Automatically selecting hot and cold pixels for satellite actual evapotranspiration estimation under different topographic and climatic conditions

Saboori, Mojtaba; Mokhtari, Ali; Afrasiabian, Yasamin; Daccache, André; Alaghmand, Sina; Mousivand, Yousef

doi:10.1016/j.agwat.2021.106763

Cited by 16 publications

(11 citation statements)

References 44 publications

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“…Additionally, the mismatch of the flux tower footprint and spatial resolution of SEBAL ET will causes errors as well. Generally, the footprint of the flux tower varied from hundreds of square meters to several square kilometers, which was determined by the height of the observation instrument, the intensity of the turbulence, terrain, environment, and vegetation status (Chen et al, 2012;Damm et al, 2020;Schmid, 1994). Moreover, a footprint probability distribution function (PDF) could characterize the footprint at a fine spatial resolution (Wang et al, 2019), but it may not be suitable for the coarse resolution in this study (kilometer scale).…”

Section: Errors In Flux Tower Measurementsmentioning

confidence: 99%

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

Cheng

Jiao

et al. 2021

Earth Syst. Sci. Data

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Abstract. Satellite observations of evapotranspiration (ET) have been widely used for water resources management in China. An accurate ET product with a high spatiotemporal resolution is required for research on drought stress and water resources management. However, such a product is currently lacking. Moreover, the performances of different ET estimation algorithms for China have not been clearly studied, especially under different environmental conditions. Therefore, the aims of this study were as follows: (1) to use multisource images to generate a long-time-series (2001–2018) daily ET product with a spatial resolution of 1 km × 1 km based on the Surface Energy Balance Algorithm for Land (SEBAL); (2) to comprehensively evaluate the performance of the SEBAL ET in China using flux observational data and hydrological observational data; and (3) to compare the performance of the SEBAL ET with the MOD16 ET product at the point scale and basin scale under different environmental conditions in China. At the point scale, both the models performed best in the conditions of forest cover, subtropical zones, hilly terrain, or summer, respectively, and SEBAL performed better in most conditions. In general, the accuracy of the SEBAL ET (rRMSE = 44.91 %) was slightly higher than that of the MOD16 ET (rRMSE = 48.72 %). In the basin-scale validation, both the models performed better than in the point-scale validation, with SEBAL obtaining results superior (rRMSE = 13.57 %) to MOD16 (rRMSE = 32.84 %). Additionally, both the models showed a negative bias, with the bias of the MOD16 ET being higher than that of the SEBAL ET. In the daily-scale validation, the SEBAL ET product showed a root mean square error (RMSE) of 0.92 mm d−1 and an r value of 0.79. In general, the SEBAL ET product can be used for the qualitative analysis and most quantitative analyses of regional ET. The SEBAL ET product is freely available at https://doi.org/10.5281/zenodo.4243988 and https://doi.org/10.5281/zenodo.4896147 (Cheng, 2020a, b). The results of this study can provide a reference for the application of remotely sensed ET products and the improvement of satellite ET observation algorithms.

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Section: Errors In Flux Tower Measurementsmentioning

confidence: 99%

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

Cheng

Jiao

et al. 2021

Earth Syst. Sci. Data

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“…Considering that cloud boundaries usually remain in the CFMask output, pixels with LST less than 270 K were also removed in accordance with [43]. Water bodies were also masked by the normalized difference water index (NDWI) and the NDVI by defining NDW I > −0.1 [63] and NDV I < 0 thresholds in accordance with [32,40,63,64]. Unlike cropland, which includes vegetated and bare agricultural fields, the other three classes may lack bare soil for hot pixel selection.…”

Section: Automated Endmember Selectionmentioning

confidence: 99%

“…Given that built-up and stony areas were masked in step 1, the chance of an appropriate hot pixel selection in fallow or bare fields increased. However, the fallow and bare soil classes have relatively high albedo, and non-saline soils usually show an albedo lower than 0.3 followed by salty soil (usually Albedo > 0.3) [40,66]. Hence, a middle range of albedo can be an apt choice for hot pixel selection (0.15 < Albedo hot_intended_class < 0.3).…”

Section: Evaluation Of Endmember Selectionmentioning

confidence: 99%

“…These parameters are empirically estimated and controlled by ground-sourced data in contrast to our approach being independent of in situ data. Saboori et al [40] implemented a simple method using vegetation and topographic index thresholding as well as endmember distance from flux towers without the need for land cover maps. Although the ET estimates were satisfactory, the study was mostly conducted for crop flux tower sites.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

“…Ref. [40] implemented a simple method using vegetation and topographic index thresholding as well as endmember distance from flux towers, without the need for land cover maps. Although the ET estimates were satisfactory, the study was mostly conducted for crop flux tower sites.…”

Section: Introductionmentioning

confidence: 99%

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An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data

et al. 2022

Self Cite

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The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and meteorological data from the Global Land Data Assimilation System (GLDAS) were used to estimate long-term daily actual ET based on three endmember selection procedures: two land cover-based models, one with (WF) and the other without (WOF) morphological functions, and the Allen method (with the default percentiles) for 2270 Landsat images. Models were evaluated for 23 flux tower sites with four main vegetation cover types as well as different climate types. Results showed that endmember selection with morphological functions (WF_ET) generally performed better than the other endmember approaches. Climate-based classification assessment provided the clearest discrimination between the performance of the different endmember selection approaches for the humid category. For humid zones, the land cover-based methods, especially WF, appropriately outperformed Allen. However, the performance of the three approaches was similar for sub-humid, semi-arid and arid climates together; the Allen approach was therefore recommended to avoid the need for dependency on land cover maps. Tower-by-tower validation also showed that the WF approach performed best at 12 flux tower sites, the WOF approach best at 5 and the Allen approach best at 6, suggesting that the use of land cover maps alone does not explain the differences between the performance of the land cover-based models and the Allen approach. Additionally, the satisfactory error metrics results when comparing the EC estimations with EC measurements, with root mean square error (RMSE) ≈ 0.91 and 1.59 mm·day−1, coefficient of determination (R2) ≈ 0.71 and 0.41, and bias percentage (PBias) ≈ 2% and 60% for crop and non-crop flux tower sites, respectively, supports the use of GLDAS meteorological forcing datasets with the different automated ET estimation approaches. Overall, given that the thorough evaluation of different endmember selection approaches at large scale confirmed the validity of the WF approach for different climate and land cover types, this study can be considered an important contribution to the global retrieval of long time series of ET.

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RETRACTED: Monitoring system of agricultural performance based on multisensor data and cross‐validation technique in merging temporal images

Bhavani

Chillakuru²,

Adusumilli³

et al. 2022

Agronomy Journal

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Several approaches to forecasting agricultural production have been used across the country, but they have focused on information widely detected with technology that was not very successful. Unfortunately, because of many difficulties such as climate variables (50% global fog cover) with limited temporal accuracy, the remotely detected information necessary to predict crop production was often insufficient. As a result of these problems, existing methods of estimating agricultural production are ineffective or out of date. Several efforts have been made to overcome these challenges by combining images with high temporal accuracy but poor geographic detail. On the other hand, it is this kind of situation that is most suitable for extremely large and homogeneous agricultural areas. An innovative theoretical framework has developed that explains this absence of high‐quality satellite images. This intelligent method was built around this new theoretical framework, which incorporates its use of something like the energy equation to improve the predictions of multiple cultures. Many producers were contacted and data regarding agricultural production were obtained to validate the results of the smart technology. The excellent reliability of this intelligent method has been shown by a comparative contrast between projected crop yields and actual output in various areas.

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Automatically selecting hot and cold pixels for satellite actual evapotranspiration estimation under different topographic and climatic conditions

Cited by 16 publications

References 44 publications

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data

RETRACTED: Monitoring system of agricultural performance based on multisensor data and cross‐validation technique in merging temporal images

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