Using Remote Sensing to Estimate Evapotranspiration of Irrigated Crops Under Flood and Sprinkler Irrigation Systems

Abstract

The objectives of this study are to: (i) estimate the actual evapotranspiration (ETa) of the different irrigated crops in the South Platte River Basin, Colorado, USA; (ii) compare the consumption use for the irrigated areas for 2001 and 2010; (iii) investigate the impact of irrigation system (flood/sprinkler) on the estimated ETa. The ReSET model, a surface energy balance remote sensing based model, is used in this study to estimate the ETa of the South Platte River Basin irrigated crops for 2001 and 2010. Landsat 5/7 satellite images, weather data, shape files of irrigated crops, and a digital elevation model (DEM) were acquired and processed for the study area. The results of this study show that the estimated ETa using the ReSET model showed a reduction of 7.1% from 2.51 × 109 m3 in 2001 to 2.33 × 109 m3 in 2010, mainly due to the reduction of the irrigated area from 2001 to 2010 by 6.6%. The estimated total ETa of crops irrigated with flood irrigation systems decreased from 66.1 to 54.6%, while the estimated total ETa of crops irrigated with sprinkler systems increased from 33.9 to 45.4%. For 2001, the average seasonal ETa of all crops for flood irrigation and sprinkler irrigation, respectively, were 665 and 712 mm, with a 7% greater ETa for sprinkler systems. The ReSET model ETa estimates have been validated in a previous study of lysimeter measurements. However, in this study and due to the unavailability of a lysimeter, the model ETa estimates were verified with the reference evapotranspiration (ETr) of the weather stations in the study area. The results of the t‐test shows a p‐value = 0.124, greater than 0.05, which indicates that the means of the ReSET ETa estimates and the ETr are not significantly different. Additionally, the results of the F‐test shows a p‐value = 0.674, greater than 0.05, which indicates that the two variances of the ReSET ETa estimates and the ETr are homogeneous. Copyright © 2015 John Wiley & Sons, Ltd.

Crop Data Retrieval Using Earth Observation Data to Support Agricultural Water Management

“…EO methodologies have been widely used for crop evapotranspiration (ETc) and IWR estimation because of the reflective properties of vegetation that allow one to estimate crop biophysical parameters and plant processes such as transpiration (Neale et al, 1989;Calera Belmonte et al, 2005;D'Urso et al, 2010;Paço et al, 2014;Vuolo et al, 2015;Ferreira et al, 2016;Oliveira et al, 2016). ETc can be estimated from EO data using empirical methods based on the use of vegetation indices (VIs) to estimate crop coefficients (Neale et al, 1989;Calera Belmonte et al, 2005;D'Urso et al, 2010) or using physics-based methods based on the surface energy balance to estimate the latent heat flow based on EO thermal images (Bastiaanssen et al, 1998;Allen et al, 2007;Eldeiry et al, 2016). The empirical methods have been more widely used because of their simplicity, with the most common VI, the normalized difference vegetation index (NDVI), used to estimate several crop parameters, such as fraction of ground cover (fc), single crop coefficient (Kc), and basal Engenharia Agrícola, Jaboticabal, v.39, n.3, p.380-390, may/jun.…”

Is the crop evapotranspiration rate a good surrogate for the recommended irrigation rate?

“…Three general control strategies are (Romero et al, 2012): (1) open loop (no control) in which no measurements are used to modify the inputs and the decisions are taken a priori based on heuristics, expert knowledge or a model of the system; (2) feed forward control (a particular open‐loop case) in which known or estimated values of future disturbances are used to compensate for their effects in advance and (3) feedback control using feedback information on the consequences of previous actions from relevant sensors to calculate the next action. In the context of determining the daily irrigation rate (irrigation dose/application depth), these can be as follows: (1) calendar irrigation scheduling according to given, previously determined, irrigation tables (/charts) (Fessehazion et al, 2014; Hill & Allen, 1996); (2) irrigation according to given, previously determined, crop coefficients and real‐time meteorological measurements evaluating the potential evapotranspiration (Allen et al, 1998, Pereira et al, 2021a, 2021b; FAO56 approach) and (3) irrigation according to proxy and remote sensing of plant and soil water status indicators (Campbell et al, 1982; Eldeiry et al, 2016; Goldhamer & Fereres, 2001; Jimenez et al, 2020; Jones, 2004; Möller et al, 2007; Naor, 2006). Over the years, hybrids of the main three strategies have also been proposed (Kassing et al, 2020; McCarthy et al, 2013).…”

Deficit drip irrigation based on crop evapotranspiration and precipitation forecast improves water‐ use efficiency and grain yield of summer maize

“…Evapotranspiration is the largest user of crop water consumption 23 . It is commonly estimated using reference evapotranspiration (ET 0 ) multiplied by the corresponding crop coefficient (K c ) 8,24,25 .…”

“…Evapotranspiration is the largest user of crop water consumption. 23 It is commonly estimated using reference evapotranspiration (ET 0 ) multiplied by the corresponding crop coefficient (K c ). 8,24,25 In previous studies, the irrigation amount was determined by crop evapotranspiration 11,26 or field capacity subtracting soil moisture, 27,28 and irrigation time is generally the fixed time interval (e.g., 10-day or 12-day) 11,26,29 or the soil moisture reaches a certain lower limit.…”