While yearly budgets of CO2 and evapotranspiration (ET) above forests can be readily obtained from eddy-covariance measurements, the quantification of their respective soil (respiration and evaporation) and canopy (photosynthesis and transpiration) components remains an elusive yet critical research objective. To this end, methods capable of reliably partitioning the measured ET and F_c fluxes into their respective soil and plant sources and sinks are highly valuable. In this work, we investigate four partitioning methods (two new, and two existing) that are based on analysis of conventional high frequency eddy-covariance (EC) data. The physical validity of the assumptions of all four methods, as well as their performance under different scenarios, are tested with the aid of large eddy simulations, which are used to replicate eddy-covariance field experiments. Our results indicate that canopies with large, exposed soil patches increase the mixing and correlation of scalars; this negatively impacts the performance of the partitioning methods, all of which require some degree of uncorrelatedness between CO2 and water vapor. In addition, best performance for all partitioning methods were found when all four flux components are non-negligible, and measurements are collected close to the canopy top. Methods relying on the water-use efficiency (W) perform better when W is known a priori, but are shown to be very sensitive to uncertainties in this input variable especially when canopy fluxes dominate. We conclude by showing how the correlation coefficient between CO2 and water vapor can be used to infer the reliability of different W parameterizations.

Landsat-based monitoring of seasonal and near real-time evapotranspiration (ET) in California vineyards is currently challenged by its low temporal revisit period and missing data under cloudy conditions. Gap-filling approaches, such as data fusion with high-temporal resolution images (e.g., MODIS) and interpolation of actual to potential ET ratio (ET/ETo) between image acquisition dates are now commonly used to overcome this challenge. However, these methods may not fully capture non-linear changes in crop condition due to scheduled irrigation, and other management decisions affecting ET during days when satellite images are unavailable and can lead to biased ET estimates. In this study, we combined Landsat-8 and Sentinel-2 data to develop a Shuttleworth-Wallace (SW) based near real-time ET modeling framework for mapping daily ET across three California Vineyard sites. In addition, we utilized daily Leaf area index (LAI) products derived from the Harmonized Landsat and Sentinel-2 (HLS) surface reflectance and MODIS LAI data products to constrain key resistance parameters in the SW model and tested the model across nine flux towers covering three vineyard sites in California. Results suggest that compared to the linear interpolation-based ET/ETo approach, this framework can help reduce biases and root mean squared error of estimated daily ET by over 10%. Results point to a potential utility of the combined Landsat-8 and Sentinel-2 based approach to monitor near real-time ET and complement ongoing thermal remote sensing-based ET modeling approaches to better characterize near real-time crop water status in California vineyards.

Efficient use of available water resources is key to sustainable viticulture management in California (CA) and other regions with limited water availability in the western US and abroad. This requires remote and frequent field-scale information on vineyard water status. Though the Sentinel-2 sensors offer good spatial (10-60m) and temporal (~5 days) coverages, their utility in monitoring vineyard evapotranspiration (ET) has not been considered viable primarily due to the lack of a thermal band. However, recently, a new spectral-based Shuttleworth Wallace (SW) ET model, which uses a contextual framework to determine dry and wet extremes from the Sentinel-2 (SW-S2) surface reflectance data, has shown promise when tested over a single GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) site in CA. However, current knowledge on its applicability across a climate gradient in CA with different topography, soils, trellis design and vine variety is lacking. Moreover, how the selection of modeling domain and meteorological forcing data influence model output is limited. Consequently, this presentation expands the evaluation of the SW-S2 model across multiple domains and meteorological inputs covering all three GRAPEX vineyard sites spanning a north to south climate gradient over three recent growing seasons (2018-2020). In comparison with flux tower observations, the size of the modeling domain and the source and quality of meteorological forcing data on the performance of the SW-S2 model as well as application to the three different vineyard study sites will be presented. Future research on merging output from more-frequent spectral and less-frequent thermal-based ET models to reduce latency in ET monitoring of California vineyards will also be discussed.

Most remote sensing-based surface energy balance (SEB) models are limited by data availability and physical constraints to fully capture the non-linear and temporally varying nature of atmospheric, biophysical, and environmental controls on evapotranspiration (ET). As such, currently, no single SEB model is considered to work best under all conditions particularly in irrigated croplands where surface moisture conditions could change dramatically in a short amount of time. Hence, irrigation water management based on a single remotely sensed ET model is often required to cope with model limitations and data latency issues, which could lead to unsustainable and unreliable accounting of water use over time. The recent inception of ensemble-based ET modeling takes the advantage of the strengths of the several SEB models under different conditions and is found to perform better as compared to an Individual model. Yet, challenges remain in how high-temporal ET outputs from different models are accurately assembled in a way that yields the most reliable estimates of ET across any environmental and surface conditions. Specifically, existing simple or Bayesian average and machine learning-based ensemble approaches have not been able to optimally utilize the comprehensive suite of existing SEB models and the availability of multiple remotely sensed datasets. Here, we discuss the utility of convolutional neural networks (CNNs) to assemble the outputs from a host of SEB models that can robustly capture the non-linear dynamics of ET under all conditions. We will also discuss the advantage and potential limitations of using the CNN-based ensemble ET modeling framework with respect to the individual, simple or Bayesian average, and other machine learning approaches and their implications for use in allocating water use across critically dry regions. Several ensemble models will be trained using eddy covariance flux data globally and will be evaluated based on their ability to estimate ET from MODIS and Landsat sensors with both individual and fused products and minimal weather inputs. The results can provide useful insights into how multiple datasets and SEB models could be optimally utilized to accurately monitor crop water status and support sustainable water resource management in drylands.

To explain the inequality between aerodynamic and radiometric surface temperature, we used an analytical surface energy balance model where evaporation is directly estimated by constraining the state equations of aerodynamic temperature and biophysical conductances through radiometric temperature. While the derived aerodynamic temperature was comparable with a flux-inverted counterpart, evaporation and sensible heat fluxes also showed good correspondence with in-situ eddy covariance observations over contrasting aridity in Australia. Results showed aerodynamic temperature frequently exceeds the radiometric temperature in arid and semiarid ecosystems for two reasons: (i) declining canopy-surface conductance and evaporative fraction due to escalated water stress and vapor pressure deficit, and (ii) a simultaneous increase in aerodynamic conductance, air temperature and sensible heat flux. The analytical approach provides valuable insights into the long-lasting debate of aerodynamic versus radiometric temperature paradox by recognizing the feedback between biophysical conductances and the supply-demand limit of solar radiation, soil moisture, and vapor pressure deficit.