Suppr超能文献

作物水分含量对揭示有限灌溉下冬小麦产量和土壤水分变异性的能力。

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

机构信息

Key Laboratory of Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest Agriculture and Forestry University, Yangling, China; Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada.

Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada.

出版信息

Sci Total Environ. 2018 Aug 1;631-632:677-687. doi: 10.1016/j.scitotenv.2018.03.004. Epub 2018 Mar 16.

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), leaf water content (LWC: %) and canopy water content (CWC: kg m), 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 R 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 (R=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.

摘要

在中国关中平原,冬小麦(Triticum aestivum L.)是主要作物。了解其水分状况对于灌溉规划非常重要。一些作物水分指标,如叶等效水厚度(EWT:g cm)、叶片水分含量(LWC:%)和冠层水分含量(CWC:kg m),已经通过遥感技术在广泛的作物上进行了估算,但是它们对于揭示冬小麦生长和土壤水分状况的适用性和实用性尚未得到很好的研究。为了弥补这一知识空白,在连续两年(2014 年和 2015 年)进行了田间尺度的灌溉实验,以研究作物水分含量与土壤水分和籽粒产量的关系,并评估四种光谱处理方法用于反演这三种作物水分指标的性能。结果表明,在拔节期之前,水分指标对土壤水分变化更敏感。在返青期,所有三种水分指标与土壤水分均呈显著相关,在拔节期,叶片水分指标与冠层水分指标的相关性强于冠层水分指标。在抽穗期后没有相关性。在拔节期,所有三种水分指标均能很好地反映籽粒产量的变化,相关系数高达 0.89。CWC 在不同生长季节与籽粒产量均呈一致关系,但 EWT 和 LWC 的性能具有生长季节特异性。偏最小二乘回归是估算 LWC 最准确的方法(R=0.72;RMSE=3.6%),对 EWT 和 CWC 也具有相当的能力。最后,这项工作强调了作物水分指标在评估作物生长、生产力和土壤水分状况方面的有用性,并展示了各种光谱处理方法从冠层反射光谱中反演作物水分含量的潜力。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验