Thermal-Infrared Technology for Local and Regional Scale Irrigation Analyses in Horticultural Systems

Bastiaanssen, W.G.M.; Pelgrum, H.; Soppe, R.W.O.; Allen, Richard G.; Thoreson, Bryan; Teixeira, A. H. de C.

doi:10.17660/actahortic.2008.792.2

Cited by 26 publications

(14 citation statements)

References 36 publications

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“…Nevertheless, the adopted computation of surface temperature using a three source condition helps overcoming the limitation of a onesource model, as discussed by Pôças et al (2014). The results obtained in the current study agree with those reported by Bastiaanssen et al (2008) for applications of the one-source model SEBAL to several tree crops with mean biases ranging from 2.5% to 24.6%. The comparison of the results derived from METRIC and SIMDualKc for the set of image dates considered showed that ET METRIC and ET sim follow a similar pattern (Fig.…”

Section: Et Derived From Metric and Satellite Datasupporting

confidence: 88%

Evapotranspiration and crop coefficients for a super intensive olive orchard. An application of SIMDualKc and METRIC models using ground and satellite observations

Paço

Pôças

Cunha

et al. 2014

Journal of Hydrology

104

101

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The estimation of crop evapotranspiration (ETc) from the reference evapotranspiration (ETo) and a standard crop coefficient (Kc) in olive orchards requires that the latter be adjusted to planting density and height. The use of the dual Kc approach may be the best solution because the basal crop coefficient Kcb represents plant transpiration and the evaporation coefficient reproduces the soil coverage conditions and the frequency of wettings. To support related computations for a super intensive olive orchard, the model SIMDualKc was adopted because it uses the dual Kc approach. In alternative, to consider the physical characteristics of the vegetation, the satellite--based surface energy balance model METRICTM --Mapping EvapoTranspiration at high Resolution using Internalized Calibration --was used to estimate ETc and to derive crop coefficients. Both approaches were compared in this study. SIMDualKc model was calibrated and validated using sap--flow measurements of the transpiration for 2011 and 2012. In addition, eddy covariance estimation of ETc was also used. In the current study, METRICTM was applied to Landsat images from 2011 and 2012. Adaptations for incomplete cover woody crops were required to parameterize METRIC. It was observed that ETc obtained from both approaches was similar and that crop coefficients derived from both models showed similar patterns throughout the year. Although the two models use distinct approaches, their results are comparable and they are complementary in spatial and temporal scales.

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Section: Et Derived From Metric and Satellite Datasupporting

confidence: 88%

Evapotranspiration and crop coefficients for a super intensive olive orchard. An application of SIMDualKc and METRIC models using ground and satellite observations

Paço

Pôças

Cunha

et al. 2014

Journal of Hydrology

104

101

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“…The mean bias of 20% for the whole set of images integrates the effects of changes in canopy conditions in 2012 and would certainly be lower if these changes have not occurred, since in 2011 it was 12.6%. Nevertheless, it is within the range of values reported by Bastiaanssen et al (2008) relative to the onesource model SEBAL applied to tree crops in various countries. That mean bias is also within the range of values presented by Allen, Tasumi, Morse et al (2007) for the application of METRIC to several annual crops.…”

Section: Doy (Year)supporting

confidence: 68%

Satellite-based evapotranspiration of a super-intensive olive orchard: Application of METRIC algorithms

Pôças

Paço

Cunha

et al. 2014

Biosystems Engineering

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Leaf area indexMomentum roughness length Remote sensing Surface energy balance Surface temperature METRIC™ is a satellite-based surface energy balance model aimed at estimating and mapping crop evapotranspiration (ET). It has been applied to a large range of vegetation types, mostly annual crops. When applied to anisotropic woody canopies, such as olive orchards, extensions are required to algorithms for estimating the leaf area index (LAI), surface temperature, and momentum roughness length (Z om ). The computation of the radiometric surface temperature needs to consider a three-source condition, thus differentiating the temperature of the canopy (T c ), of the shaded ground surface (T shadow ), and of the sunlit ground surface (T sunlit ). The estimation of the Z om for tall and incomplete cover is based upon the LAI and crop height using the Perrier equation. The LAI, Z om , and temperature derived from METRIC after these adjustments were tested against field collected data with good results. The application of METRIC to a two year set of Landsat images to estimate ET of a super-intensive olive orchard in Southern Portugal produced good ET estimates that compared well with ground-based ET. The analysis of METRIC performance showed a quantitative improvement of ET estimates when applying the three-source condition for temperature estimation, as well as the Z om computation with the Perrier equation. Results show that METRIC can be used operationally to estimate and mapping ET of super-intensive olive orchards aiming at improving irrigation water use and management. ScienceDirect journal homepa ge: www .e lsev ie r.com/locate/issn/153 75110 b i o s y s t e m s e n g i n e e r i n g x x x ( 2 0 1 4 ) 1 e1 3Please cite this article in press as: Pôç as, I., et al., Satellite-based evapotranspiration of a super-intensive olive orchard: Application of METRIC algorithms, Biosystems Engineering (2014), http://dx.

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“…This estimate was recently confirmed by independent researchers from Nebraska (Singh et al 2008). Four years of ET field measurements over irrigated fruit crops in Brazil confirmed this accuracy with SEBAL to be attainable (Texeira 2008). A compilation of earlier SEBAL research results on grapes in Spain, Turkey and Brazil showed that the model performance is very consistent between rainfed rosins and intensely irrigated table grapes .…”

Section: Remote Sensing In Combination With Sebalmentioning

confidence: 53%

Combining remote sensing and economic analysis to support decisions that affect water productivity

et al. 2008

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In this paper, an innovative method-that combines a technical and socio-economic analysis-is presented to assess the implications of policy decisions on water productivity. In the technical part, the variability in crop water productivity (CWP) is analyzed on the basis of actual water consumption and associated biomass production using the Surface Energy Balance Algorithm for Land (SEBAL). This generates input for the socio-economic analysis, which aims to quantify the foregone economic water productivity (EWP) of policy decisions to allocate water in a social optimal way. The basis for arguments to transfer water between categories of users will be strengthened and be more objective when the productivity in existing and alternative uses is known. The usefulness of such an approach is shown in the South African part of the Inkomati Basin, where according to the Water Act, water has to be reserved for basic human needs and to protect aquatic ecosystems. The opportunity costs, in terms of foregone EWP, of decisions to divert water away from agriculture are assessed. The results show that diverting water away from crops with a low CWP is not always the most cost-effective way in terms of foregone EWP.

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Thermal-Infrared Technology for Local and Regional Scale Irrigation Analyses in Horticultural Systems

Cited by 26 publications

References 36 publications

Evapotranspiration and crop coefficients for a super intensive olive orchard. An application of SIMDualKc and METRIC models using ground and satellite observations

Evapotranspiration and crop coefficients for a super intensive olive orchard. An application of SIMDualKc and METRIC models using ground and satellite observations

Satellite-based evapotranspiration of a super-intensive olive orchard: Application of METRIC algorithms

Combining remote sensing and economic analysis to support decisions that affect water productivity

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