宇宙射线快中子法在农田土壤水分测量中的研究与应用

doi:10.11867/j.issn.1001-8166.2013.10.1136

地球科学进展 ›› 2013, Vol. 28 ›› Issue (10): 1136 -1143. doi: 10.11867/j.issn.1001-8166.2013.10.1136

焦其顺 ¹, 朱忠礼 ^1*, 刘绍民 ¹, 晋锐 ², 杜帆 ¹

1. 北京师范大学地理学与遥感科学学院遥感科学重点实验室, 北京100875; 2 .中国科学院寒区旱区环境与工程研究所, 甘肃兰州 730000

收稿日期:2013-04-07 出版日期:2013-10-10
通讯作者: 朱忠礼(1972-), 男, 河南商丘人, 讲师, 主要从事遥感水文方面的研究. E-mail: zhuzl@bnu.edu.cn
基金资助:
国家自然科学基金重点项目“黑河流域生态—水文过程综合遥感观测试验：水文气象要素与多尺度蒸散发观测”(编号：91125002)

Research and Application of Cosmic-ray Fast Neutron Method to Measure Soil Moisture in the Field

Jiao Qishun ¹, Zhu Zhongli ¹, Liu Shaomin ¹, Jin Rui ², Du Fan ¹

1. State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China; 2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China

Received:2013-04-07 Online:2013-10-10 Published:2013-10-10

全文 ( 6 ) 下载PDF ( 1061 )

区域地表土壤水分的获取是长期以来的研究难点。近年来, 利用宇宙射线快中子法测量区域地表土壤含水量的研究得到了长足发展。作为一种新型的中尺度土壤水分无损测量方法, 宇宙射线快中子法能够获得半径300 m左右源区内的土壤水分, 较大的测量源区使其填补了传统点观测和遥感大面积观测尺度之间的空白。利用宇宙射线土壤水分观测系统(COSMOS)、无线传感器网络对张掖绿洲农田下垫面的土壤水分进行观测。研究结果表明COSMOS观测数据很好地反映了农田尺度下土壤水分的变化趋势。COSMOS土壤水分主要受源区内灌溉和降水的影响, 源区内不同地区灌溉顺序的不同使观测的COSMOS土壤水分呈现明显的双峰变化趋势。利用COSMOS源区内无线传感器网络数据对观测结果进行了验证, 灌溉期受地面积水的影响, COSMOS观测结果偏高, 剔除灌溉期数据后两者一致性较好, 均方根误差(RMSE)由0.037 m³/m³降低到0.028 m³/m³。宇宙射线土壤水分观测系统能够有效测量高异质性状况下的农田区域土壤水分, 为遥感反演的土壤水分提供真正意义上的面状地面验证数

关键词: 农田尺度, 土壤含水量, 无线传感器网络, 黑河流域

It is very difficult to measure large region soil moisture. In recent years, measurement of surface soil moisture by the cosmicray fast neutron probe has gradually attracted more attention. By this passive, non-invasive and intermediate scale measurement, soil moisture at a horizontal scale of around 300 m can be observed, which makes this method available to fill the gap between little scale of traditional point measurement and large scale of remote sensing in the measurement of soil moisture. In this paper, Cosmic-ray Soil Moisture Observing System (COSMOS) and Wireless Sensor Network (WSN) were used to observe field soil moisture in Zhangye Oasis. The results of the COSMOS soil moisture well reflected the variation trend of soil moisture at the field scale. There were some regular changes with the cosmicray soil moisture during the irrigation period. Combined with irrigation data in cosmic-ray probe footprint, a bimodal change was showed in the cosmic-ray soil moisture figure during irrigation period. This was because that the order of irrigation of the three communities was different in cosmic-ray probe footprint. WSN data were used to validate this result and we found that root-mean-square error between cosmic-ray soil moisture and SoilNET average soil moisture was very large during irrigation periods because of the impact of cosmic-ray measurement theory. While root-mean-square error would decrease from 0.037 m³/m³ to 0.028 m³/m³ after we eliminated the data in irrigation period. So COSMOS can be used to measure field soil moisture under high heterogeneity condition and provide truly ground data for the validation of remote sensing

Key words: Field scale, Soil water content, Heihe River Basin., Wireless Sensor Network

中图分类号:

P343.8

[1]Deng Yingchun, Xu Yonghui. Introduction to the methods of soil moisture content measuring[J]. Journal of China Hydrology, 2007, 27(4): 20-24.[邓英春, 许永辉. 土壤水分测量方法研究综述 [J]. 水文, 2007, 27(4): 20-24.]
[2]Topp G C, Davis J L, Annan A P. Electromagnetic determination of soil water content:Measurements in coaxial transmission lines[J].Water Resources Research, 1980, 16 (3): 574-582.
[3]Robinson D A, Campbell C S, Hopmans J W, et al. Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review[J].Vadose Zone Journal, 2008, 7 (1): 358-389.
[4]Vinnikov K Y, Robock A, Qiu S, et al. Satellite remote sensing of soil moisture in Illinois, United States[J].Journal of Geophysical Research—Atmospheres, 1999, 104 (D4): 4 145-4 168.
[5]Famiglietti J S, Devereaux J A, Laymon C A, et al. Ground—Based investigation of soil moisture variability within remote sensing footprints during the Southern Great Plains 1997 (SGP97) hydrology experiment[J].Water Resources Research, 1999, 35(6): 1 839-1 851.
[6]Weihermuller L, Huisman J A, Lambot S, et al. Mapping the spatial variation of soil water content at the field scale with different ground penetrating radar techniques[J].Journal of Hydrology, 2007, 340 (3/4): 205-216.
[7]Zreda M, Desilets D, Ferré T P A, et al. Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons[J].Geophysical Research Letters, 2008, 35 (21): L21402.
[8]Shuttleworth W J, Zreda M, Zeng X, et al. The COsmic-ray Soil Moisture Observing System(COSMOS): A non-invasive, intermediate scale soil moisture measurement network[C]∥Proceedings of the British Hydrological Society’s Third International Symposium. UK:Newcastle University, 2010.
[9]Jin Rui, Li Xin, Yan Baoping, et al. Introduction of eco-hydrological wireless sensor network in the Heihe River Basin[J]. Advances in Earth Science, 2012, 27(9): 993-1 005.[晋锐, 李新, 阎保平, 等. 黑河流域生态水文传感器网络设计 [J]. 地球科学进展, 2012, 27(9): 993-1 005.]
[10]Li X, Cheng G, Liu S, et al. Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific objectives and experimental design[J].Bulletin of American Meteorological Society, 2013, 94(8): 1 145-1 160.
[11]Li Xin, Liu Shaomin, Ma Mingguo, et al. HiWATER:An integrated remote sensing experiment on hydrological and ecological processes in the Heihe River Basin[J]. Advances in Earth Science, 2012, 27(5):481-498.[李新, 刘绍民, 马明国, 等.黑河流域生态—水文过程综合遥感观测联合试验总体设计[J].地球科学进展, 2012, 27(5): 481-498.]
[12]Liu S. Multi-Scale Observation Experiment on Evapotranspiration over heterogeneous land surfaces (MUSOEXE-12): Flux observation matrix[J].Journal of Geophysical Research: Atmospheres, 2013, in press.
[13]Mittelbach H, Lehner I, Seneviratne S I. Comparison of four soil moisture sensor types under field conditions in Switzerland[J].Journal of Hydrology, 2012, 430/431:39-49.
[14]Schlaeger S, Huebner C, Becker R. Simple soil moisture probe for low-cost measurement applications[C]∥Proceedings of the 6th Conference on Electromagnetic Wave Interaction with Water and Moist Substances. ISEMA, 2005: 258-265.
[15]Zreda M, Zeng X, Shuttleworth W J, et al. Cosmic-ray neutrons, an innovative method for measuring area-average soil moisture[J].GEWEX News, 2011, 21(3): 6-10.
[16]Rosolem R, Shuttleworth W, Zreda M, et al. The effect of atmospheric water vapor on the Cosmic-ray soil moisture observing system[J].Journal of Hydrometeorology, 2013, doi:1175/JHM-D-12-0120.1.
[17]Franz T E, Zreda M, Rosolem R, et al. A universal calibration function for determination of soil moisture with cosmic-ray neutrons[J].Hydrology and Earth System Sciences Discussions, 2013, 17(2):453-460.
[18]Zreda M, Shuttleworth W J, Zeng X, et al. COSMOS: The Cosmic-ray soil moisture observing system[J].Hydrology and Earth System Sciences, 2012, 16 (11): 4 079-4 099.
[19]Desilets D, Zreda M, Ferré T P A. Nature’s neutron probe: Land surface hydrology at an elusive scale with cosmic rays[J].Water Resources Research, 2010, 46(11), doi:10.1029/2009WR008726.
[20]Pelowitz D. MCNPX User’s Manual, Version 2.5. 0. LA-CP-05-0369[M]. Los Alamos: Los Alamos National Labs, 2005.
[21]Glasstone S, Edlund M C. The Elements of Nuclear Reactor Theory[M]. New York: Van Nostrand, 1952.
[22]Hess W N, Patterson H W, Wallace R, et al. Cosmic-ray neutron energy spectrum[J].Physical Review, 1959, 116(2): 445-457.
[23]Desilets D, Zreda M. Footprint diameter for a cosmic-ray soil moisture probe: Theory and monte carlo simulations[J].Water Resources Research, 2013, doi:10.1022/wrcr.20187.
[24]Franz T E, Zreda M, Ferre T P A, et al. Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources[J].Water Resources Research, 2012, 48(8): W08515.
[25]Franz T E, Zreda M, Rosolem R, et al. Field validation of a cosmic-ray neutron sensor using a distributed sensor network[J].Vadose Zone Journal, 2012, 11(4), doi:10.2136/vzj2012.0046.

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