Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing

Laipelt, Leonardo; Kayser, Rafael Henrique Bloedow; Fleischmann, Ayan Santos; Ruhoff, Anderson Luís; Bastiaanssen, W.G.M.; Erickson, Tyler; Melton, F. S.

doi:10.1016/j.isprsjprs.2021.05.018

Cited by 88 publications

(62 citation statements)

References 81 publications

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“…One of them is a new python version of SEBAL (pySEBAL), which incorporates an automated pixel selection procedure and is currently under development and testing at the IHE-Delft Institute [129]. In addition, a new tool based on the SEBAL algorithm, modified with a simplified version of the Calibration using Inverse Modeling at Extreme Conditions process [130] for the endmembers selection, was developed within the Google Earth Engine (geeSEBAL) environment [131].…”

Section: Surface Energy Balance System (Sebs) Modelmentioning

confidence: 99%

Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review

2022

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Almost fifty years have passed since the idea to retrieve a value for Evapotranspiration (ET) using remote sensing techniques was first considered. Numerous ET models have been proposed, validated and improved along these five decades, as the satellites and sensors onboard were enhanced. This study reviews most of the efforts in the progress towards providing a trustworthy value of ET by means of thermal remote sensing data. It starts with an in-depth reflection of the surface energy balance concept and of each of its terms, followed by the description of the approaches taken by remote sensing models to estimate ET from it in the last thirty years. This work also includes a chronological review of the modifications suggested by several researchers, as well as representative validations studies of such ET models. Present limitations of ET estimated with remote sensors onboard orbiting satellites, as well as at surface level, are raised. Current trends to face such limitations and a future perspective of the discipline are also exposed, for the reader’s inspiration.

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Section: Surface Energy Balance System (Sebs) Modelmentioning

confidence: 99%

Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review

2022

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“…CFmask algorithm provides the best overall accuracy among many algorithms on Landsat scenes; it is also derived from a priori knowledge of physical phenomena and is operable without geographic restriction [87]. Since 2013, a dramatic increase in the use of CFMask has been seen in the detection of changes using Landsat time series [12] and has been used in GEE applications such as GEESE-BAL [88]. For Sentinel-2 images, the cloud removal model s2cloudless (GEE library COPER-NICUS/S2_CLOUD_PROBABILITY) was applied and additional scripts were used to mask shadows and snow/ice coverage.…”

Section: Tool Development In the Google Earth Engine (Gee) Platformmentioning

confidence: 99%

VICAL: Global Calculator to Estimate Vegetation Indices for Agricultural Areas with Landsat and Sentinel-2 Data

et al. 2022

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The vegetation indices (VIs) estimated from remotely sensed data are simple and based on effective algorithms for quantitative and qualitative evaluations of the dynamics of biophysical crop variables such as vegetation cover, leaf area, vigor and development, and many others. Over the last decade, many VIs have been proposed and validated to enhance the vegetation signal by reducing the noise from effects produced either by the soil or by vegetation such as brightness, shadows, color, etc. VIs are commonly calculated from satellite images such as ones from Landsat and Sentinel-2 because of their medium resolution and free availability. However, despite the VIs being fairly simple algorithms, it can take hours to calculate them for an established agricultural area, mainly due to the pre-processing of the images (including atmospheric corrections, the detection of clouds and shadows), size and download time of the images, and the capacity of the computer equipment used. Time increases as the number of images increases. In this sense, the free to use Google Earth Engine (GEE) platform was here used to develop an application called VICAL to calculate 23 VIs map (VIs commonly used in agricultural applications) and time series of any agricultural area in the world with images (cloud-free) from Landsat and Sentinel-2 data. It was found that VICAL can calculate these 23 VIs accurately, and shows the potential of the GEE cloud-based tools using multispectral dataset to assess many spectral VIs. This tool is very beneficial for researchers with poor access to satellite data or in institutions with a lack of computational infrastructure to handle the large volumes of satellite datasets, since it is not necessary for the user writing a single line of code. The VICAL is open-access image analysis platform that can be modified to carry out more complex analysis or adapt it to a specific VI application.

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“…The results of this study indicate that the SEBAL model has reasonable accuracy when predicting the ET of subtropical regions. In Tan et al's previous study in 2021 [43], the SEBAL model was applied to estimate the ET characteristics in a subtropical region (Huaihe River Basin, China) and the SEBAL model performance was evaluated by fitting the regression with the daily reference ET calculated by multiple theoretical methods. The results showed that the bias between the ET estimated by the SEBAL model and daily reference ET was less than 1.5%.…”

Section: Estimation Accuracy Of the Sebal Model For Daily Et In The G...mentioning

confidence: 99%

“…The SEBAL model has been applied to several regions in China: the northeast Sanjiang River Basin [35,36], the North China Plain [37], the Loess Plateau [33,38], the agro-pastoral ecotone of Northwestern China [39], the Taklimakan Desert [40], and the Yellow River Delta [21]. Additionally, the SEBAL model has been widely applied over a multitude of different climatic conditions, including subtropical areas [41][42][43], but the cloudy and rainy weather in the subtropical climate zone-which is represented by the southern region of China, where mixed double-season early and late rice and single-season middle rice are cultivated-presents a greater challenge for accurate estimations of ET using the SEBAL model.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Variation in Paddy Evapotranspiration in Subtropical Climate Regions Based on the SEBAL Model: A Case Study of the Ganfu Plain Irrigation System, Southern China

et al. 2022

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The surface energy balance algorithm for land (SEBAL) is a commonly used method for estimating evapotranspiration (ET) at a regional scale; however, the cloudy and rainy characteristics of subtropical monsoon regions pose a greater challenge for estimating paddy field ET based on remote sensing technology. To this end, a typical subtropical climate region in southern China (Ganfu Plain irrigation system) was selected as the study area. Subsequently, we evaluated the applicability of the SEBAL model for estimating the ET of paddy fields at the daily scale; derived the interannual variation (2000–2017) characteristics of early, middle, and late rice ET; and finally analyzed the spatial distribution patterns of rice in different hydrological years. The results demonstrated that: (1) the SEBAL model estimated ET accurately on a daily scale, with R2, NSE, and RMSE values of 0.85, 0.81, and 0.84 mm/day, respectively; (2) the ET of paddy fields in the irrigated area was higher in July and August and the interannual trend of ET of early rice was not obvious, with a declining trend observed in middle rice and late rice from 2000 to 2009, which was followed by an increasing trend from 2009 to 2017; and (3) variations in the spatial distribution of ET were significant for early and late rice at different precipitation levels and less obvious for middle rice in wet years but significant in dry years. Overall, this study verified the applicability of the SEBAL model for estimating ET in paddy fields in subtropical regions and provided a basis and reference for the rational allocation of water resources at a regional scale.

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Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing

Cited by 88 publications

References 81 publications

Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review

Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review

VICAL: Global Calculator to Estimate Vegetation Indices for Agricultural Areas with Landsat and Sentinel-2 Data

Spatial-Temporal Variation in Paddy Evapotranspiration in Subtropical Climate Regions Based on the SEBAL Model: A Case Study of the Ganfu Plain Irrigation System, Southern China

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