understood due to currently missing observations. We hypothesize that an appropriate cross-scale monitoring of plant-water relations can be achieved by combined observational and modelling approaches. This paper reviews suitable remote sensing approaches to assess plant-water relations ranging from pure observational to combined observational-modelling approaches. We use a combined energy balance and radiative transfer model to assess the explanatory power of pure observational approaches focussing on plant parameters to estimate plant-water relations, followed by an outline for a more effective use of remote sensing by their integration into soil-plant-atmosphere continuum (SPAC) models. We apply a mechanistic model simulating water movement in the SPAC to reveal insight into the complexity of relations between soil, plant and atmospheric parameters, and thus plant-water relations. We conclude that future research should focus on strategies combining observations and mechanistic modelling to advance our knowledge on the interplay between the plant-water system and environmental change, e.g. through plant transpiration.

Estimation of the refractive index structure parameter from single-level daytime routine weather data.

Atmospheric scintillations cause difficulties for applications where an undistorted propagation of electromagnetic radiation is essential. These scintillations are related to turbulent fluctuations of temperature and humidity that are in turn related to surface heat fluxes. We developed an approach that quantifies these scintillations by estimating C(n(2)) from surface fluxes that are derived from single-level routine weather data. In contrast to previous methods that are biased to dry and warm air, our method is directly applicable to several land surface types, environmental conditions, wavelengths, and measurement heights (lookup tables for a limited number of site-specific parameters are provided). The approach allows for an efficient evaluation of the performance of, e.g., infrared imaging systems, laser geodetic systems, and ground-to-satellite optical communication systems. We tested our approach for two grass fields in central and southern Europe, and for a wheat field in central Europe. Although there are uncertainties in the flux estimates, the impact on C(n(2)) is shown to be rather small. The C(n(2)) daytime estimates agree well with values determined from eddy covariance measurements for the application to the three fields. However, some adjustments were needed for the approach for the grass in southern Europe because of non-negligible boundary-layer processes that occur in addition to surface-layer processes.