Estimation of High-Resolution Evapotranspiration in Heterogeneous Environments Using Drone-Based Remote Sensing

Evapotranspiration (ET) is a key element of hydrological cycle analysis, irrigation demand, and for better allocation of water resources in the ecosystem. For successful water resources management activities, precise estimate of ET is necessary. Although several attempts have been made to achieve that, variation in temporal and spatial scales constitutes a major challenge, particularly in heterogeneous canopy environments such as vineyards, orchards, and natural areas. The advent of remote sensing information from different platforms, particularly the small unmanned aerial systems (sUAS) technology with lightweight sensors allows users to capture high-resolution data faster than traditional methods, described as “flexible in timing”. In this study, the Two Source Energy Balance Model (TSEB) along with high-resolution data from sUAS were used to bridge the gap in ET issues related to spatial and temporal scales. Over homogeneous vegetation surfaces, relatively low spatial resolution information derived from Landsat (e.g., 30 m) might be appropriate for ET estimate, which can capture differences between fields. However, in agricultural landscapes with presence of vegetation rows and interrows, the homogeneity is less likely to be met and the ideal conditions may be difficult to identify. For most agricultural settings, row spacing can vary within a field (vineyards and orchards), making the agricultural landscape less homogenous. This leads to a key question related to how the contextual spatial domain/model grid size could influence the estimation of surface fluxes in canopy environments such as vineyards. Furthermore, temporal upscaling of instantaneous ET at daily or longer time scales is of great practical importance in managing water resources. While remote sensing-based ET models are promising tools to estimate instantaneous ET, additional models are needed to scale up the estimated or modeled instantaneous ET to daily values. Reliable and precise daily ET (ETd) estimation is essential for growers and water resources managers to understand the diurnal and seasonal variation in ET. In response to this issue, different existing extrapolation/upscaling daily ET (ETd) models were assessed using eddy covariance (EC) and sUAS measurements. On the other hand, ET estimation over semi-arid naturally vegetated regions becomes an issue due to high heterogeneity in such environments where vegetation tends to be randomly distributed over the land surface. This reflects the conditions of natural vegetation in river corridors. While significant efforts were made to estimate ET at agricultural landscapes, accurate spatial information of ET over riparian ecosystems is still challenging due to various species associated with variable amounts of bare soil and surface water. To achieve this, the TSEB model with high-resolution remote sensing data from sUAS were used to characterize the spatial heterogeneity and calculate the ET over a natural environment that features arid climate and various vegetation types at the San Rafael River corridor

Testing the maximum entropy production approach for estimating evapotranspiration from closed canopy shrubland in a low-energy humid environment

We would like to thank The Leverhulme Trust (project PLATO, RPG-2014-016) and the European Research Council (ERC, project GA 335910 VeWa) for funding. We also thank three anonymous reviewers for their invaluable comments that improved the manuscript substantially. Data in this study can be accessed upon request to the authors.Peer reviewedPostprin

White paper – On the use of LiDAR data at AmeriFlux sites

Our aim is to inform the AmeriFlux community on existing and upcoming LiDAR technologies (atmospheric Doppler or Raman LiDAR often deployed at flux sites are not considered here), how it is currently used at flux sites, and how we believe it could, in the future, further contribute to the AmeriFlux vision. Heterogeneity in vegetation and ground properties at various spatial scales is omnipresent at flux sites, and 3D mapping of canopy, understory, and ground surface can help move the science forward

Evaluation of six satellite-based terrestrial latent heat flux products in the vegetation dominated Haihe river basin of north China

In this study, six satellite-based terrestrial latent heat flux (LE) products were evaluated in the vegetation dominated Haihe River basin of North China. These LE products include Global Land Surface Satellite (GLASS) LE product, FLUXCOM LE product, Penman-Monteith-Leuning V2 (PML_V2) LE product, Global Land Evaporation Amsterdam Model datasets (GLEAM) LE product, Breathing Earth System Simulator (BESS) LE product, and Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD16) LE product. Eddy covariance (EC) data collected from six flux tower sites and water balance method derived evapotranspiration (WBET) were used to evaluate these LE products at site and basin scales. The results indicated that all six LE products were able to capture the seasonal cycle of LE in comparison to EC observations. At site scale, GLASS LE product showed the highest coefficients of determination (R2) (0.58, p 2), followed by FLUXCOM and PML products. At basin scale, the LE estimates from GLASS product provided comparable performance (R2 = 0.79, RMSE = 18.8 mm) against WBET, compared with other LE products. Additionally, there was similar spatiotemporal variability of estimated LE from the six LE products. This study provides a vital basis for choosing LE datasets to assess regional water budget

Implications of Soil and Canopy Temperature Uncertainty in the Estimation of Surface Energy Fluxes Using TSEB2T and High-Resolution Imagery in Commercial Vineyards

Estimation of surface energy fluxes using thermal remote sensing–based energy balance models (e.g., TSEB2T) involves the use of local micrometeorological input data of air temperature, wind speed, and incoming solar radiation, as well as vegetation cover and accurate land surface temperature (LST). The physically based Two-source Energy Balance with a Dual Temperature (TSEB2T) model separates soil and canopy temperature (Ts and Tc) to estimate surface energy fluxes including Rn, H, LE, and G. The estimation of Ts and Tc components for the TSEB2T model relies on the linear relationship between the composite land surface temperature and a vegetation index, namely NDVI. While canopy and soil temperatures are controlling variables in the TSEB2T model, they are influenced by the NDVI threshold values, where the uncertainties in their estimation can degrade the accuracy of surface energy flux estimation. Therefore, in this research effort, the effect of uncertainty in Ts and Tc estimation on surface energy fluxes will be examined by applying a Monte Carlo simulation on NDVI thresholds used to define canopy and soil temperatures. The spatial information used is available from multispectral imagery acquired by the AggieAir sUAS Program at Utah State University over vineyards near Lodi, California as part of the ARS-USDA Agricultural Research Service’s Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The results indicate that LE is slightly sensitive to the uncertainty of NDVIs and NDVIc. The observed relative error of LE corresponding to NDVIs uncertainty was between -1% and 2%, while for NDVIc uncertainty, the relative error was between -2.2% and 1.2%. However, when the combined NDVIs and NDVIc uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ΔLE| was between 0% and 4%

Remote Sensing of Sun-Induced Chlorophyll Fluorescence for Advanced Ecosystem Evapotranspiration Estimates

A precise ecosystem evapotranspiration (ET) estimate is essential for understanding the complex relationship between plants' energy-water-carbon fluxes. Besides, robust ecosystem ET estimation under different water stresses can provide insight into plants' response to extreme weather and environmental conditions. However, for such very preciseness, we must accept the individual and comprehensive interlinked mechanistic relationships between ecosystem ET and its controlling variables for determining the response of ecosystem ET towards extreme climate events. Due to recent drought events in the European continent since the 2000s, many geographical hotspots are getting attention to understanding the complicated mechanistic relationship between ecosystem ET and its controlling variables under such extreme water stress conditions. Consequently, precise ecosystem ET estimation of the European continent's water-stressed ecosystems, i.e. agriculture, will give insight into the sustainability of Europe's agricultural production to ensure sufficient food for millions of people in future. The recent heatwaves and drought in 2018 impacted ecosystem ET substantially over the European continent, which may be a big concern for the European ecosystems' future water, energy, and CO2 balance. The research outcomes of the first research article (c.f. Ahmed et al., 2021) showed that the European continent had up to 50% reduced ecosystem ET compared to a 10-year reference period due to a combined heatwave and drought event in 2018. The results also showed extreme surface air temperature (Tsa) and precipitation (P) anomalies. Due to such extreme climatic phenomena, agricultural land, mixed natural vegetation, and the European continent's non-irrigated agricultural areas were mainly affected. In conclusion, the first research article explains the importance of modelling precise ecosystem ET in variable time and space. However, modelling and estimating precise ecosystem ET is still challenging, especially under extreme climates within continuous time and ample variable space. Remote sensing (RS) data based modelling approaches often encounter uncertainties due to complex parameterizations of different variables for ecosystem ET modelling schemes. Further, uncertainties may be introduced by different data types, data quality, multi-sensor systems, and spatio-temporal resolution of satellite images. The growing advancement of using RS based sun-induced chlorophyll fluorescence (SIF) for ecosystem studies has introduced SIF's use case for ecosystem ET estimates. However, previous studies have limitations due to applying specific ET models, only considering energy or water constraints and different strategies to add SIF in such specifically selected models. The second research article (c.f. Ahmed et al., 2023) investigated possible SIF integration in an advanced ecosystem ET modelling scheme. The research considers the mechanistic relationships between SIF and ecosystem ET and their abiotic and biotic drivers. The results concluded the best possible ways of empirically applying SIF for ecosystem ET estimates under water-limited and well-watered conditions under an experimental setup in maize crop fields in northern Italy. The research assesses the absolute and relative sensitivity of several SIF based ecosystem ET estimation strategies for evolving soil water limitation using extensive in-situ and airborne RS data acquired during the water limitation experiment. The study evaluated five strategies to integrate SIF in an ecosystem ET modelling framework based on the Penman Monteith (PM) and the Ball-Berry-Leuning (BBL) models. The results showed that replacing canopy conductance (including canopy resistance and leaf's net CO2 assimilation rate), leaf area index and net radiation with SIF significantly correspond with in-situ reference ecosystem ET (unit based conversion of measured sap flow) under evolving water limitation. Indeed, considering a single SIF as an indirect proxy for ecosystem ET with a one-to-one relationship showed inconsequential outcomes. In conclusion, the research's outcomes give insight into the importance and scientific advantage of applying SIF in a multi-sensors RS data based framework to increase the sensitivity of SIF based ecosystem ET estimates for evolving water limitations. Besides, the results highlighted the uses of SIF for the scientific advancement of ecosystem drought monitoring. Recent studies have proposed the usability of SIF to establish SIF-based drought indices (DIs) using comparatively coarse spatio-temporal resolution RS data. However, the temporal and spatial sensitivity of such newly proposed SIF-based DIs for growing crop water limitation with higher spatio-temporal resolution RS data must be determined. Therefore, the third Ph.D. research article (in review) conducted a temporal and spatial sensitivity analysis of SIF-based DI for gradually increasing soil and crop water limitation for different crop types. Temporal sensitivity analysis of the study showed that SIF based DI is sensitive throughout evolving soil water limitations, and traditional optical index (OI) based DI is only sensitive at extreme soil water limitations. However, both DIs showed their sensitivity towards the highest soil water limitation. Spatial sensitivity analysis reveals that SIF based DI is sensitive towards decreasing plant available water (PAW) zones and continues till the lowest PAW zones, and OI based DI is only sensitive in the lowest PAW zones. Furthermore, like the temporal analysis, from the spatial analysis, it is also visible that both DIs are sensitive towards the lowest PAW. The research concludes that both SIF based and traditional OI based DIs are sensitive to increasing soil and crop water limitation; however, the experimental setup was not sufficient to say that SIF based DI can be more beneficial for monitoring crop water limitation throughout drought events than OI based DI, instead, both DIs can be applied for monitoring evolving soil and crop water limitation within shorter spatio-temporal scales. Besides, SIF based DI can be applicable for predicting early crop water limitation and promoting incentive preparation for drought, but further studies within different ecosystems with different environmental conditions are needed. In contrast, resulting ecosystem ET values and SIF have been examined with their absolute, relative, temporal, and spatial sensitivities under different soil and crop water availability for monitoring and predicting early plants’ water limitation within different spatio-temporal scales in various spaces and times. Combining all three research articles gives a forward consideration towards the sensitivity of SIF for robust forward ecosystem ET modelling and SIF embedded drought monitoring application within an advanced multi-sensors RS data modelling approach

Triennial Report: 2009-2011

Triennial Report Purpose [Page] 3 Geographical Information Science Center of Excellence [Page] 4 SDSU Faculty [Page] 6 EROS Faculty [Page] 13 Research Professors [Page] 18 Postdoctoral Fellows [Page] 21 GSE Ph.D Program [Page] 30 Ph.D. Students [Page] 31 Ph.D. Fellowships [Page] 44 Recent Ph.D. Graduates [Page] 45 Center Scholars Program and Masters Students [Page] 51 Research Staff [Page] 52 Administrative and Information Technology Staff [Page] 55 Computer Resources [Page] 58 Research Funding [Page] 60 Looking Forward [Page] 61 Appendix I Alumni Faculty and Staff Appendix II Cool Faculty Research and Locations Appendix III Non-Academic Fun Things To Do Appendix IV Publications 2009-2011 Appendix V Directory Appendix VI GIScCE Birthplace Map Appendix VII How To Get To The GIScC

Incorporating an iterative energy restraint for the Surface Energy Balance System (SEBS)

© 2017 Elsevier Inc. The Surface Energy Balance System (SEBS) has proven itself as an effective remotely sensed estimator of actual evapotranspiration (ETa). However, it has several vulnerabilities associated with the partitioning of the available energy (AE) at the land surface. We introduce a two stage energy restraint process into the SEBS algorithm (SEBS-ER) to overcome these vulnerabilities. The first offsets the remotely sensed surface temperature to ensure the surface to air temperature difference reflects AE, while the second stage uses a domain based image search process to identify and adjust the proportions of sensible (H) and latent (λE) heat flux with respect to AE. We effectively implemented SEBS-ER over 61 acquisitions over two Landsat tiles (path 90 row 84 and path 91 row 85) in south-eastern Australia that feature heterogeneous land covers. Across the two areas we showed that the SEBS-ER algorithm has: greater resilience to perturbed errors in surface energy balance algorithm inputs; significantly improved accuracy (p < 0.05) at two eddy covariance flux towers in heavily forested (RMSE 62.3 W m− 2, R2 0.879) and sub-alpine grassland (RMSE 33.2 W m− 2, R2 0.939) land covers; and greater temporal stability across 52 daily actual evapotranspiration (ETa) estimates compared to a temporally stable and independent ETa dataset. The energy restraint within SEBS-ER has reduced exposure to the complex errors and uncertainties within remotely sensed, meteorological, and land type SEBS inputs, providing more reliable and accurate spatially distributed ETa products

Robust estimates of soil moisture and latent heat flux coupling strength obtained from triple collocation

Land surface models (LSMs) are often applied to predict the one-way coupling strength between surface soil moisture (SM) and latent heat (LH) flux. However, the ability of LSMs to accurately represent such coupling has not been adequately established. Likewise, the estimation of SM/LH coupling strength using ground-based observational data is potentially compromised by the impact of independent SM and LH measurements errors. Here we apply a new statistical technique to acquire estimates of one-way SM/LH coupling strength which are nonbiased in the presence of random error using a triple collocation approach based on leveraging the simultaneous availability of independent SM and LH estimates acquired from (1) LSMs, (2) satellite remote sensing, and (3) ground-based observations. Results suggest that LSMs do not generally overestimate the strength of one-way surface SM/LH coupling