HS6.6

Evapotranspiration (ET) is a major component of the hydrologic cycle and plays a fundamental role in water and land management. However, previous studies have shown that estimating ET is quite challenging, particularly at fine temporal and spatial scales. Dense time series of harmonized Landsat 8 and Sentinel-2 imagery (HLS) combined with ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) data provide a unique opportunity to enhance monitoring, mapping, and characterizing ET at unprecedented spatial and temporal resolutions. In this study, we develop and evaluate an improved ET estimation method based on Priestley-Taylor Jet Propulsion Laboratory algorithm (PT-JPL) with HLS optical reflectance imagery and ECOSTRESS land surface temperature (LST) and surface emissivity, in addition to MODIS surface albedo and ERA5-Land climate reanalysis data. The new approach creates denser times series of ET from HLS imagery, which can also be used to map ET at 30 m spatial resolution. The new ET estimates are evaluated against ground-based observations from the AmeriFlux network and compared with the performance of the original ECOSTRESS PT-JPL ET estimates across different ecosystems and landcover settings over the continental United States. We present results for evaluating ET estimates, the remote sensing and climate reanalysis inputs, and illustrating the sensitivity and uncertainty of the improved ET method and its performance related to land cover and terrestrial ecosystem properties.

How to cite: Rashid, T. and Tian, D.: Improving ECOSTRESS-based Evapotranspiration Estimates Using Harmonized Landsat Sentinel-2 Imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-685, https://doi.org/10.5194/egusphere-egu22-685, 2022.

Standard Evapotranspiration (ETo) is an indicator of water losses given by evaporation and plant transpiration. Its quantification is particularly important for irrigation purposes, however in-situ data is not always accessible. This research aims to develop a methodology for Eto ‘weather’ classification through clustering Eto zones over Ecuador using remotely sensed data and an unsupervised learning algorithm. Thus, we obtained climatological variables from the Weather Research and Forecasting model  corresponding to years 2017, 2018, 2019, 2020, 2021. Following, we pre-processed the raw variables into eight parameters for Eto estimation, as in the Penman-Monteith equation, providing the model input variables for each year of study. Hence, we implemented a Self-Organizing Map (SOM) Artificial Neural Network over each dataset to obtain maps representing Eto clustered classes. Moreover, we tested the methodology's repeatability by applying SOM ten different times over each dataset and by applying the modified Cramers’ V-index to quantify the differences between map comparisons. Accordingly, we selected the SOM parameters that produced a Cramer’s V-index > 0.9  and differences between clustered maps < 0.0001. The outcomes of this research contribute to the classification of Eto ‘weather’ in mesoscale regions with future prospects to Eto ‘climate’ classification over larger temporal and spatial resolutions.

How to cite: Solis-Aulestia, M., Pineda, I., Piispa, E., and Williams, S.: A new methodology for Standard Evapotranspiration classification over mesoscale regions: Application and evaluation of SOM over remotely sensed data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12163, https://doi.org/10.5194/egusphere-egu22-12163, 2022.

Among various measurement techniques, eddy covariance (EC) is the most direct one for measuring evapotranspiration (ET) fluxes at field to ecosystem scales (Aubinet et al., 2000). In the past two decades, EC flux towers around the world, particularly those within the FLUXNET, have served as a worldwide network of calibration and validation for surface-atmosphere energy and ET flux data obtained from remote sensing-based models or hydrological process-based models (Wang and Dickinson, 2012). 

One of the major challenges in model-data benchmarking is the spatial mismatch issue. For example, the grid cell size of around 10⁶ – 10⁸ m² in typical Earth system regional modeling cases is often several orders of magnitude larger than the EC flux footprints of around 10³–10⁷ m². Since most flux tower sites are located in more-or-less heterogeneous landscapes, multiple measurement units for spatially adequate sampling and representative fluxes are of interest for capturing the fine-scale spatial variation. However, the deployment of higher density sampling points was mainly limited by the costs of conventional analyzers. Therefore, there is increasing demand in the development of low-cost water vapor analyzers specifically for more spatial representative terrestrial ET flux footprints measurements based on EC methods.

In recent years, laser-based gas spectrometers have shown good reliability and effectiveness in the high-frequency and high-sensitivity measurement of various atmospheric trace gases. In this work, we have developed an open-path analyzer (HT1800, HealthyPhoton Co., Ltd.) for fast and sensitive measurements of atmospheric water vapor density. The analyzer employs a low-power vertical cavity surface emitting laser (VCSEL) and a near-infrared Indium Galinide Arsenide (InGaAs) photodetector. An open-path configuration with 0.5 m effective optical path length is used for selective and sensitive detection of the single spectral transition of H₂O at 1392 nm, which has been extensively studied in the field of spectroscopic analysis. Using this spectral line to realize the single-component measurement of water vapor density can avoid the complex cross-calibration process due to the H₂O-CO₂ spectral interference as happened in traditional nondispersive infrared (NDIR) analyzers. On the other hand, the semiconductor nature of lasers and detectors can borrow the mature optical communication industry fabrication process, so that the cost of the core optoelectronic devices is expected to be reduced in mass production.

The analyzer has a precision (1σ noise level) of 15 μmol mol−1 (ppmv) at a sampling frequency of 10 Hz. Due to its open-path configuration, there is no delay or high-frequency damping due to surface adsorption. The analyzer head has a weight of ~2.8 kg and dimensions of 46 cm (length) and 9.5 cm (diameter). It can be powered by solar cells, with a total power consumption of as low as 10 W under normal operations. With good performance in terms of response time and precision, this instrument is an ideal tool for ET flux measurements based on the EC technique. An EC flux tower was built based on the open-path analyzer, which also included an integrated CO₂ and H₂O open-path gas analyzer and 3-D sonic anemometer (IRGASON, Campbell Scientific) for comparison of ET flux measurement.

How to cite: Wang, Y., Liu, Z., Lin, T.-J., Zhen, X., Zhang, X., Wang, K., and Zheng, X.: A low-cost, open-path water vapor analyzer for eddy covariance measurement of evapotranspiration, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4761, https://doi.org/10.5194/egusphere-egu22-4761, 2022.

Associated with drought, the increased agricultural water consumption in arid and semi-arid regions has caused water competition among water users and worsened water scarcity and food security. Hence, there is an imminent need for accurate and reliable estimation of actual evapotranspiration (ETa) for large scales, as a key component of the water cycle due to its critical role in determining crop water requirement. This work aims to investigate the impact of changes in harvested area (HA) over time on ETa estimates using remote sensing (RS) in the Zayandehrud River Basin, Iran, where croplands are highly dependent on irrigation and strongly influenced by aridity and recurring drought events. RS provides a dependable basis for ETa quantification across large areas, particularly regions where lack of ground data hampers ETa estimation. The efficient RS data handling is of importance. In this regard, Google Earth Engine (GEE), a cloud-based open-access platform for accessing and analysing the RS data, was used to derive ETa and HA. The Vegetation Index-based ETa (ET-VI) approach is one of the RS-based methods which combines a vegetation index as a proxy of crop factor and reference ET (ET0) to estimate ETa. We calculated ET-VI as a tool to monitor agricultural water consumption and drought over croplands (2000-2019). Since reducing crop area is a common strategy employed by farmers against drought, HA tends to show considerable inter-annual variability, particularly in semi-arid regions, where drought is a major factor affecting rainfed and irrigated agriculture. Shrinkage of HA during water-stressed periods often leads to an ETa underestimation when HA changes are ignored in ETa estimation (i.e., HA is assumed static for ETa calculation). To assess the effect of cropping patterns’ inter-annual change on the annual ETa, annual maximum Normalized Difference Vegetation Index layers were derived. Analyses of HA and ET-VI showed that inter-annual variability in cropland extent affects ETa considerably, therefore using static cropland extent is not recommended in drought studies. ETa remained less variable while cropped areas changed in response to dry years. This means that drought has forced farmers to use the limited available water on a smaller area to cope with drought and safeguard reliable crop production. The average difference between ET-VI estimated based on static and dynamic HA was 221.7 mm per annum in our study area. Our findings highlight the necessity of incorporating both cropped areas and ETa rates in water management and drought monitoring of croplands. Our analysis offers insights into the capability and suitability of RS-based ETa as an efficient and quick tool for understanding spatio-temporal variability of ETa across croplands and monitoring water resources. Future research should evaluate the potential of high-resolution sensors with frequent return in the ETa derivation to monitor drought, vegetation health, and water consumption at different time scales, and whether they can improve the accuracy of drought mapping and monitoring.

How to cite: Abbasi, N., Nouri, H., Nagler, P., Chavoshi Borujeni, S., Barreto-Muñoz, A., Opp, C., Didan, K., and Siebert, S.: How Changes in Harvested Area Impacts the Actual Evapotranspiration of Croplands Using Optical Remote Sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3224, https://doi.org/10.5194/egusphere-egu22-3224, 2022.

Drought is one of the globally extreme events and is physically defined as an extended imbalance between moisture supply and demand, which might severely affect crop growth and water preservation. More specifically, agricultural and meteorological droughts are manifest as the deficits in actual evapotranspiration (ET) and a surplus in atmospheric evaporative demand ET₀ (sometimes referred to as potential ET). Therefore, the accurate estimations and reliable information regarding to ET might enhance the drought monitoring and provide better agricultural management policies. However, ET estimations and its components remain many challenges. For example, as three ET contributors are soil evaporation (ET_soil), plant transpiration (ET_veg), and vegetation interception evaporation (ET_ic), current ET models tend to ignore the ET_ic and consider it as residual of two others. This leads to the uncertainties and incomprehensive reflection of ET. Additionally, ET models-based flux measurement might produce good accurate results, but they have limitations of spatial coverage. With the rapid development of remote sensing platforms, the models-based remote sensing are able to cover a large and regional scale, but they remain higher uncertainties due to the low spatial resolution, complexities in processing, requirements of many input data. Currently, the optical Sentinel-2 is a newly launched product with the Multi Spectral Instruments that might provide 10, 20, and 60-m spatial resolution, which potentially supports to design and improve ET models with superior performance. To overcome these mentioned disadvantages of ET models, the main objective of this study are to propose a simple and objective method using only optical Sentinel-2 dataset to improve the accuracy of ET estimation; and to project the enhanced ET partitioning as feasible method for further monitoring the agricultural drought. This study might bridge the gap in knowledge and applicability of ET in monitoring the hydrological disasters under severe climate change context.

How to cite: Nguyen, M., Jeon, H., Kim, W., and Choi, M.: An application of evapotranspiration partitioning for monitoring drought from Sentinel-2 data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9019, https://doi.org/10.5194/egusphere-egu22-9019, 2022.

The role of water in shaping and developing cities has been known and referred to in numerous studies in the last two decades. Urban water management encounters compelling features, including rapid urban expansion and consequent demographic change, climate change, and environmental limitations. Urban green spaces bridge the relationship between humans and nature. As the major feature of green infrastructure, urban green space (UGS) has a crucial role in cities' human health and quality of life. UGS makes cities more habitable and promotes psychological and physical health by filtering air, enhancing water quality, reducing traffic noise, and adjusting wind speed, among other benefits. One of the most important features of urban greenery is its contribution to reducing urban heat islands and cooling the city. In order to attain a water-resilient city, we need to overcome challenges associated with water scarcity, such as drought events. While the impact of drought on forestry, agriculture, and riparian corridors has already been studied, this study is one of the first to assess the effect of drought on the UGS. The main objective of this study is to find a sustainable approach toward a green, livable city under climate change by optimizing the water footprint of UGS. As the third most liveable city in the world in 2021, Adelaide city was selected as the case study. The changes in greenness and water requirement of UGS in Greater Adelaide were studied to detect the impact of drought from 2000 to 2020. The optical remote sensing techniques were employed using Landsat, MODIS, and Sentinel images. The study area's greenness and ETa time series were simulated on the Google Earth Engine platform. Preliminary results show that the water footprint of Adelaide's urban green space is the highest in December with the highest rate of heat-wave and the lowest in June.

How to cite: Chavoshi Borujeni, S., Nouri, H., Nagler, P., Abassi, N., Pradhan, B., and Huete, A.: The impact of drought on urban green space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13187, https://doi.org/10.5194/egusphere-egu22-13187, 2022.

As global urbanization has become more dominant in recent years so have the negative consequences of dense, artificial, urban environments on their inhabitants. The urban heat island is one such phenomena, describing an increase of air temperature in the inner city in comparison to the periphery, with negative effects for human health. Urban green spaces are crucial to mitigating this heat stress due to their higher levels of evapotranspiration (ET) and shading. To maximize cooling potential, the individual contribution of typically heterogenous urban green space to ET and heat fluxes needs to be better understood. Higher ET rates of urban vegetation need to be balanced against on-site water availability to plan the most efficient green spaces. Moreover, trees and shrubs possess the additional benefit of shading the surface below.
Traditional remote sensing methods have focused on the use of satellite data with multi-meter spatial resolution as a cost-effective way to observe and analyze the large spatial extent of cities. However, the individual vegetation compositions of urban green spaces cannot be resolved through these systems, making it hard to evaluate their superimposed spectral signal. Unmanned Aerial Vehicles (UAVs) record data with very high spatial resolution and also allow multiple flights per day to cover the temporal and spatial urban green space heterogeneity.
Estimation of vegetation indices, land surface temperature (LST) or ET can reveal the high spatial heterogeneity of urban vegetation patches and help to better understand the spatial and temporal patterns of ET and urban cooling at a plot scale.
In our study, we assessed multiple remote sensing-based ET modelling techniques for thermal and multispectral UAV remote sensing data and validated them against in-situ measurements. Data has been recorded at a monthly interval from April to October at an urban research garden in Berlin, Germany consisting of representative urban vegetation types. An inference method was tested with different vegetation indices to estimate ET for three different green space classes (trees, shrubs, grass). In situ measurements for sap flow, soil moisture, leaf area index and meteorological conditions were used to compare and validate the UAV-based ET, LST, and greenness estimates.
Results showed a significant difference for ET and surface cooling between green space classes throughout the year, with trees and shrubs showing consistently lower temperatures then grassland. The influence of shadow on cooling potential (ET and LST) also became apparent.
The findings of our study provide further insights into the influence of different urban green spaces on ET and cooling potential and are valuable for upscaling approaches to the city scale. This knowledge can further support valuable decision-making for planning and managing urban green spaces to mitigate heat risks and optimize urban water supply.

How to cite: Jordan, P., Kleinschmit, B., Duarte Rocha, A., Graenzig, T., and Vulova, S.: Using multispectral and thermal UAV data to infer the influence of contrasting urban green space on evapotranspiration and heat fluxes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8129, https://doi.org/10.5194/egusphere-egu22-8129, 2022.

Unmanned aerial vehicles (UAVs) constitute a new frontier in remote sensing of ET that bridges the gap between in situmeasurements and remotely sensed observations of plant water use. While a single satellite pixel often comprises a mixture of plant types and bare soil, UAV imagery can resolve fine- (m- to cm-) scale differences in surface temperature without thermal unmixing. Furthermore, they can be used to observe diurnal patterns of plant water use and photosynthesis, providing critical insights into the timing and severity of plant water stress. We highlight a novel approach for estimating ET at leaf- to canopy-scales using thermal infrared (TIR) imagery, structural data, and a suite of environmental sensors mounted on a UAV platform. ET is calculated solely from these UAV-acquired data using a combined atmospheric profile and surface energy balance algorithm. Centimeter-scale leaf position and orientation information derived from Structure-from-Motion (SfM) are integrated with the functional data to constrain available energy, allowing for multi-scale estimation of plant water use within and across canopies.

We present UAV-derived ET across diurnal and seasonal time scales for two landscapes, a native California grassland and a riparian oak woodland. Grassland flights were conducted at 90-minute intervals spanning early morning to late afternoon during the 2021 and 2022 growing seasons. Results show good agreement (<20%) with measured ET fluxes from a collocated eddy covariance tower throughout the growing season. Riparian oak canopies were observed monthly and diurnally over the summer of 2021. Ground measurements of surface temperature, stomatal conductance, and soil moisture were collected during each flight. Water-stressed tress at the driest site showed peak ET at midday, decreasing into afternoon, reflecting down-regulation of photosynthesis to preserve hydraulic function. Relative canopy water use and stress across a range of tree sizes will also be discussed using measurements of stem and canopy area and ET for individual tree crowns extracted from the UAV imagery. By collecting comprehensive meteorological data from sensors on the UAV itself, our approach eliminates the need for extensive field data collection and enables characterization of highly spatially and temporally resolved fluxes within and across complex landscapes. This work opens up new avenues to investigate how ecologically important species—and even individual trees—respond to drought and the impacts of these responses on water use, water stress, and the ecological health of critical habitats like riparian forests.

How to cite: Morgan, B. and Caylor, K.: Diurnal and seasonal variation in ET at canopy scales using a novel UAV-based approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10751, https://doi.org/10.5194/egusphere-egu22-10751, 2022.

Accurate estimates of natural plant area water use or evapotranspiration (ET, mm/day) are important to quantify so that in-stream use can be partitioned for human and natural environments. The natural grasses, shrubs and trees that grow alongside rivers and streams are collectively called riparian vegetation and their leaves transpire water that is considered a loss to the ecosystem. Bare soil also loses water through evaporation. In the landscape, we quantify both losses as one variable, actual evapotranspiration (ETa). ETa is the most difficult component of the water cycle to measure. Furthermore, estimates of ETa in uncultivated lands are a fraction of the estimates studied compared with cropped, agricultural lands. Riparian areas of the Little Colorado River are of critical importance to the Navajo Nation. Select riparian reaches were delineated using digitized shrubs and trees so that we could track plant health and its evapotranspiration (ET) with Landsat for the recent seven years (2014-2020). We acquired six Landsat scenes, processed and filtered the data and computed the two-band Enhanced Vegetation Index (EVI2) as a proxy for vegetation at a 16-day interval. We then computed daily potential ET (ETo, mmd^-1) using Blaney-Criddle with input temperature data from two sources, Daymet (1 km) and PRISM (4 km) data. ETo from Blaney-Criddle was then averaged over 16-days using the 8-days before- and after- the Landsat overpass date. The riparian corridor’s high-definition digitized shrubs and trees were aggregated using a 10 m buffer in ArcGIS and then rasterized to match the Landsat 30 m grid pixels. Two raster masks were created; the first used a 50% threshold majority option to include/exclude the grid pixels resulting in a ‘conservative’ estimate of the riparian area, and the second considered all pixels that intersected the vegetation buffered outline, resulting in the ‘best-approximation’ estimate of the riparian acreage. The best-approximation raster-area for the riparian corridor was 25,615 ha (63,296 acres) and the conservative raster-area estimate was 19,362 ha (47,846 acres), whereas the digitized area included only a fraction of the total vegetative area, was only 4,974 ha (12,291 acres). We utilized ETo to estimate actual ET (ETa) using EVI2 (mmd^-1). Including seven years, 2014 through 2020, the average annual ETa (mmyr^-1) increased from 423.9 to 489.2 mmyr^-1 or 65.3 mmyr^-1 (15%) over the recent seven years, 2014-2020. Precipitation decreased by 73.1 mmyr^-1 (38%) from 190.8 mmyr^-1 (2014) to 117.7 mmyr^-1 (2020). The water deficit (WD), like ETa, shows an increasing trend from 235.6 mmyr^-1 (2014) to 373.5 mmyr^-1 (2020); this is an increase in WD of 137.9 mmyr^-1 (59%). We produced three estimates of consumptive water use (CU) based on riparian area using a best-approximation and conservative-estimate from the rasterized area, and vector area. Our CU estimates for the riparian corridor range from 31,648 (conservative) to 36,983 (best; Daymet) to 41,585 (PRISM) acre-feet. These findings refine predictions in the range between 25,387 and 46,397 acre-feet using only literature for similar areas. Better estimates of water use are valuable to the Navajo Nation in the adjudication of water rights.

How to cite: Nagler, P., Barreto-Muñoz, A., Sall, I., and Didan, K.: Consumptive Water Use for the Riparian Areas of the Little Colorado River within Navajo Nation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1380, https://doi.org/10.5194/egusphere-egu22-1380, 2022.