Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2015, Vol. 27 Issue (3) : 77-83     DOI: 10.6046/gtzyyg.2015.03.14
Technology and Methodology |
Estimation of soil moisture based on crop water stress index
YU Wendan1, ZHANG Youjing1,2, ZHENG Shuqian3
1. School of Earth Sciences and Engineer, Hohai University, Nanjing 210098, China;
2. State Key Laboratory of Hydrology-Water Resource and Hydraulic Engineer, Hohai University, Nanjing 210098, China;
3. Zhejiang East China Surveying and Mapping Co. Ltd, Hangzhou 310030, China
Download: PDF(5435 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  In this paper, the authors calculated the amount of the actual evapotranspiration based respectively on double layer model and improved double layer model in consideration of the available water rate of soil with MODIS data and meteorological data so as to investigate the temporal and spatial distribution of soil moisture and dynamic changes in Xuzhou City, Jiangsu Province. The amount of the potential evapotranspiration was calculated by using Penman-Monteith formula. Models were built to estimate the relative content of water of Xuzhou in July and November 2010, by crop water stress index(CWSI) obtained by the actual evapotranspiration and the potential evapotranspiration. The result shows that the relative error of the estimated data based on the improved double layer model and that of the measured data are 3.47% and 6.03% respectively, with the correlation coefficient being 0.84 and 0.84, which are better than the results obtained by the model based on the double layer model, whose relative error is 5.89% and 9.6%, and whose correlation coefficient is 0.53 and 0.72.
Keywords remote sensing      ZY1-02C satellite      hydrogeology      Karst water resources     
:  TP75  
Issue Date: 23 July 2015
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
CHENG Yang
TONG Liqiang
GUO Zhaocheng
MO Yuanfu
JI Yiqun
Cite this article:   
CHENG Yang,TONG Liqiang,GUO Zhaocheng, et al. Estimation of soil moisture based on crop water stress index[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 77-83.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.03.14     OR     https://www.gtzyyg.com/EN/Y2015/V27/I3/77
[1] 冯蜀青,殷青军,肖建设,等.基于温度植被旱情指数的青海高寒区干旱遥感动态监测研究[J].干旱地区农业研究,2006,24(5):141-145. Feng S Q,Yin Q J,Xiao J S,et al.Monitoring drought dynamic variation based on temperature vegetation drought index in Qinghai high and cold area[J].Agricultural Research in the Arid Areas,2006,24(5):141-145.
[2] 袁国富,唐登银,罗毅,等.基于冠层温度的作物缺水研究进展[J].地球科学进展,2000,16(1):49-54. Yuan G F,Tang D Y,Luo Y,et al.Advances incanopy-temperature based crop water stress research[J].Advances in Earth Science,2000,16(1):49-54.
[3] Jackson R D,Idso S B,Reginato R J,et al.Canopy temperature as a crop water stress indicator[J].Water Resources Research,1981,17(4):1133-1138.
[4] Zhang R H.A new model for estimating crop water stress based on infrared radiation information[J].Science in China B,1986,7:776-784.
[5] 申广荣,田国良.作物缺水指数监测旱情方法研究[J].干旱地区农业研究,1998,16(1):123-128. Shen G R,Tian G L.Drought monitoring with crop water stress index[J].Agricultural Research in the Arid Areas,1998,16(1):123-128.
[6] 王纯枝,毛留喜,吕厚荃,等.基于作物缺水指数的区域旱情遥感监测[C]//中国气象学会2007年年会生态气象业务建设与农业气象灾害预警分会场论文集.北京:中国气象学会,2007. Wang C Z,Mao L X,Lv H Q,et al.Monitor regional drought based on crop water stress index[C]//Meteorological Service Construction and Agricultural Meteorological Disaster Warning Breakout of China Meteorological Society in 2007.Beijing:China Meteorological Society,2007.
[7] 宋小宁,赵英时.改进的区域缺水遥感监测方法[J].中国科学(D辑:地球科学),2006,36(2):188-194. Song X N,Zhao Y S.Improved regional drought test method[J].Science in China(Ser D:Earth Sciences),2006,36(2):188-194.
[8] 刘振华,赵英时,李笑宇,等.基于蒸散发模型的定量遥感缺水指数[J].农业工程学报,2012,28(2):114-120. Liu Z H,Zhao Y S,Li X Y,et al.Quantitative remote sensing of water deficit index based on evapotranspiration[J].Transactions of the CSAE,2012,28(2):114-120.
[9] Jackson T J,Le Vine D M,Hsu A Y,et al.Soil moisture mapping at regional scales using microwave radiometry:The Southern Great Plains Hydrology Experiment[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(5):2136-2151.
[10] 刘钰,Pereira L S,Teixeira J L,等.参照腾发量的新定义及计算方法对比[J].水利学报,1997(6):27-33. Liu Y,Pereira L S,Teixeira J L,et al.Update definition and computation of reference evapotranspiration comparison with former method[J].Journal of Hydraulic Engineering,1997(6):27-33.
[11] 辛晓洲,田国良,柳钦火.地表蒸散定量遥感的研究进展[J].遥感学报,2003,7(3):233-240. Xin X Z,Tian G L,Liu Q H.A review of researches on remote sensing of land surface evapotranspiration[J].Journal of Remote Sensing,2003,7(3):233-240.
[12] James Shuttleworth W,Gurney R J.The theoretical relationship between foliage temperature and canopy resistance in sparse crops[J].Quarterly Journal of the Royal Meteorological Society,1990,116(492):497-519.
[13] Norman J M,Kustas W P,Humes K S.Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature[J].Agricultural and Forest Meteorology,1995,77(3):263-293.
[14] 张仁华,孙晓敏,王伟民,等.一种可操作的区域尺度地表通量定量遥感二层模型的物理基础[J].中国科学(D辑:地球科学),2004,34(s2):200-216. Zhang R H,Sun X M,Wang W M,et al.An operational regional scale surface fluxes of quantitative remote sensing model of two layers of basic physics[J].Science in China(Ser D:Earth Sciences),2004,34(s2):200-216.
[15] 谢贤群.遥感瞬时作物表面温度估算农田全日蒸散总量[J].环境遥感,1991,6(4):253-260. Xie X Q.Estimation of daily evapo-transpiration(ET) from one time-of-day remotely sensed canopy temperature[J].Remote Sensing of Environment China,1991,6(4):253-260.
[16] Lubczynski M W,Gurwin J.Integration of various data sources for transient groundwater modeling with spatio-temporally variable fluxes-Sardon study case,Spain[J].Journal of Hydrology,2005,306(1/4):71-96.
[17] Gokmen M,Vekerdy Z,Verhoef A,et al.Integration of soil moisture in SEBS for improving evapotranspiration estimation under water stress conditions[J].Remote Sensing of Environment,2012,121:261-274.
[1] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[2] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[3] LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[4] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[5] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[6] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[7] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[8] DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[9] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[10] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[11] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[12] YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.
[13] LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[14] AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[15] LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech