Introducing QWaterModel, a QGIS plugin for predicting evapotranspiration from land surface temperatures

Ellsäßer, Florian; Röll, Alexander; Stiegler, Christian; Hendrayanto, Hendrayanto; Hölscher, Dirk

doi:10.1016/j.envsoft.2020.104739

Cited by 27 publications

(27 citation statements)

References 19 publications

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“…QWaterModel developers also suggested excluding other artifacts from the thermal image as a necessary step and manually defining minimal and maximal temperatures for further estimations, since the model solely depends on the LST image. It is also recommended to first calibrate satellite images from ET by planes, drones, and handheld or fixed thermal cameras (e.g., using QWaterModel) to be used for temporal and spatial analysis [56]. This research also confirms that the use of Landsat images in the QWaterModel with ET calibration is essential.…”

Section: Evaluation Of Model-based Evapotranspirationsupporting

confidence: 57%

“…QWaterModel showed tremendous difference with 0.4-0.8 mm day −1 values in June, lower than the expected range (3-4 mm day −1 ), whereas neither it could give satisfactory results during the maize growing season. The reason could be its sensitivity to the in situ measurements of net radiation and shortwave irradiance, as mentioned in [55,56] and also observed during analysis. This application is perhaps more effective to thermal camera images, as tested previously on oil palm plantations since no good results were obtained for the Landsat images in this study.…”

Section: Evaluation Of Model-based Evapotranspirationmentioning

confidence: 97%

“…Previously, the QWaterModel was applied in a tropical environment using high resolution drone-based thermal images for predicting ET a in an Indonesian oil palm plantation against eddy covariance measurements. Landsat images were not tested in the study due to high cloud cover in their specific time span [56], however, they were used in the original DATTUTDUT model study for its comparison with other conventional energy balance models (SEBAL, SEBS) [55]. The original DATTUTDUT model estimates the radiation budget from a set of parameters and Sun-Earth geometrics, applicable to cloud-free images only.…”

Section: Qwatermodelmentioning

confidence: 99%

“…The performance was tested against other open-source applications (i.e., SEBS and METRIC). In the application, ET a was derived from latent heat fluxes by calculating the latent heat of vaporization using in situ air temperature and the lowest pixel temperatures from the LST maps for the plugin estimates following the methodology explained in [56].…”

Section: Qwatermodelmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of SEBS, METRIC-EEFlux, and QWaterModel Actual Evapotranspiration for a Mediterranean Cropping System in Southern Italy

Nisa

Khan

Govind³

et al. 2021

Agronomy

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Remote sensing-based evapotranspiration (ET) models with various levels of sophistication have emerged recently with the possibilities of user-defined model calibrations. Their application for water resources management and climate studies from regional to global scale has been rapidly increasing, which makes it important to validate field scale ET in a complex crop assemblage before operational use. Based on in situ flux-tower measurements by the eddy-covariance (EC) system, this study tested three single-source energy balance models for estimating daily ET from fennel/maize/ryegrass-clover cropland rotations in a Mediterranean context in southern Italy. The sensitivity of three user-friendly ET models (SEBS, QWaterModel, and METRIC-EEFlux) with reference to the EC system over a center pivot irrigated cropland is discussed in detail. Results in terms of statistical indicators revealed that SEBS and METRIC-EEFlux showed reasonable agreements with measured ET (r2 = 0.59SEBS, RMSE = 0.71 mm day−1; r2 = 0.65METRIC, RMSE = 1.13 mm day−1) in terms of trends and magnitudes. At 30 m spatial resolution, both models were able to capture the in-field variations only during the maize development stage. The presence of spurious scan lines due to sensor defects in Landsat L7 ETM+ can contribute to the qualities of the METRIC-Efflux’s ET product. In our observation, the QWaterModel did not perform well and showed the weakest congruency (r2 = 0.08QWaterModel) with ground-based ET estimates. In a nutshell, the study evaluated these automated remote sensing-based ET estimations and suggested improvements in the context of a generic approach used in their underlying algorithm for robust ET retrievals in rotational cropland ecosystems.

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Section: Evaluation Of Model-based Evapotranspirationsupporting

confidence: 57%

Section: Evaluation Of Model-based Evapotranspirationmentioning

confidence: 97%

Section: Qwatermodelmentioning

confidence: 99%

Section: Qwatermodelmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of SEBS, METRIC-EEFlux, and QWaterModel Actual Evapotranspiration for a Mediterranean Cropping System in Southern Italy

Nisa

Khan

Govind³

et al. 2021

Agronomy

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“…To implement the DATTUTDUT model, we used the QGIS3 plugin QWaterModel (Ellsäßer et al, 2020) that is provided with an easy-to-use graphical user interface.…”

Section: Dattutdutmentioning

confidence: 99%

Predicting evapotranspiration from drone-based thermography – a method comparison in a tropical oil palm plantation

Ellsäßer¹,

Stiegler²,

Röll³

et al. 2021

Biogeosciences

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Abstract. For the assessment of evapotranspiration, near-surface airborne thermography offers new opportunities for studies with high numbers of spatial replicates and in a fine spatial resolution. We tested drone-based thermography and the subsequent application of the DATTUTDUT energy balance model using the widely accepted eddy covariance technique as a reference method. The study site was a mature oil palm plantation in lowland Sumatra, Indonesia. For the 61 flight missions, latent heat flux estimates of the DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature) model with measured net radiation agreed well with eddy covariance measurements (r2 = 0.85; MAE = 47; RMSE = 60) across variable weather conditions and times of day. Confidence intervals for slope and intercept of a model II Deming regression suggest no difference between drone-based and eddy covariance methods, thus indicating interchangeability. The DATTUTDUT model is sensitive to the configuration of the net radiation assessment. Overall, we conclude that drone-based thermography with energy balance modeling is a reliable method complementing available methods for evapotranspiration studies. It offers promising, additional opportunities for fine grain and spatially explicit studies.

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Combining UAV thermography, point cloud analysis and machine learning for assessing small‐scale evapotranspiration patterns in a tropical rainforest

Cortés‐Molino,

Valdés‐Uribe,

Ellsäßer

et al. 2023

Ecohydrology

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Microclimate and vegetation structure control evapotranspiration (ET) from land surfaces at stand and landscape scales. Tropical rainforests are among the most diverse and complex terrestrial ecosystems, harbouring vast plant and animal species throughout their dense multistory canopy. They contribute substantially to global precipitation through their high ET. However, there is little information about ET influences at very small spatial scales under given climatic conditions. In a tropical rainforest on Sumatra, we studied the relationship between pixel‐level ET as derived from high‐resolution (~10 cm), near‐surface thermography from an unmanned aerial vehicle (UAV) and canopy structure as derived from red–green–blue (RGB) image and three‐dimensional (3D) point cloud analyses. The 16 derived potential predictors encompassed vegetation height, height variability, vegetation density and reflectance variables. Using regression models, several of the studied variables had a significant linear relationship with ET, but the explained variance was only marginal. However, applying a random forest algorithm including forward feature selection and target oriented cross validation explained substantial parts of the pixel‐level variance in ET (R2 = 0.56–0.65), thus indicating multiple non‐linear relationships with interactions among predictor variables. Therein, green leaf index, leaf area density and vegetation height were often the most important variables for the prediction outcome, but their sequence varied among the four study plots. Overall, combining canopy structure variables derived from RGB photogrammetry explained relatively large parts of spatial ET variations. Our study thus indicates the large potential of combining UAV‐based thermography and photogrammetry techniques with machine learning approaches to better understand ET but also suggests that more work remains to be done in explaining ET patterns at very small spatial scales.

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Introducing QWaterModel, a QGIS plugin for predicting evapotranspiration from land surface temperatures

Cited by 27 publications

References 19 publications

Evaluation of SEBS, METRIC-EEFlux, and QWaterModel Actual Evapotranspiration for a Mediterranean Cropping System in Southern Italy

Evaluation of SEBS, METRIC-EEFlux, and QWaterModel Actual Evapotranspiration for a Mediterranean Cropping System in Southern Italy

Predicting evapotranspiration from drone-based thermography – a method comparison in a tropical oil palm plantation

Combining UAV thermography, point cloud analysis and machine learning for assessing small‐scale evapotranspiration patterns in a tropical rainforest

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