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生物质水动力对宇宙射线中子传感器观测的影响:内布拉斯加州玉米-大豆轮作的长期分析。

Effect of Biomass Water Dynamics in Cosmic-Ray Neutron Sensor Observations: A Long-Term Analysis of Maize-Soybean Rotation in Nebraska.

机构信息

School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68503, USA.

出版信息

Sensors (Basel). 2024 Jun 24;24(13):4094. doi: 10.3390/s24134094.

Abstract

Precise soil water content (SWC) measurement is crucial for effective water resource management. This study utilizes the Cosmic-Ray Neutron Sensor (CRNS) for area-averaged SWC measurements, emphasizing the need to consider all hydrogen sources, including time-variable plant biomass and water content. Near Mead, Nebraska, three field sites (CSP1, CSP2, and CSP3) growing a maize-soybean rotation were monitored for 5 (CSP1 and CSP2) and 13 (CSP3) years. Data collection included destructive biomass water equivalent () biweekly sampling, epithermal neutron counts, atmospheric meteorological variables, and point-scale SWC from a sparse time domain reflectometry (TDR) network (four locations and five depths). In 2023, dense gravimetric SWC surveys were collected eight (CSP1 and CSP2) and nine (CSP3) times over the growing season (April to October). The parameter exhibited a linear relationship with , suggesting that a straightforward vegetation correction factor may be suitable (). Results from the 2023 gravimetric surveys and long-term TDR data indicated a neutron count rate reduction of about 1% for every 1 kg m (or mm of water) increase in . This reduction factor aligns with existing shorter-term row crop studies but nearly doubles the value previously reported for forests. This long-term study contributes insights into the vegetation correction factor for CRNS, helping resolve a long-standing issue within the CRNS community.

摘要

精确的土壤水分含量 (SWC) 测量对于有效的水资源管理至关重要。本研究利用宇宙射线中子传感器 (CRNS) 进行区域平均 SWC 测量,强调需要考虑所有的氢源,包括随时间变化的植物生物量和含水量。在内布拉斯加州米德附近,三个田间站点 (CSP1、CSP2 和 CSP3) 种植了玉米-大豆轮作,监测时间分别为 5 年 (CSP1 和 CSP2) 和 13 年 (CSP3)。数据采集包括破坏性生物量水当量 () 每隔两周采样一次、超热中子计数、大气气象变量以及稀疏时域反射仪 (TDR) 网络的点尺度 SWC (四个位置和五个深度)。2023 年,在生长季节 (4 月至 10 月) 进行了八次 (CSP1 和 CSP2) 和九次 (CSP3) 密集的重量法 SWC 调查。参数与 呈线性关系,表明简单的植被校正因子可能是合适的 ( )。2023 年重力调查和长期 TDR 数据的结果表明,中子计数率每增加 1kg m (或 mm 水),就会降低约 1%。这个减少因子与现有的短期作物研究一致,但几乎是以前报道的森林的两倍。这项长期研究为 CRNS 的植被校正因子提供了深入的了解,有助于解决 CRNS 社区内长期存在的问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5162/11244298/a8758d10908c/sensors-24-04094-g001.jpg

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