TRANSPIRATION AND CROP COEFFICIENTS FOR IRRIGATED OLIVES WITH PREDICTIVE EQUATIONS DERIVED FROM MODIS REMOTELY SENSED VEGETATION INDICES AND GROUND-BASED TEMPERATURE DATA

Abstract

Olive transpiration T can be predicted by combining MODIS remotely sensed vegetation indices (EVI* and NDVI*), tree ground-based transpiration derived from sap flow measurements and maximum daily air temperature ta. The feasibility of developing a single predictive equation of olive orchard transpiration through the relationship between sap flow based transpiration (T) and remotely sensed Enhanced and Normalized Difference Vegetation Indexes (EVI and NDVI) of an irrigated orchard in southern Portugal was tested. A correlation matrix relating T as the dependent variable to VIs and micrometeorological data as independent variables was constructed. Regression equations were then developed from the micrometeorological variable that most closely correlated with ground transpiration T data, and finally predictive multivariate equations were derived from EVI*- ta and NDVI*- ta, being the maximum air temperature ta the ground-measured micrometeorological variable found most closely correlated with field T. Such predictive responses were validated with olive sap flow ground based transpiration data, being the measured and predicted T based on EVI*-Ta within 11% of the 1:1 line. The robustness of the method is attributed to spectral vegetation indices being able to describe well vegetation amount and condition and strongly correlate with micrometeorological variables that drive olive transpiration. The predictive responses were used here to calculate and propose crop coefficients that can be made routinely operational and available to guide irrigation. The modeling study also shows that the method can offer a reliable way for verification and scaling up of sap flow measurements to wider olive growing areas, and for providing data for other applications.