Evapotranspiration (ET), the key component of water and energy balances, has myriad challenges to measure it precisely. In the last two decades, innovative approaches for remote sensing (RS) based measurements of ET has allowed for its measurement in a range of climates on most continents for different green covers. Remotely-sensed ET methods have been proved to be reliable, affordable and applicable to a broad range of scales from plot/field to regional to global in different landscapes including agricultural, forested, riparian zones and urban green spaces.
We invite researchers to contribute abstracts to share their advances and challenges in the development, application, validation, calibration and accuracy assessment of landscape ET through remote sensing platforms. We welcome studies that estimate ET using both prognostic and diagnostic approaches from process-based models that rely on the integration of gridded precipitation and soil-vegetation dynamics to a more direct estimation of ET using remote sensing-based data streams. The scope of the session will include: (1) advances in remote sensing-based ET estimation, (2) applications for a range of land covers and spatiotemporal scales, and (3) accuracy enhancement.
vPICO presentations: Thu, 29 Apr
Warming conditions represent a threat to food security and livelihood in countries in which agriculture is an important share of the national income. Central America is regarded as a climate change hotspot where significant changes in temperature and rainfall have been projected. Coffee is one of the most traditional crops in the area, with Costa Rican coffee recognized worldwide for its quality. However, increasing temperatures and rainfall extremes will likely compromise coffee plantations. A similar challenge has already been faced by farmers on interannual time scales related to the El Niño-Southern Oscillation phenomena, which is associated with yield disruptions and the spread of the coffee rust. A better understanding of the weather and climate dependency of coffee crops is needed to develop water use efficiency strategies for farms. To this end, the present study centers on the integration of long-term meteorological records and a set of measurements that cover the soil-plant-atmosphere continuum. Surface fluxes derived using the eddy covariance technique and the deployment of soil moisture sensors are combined to evaluate the performance of the Soil Vegetation Energy TraNsfer (SVEN) model. One year of micrometeorological and soil measurements in a sun-exposed coffee plantation is used to assess the skills of the SVEN model using a scheme based on MODIS and Sentinel derived products. The aim of this work is to evaluate the skills of the SVEN model to reproduce the intraseasonal seasonal and diurnal variability of evapotranspiration. Given the size of Costa Rica and the scale of the crops, satellite products are often considered of limited use. Nevertheless, given the strong need, the goal of this project is to provide a detailed evaluation of the use of these products in models and support strategies that could expand the use of satellite retrievals in areas currently considered marginal.
How to cite: Durán-Quesada, A. M., Pateromichelaki, I., García, M., Wang, S., Serra, Y., Gutiérrez, M., and Chinchilla, C.: Application of SVEN model to estimate evapotranspiration on a coffee plantation using MODIS and Sentinel products., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13739, https://doi.org/10.5194/egusphere-egu21-13739, 2021.
Precision agriculture needs accurate information on crop water use (via evaporation) at high spatiotemporal resolutions. Conventional satellite missions have traditionally required a compromise between having high spatial resolution retrievals occasionally; or coarse resolution captures regularly. The development of CubeSats is relaxing the need for such a compromise by monitoring the Earth at high spatiotemporal resolutions. Here, we show the results of using Planet’s daily CubeSat imagery to derive evaporation at 3 m spatial resolution over three agricultural fields in Nebraska USA. Our results indicate that the derived evaporation estimates can provide accurate information on crop water use when evaluated against eddy covariance measurements (r2 of 0.86-0.89; mean absolute error between 0.06-0.08mm/h) and deliver new insights to enhance water security efforts and in-field decision making.
How to cite: Aragon Solorio, B. J. L., Ziliani, M. G., and McCabe, M. F.: CubeSats deliver daily crop water use at 3 m resolution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13248, https://doi.org/10.5194/egusphere-egu21-13248, 2021.
Accurate estimation of evapotranspiration (ET) helps to create a better understanding of water allocation, irrigation scheduling, and crop management especially in arid and semiarid regions where agricultural areas are far more affected by water shortage and drought events. Remote sensing (RS) facilitates estimating the ET in regions where long-term field measurements are missed. In this study, we compare the performance of free open-access remotely sensed actual ET products at eleven counties of the Zayandehrud basin. The Zayandehrud basin, one of the major watersheds of Iran, suffers from recurrent droughts and long-term impacts of aridity. The RS products used in this study are namely WaPOR (2009-2019), MOD16A2 (2003-2019), SSEBOp (2003-2019). We also merged the two products of SSEBOp and WaPOR and assessed its performance. To prepare the Merged ETa Product (MEP), WaPOR was resampled to the spatial resolution of SSEBOp. Then, the average pixel values of the resampled ETa product and SSEBOp were calculated. To compare ETa estimations over croplands in each county, maximum Normalized Difference Vegetation Index (NDVI) maps at annual scale (2003-2019) were prepared using LANDSAT 5, 7, and 8 images. Annual mean ETa estimations were then extracted over croplands by using annual maximum NDVI layers. We compared the RS-based ETa with reported long-term ETa values extracted from the local available literature. Our results showed a consistent underestimation of MOD16A2 in all counties. The MEP and WaPOR outperformed other products in the estimation of ETa in seven. Estimations of WaPOR and SSEBOp agreed in most of the counties. Our analysis displayed that, although MOD16A2 underestimated ETa values, it could together with SSEBOp capture the drought better than that of WaPOR and MEP in the lower reaches of the basin. Further study is needed to evaluate the monthly and seasonal performance of RS-based ETa products.
How to cite: Abbasi, N., Nouri, H., Chavoshi Borujeni, S., Nagler, P., Opp, C., Barreto Munez, A., Didan, K., and Siebert, S.: Inter-comparison of remotely-sensed actual evapotranspiration products in the Zayandehrud river basin, Iran, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-103, https://doi.org/10.5194/egusphere-egu21-103, 2020.
Irrigated rice agriculture, which is traditionally conducted applying continuous flooding, requires much more irrigation water than non-ponded crops. This can be a constraint in areas facing water scarcity issues, where the pursue for water resources optimization requires that water use efficiency is increased. Therefore, main local challenges for rice production are often to identify and apply more favorable and efficient irrigation and crop management practices, while guaranteeing high crop yields. For this purpose, the knowledge of rice crop water requirements is an important practical consideration. However, there are usually several limiting factors to obtain relevant data for the local conditions. Several recent approaches and methodologies based on remote sensing data, such as images obtained from satellites and Unmanned Aerial Systems (UAS), are offering attractive alternative routes to estimate crop evapotranspiration in a fast and easy way, including in rice fields.
For the rice producing area of the Lower Mondego region (Portugal), we report on exploring the usefulness of remote sensing tools for the local rice agriculture monitoring and management. Data include 25 land surface images of rice cultivated areas obtained from satellite Sentinel-2A during 2020, which together with weather data and crop parameters permits to calculate biophysical indicators and indices of vegetation water stress. Although remote sensing data available from satellite imagery presents some practical constraints (e.g. cloud cover, resolution), preliminary results from this study show that they allow to improve the characterization of the rice local cultivation conditions, therefore contributing to evaluate the impact of applying different irrigation and agriculture management practices, in particular those that have the potential to lead to significant savings of irrigation water.
This work was conducted under the umbrella of the international project MEDWATERICE (www.medwaterice.org) that focus on improving the sustainable use of water in the Mediterranean rice agro-ecosystem and aims to exploring the opportunity to apply water-saving, alternative, rice irrigation methods.
How to cite: P. de Lima, I., G. Jorge, R., and L.M.P. de Lima, J.: Rice water requirements: local assessment based on remote sensing data in the Lower Mondego (Portugal), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12498, https://doi.org/10.5194/egusphere-egu21-12498, 2021.
Remote Sensing (RS) information has progressively found, in recent years, more and more applications in hydrological modelling as a valuable tool for easy and frequent collection of geophysical data. However, this kind of data should be handled carefully, minding its characteristics, spatial resolution and the heterogeneity of the target area.
In this work, a scale analysis on evapotranspiration estimates over heterogeneous crops is performed combining a distributed energy-water balance model (FEST-EWB) and high-resolution remotely-sensed Land Surface Temperature (LST) and vegetation data.
The FEST-EWB model is calibrated on measured LST, based on a procedure where every single pixel is modified independently one from the other; hence in each pixel of the analysed domain the minimum of the pixel difference between modelled RET and satellite observed LST is searched over the period of calibration.
The case study is a Sicilian vineyard, with test dates in the summer of 2008. Meteorological and energy fluxes data are available from an eddy-covariance station, while LST and vegetation data are obtained from low-altitude flights at the high resolution of 1.7 metres.
After a preliminary calibration on LST data and validation on energy fluxes, the scale analysis is performed in two ways: model input aggregation and model output aggregation. Four coarser scales are selected in reference to some common satellite products resolution: 10.2 m (in reference to Sentinel’s 10 m), 30.6 m (Landsat, 30 m), 244.8 m (MODIS visible, 250 m) and 734.4 m (MODIS, 1000 m). First, modelled surface temperature and evapotranspiration are aggregated to each scale by progressive averaging. Then, model inputs are upscaled to the same spatial resolutions and the model is calibrated anew, obtaining independent results directly at the target scale.
The results of the two procedures are found to be quite similar, testifying to the capacity of the model to provide accurate products for a heterogeneous area even at low resolutions. The robustness of the analysis is strengthened by a further comparison with two well-established energy-balance algorithms: the one source Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) model.
How to cite: Paciolla, N., Corbari, C., Ciraolo, G., Maltese, A., and Mancini, M.: Scale analysis of evapotranspiration estimates from an energy-water balance model and remotely sensed LST, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12870, https://doi.org/10.5194/egusphere-egu21-12870, 2021.
‘Aerodynamic resistance’ (hereafter ra) is a preeminent variable in the modelling of evapotranspiration (ET), and its accurate quantification plays a critical role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links ra with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates.
The present study investigates the influence of ra and its relation to LST uncertainties on the performance of three structurally different SEB models by combining nine OzFlux eddy covariance datasets from 2011 to 2019 from sites of different aridity in Australia with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the latent heat flux (LE, energy equivalent of ET in W/m2) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST, LAI, and in-situ meteorological datasets were evaluated using observed flux data across water-limited (semi-arid and arid) and radiation-limited (mesic) ecosystems.
Our results revealed that the three models tend to overestimate instantaneous LE in the water-limited shrubland, woodland and grassland ecosystems by up to 60% on average, which was caused by an underestimation of the sensible heat flux (H). LE overestimation was associated with discrepancies in ra retrievals under conditions of high atmospheric instability, during which errors in LST (expressed as the difference between MODIS LST and in-situ LST) apparently played a minor role. On the other hand, a positive bias in LST coincides with low ra and causes slight underestimation of LE at the water-limited sites. The impact of ra on the LE residual error was found to be of the same magnitude as the influence of errors in LST in the semi-arid ecosystems as indicated by variable importance in projection (VIP) coefficients from partial least squares regression above unity. In contrast, our results for mesic forest ecosystems indicated minor dependency on ra for modelling LE (VIP<0.4), which was due to a higher roughness length and lower LST resulting in dominance of mechanically generated turbulence, thereby diminishing the importance of atmospheric stability in the determination of ra.
How to cite: Trebs, I., Mallick, K., Bhattarai, N., Sulis, M., Cleverly, J., Woodgate, W., Silberstein, R., Hinko-Najera, N., Beringer, J., Su, Z., and Boulet, G.: The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2186, https://doi.org/10.5194/egusphere-egu21-2186, 2021.
Evapotranspiration (ET) links the water and carbon cycles in the atmosphere, hydrosphere and biosphere, and is of great important in earth system science, hydrology and resource management researches. Commonly used ET estimating approaches usually contains type-based parameters, which requires calibration and associates with land cover product. Parameterization structure, representativity of training group and accuracy of land cover information all influences the performance of model extrapolation. In this study, we develop an ET modelling framework based on the hypothesis that canopy conductance acclimates to plant growth conditions so that the total costs of maintaining carboxylation and transpiration capacities are minimized. This is combined with the principle of co-ordination between the light- and Rubisco-limited rates of photosynthesis to predict gross primary production (GPP). Transpiration (T) is predicted from GPP via canopy conductance. No plant type- or biome-specific parameters are used. ET is estimated from T by calibrating a site-specific (but time-invariant) ratio of modelled average T to observed average ET. Predicted GPP were well supported by (weekly) GPP records at 112 widely distributed eddy-covariance flux sites (FLUXNET 2015 dataset), with R2 = 0.61, and RMSE = 2.73gC/day (N = 30129). ET were also well supported at site scale, with R2 = 0.65, and RMSE = 0.73mm/day (N = 30129). Global ET mapping was carried out with the help of Google Earth Engine (GEE). Basin-scale water balance validation in several globally distributed watersheds also indicates the robustness of our model.
How to cite: Tan, S., Wang, H., and Prentice, C.: Estimating land-surface evapotranspiration based on a first-principles primary productivity model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1026, https://doi.org/10.5194/egusphere-egu21-1026, 2021.
We studied the health and water use of seven riparian reaches of the Lower Colorado River from Hoover to Morelos Dam over the last 20-years, since 2000, to evaluate trends in the riparian ecosystem. This ecosystem has been in decline based on myriad pressures related to drought, water diversions and land use changes, such as defoliation events from the tamarisk leaf beetle, Diorhabda spp. We provide remotely sensed measurements of vegetation index (VI), daily evapotranspiration (ET, mmd-1) and annualized ET (mmyr-1). We used 250m Moderate Resolution Imaging Spectroradiometer (MODIS) and 30m Landsat EVI2 time-series. We selected EVI2 to parameterize our ET algorithm and tested the ET relationship between sensors by regression approaches and found a significant correlation between EVI2Landsat and EVI2MODIS. A key finding is that riparian health and its water use between Hoover and Morelos Dams has been in decline since 2000, as measured by Landsat with daily water use dropping from 4.79 mmd-1 to 3.18 mmd-1. Our results show that over the past two decades, the average greenness (EVI2Landsat) loss was 29% and total annual ET loss was 34% (-1.61 mmd-1 or -386 mmyr-1; a drop from 1163 mmyr-1 down to 777 mmyr-1). Greenness declined on average 29%, but certain reaches declined 42% or ca. -2.28 mmd-1, or -575 mmyr-1 (Reach 6). Reach 3 showed an ET loss of 39% (-1.94 mmd-1, -410 mmyr-1). Our findings are significant because riparian plant species have declined so drastically, suggesting further deterioration of biodiversity, wildlife habitat and other key ecosystem services.
How to cite: Nagler, P., Barreto-Muñoz, A., Chavoshi Borujeni, S., Nouri, H., Jarchow, C., and Didan, K.: Changes in Water Use on the Lower Colorado River in the USA from 2000-2020, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-138, https://doi.org/10.5194/egusphere-egu21-138, 2020.
Accurate estimation of evapotranspiration (ET) and water demand of urban green spaces (UGS) remain critical, especially in water-limited cities. Measuring ET helps decision‐makers, urban planners and urban water managers formulate strategies and plans for sustainable green cities worldwide. In this study, we used three satellites, WorldView2, Landsat (OLI, TM5 and ETM+), and MODIS to measure the greenness and ET of a 780‐ha public green space, the Adelaide Parklands in Australia. Different satellite‐based vegetation indices (VIs) including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2) were assessed. The VI-based ET from these three satellites were estimated. We then validated these remote sensing-based ET with a field-based method of Soil Water Balance (SWB) using Artificial Neural Network (ANN). Inter‐ and intra‐annual changes of VIs and their relevant ET were mapped and analyzed during 2010-2018. Our study, using multi-sensor remote sensing data fusion, systematic methods and machine learning techniques confirmed the suitability and feasibility of remote sensing-based ET as accurate long‐term monitoring mean for ET trends over large UGS. Our techniques rely on public and free-access satellite images, and therefore, can be adapted to other water-limited cities.
How to cite: Chavoshi Borujeni, S., Nouri, H., Nagler, P., Barreto-Muñoz, A., and Didan, K.: Spatio-temporal changes in water demand of urban greenery , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-104, https://doi.org/10.5194/egusphere-egu21-104, 2020.
How to cite: Duarte Rocha, A., Vulova, S., van der Tol, C., Förster, M., and Kleinschmit , B.: A correction factor for evapotranspiration prediction in urban environments using physical-based models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13367, https://doi.org/10.5194/egusphere-egu21-13367, 2021.
An increasing number of urban residents are affected by the urban heat island effect and water scarcity as urbanization and climate change progress. Evapotranspiration (ET) is a key component of urban greening measures aimed at addressing these issues, yet methods to estimate urban ET have thus far been limited. In this study, we present a novel approach to model urban ET at a half-hourly scale by fusing flux footprint modeling, remote sensing (RS) and geographic information system (GIS) data, and artificial intelligence (AI). We investigated this approach with a two-year dataset (2018-2020) from two eddy flux towers in Berlin, Germany. Two AI algorithms (1D convolutional neural networks and random forest) were compared. The land surface characteristics contributing to ET measurements were estimated by combining footprint modeling with RS and GIS data, which included Normalized Difference Vegetation Index (NDVI) derived from the Harmonized Landsat and Sentinel-2 (HLS) NASA product and indicators of 3D urban structure (e.g. building height). The contribution of remote sensing and meteorological data to model performance was examined by testing four predictor scenarios: (1) only reference evapotranspiration (ETo), (2) ETo and RS/ GIS data, (3) meteorological data, and (4) meteorological and RS/ GIS data. The inclusion of GIS and RS data extracted using flux footprints improved the predictive accuracy of models. The best-performing models were then used to model ET values for the year 2019 and compute monthly and annual sums of ET. A variable importance analysis highlighted the importance of the NDVI and impervious surface fraction in modeling urban ET. The 2019 ET sum was considerably higher at the site surrounded by more urban vegetation (366 mm) than at the inner-city site (223 mm). The proposed method is highly promising for modeling ET in a heterogeneous urban environment and can bolster sustainable urban planning efforts.
How to cite: Vulova, S., Meier, F., Duarte Rocha, A., Quanz, J., Nouri, H., and Kleinschmit, B.: A data-driven approach to quantifying urban evapotranspiration using remote sensing, footprint modeling, and deep learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-112, https://doi.org/10.5194/egusphere-egu21-112, 2020.
Evapotranspiration (ET) from an urban area and its components are important when estimating the urban ‘heat island’ effect and the urban hydrological cycle. Multi-source satellite-based ET models for ecosystems (e.g. farmland, forest, and wetland) have been developed and applied, but satellite-based ET model dimensions for urban areas are lacking, especially since all currently available models are designed for single-source schemes. This paper proposes the first Three-source Remote sensing model for Urban areas (TRU) to discriminate between soil evaporation, vegetation transpiration, and impervious surface evaporation. TRU uses a new parameterization scheme, based on the use of a complementary relationship integrating soil surface temperature to estimate soil evaporation. An iterative procedure was developed for decomposing land surface temperature (LST) into component temperatures. Also, the ET for impervious areas was independently delineated using the “patch” approach. The model was tested for 45 cloudless days in Tianjin for 2017-2020 based on 30 m Operational Land Imager (OLI)/Enhanced Thematic Mapper Plus (ETM+) images. Results indicated the root mean square error (RMSE) of 38.8 W/m2 and Bias of 9.9 W/m2 compared with two Eddy Correlation (EC) observations for instantaneous latent heat (LE) simulation and RMSE was 0.087 and Bias was -0.012, compared with stable water isotope measurements for the estimation of the ratio of vegetation latent heat flux to latent heat flux (LEv/LE).Comparison with urban single-source models and two-source models for ecosystem suggest TRU provide best accuracy for ET and its components simulation. The spatial pattern suggested impervious surface evaporation exhibited minimal seasonal variation and maintained a very lower level due to limited availability of water. The results emphasized the importance of using land use and land cover (LULC) in urban ET modeling and the necessity to calculate ET as independent of impervious areas. TRU represents a groundbreaking development of multi-source urban satellite-based ET models and facilitates the mapping of urban ET components.
How to cite: Chen, H., Huang, J. J., McBean, E., Lan, Z., Gao, J., Li, H., and Zhang, J.: Development of a Three-Source Remote Sensing Model for Estimation of Urban Evapotranspiration (TRU), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9065, https://doi.org/10.5194/egusphere-egu21-9065, 2021.
A changing climate accompanied by an increasing number of extreme weather events puts pressure on ecosystems around the globe. Evapotranspiration is one of the key metrics for understanding vegetation dynamics and changes in an ecosystem. Due to its complex nature, evapotranspiration is difficult to determine on a larger scale.
Existing approaches to correlate evapotranspiration measurements and radar backscatter signals were completed in boreal forests using ground-based scatterometers for short time series (several months) with much higher temporal resolution (multiple observations per hour) for small test sites. Our goal is to build upon this research to establish a broader understanding on the influences of evapotranspiration on the signal of the widely used Copernicus Sentinel-1 C-Band SAR for managed temperate coniferous forests. Variations of the observed backscatter signals (VV, VH) over several growing seasons and years (2016-2020) are investigated.
Besides wind, temperature or precipitation as some of the influencing parameters on the C-band SAR signal, we focus our analyses on the influence of evapotranspiration on the Sentinel-1 C-band signal. Therefore, we recorded long time series of Sentinel-1 data to investigate and estimate the correlation between forest evapotranspiration dynamics and SAR signal variations. For this purpose, Sentinel-1 and weather data from July 2016 to December 2020 were obtained for forested areas in the southeastern part of the Free State of Thuringia, central Germany.
We use four different weather station datasets with daily measurements to calculate evapotranspiration values following the Penman-Monteith approach and apply regression analyses to gain a better understanding about the influence on the SAR signal. To obtain regions with speckle-suppressed backscatter for in situ comparison, forest areas in a radius of five kilometers around the four weather stations are considered. For the analysis, radar datasets are differentiated in co- and cross-polarized data as well as descending and ascending flight directions. It seems also important to distinguish between frozen and no-frozen conditions as we discover strong changes in the C-band SAR signal but only minor changes in evapotranspiration values for temperatures below freezing level. Excluding frozen conditions, in situ evapotranspiration measurements and the SAR backscatter variations over four years directly correlate with R2-values up to 0.48 without any parameterization or calibration on both sides (SAR & in situ). Currently we are investigating statistical methods for in-depth analysis of the correlation between the two datasets. As the SAR backscatter signal at C-band is not a direct and sole function of evapotranspiration, future work will combine the modelling of the different influence parameters of the environment on the SAR backscatter signal and aim at quantifying their respective influence on the signal to better understand the seasonal signal variations.
How to cite: Mueller, M. M., Dubois, C., Jagdhuber, T., Pathe, C., and Schmullius, C.: Comparison of seasonal evapotranspiration of temperate coniferous forests with Copernicus Sentinel-1 time series, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9851, https://doi.org/10.5194/egusphere-egu21-9851, 2021.
Dual source energy balance models are often used in estimating and partitioning evapotranspiration between the soil and vegetation. The use of multi-angular remotely-sensed thermal data in such methods makes them susceptible to directional anisotropy (hotspot) effects that may result from the satellite’s geometry, relative to the sun, at overpass time. It is therefore important to have these effects accounted for to ensure realistic flux retrievals irrespective of sensor viewing position. At present, dual source models generally interpret surface temperature according to two sources, which may be insufficient to adequately represent the limiting temperature conditions that not only depend on the source type but also their exposure to the sun. Here, we present an extended SPARSE (Soil Plant Atmosphere Remote Sensing Evapotranspiration) scheme, wherein the original SPARSE is extended to incorporate sunlit/shaded soil/vegetation elements and coupled with a radiative transfer model that links these four component emissions to out-of-canopy radiances as observed by remote sensors. An initial evaluation is carried out to check the model’s capability in retrieving surface fluxes over diverse environments instrumented with in-situ thermo-radiometers. When run with nadir-acquired thermal data, which have no hotspot signal influence, both algorithms show, as expected, no observable difference in their retrieval of total fluxes. We nonetheless show that by incorporating the solar direction and discriminating between sunlit and shaded elements, the partitioning of these overall fluxes between the soil and vegetation can be improved especially in water stressed environments. We also test the sensitivity of flux and component temperature estimates to the viewing direction of the thermal sensor by using two sets of TIR data (nadir and oblique) to force the models and show that angular sensitivity is reduced. This is key particularly when using high spatial and temporal data from earth observation missions that inherently have to consider a wide-range of viewing angles in their design.
Keywords: Evapotranspiration, thermal infrared (TIR), Soil Vegetation Atmosphere Transfer (SVAT), temperature inversion.
How to cite: Mwangi, S., Boulet, G., and Olioso, A.: Estimating evapotranspiration from thermal infrared data : extension of the two source SPARSE model to a four source representation in order to account for the sun-earth-sensor configuration, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10895, https://doi.org/10.5194/egusphere-egu21-10895, 2021.
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