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Remote Sens. 2018, 10(9), 1452; https://doi.org/10.3390/rs10091452

Mapping Maize Evapotranspiration at Field Scale Using SEBAL: A Comparison with the FAO Method and Soil-Plant Model Simulations

1
Mario Gulich Institute, National Agency of Spatial Activities (CONAE), X5000 Córdoba, Argentina
2
Institute of Environmental Engineering, ETH Zurich, 8093 Zurich, Switzerland
3
Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, 35020 Legnaro PD, Italy
4
Harvesting, Silicon Valley, CA 94027, USA
5
INTA EEA Manfredi, 5988 Córdoba, Argentina
6
Department of Civil, Environmental and Architectural Engineering, University of Padova, 35131 Padova, Italy
*
Author to whom correspondence should be addressed.
Received: 8 July 2018 / Revised: 3 September 2018 / Accepted: 7 September 2018 / Published: 11 September 2018
(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))
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Abstract

The surface energy balance algorithm for land (SEBAL) has been successfully applied to estimate evapotranspiration (ET) and yield at different spatial scales. However, ET and yield patterns have never been investigated under highly heterogeneous conditions. We applied SEBAL in a salt-affected and water-stressed maize field located at the margin of the Venice Lagoon, Italy, using Landsat images. SEBAL results were compared with estimates of evapotranspiration by the Food and Agriculture Organization (FAO) method (ETc) and three-dimensional soil-plant simulations. The biomass production routine in SEBAL was then tested using spatially distributed crop yield measurements and the outcomes of a soil-plant numerical model. The results show good agreement between SEBAL evapotranspiration and ETc. Instantaneous ET simulated by SEBAL is also consistent with the soil-plant model results (R2 = 0.7047 for 2011 and R2 = 0.6689 for 2012). Conversely, yield predictions (6.4 t/ha in 2011 and 3.47 t/ha in 2012) are in good agreement with observations (8.64 t/ha and 3.86 t/ha, respectively) only in 2012 and the comparison with soil-plant simulations (8.69 t/ha and 5.49 t/ha) is poor. In general, SEBAL underestimates land productivity in contrast to the soil-plant model that overestimates yield in dry years. SEBAL provides accurate predictions under stress conditions due to the fact that it does not require knowledge of the soil/root characteristics. View Full-Text
Keywords: surface energy balance algorithm for land (SEBAL); evapotranspiration; yield; remote sensing; heterogeneous conditions surface energy balance algorithm for land (SEBAL); evapotranspiration; yield; remote sensing; heterogeneous conditions
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Grosso, C.; Manoli, G.; Martello, M.; Chemin, Y.H.; Pons, D.H.; Teatini, P.; Piccoli, I.; Morari, F. Mapping Maize Evapotranspiration at Field Scale Using SEBAL: A Comparison with the FAO Method and Soil-Plant Model Simulations. Remote Sens. 2018, 10, 1452.

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