Literature DB >> 29539596

Capability of crop water content for revealing variability of winter wheat grain yield and soil moisture under limited irrigation.

Chao Zhang1, Jiangui Liu2, Jiali Shang2, Huanjie Cai3.   

Abstract

Winter wheat (Triticum aestivum L.) is a major crop in the Guanzhong Plain, China. Understanding its water status is important for irrigation planning. A few crop water indicators, such as the leaf equivalent water thickness (EWT: g cm-2), leaf water content (LWC: %) and canopy water content (CWC: kg m-2), have been estimated using remote sensing techniques for a wide range of crops, yet their suitability and utility for revealing winter wheat growth and soil moisture status have not been well studied. To bridge this knowledge gap, field-scale irrigation experiments were conducted over two consecutive years (2014 and 2015) to investigate relationships of crop water content with soil moisture and grain yield, and to assess the performance of four spectral process methods for retrieving these three crop water indicators. The result revealed that the water indicators were more sensitive to soil moisture variation before the jointing stage. All three water indicators were significantly correlated with soil moisture during the reviving stage, and the correlations were stronger for leaf water indicators than that of the canopy water indicator at the jointing stage. No correlation was observed after the heading stage. All three water indicators showed good capabilities of revealing grain yield variability in jointing stage, with R2 up to 0.89. CWC had a consistent relationship with grain yield over different growing seasons, but the performances of EWT and LWC were growing-season specific. The partial least squares regression was the most accurate method for estimating LWC (R2=0.72; RMSE=3.6%) and comparable capability for EWT and CWC. Finally, the work highlights the usefulness of crop water indicators to assess crop growth, productivity, and soil water status and demonstrates the potential of various spectral processing methods for retrieving crop water contents from canopy reflectance spectrums.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Crop water content; Machine learning; Partial least squares regression; Soil moisture; Triticum aestivum L; Water deficit

Mesh:

Substances:

Year:  2018        PMID: 29539596     DOI: 10.1016/j.scitotenv.2018.03.004

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


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  3 in total

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