Predicting Crop Evapotranspiration by Integrating Ground and Remote Sensors with Air Temperature Forecasts.

Water use efficiency in agriculture can be improved by implementing advisory systems that support on-farm irrigation scheduling, with reliable forecasts of the actual crop water requirements, where crop evapotranspiration (ET) is the main component. The development of such advisory systems is highly dependent upon the availability of timely updated crop canopy parameters and weather forecasts several days in advance, at low operational costs. This study presents a methodology for forecasting ET, based on crop parameters retrieved from multispectral images, data from ground weather sensors, and air temperature forecasts.

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users.

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution.

The response of ecosystem carbon fluxes to LAI and environmental drivers in a maize crop grown in two contrasting seasons.

The eddy correlation technique was used to investigate the influence of biophysical variables and crop phenological phases on the behaviour of ecosystem carbon fluxes of a maize crop, in two contrasting growing seasons. In 2009, the reduced water supply during the early growing stage limited leaf area expansion, thus negatively affecting canopy photosynthesis. The variability of gross primary production (GPP) and ecosystem respiration (R eco) was mainly explained by seasonal variation of leaf area index (LAI).