Diagnosing and predicting evaporation through satellite-based surface energy balance (SEB) and land surface models (LSMs) is challenging due to the non-linear responses of aerodynamic (ga) and stomatal conductance (gcs) to the coalition of soil and atmospheric drought. Despite a soaring popularity in refining gcs formulation in the LSMs by introducing a link between soil-plant hydraulics and gcs, the utility of gcs has been surprisingly overlooked in SEB models due to the overriding emphasis on eliminating ga uncertainties and the lack of coordination between these two different modeling communities. Therefore, a persistent challenge is to understand the reasons for divergent evaporation estimates from different models during strong soil-atmospheric drought. Here we present a virtual reality experiment over two contrasting European forest sites to understand the apparent sensitivity of the two critical conductances and evaporative fluxes to a water-stress factor (b-factor) in conjunction with land surface temperature (soil drought proxy) and vapor pressure deficit (atmospheric drought proxy) by using a non-parametric diagnostic model (Surface Temperature Initiated Closure, STIC1.2) and a prognostic model (Community Land Model, CLM5.0). Results revealed the b-factor and different functional forms of the two conductances to be a significant predictor of divergent response of the conductances to soil and atmospheric drought, which subsequently propagated in the evaporative flux estimates between STIC1.2 and CLM5.0. This analysis reaffirms the need for consensus on theory and models that capture the sensitivity of the biophysical conductances to the complex coalition of soil and atmospheric drought for better evaporation prediction.

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.

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.