A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration

Mahour, Milad; Tolpekin, V.A.; Stein, Alfred; Sharifi, Alimorad

doi:10.1016/j.isprsjprs.2017.02.004

Cited by 24 publications

(20 citation statements)

References 30 publications

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“…These two seasons were characterized because in the initial stage crop needs smaller water requirements, whereas in the mid-season crop needs higher water requirements, as we can see in the next section. The ET c maps created in this study are in agreement with other researchers; for example, in [2,4,58,59], they generated ET c maps using c derived from remote sensing-based vegetation indices. Other researchers reported that c derived from canopy reflectance based vegetation index had the potential to estimate crop evapotranspiration at regional and field scales, for example, in [17,18,29,55,60,61].…”

Section: 3supporting

confidence: 88%

Estimation of Crop Evapotranspiration Using Satellite Remote Sensing-Based Vegetation Index

Reyes-González

Kjaersgaard

Trooien

et al. 2018

Advances in Meteorology

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Irrigation water is limited and scarce in many areas of the world, including Comarca Lagunera, Mexico. Thus better estimations of irrigation water requirements are essential to conserve water. The general objective was to estimate crop water demands or crop evapotranspiration (ET c ) at different scales using satellite remote sensing-based vegetation index. The study was carried out in northern Mexico (Comarca Lagunera) during four growing seasons. Six, eleven, three, and seven clear Landsat images were acquired for 2013, 2014, 2015, and 2016, respectively, for the analysis. The results showed that ET c was low at initial and early development stages, while ET c was high during mid-season and harvest stages. These results are not new but give us confidence in the rest of our ET c results. Daily ET c maps helped to explain the variability of crop water use during the growing season. Based on the results we can conclude that ET c maps developed from remotely sensed multispectral vegetation indices are a useful tool for quantifying crop water consumption at regional and field scales. Using ET c maps at the field scale, farmers can supply appropriate amounts of irrigation water corresponding to each growth stage, leading to water conservation.

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Section: 3supporting

confidence: 88%

Estimation of Crop Evapotranspiration Using Satellite Remote Sensing-Based Vegetation Index

Reyes-González

Kjaersgaard

Trooien

et al. 2018

Advances in Meteorology

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“…Table 7 illustrates the statistical parameters between the fused LEs and measured LEs on the selected days for different land cover types. The results show an overall reasonably good agreement between the instantaneously fused LEs and measured LEs, with a slight underestimation of 13.1 W/m 2…”

Section: Validation Of Et Data Fused By the Sadfaet Model Using Grounsupporting

confidence: 55%

“…For each fine-resolution pixel at t m and t n , record T * and the LST retrieved from remote sensing data (T R ). (2). For a given pixel, if the remotely sensed T R at a fine resolution at t m is equal to or greater than T * and T R at t n is equal to or greater than T * as well, this pixel is considered to fall into CLASS 1, where no water is available to be evaporated for the soil component and the vegetation component transpires with a certain degree of soil water stress between t m and t n .…”

Section: Selection Of Similar Neighboring Pixelsmentioning

confidence: 99%

“…To overcome this limitation, previous studies have proposed several methods, mainly including traditional downscaling methods and data fusion methods [2]. Downscaling methods comprise a scaling process of converting coarse spatial resolution images into finer spatial resolution images; however, these methods cannot simultaneously enhance the temporal resolution of the sensor [3] and have rarely been used in recent years [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping

Wang

Tang

et al. 2019

Remote Sensing

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Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites cannot generally provide ET data at both high spatial and temporal resolutions. Data fusion methods have been widely applied to estimate ET at a high spatio-temporal resolution. Nevertheless, most fusion methods applied to ET are initially used to integrate land surface reflectance, the spectral index and land surface temperature, and few studies completely consider the influencing factor of ET. To overcome this limitation, this paper presents an improved ET fusion method, namely, the spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping (SADFAET), by introducing critical surface temperature (the corresponding temperature to decide soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET, which is estimated from remote sensing data) and modifying the spectral similarity (the differences in spectral characteristics of different spatial resolution images) for the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We fused daily Moderate Resolution Imaging Spectroradiometer (MODIS) and periodic Landsat 8 ET data in the SADFAET for the experimental area downstream of the Heihe River basin from April to October 2015. The validation results, based on ground-based ET measurements, indicated that the SADFAET could successfully fuse MODIS and Landsat 8 ET data (mean percent error: −5%), with a root mean square error of 45.7 W/m2, whereas the ESTARFM performed slightly worse, with a root mean square error of 50.6 W/m2. The more physically explainable SADFAET could be a better alternative to the ESTARFM for producing ET at a high spatio-temporal resolution.

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“…Alternatively, soil moisture downscaling approaches, as explained in Wang et al (2016) and Jiang and Weng (2017), could be employed to obtain finer resolution soil moisture products for incorporating into SEB models. Recently proposed downscaled land surface temperature (LST) methods, particularly for the purpose of ET mapping (Ke et al, 2017;Mahour et al, 2017), could produce reliable ET maps by capturing temporal variation in soil moisture for days when Landsat images are not available.…”

Section: 4comparison Of Seasonal Sums Of Et Ec Et 0 and Predicmentioning

confidence: 99%

Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum

Wagle

Bhattarai

Gowda

et al. 2017

ISPRS Journal of Photogrammetry and Remote Sensing

111

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A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration

Cited by 24 publications

References 30 publications

Estimation of Crop Evapotranspiration Using Satellite Remote Sensing-Based Vegetation Index

Estimation of Crop Evapotranspiration Using Satellite Remote Sensing-Based Vegetation Index

An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping

Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum

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