Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance model

Huihui Zhang, Ming Han, José L. Chávez, Yubin Lan


Abstract: In this study, an approach that integrates airborne imagery data as inputs was used to improve the estimation of soil water deficit (SWD) for maize and sunflower grown under full and deficit irrigation treatments. The proposed model was applied to optimize the maximum total available soil water (TAWr) by minimizing the difference between a water stress coefficient ks and crop water stress index (1-CWSI). The optimal value of maximum TAWr was then used to calibrate a soil water balance model which in turn updated the estimation of soil water deficit. The estimates of SWD in the soil profile of both irrigated maize and sunflower fields were evaluated with the crop root zone SWD derived from neutron probe measurements and the FAO-56 SWD procedure. The results indicated a good agreement between the estimated SWD from the proposed approach and measured SWD for both maize and sunflower. The statistical analyses indicated that the maximum TAWr estimated from CWSI significantly improved the estimates of SWD, which reduced the mean absolute error (MAE) and root mean square error (RMSE) by 40% and 44% for maize and 22% for sunflower, compared with the FAO-56 model. The proposed procedure works better for crops under deficit irrigation condition. With the availability of higher spatial and temporal resolution airborne imagery during the growing season, the optimization procedure can be further improved.
Keywords: soil water deficit, soil water balance model, airborne imagery, total available water, CWSI, deficit irrigation
DOI: 10.3965/j.ijabe.20171003.3081

Citation: Zhang H H, Han M, Chávez J L, Lan Y B. Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance model. Int J Agric & Biol Eng, 2017; 10(3): 37–46.


soil water deficit, soil water balance model, airborne imagery, total available water, CWSI, deficit irrigation


Allen R G, Pereira L S, Raes D, Smith M. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome. 1998; 300(9).

Annandale J G, Campbell G S, Olivier F C, Jovanovic N Z. Predicting crop water uptake under full and deficit irrigation: An example using pea (Pisum sativum L. cv. Puget). Irrigation Science, 2000; 19(2): 65–72.

Campos I, González-Piqueras J, Carrara A, Villodre J, Calera A. Estimation of total available water in the soil layer by integrating actual evapotranspiration data in a remote sensing-driven soil water balance. Journal of Hydrology, 2016; 534: 427–39.

Steduto P, Hsiao T C, Raes D, Fereres E. AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 2009; 101(3): 426–37.

Hsiao T C, Heng L, Steduto P, Rojas-Lara B, Raes D, Fereres E. AquaCrop—the FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agronomy Journal, 2009; 101(3): 448–59.

Sánchez N, Martínez-Fernández J, Calera A, Torres E, Pérez-Gutiérrez C. Combining remote sensing and in situ soil moisture data for the application and validation of a distributed water balance model (HIDROMORE). Agricultural Water Management, 2010; 98(1): 69–78.

Neale C M U, Geli H M E, Kustas W P, Alfieri J G, Gowda P H, Evett S R, et al. Soil water content estimation using a remote sensing based hybrid evapotranspiration modeling approach. Advances in Water Resources, 2012; 50: 152–61.

Campos I, Balbontín C, González-Piqueras J, González-Dugo M P, Neale C M U, Calera A. Combining a water balance model with evapotranspiration measurements to estimate total available soil water in irrigated and rainfed vineyards. Agricultural Water Management, 2016; 165: 141–52.

Padhi J, Misra R K, Payero J O. Estimation of soil water deficit in an irrigated cotton field with infrared thermography. Field Crops Research, 2012; 126: 45–55.

Li B, Wang T L, Sun J. Crop water stress index for off-season greenhouse green peppers in Liaoning, China. Int J Agric & Biol Eng, 2014; 7(3): 28–35

Zia-Khan S, Du W Y, Spreer W, Spohrer K, He X K, Müller J. Assessing crop water stress of winter wheat by thermography under different irrigation regimes in North China Plain. Int J Agric & Biol Eng, 2012; 5(3): 24–34.

Zia-Khan S, Spohrer K, Du W Y, Spreer W, Romano G, He X K, et al. Monitoring physiological responses to water stress in two maize varieties by infrared thermography. Int J Agric & Biol Eng, 2011; 4(3): 7–15.

Idso S B, Jackson R D, Pinter P J, Reginato R J, Hatfield J L. Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 1981; 24: 45–55.

Jackson R D, Idso S B, Reginato R J, Pinter P J. Canopy temperature as a crop water stress indicator. Water Resources Research, 1981; 17(4): 1133–8.

DeJonge K C, Taghvaeian S, Trout T J, Comas L H. Comparison of canopy temperature-based water stress indices for maize. Agricultural Water Management, 2015; 156: 51–62.

Taghvaeian S, Comas L, DeJonge K C, Trout T J. Conventional and simplified canopy temperature indices predict water stress in sunflower. Agricultural Water Management, 2014; 144:69–80.

Allen R G, Pereira L S, Raes D, Smith M. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 1998; 300(9): D05109.

Comas L H, Becker S R, Cruz V M, Byrne P F, Dierig D A. Root traits contributing to plant productivity under drought. Frontiers in plant science, 2013; 4:442.

Colaizzi P D, Barnes E M, Clarke T R, Choi C Y, Waller P M. Estimating soil moisture under low frequency surface irrigation using crop water stress index. Journal of Irrigation and Drainage Engineering, 2003; 129(1): 27–35.

Jensen M E, Robb D C, Franzoy C E. Scheduling irrigations using climate-crop-soil data. Proceedings of the American Society of Civil Engineers, Journal of the Irrigation

and Drainage Division, 1970; 96(IRI): 25–38.

Trout T J, DeJonge K C. Water Productivity of Maize in the U.S. High Plains. Irrigation Science, 2017; 1–16.

Mersmann O, Trautmann H, Steuer D, Bischl B, Deb K. Package “mco”: Multiple Criteria Optimization Algorithms and Related Functions. URL: 2014.

Neale C M U, Crowther B G. An airborne multispectral video/radiometer remote sensing system: Development and calibration. Remote Sensing of Environment, 1994; 49(3): 187–94.

Cai B, Neale C M U. A method for constructing three dimensional models from airborne imagery. 17th Biennial Workshop on Color Photography and Videography in Resource Assessment, Reno, NV 1999.

Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote sensing of environment, 1996; 55(2): 95–107.

Anderson M, Neale C, Li F, Norman J, Kustas W, Jayanthi H, et al. Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote sensing of environment, 2004; 92(4): 447–64.

Kustas W P, Norman J M. Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agricultural and Forest Meteorology, 1999; 94(1): 13–29.

Nash J E, Sutcliffe J V. River flow forecasting through conceptual models part I—A discussion of principles. Journal of hydrology, 1970; 10(3): 282–90.

Willmott C J. Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 1982; 63(11): 1309–13.

Hunsaker D J, Pinter P J, Barnes E M, Kimball B A. Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index. Irrigation Science, 2003; 22(2): 95–104.

Hunsaker D J, Pinter P J, Kimball B A. Wheat basal crop coefficients determined by normalized difference vegetation index. Irrigation Science, 2005; 24(1): 1–14.

Allen R G, Pereira L S. Estimating crop coefficients from fraction of ground cover and height. Irrigation Science, 2009; 28(1): 17–34.

Trout T J, Johnson L F, Gartung J. Remote sensing of canopy cover in horticultural crops. HortScience, 2008; 43(2): 333–7.

Bryla D R, Trout T J, Ayars JE. Weighing lysimeters for developing crop coefficients and efficient irrigation practices for vegetable crops. HortScience, 2010; 45(11): 1597–604.

Neale C M, Bausch W C, Heermann D F. Development of reflectance-based crop coefficients for corn. Transactions of the ASAE, 1990; 32(6): 1891–900.

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