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

Anna Pelosi,Giovanni Battista Chirico,Guido D’Urso,Paolo Villani,Salvatore Falanga Bolognesi

doi:10.3390/s20061740

Anna Pelosi, Giovanni Battista Chirico + Show 3 more

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https://doi.org/10.3390/s20061740

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Abstract

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 (ETc) 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 ETc, based on crop parameters retrieved from multispectral images, data from ground weather sensors, and air temperature forecasts. Crop multispectral images are freely provided by recent satellite missions, with high spatial and temporal resolutions. Meteorological services broadcast air temperature forecasts with lead times of several days, at no subscription costs, and with high accuracy. The performance of the proposed methodology was applied at 18 sites of the Campania region in Italy, by exploiting the data of intensive field campaigns in the years 2014–2015. ETc measurements were forecast with a median bias of 0.2 mm, and a median root mean square error (RMSE) of 0.75 mm at the first day of forecast. At the 5th day of accumulated forecast, the median bias and RMSE become 1 mm and 2.75 mm, respectively. The forecast performances were proved to be as accurate and as precise as those provided with a complete set of forecasted weather variables.

Highlights

Optimal irrigation water management is one of the main challenges of precision agriculture, especially in open field crops, where farmers must deal with the uncertainty of both weather conditions and water availability, under climate change scenarios [1]
In many regions of the world, such as Mediterranean areas, where open field irrigation is practiced during dry seasons characterized by negligible rain contributions, irrigation water requirements are essentially driven by crop evapotranspiration
The root mean square error (RMSE) of the forecasted weather variables was computed to better understand the performance of the evapotranspiration predicted with the Hargreaves–Samani and Penman–Monteith equations

Summary

Introduction

Optimal irrigation water management is one of the main challenges of precision agriculture, especially in open field crops, where farmers must deal with the uncertainty of both weather conditions and water availability, under climate change scenarios [1]. 56 [2], can be estimated by models, such as the Penman–Monteith equation, that require data reflecting the crop canopy properties and ground surface weather conditions. The availability of these data at reasonable costs represents a prerequisite for the implementation of these models within operational advisory systems, that can help farmers in assessing the actual crop water requirements, and optimizing irrigation scheduling. Programs like Landsat 8 by NASA and Sentinel-2 by ESA, by offering free access to the satellite images, have prompted the development of agricultural advisory services based on the remote assessment of the crop canopy properties and evapotranspiration

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