Suppr超能文献

基于遥感和地面数据耦合的灌溉效率评估:以俄罗斯萨拉托夫地区苜蓿地面喷灌为例。

Assessment of Irrigation Efficiency by Coupling Remote Sensing and Ground-Based Data: Case Study of Sprinkler Irrigation of Alfalfa in the Saratovskoye Zavolgie Region of Russia.

机构信息

Department of Applied Informatics, Institute of Economics and Management in Agribusiness, Russian State Agrarian University-Moscow Timiryazev Agricultural Academy, Timiryazevskaya Str., 49, 127550 Moscow, Russia.

出版信息

Sensors (Basel). 2023 Feb 26;23(5):2601. doi: 10.3390/s23052601.

Abstract

Nowadays, the leading role of data from sensors to monitor crop irrigation practices is indisputable. The combination of ground and space monitoring data and agrohydrological modeling made it possible to evaluate the effectiveness of crop irrigation. This paper presents some additions to recently published results of field study at the territory of the Privolzhskaya irrigation system located on the left bank of the Volga in the Russian Federation, during the growing season of 2012. Data were obtained for 19 crops of irrigated alfalfa during the second year of their growing period. Irrigation water applications to these crops was carried out by the center pivot sprinklers. The actual crop evapotranspiration and its components being derived with the SEBAL model from MODIS satellite images data. As a result, a time series of daily values of evapotranspiration and transpiration were obtained for the area occupied by each of these crops. To assess the effectiveness of irrigation of alfalfa crops, six indicators were used based on the use of data on yield, irrigation depth, actual evapotranspiration, transpiration and basal evaporation deficit. The series of indicators estimating irrigation effectiveness were analyzed and ranked. The obtained rank values were used to analyze the similarity and non-similarity of indicators of irrigation effectiveness of alfalfa crops. As a result of this analysis, the opportunity to assess irrigation effectiveness with the help of data from ground and space-based sensors was proved.

摘要

如今,传感器数据在监测作物灌溉实践中的主导作用是不可争议的。地面和空间监测数据与农业水文学模型的结合使得评估作物灌溉效果成为可能。本文对在俄罗斯联邦伏尔加河左岸的普里沃尔日斯克灌溉系统的土地上进行的田间研究的最近发表的结果进行了一些补充,该研究在 2012 年的生长季节进行。为灌溉紫花苜蓿的 19 种作物获得了数据,这些作物处于其生长周期的第二年。这些作物的灌溉用水是由中心枢轴式喷灌机进行灌溉的。通过从 MODIS 卫星图像数据中使用 SEBAL 模型,得出了实际作物蒸散量及其组成部分的数据。结果,为这些作物中的每一种作物的占地面积获得了每日蒸散量和蒸腾量的时间序列。为了评估苜蓿作物灌溉的效果,使用了基于产量、灌溉深度、实际蒸散量、蒸腾量和基态蒸发亏缺数据的六个指标来评估。对估计灌溉效果的指标系列进行了分析和排序。所获得的等级值用于分析苜蓿作物灌溉效果指标的相似性和非相似性。通过这种分析,证明了可以借助地面和基于空间的传感器数据来评估灌溉效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d8b/10007418/6eacf37a52cf/sensors-23-02601-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验