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Proceedings Paper

A synergistic approach using optical and SAR data to estimate crop's irrigation requirements
Author(s): João Rolim; Ana Navarro Ferreira; Cátia Saraiva; João Catalão
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Paper Abstract

A study conducted in the scope of the Alcantara initiative in Angola shown that optical and SAR images allows the estimation of crop's irrigation requirements (CIR) based on a soil water balance model (IrrigRotation). The methodology was applied to east central Portugal, to evaluate its transferability in cases of different climatic conditions and crop types. SPOT-5 Take-5 and Sentinel-1A data from April to September 2015 are used to generate NDVI and backscattering maize crop time series. Both time series are then correlated and a linear regression equation is computed for some maize parcels identified in the test area. Next, basal crop coefficients (Kcb) are determined empirically from the Kcb-NDVI relationships applied within the PLEIADeS project and also from the Kcb-SAR relationships retrieved from the linear fit of both EO data for other maize parcels. These Kcb allow to overcome a major drawback related to the use of the FAO tabulated Kcb, only available for the initial, mid and late season of a certain crop type. More frequent Kcb values also allow a better identification of the crop's phenological stages lengths. CIR estimated from EO data are comparable to the ones obtained with tabulated FAO 56 Kcb values for crops produced under standard conditions, while for crops produced in suboptimal conditions, EO data allow to improve the estimation of the CIR. Although CIR results are promising, further research is required in order to improve the Kcb initial and Kcb end values to avoid the overestimation of the CIR.

Paper Details

Date Published: 25 October 2016
PDF: 16 pages
Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 99980W (25 October 2016); doi: 10.1117/12.2241054
Show Author Affiliations
João Rolim, Univ. de Lisboa (Portugal)
Ana Navarro Ferreira, Univ. de Lisboa (Portugal)
Cátia Saraiva, Univ. de Lisboa (Portugal)
João Catalão, Univ. de Lisboa (Portugal)


Published in SPIE Proceedings Vol. 9998:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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