Scientia Agricultura Sinica ›› 2018, Vol. 51 ›› Issue (9): 1717-1724.doi: 10.3864/j.issn.0578-1752.2018.09.009

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

Spectral Characteristics and Quantitative Prediction of Soil Water Content under Different Soil Particle Sizes

LU YanLi, BAI YouLu, WANG Lei, YANG LiPing   

  1. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081
  • Received:2017-08-03 Online:2018-05-01 Published:2018-05-01

Abstract: 【Objective】Hyper spectral technology is more and more widely applied in monitoring soil properties. Soil Water Content (SWC) is a key parameter of soil properties. This paper aimed to make clear the spectral response of soil moisture under different soil roughness to predict quantitatively soil water content, and further to provide the basis for rapid monitoring of farmland moisture and other soil properties. 【Method】 Soil samples were sieved through different mesh sizes to classify into different particle sizes, and then set different moisture levels. The spectral reflectance of those samples were compared, and the quantitative prediction models of soil water content were established by Partial Least Squares Regression (PLSR) method. 【Result】The results showed that the spectral reflectance decreased with the increase of soil water content, and the difference became bigger as the increase of wavelength and decrease of soil water content. The absorptions got deeper in 1 400 nm and 1 900 nm with the increase of water content. In those soil samples passed through a sieve with an aperture of 0.15 mm (denoted as D-1), the spectral reflectance increased in 350-1 240 nm and then decreased after 1 240 nm when the water content was more than 40%. Compared with the model constructed from all samples with different sizes, models from the same size were improved in predicting accuracy and stability: the smaller the particle size was, the better the prediction effect and stability of the predicting model were. The RMSE (root mean square error) and R2 of the optimal model were 4.13% and 0.90, respectively. Additionally, normalization of spectral data reduced the influence of noise, and improved the predicting accuracy and stability of the model. 【Conclusion】The spectral generally decreased with soil water content increasing, but soil with small size showed opposite in 350-1 240 nm when the moisture content was greater than 40%. The predicting model for soil water content was improved as size getting smaller and sample number involved getting larger, and the spectral data normalization also improved predicting accuracy and stability of model.

Key words: spectral, soil water content, particle size, model

[1]    孙家柄. 遥感原理与应用. 武汉: 武汉大学出版社, 2009.
SUN J B. Principles and Applications of Remote Sensing. Wuhan: Wuhan University Press, 2009. (in Chinese)
[2]    IRONS J R, WEISMILLER R A, PETERSEN G W. Soil reflectance//ASRAR G. Theory and Application of Optical Remote Sensing. New York: John Wiley and Sons, 1989: 66-106.
[3]    IDSO S B, JACKSON R D, REGINATO R J, KIMBALL B A, NAKAYAMA F S. The dependence of bare soil albedo on soil water content. Journal of Applied Meteorology, 1975, (14): 109-113.
[4]    张俊华, 贾科利. 典型龟裂碱土土壤水分光谱特征及预测. 应用生态学报, 2015, 26(3): 884-890.
ZHANG J H, JIA K L. Spectral reflectance characteristics and modeling of typical Takyr Solonetzs water content. Chinese Journal of Applied Ecology, 2015, 26(3): 884-890. (in Chinese)
[5]    刁万英. 基于可见-近红外波段反射率估算表层土壤含水量[D]. 北京: 中国农业大学, 2016.
DIAO W Y. Estimation of surface soil moisture based on visible near infrared reflectance[D]. Beijing: China Agricultural University, 2016. (in Chinese)
[6]    王德彩, 张俊辉, 韩光中. 土壤含水量对采用vis-nir光谱分析土壤质地的影响. 地理与地理信息科学, 2015, 31(6): 52-55.
WANG D C, ZHANG J H, HAN G Z. Effects of soil water content on soil texture using Vis-NIR spectroscopy. Geography and geographic information science, 2015, 31 (6): 52-55. (in Chinese)
[7]    MULLER E, DECAMPS H.Modeling soil moisture-reflectance. Remote Sensing of Environment, 2001, 76(2): 173-180.
[8]    VERHOEST N E, LIEVENS H, WAGNER W, ÁLVAREZ-MOZOS J, MORAN M S, MATTIA F. On the soil roughness parameterization problem in soil moisture retrieval of bare surfaces from synthetic aperture radar. Sensors, 2008, 8(7): 4213-4248.
[9]    , 丁潇, 刘焕军, 张新乐, 曲长祥, 胡文, 臧红婷. 黑土土壤水分反射光谱特征定量分析与预测. 土壤学报, 2014(5): 1021-1026.
Liu Y, Ding X, Liu H J, Zhang X L, Hu W, Qu C X, Zang H T. Analysis and prediction of soil moisture reflectance characteristics of quantitative black soil. Acta Pedologica Sinica, 2014 (5): 1021-1026. (in Chinese)
[10]   SANTRA P, SAHOO R N, DAS B S, SAMAL R N, PATTANAIK A  K, GUPTA V K. Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwave- infrared (vis-nir-swir) region. Geoderma, 2009, 152(3/4): 338-349.
[11]   HUMMEL J W, SUDDUTH K A, HOLLINGER S E. Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor. Computers and Electronics in Agriculture, 2001, 32: 149-165.
[12]   徐金鸿, 徐瑞松, 夏斌, 朱照宇. 土壤遥感监测研究进展. 水土保持研究, 2006, 13(2): 17-20.
XU J H, XU R S, XIA B, ZHU Z Y. Research advances on soil monitor by remote sensing. Research of soil and water conservation, 2006, 13(2): 17-20. (in Chinese)
[13]   Demattê J A M, Antonio A, Sousa M C. Alves Marcos R, Nanni Peterson R, Fiorio, Rogério Costa Campos. Determining soil water status and other soil characteristics by spectral proximal sensing. Geoderma, 2006, 135(11): 179-195.
[14]   刘伟东, F. Baret, 张兵, 郑兰芬, 童庆禧. 应用高光谱遥感数据估算土壤表层水分的研究. 遥感学报, 2004, 8(5): 434-442.
LIU W D, BARET F, ZHANG B, ZHENG L F, TONG Q X. Estimation of soil surface water using hyperspectral remote sensing data. Journal of Remote Sensing, 2004, 8(5): 434-442. (in Chinese)
[15]   Ben-Dor E. Quantitative remote sensing of soil properties. Advances in Agronomy, 2002, 73: 173-243.
[16]   ROSSEL R A V, WALVOORT D J J, MCBRATNEY A B, JANIK L J, SKJEMSTAD J O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 2006, 131(1/2): 59-75.
[17]   SHI Z, JI W, VISCARRA R R A, CHEN S, ZHOU Y. Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–nir spectral library. European Journal of Soil Science, 2015, 66(4): 679-687.
[18]   THOMAS U, CHRISTOPH E, THOMAS J. Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study. Plant and Soil, 2003, 251: 319-329.
[19]   LU Y L, BAI Y L, YANG L P, WANG H J. Hyperspectral extraction of soil organic matter content based on principle component regression. New Zealand Journal of Agricultural Research, 2007, 50: 1169-1175.
[20]   SUMMERS D, LEWIS M, OSTENDORF B, CHITTLEBOROUGH D. Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicator, 2011, 11: 123-131.
[21]   REEVES III J, McCATTY G, MIMMO T. The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils. Environmental Pollution, 2002, 116(3): 277-284.
[22]   FONTÁN J M, CALVACHE S, LÓPEZ-BELLIDO R J, LÓPEZ-BELLIDO L. Soil carbon measurement in clods and sieved samples in a Mediterranean Vertisol by Visible and Near-Infrared Reflectance Spectroscopy. Geoderma, 2010, 156(3): 93-98.
[23]   褚小立. 化学计量学方法与分子光谱分析技术. 北京: 化学工业出版社, 2002: 41-53
CHU X L. Chemometrics Methods and Molecular Spectroscopy. Beijing: Chemical Industry Press, 2002: 41-53. (in Chinese)
[24]   Rossel R A V, Behrens T, Ben-Dor E, Brown D J, Demattê J A M, Shepherd K D. A global spectral library to characterize the world's soil. Earth-Science Reviews, 2016,155: 198-230.
[25]   Brown D J, Shepherd K D, Walsh M G, Mays M D, Reinsch T G. Global soil characterization with vnir diffuse reflectance spectroscopy. Geoderma, 2006,132(3): 273-290.
[26]   JI W, LI S, CHEN S, SHI Z, ROSSEL R A V, MOUAZEN A M. Prediction of soil attributes using the Chinese soil spectral library and standardized spectra recorded at field conditions. Soil and Tillage Research, 2016,155: 492-500.
[1] CAI WeiDi,ZHANG Yu,LIU HaiYan,ZHENG HengBiao,CHENG Tao,TIAN YongChao,ZHU Yan,CAO WeiXing,YAO Xia. Early Detection on Wheat Canopy Powdery Mildew with Hyperspectral Imaging [J]. Scientia Agricultura Sinica, 2022, 55(6): 1110-1126.
[2] TAN XianMing,ZHANG JiaWei,WANG ZhongLin,CHEN JunXu,YANG Feng,YANG WenYu. Prediction of Maize Yield in Relay Strip Intercropping Under Different Water and Nitrogen Conditions Based on PLS [J]. Scientia Agricultura Sinica, 2022, 55(6): 1127-1138.
[3] MA YuYang,GUAN HaiXiang,YANG HaoXuan,SHAO Shuai,SHAO YiQun,LIU HuanJun. A New Method to Improve the Accuracy of Digital Elevation Model in Northeast China by Using Terrain, Soil and Crop Information [J]. Scientia Agricultura Sinica, 2021, 54(8): 1715-1727.
[4] ZHOU Meng,HAN XiaoXu,ZHENG HengBiao,CHENG Tao,TIAN YongChao,ZHU Yan,CAO WeiXing,YAO Xia. Remote Sensing Estimation of Cotton Biomass Based on Parametric and Nonparametric Methods by Using Hyperspectral Reflectance [J]. Scientia Agricultura Sinica, 2021, 54(20): 4299-4311.
[5] WANG GuoLi,CHANG FangDi,ZHANG HongYuan,LU Chuang,SONG JiaShen,WANG Jing,PANG HuanCheng,LI YuYi. Effects of Straw Interlayer with Different Thickness on Saline-Alkali Soil Temperature, Water Content, and Sunflower Yield in Hetao Irrigation Area [J]. Scientia Agricultura Sinica, 2021, 54(19): 4155-4168.
[6] FEI ShuaiPeng,YU XiaoLong,LAN Ming,LI Lei,XIA XianChun,HE ZhongHu,XIAO YongGui. Research on Winter Wheat Yield Estimation Based on Hyperspectral Remote Sensing and Ensemble Learning Method [J]. Scientia Agricultura Sinica, 2021, 54(16): 3417-3427.
[7] LI PengLei,ZHANG Xiao,WANG WenHui,ZHENG HengBiao,YAO Xia,ZHU Yan,CAO WeiXing,CHENG Tao. Assessment of Terrestrial Laser Scanning and Hyperspectral Remote Sensing for the Estimation of Rice Grain Yield [J]. Scientia Agricultura Sinica, 2021, 54(14): 2965-2976.
[8] LI HanTing,CHAI Qiang,WANG QiMing,HU FaLong,YU AiZhong,ZHAO Cai,YIN Wen,FAN ZhiLong,FAN Hong. Water Use Characteristics of Maize-Green Manure Intercropping Under Different Nitrogen Application Levels in the Oasis Irrigation Area [J]. Scientia Agricultura Sinica, 2021, 54(12): 2608-2618.
[9] LI MeiXuan,ZHU XiCun,BAI XueYuan,PENG YuFeng,TIAN ZhongYu,JIANG YuanMao. Remote Sensing Inversion of Nitrogen Content in Apple Canopy Based on Shadow Removal in UAV Multi-Spectral Remote Sensing Images [J]. Scientia Agricultura Sinica, 2021, 54(10): 2084-2094.
[10] FengZhi SHI,RuiYan WANG,YuHuan LI,Hong YAN,XiaoXin ZHANG. LAI Estimation Based on Multi-Spectral Remote Sensing of UAV and Its Application in Saline Soil Improvement [J]. Scientia Agricultura Sinica, 2020, 53(9): 1795-1805.
[11] ZHAO Jing,LI ZhiMing,LU LiQun,JIA Peng,YANG HuanBo,LAN YuBin. Weed Identification in Maize Field Based on Multi-Spectral Remote Sensing of Unmanned Aerial Vehicle [J]. Scientia Agricultura Sinica, 2020, 53(8): 1545-1555.
[12] SHEN Zhe,ZHANG RenLian,LONG HuaiYu,WANG Zhuan,ZHU GuoLong,SHI QianXiong,YU KeFan,XU AiGuo. Research on Spatial Distribution of Soil Particle Size Distribution in Loess Region Based on Three Spatial Prediction Methods—Taking Haiyuan County in Ningxia as an Example [J]. Scientia Agricultura Sinica, 2020, 53(18): 3716-3728.
[13] FAN KaiKai,TONG XuZe,YAN YuChun,XIN XiaoPing,WANG Xu. Effect of Fairy Rings on Soil Respiration in Hulunber Meadow Steppe [J]. Scientia Agricultura Sinica, 2020, 53(13): 2595-2603.
[14] LI Qiang, HUANG YingXin, ZHONG RongZhen, SUN HaiXia, ZHOU DaoWei. Influence of Medicago sativa Proportion on Its Individual Nitrogen Fixation Efficiency and Underlying Physiological Mechanism in Legume-Grass Mixture Grassland [J]. Scientia Agricultura Sinica, 2020, 53(13): 2647-2656.
[15] ZHOU LongFei, GU XiaoHe, CHENG Shu, YANG GuiJun, SUN Qian, SHU MeiYan. Spectral Diagnosis of Leaf Area Density of Maize at Heading Stage Under Lodging Stress [J]. Scientia Agricultura Sinica, 2019, 52(9): 1518-1528.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!