Assessment of the Relationship Between NDVI-Based Actual Evapotranspiration by SEBS

Arast, Mina; Ranjbar, A.A.; Mousavi, S. H.; Abdollahi, Khodayar

doi:10.1007/s40995-020-00895-3

Cited by 4 publications

(6 citation statements)

References 29 publications

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“…The authors of [97] found that Sentinel-1 and Sentinel-3 sensors can provide estimate values of the NDVI and LST to ascertain spatiotemporal vegetation dynamics, droughts, and water availability in water stress conditions, respectively, which are essential factors influencing, and significant driving forces of, the AET distribution. This is consistent with Arast et al [39], who demonstrated that the NDVI, net solar radiation, and other meteorological parameters influence AET. The data from the two sensors are efficiently sufficient to derive the variables under investigation and explore their associations with AET.…”

Section: Descriptive Statisticssupporting

confidence: 92%

“…Studies have employed remote-sensing-based models in AET-related studies. Using the surface energy balance index (SEBI), two-source model (TSM), surface energy balance algorithm for land (SEBAL), surface energy balance system (SEBS), Eta mapping algorithm (ETMA), and atmosphere-land exchange inverse model (ALEXI), many researchers have successfully assessed and predicted AET spatiotemporal variation in different regions across the globe [34][35][36][37][38][39][40]. In the same context, [41] suggested a more sophisticated analysis aiming to reduce the range of uncertainty in observation-based AET estimations based on a combination of the remote sensing and machine learning tools discussed in the present study.…”

Section: Introductionmentioning

confidence: 99%

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Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya

et al. 2022

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Kenya is dominated by a rainfed agricultural economy. Recurrent droughts influence food security. Remotely sensed data can provide high-resolution results when coupled with a suitable machine learning algorithm. Sentinel-1 SAR and Sentinel-3 SLSTR sensors can provide the fundamental characteristics for actual evapotranspiration (AET) estimation. This study aimed to estimate the actual monthly evapotranspiration in Busia County in Western Kenya using sentinel-1 SAR and Sentinel-3 SLSTR data with the application of the gradient boosting machine (GBM) model. The descriptive analysis provided by the model showed that the estimated mean, minimum, and maximum AET values were 116, 70, and 151 mm/month, respectively. The model performance was assessed using the correlation coefficient (r) and root mean square error (RMSE). The results revealed a correlation coefficient of 0.81 and an RMSE of 10.7 mm for the training dataset (80%), and a correlation coefficient of 0.47 and an RMSE of 14.1 mm for the testing data (20%). The results are of great importance scientifically, as they are a conduit for exploring alternative methodologies in areas with scarce meteorological data. The study proves the efficiency of high-resolution data retrieved from Sentinel sensors coupled with machine learning algorithms, focusing on GBM as an alternative to accurately estimate AET. However, the optimal solution would be to obtain direct evapotranspiration measurements.

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Section: Descriptive Statisticssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya

et al. 2022

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“…The applicability of these RS-based ETa models has the potential to facilitate and improve water management and drought monitoring, where access to in-situ data is constrained. In the ZRB, except a few short-term [65,66] or coarse-resolution [67] studies, there was no study which assessed the application of empirical ET-VI methods over croplands at basin scale using Landsat images. Most studies do not consider changes in croplands' extent over the years; however, it is critical to incorporate the impact of fallow and non-cultivated lands on the estimation of ETa.…”

Section: Discussionmentioning

confidence: 99%

Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area

et al. 2021

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Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVIMccL and EVI2MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.

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“…The computation of surface energy balance with the SEBS algorithm is based on the determination of the relative evaporative fraction ( ). The latent heat (or the evaporation) becomes zero due to the limitation of soil moisture, and the sensible heat flux is the maximum value (Arast et al 2020), thus: 2015).…”

Section: Surface Energy Balance Algorithmmentioning

confidence: 99%

“…where E daily represents the actual evaporation (mm d À1 ), k is the latent heat of vaporization (J kg À1 ), w is water density (kg m À3 ), and R n is the daily net radiation flux (Su 2002;Arast et al 2020).…”

Section: Surface Energy Balance Algorithmmentioning

confidence: 99%

Estimation of Surface Soil Moisture Based on Improved Multi-index Models and Surface Energy Balance System

Jahangir

Arast

2020

Nat Resour Res

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Soil moisture is a vital resource that plays a critical role in arid and semi-arid areas. In the present study, a new approach was adopted to estimate surface soil moisture based on multiindex models using reflective and thermal indices as well as surface energy balance system-Iran (SEBS-Iran) in pastures and farmlands in Qom province, Iran in 2016-2017. To select the best model based on remote sensing (RS) indices, 12 models were designed and after analysis, the best ones were selected. Afterward, the results of the SEBS-Iran algorithm and the improved multi-index model [normalized multi-band drought index (NMDI), normalized difference vegetation index (NDVI), land surface temperature (LST) and the temperature vegetation dryness index (TVDI)] were calibrated with field data in the two studied fields (pastures and farmlands). The findings indicated that the multi-index model NMDI-TDVI-LST-NDVI (R = 0.95) and SEBS-Iran (R = 0.93) both had significant correlations with measured soil moisture. Regarding both models in farmlands and pastures, the SEBS-Iran regression model was closer to the line of fit, and R 2 in the two fields was 0.95 and 0.96, respectively. Compared to SEBS-Iran, the multi-index model showed lower coefficient of determination in pastures (0.71) due to the higher accuracy of SEBS-Iran in areas with lower vegetation density. Generally, both methods were found to be suitable for soil moisture estimation. The multi-index model can be used to estimate soil moisture in densely vegetated areas on a large scale due to its simplicity and good accuracy. Moreover, the highly accurate SEBS-Iran model can be used even in sparsely vegetated areas.

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Assessment of the Relationship Between NDVI-Based Actual Evapotranspiration by SEBS

Cited by 4 publications

References 29 publications

Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya

Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya

Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area

Estimation of Surface Soil Moisture Based on Improved Multi-index Models and Surface Energy Balance System

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