Effects of meteorological and land surface modeling uncertainty on errors in winegrape ET calculated with SIMS.

Characterization of model errors is important when applying satellite-driven evapotranspiration (ET) models to water resource management problems. This study examines how uncertainty in meteorological forcing data and land surface modeling propagate through to errors in final ET data calculated using the Satellite Irrigation Management Support (SIMS) model, a computationally efficient ET model driven with satellite surface reflectance values. The model is applied to three instrumented winegrape vineyards over the 2017-2020 time period and the spatial and temporal variation in errors are analyzed. We illustrate how meteorological data inputs can introduce biases that vary in space and at seasonal timescales, but that can persist from year to year. We also observe that errors in SIMS estimates of land surface conductance can have a particularly strong dependence on time of year. Overall, meteorological inputs introduced RMSE of 0.33-0.65 mm/day (7-27%) across sites, while SIMS introduced RMSE of 0.55-0.83 mm/day (19-24%). The relative error contribution from meteorological inputs versus SIMS varied across sites; errors from SIMS were larger at one site, errors from meteorological inputs were larger at a second site, and the error contributions were of equal magnitude at the third site. The similar magnitude of error contributions is significant given that many satellite-driven ET models differ in their approaches to estimating land surface conductance, but often rely on similar or identical meteorological forcing data. The finding is particularly notable given that SIMS makes assumptions about the land surface (no soil evaporation or plant water stress) that do not always hold in practice. The results of this study show that improving SIMS by eliminating these assumptions would result in meteorological inputs dominating the error budget of the model on the whole. This finding underscores the need for further work on characterizing spatial uncertainty in the meteorological forcing of ET. Supplementary Information: The online version contains supplementary material available at 10.1007/s00271-022-00808-9.

Improving the spatiotemporal resolution of remotely sensed ET information for water management through Landsat, Sentinel-2, ECOSTRESS and VIIRS data fusion.

Robust information on consumptive water use (evapotranspiration, ET) derived from remote sensing can significantly benefit water decision-making in agriculture, informing irrigation schedules and water management plans over extended regions. To be of optimal utility for operational usage, these remote sensing ET data should be generated at the sub-field spatial resolution and daily-to-weekly timesteps commensurate with the scales of water management activities. However, current methods for field-scale ET retrieval based on thermal infrared (TIR) imaging, a valuable diagnostic of canopy stress and surface moisture status, are limited by the temporal revisit of available medium-resolution (100 m or finer) thermal satellite sensors. This study investigates the efficacy of a data fusion method for combining information from multiple medium-resolution sensors toward generating high spatiotemporal resolution ET products for water management. TIR data from Landsat and ECOSTRESS (both at ~ 100-m native resolution), and VIIRS (375-m native) are sharpened to a common 30-m grid using surface reflectance data from the Harmonized Landsat-Sentinel dataset. Periodic 30-m ET retrievals from these combined thermal data sources are fused with daily retrievals from unsharpened VIIRS to generate daily, 30-m ET image timeseries. The accuracy of this mapping method is tested over several irrigated cropping systems in the Central Valley of California in comparison with flux tower observations, including measurements over irrigated vineyards collected in the GRAPEX campaign. Results demonstrate the operational value added by the augmented TIR sensor suite compared to Landsat alone, in terms of capturing daily ET variability and reduced latency for real-time applications. The method also provides means for incorporating new sources of imaging from future planned thermal missions, further improving our ability to map rapid changes in crop water use at field scales.

Modification of a gas exchange system to measure active and passive chlorophyll fluorescence simultaneously under field conditions.

Solar-induced fluorescence (SIF) is a promising tool to estimate photosynthesis across scales; however, there has been limited research done at the leaf level to investigate the relationship between SIF and photosynthesis. To help bridge this gap, a LI-COR LI-6800 gas exchange instrument was modified with a visible-near-infrared (VIS-NIR) spectrometer to measure active and passive fluorescence simultaneously. The system was adapted by drilling a hole into the bottom plate of the leaf chamber and inserting a fibre-optic to measure passive steady-state fluorescence (F t , λ , analogous to SIF) from the abaxial surface of a leaf. This new modification can concurrently measure gas exchange, passive fluorescence and active fluorescence over the same leaf area and will allow researchers to measure leaf-level F t , λ in the field to validate tower-based and satellite measurements. To test the modified instrument, measurements were performed on leaves of well-watered and water-stressed walnut plants at three light levels and a constant air temperature. Measurements on these same plants were also conducted using a similarly modified Walz GFS-3000 gas exchange instrument to compare results. We found a positive linear correlation between F t , λ measurements from the modified LI-6800 and GFS-3000 instruments. We also report a positive linear relationship between F t , λ and normalized steady-state chlorophyll fluorescence (F t /F o ) from the pulse-amplitude modulation (PAM) fluorometer of the LI-6800 system. Accordingly, this modification will inform the link between spectrally resolved F t , λ and gas exchange-leading to improved interpretation of how remotely sensed SIF tracks changes in the light reactions of photosynthesis.

Predicting Stomatal Closure and Turgor Loss in Woody Plants Using Predawn and Midday Water Potential.

Knowledge about physiological stress thresholds provides crucial information about plant performance and survival under drought. In this study, we report on the triphasic nature of the relationship between plant water potential (Ψ) at predawn and midday and describe a method that predicts Ψ at stomatal closure and turgor loss exclusively from this water potential curve (WP curve). The method is based on a piecewise linear regression model that was developed to predict the boundaries (termed Θ1 and Θ2) separating the three phases of the curve and corresponding slope values. The method was tested for three economically important woody species. For all species, midday Ψ was much more negative than predawn Ψ during phase I (mild drought), reductions in midday Ψ were minor while predawn Ψ continued to decline during phase II (moderate drought), and midday and predawn Ψ reached similar values during phase III (severe drought). Corresponding measurement of leaf gas exchange indicated that boundary Θ1 between phases I and II coincided with Ψ at stomatal closure. Data from pressure-volume curves demonstrated that boundary Θ2 between phases II and III predicted Ψ at leaf turgor loss. The WP curve method described here is an advanced application of the Scholander-type pressure chamber to categorize plant dehydration under drought into three distinct phases and to predict Ψ thresholds of stomatal closure and turgor loss.