Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient

被引:40
作者
Zhao, Shaohua [1 ]
Yang, Yonghui [2 ]
Qiu, Guoyu [3 ]
Qin, Qiming [1 ]
Yao, Yunjun [1 ]
Xiong, Yujiu [4 ]
Li, Chunqiang [5 ]
机构
[1] Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China
[2] Chinese Acad Sci, Inst Genet & Dev Biol, Ctr Agr Resources Res, Key Lab Agr Water Resources, Shijiazhuang 050021, Peoples R China
[3] Peking Univ, Shenzhen Grad Sch, Sch Environm & Energy, Shenzhen 518055, Peoples R China
[4] Sun Yat Sen Univ, Dept Water Resources & Environm, Guangzhou 510275, Guangdong, Peoples R China
[5] Hebei Prov Inst Meteorol, Shijiazhuang 050021, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Remote sensing; Energy balance equation; Regional maximum temperature; MODIS; PERPENDICULAR DROUGHT INDEX; THEORETICAL-ANALYSIS; MODIS DATA; VALIDATION; RETRIEVAL; MODEL; TIME;
D O I
10.1016/j.jag.2010.04.007
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
An approach for estimating soil moisture is presented and tested by using surface-temperature-based soil evaporation transfer coefficient (h(a)), a coefficient recently proposed through the equation h(a) (T-s - T-a)/(T-sd - T-a), where T-s, T-sd, and T-a are land surface temperature (LST), reference soil (dry soil without evaporation) surface temperature, and air temperature respectively. Our analysis and controllable experiment indicated that h(a) closely related to soil moisture, and therefore, a relationship between field soil moisture and h(a) could be developed for soil moisture estimation. Field experiments were carried out to test the relationship between h(a) and soil moisture. Time series Aqua-MODIS images were acquired between 11 Sep. 2006 and 1 Nov. 2007. Then, MODIS derived h(a) and simultaneous measured soil moisture for different soil depths were used to establish the relations between the two variables. Results showed that there was a logarithmic relationship between soil moisture and h(a) (P < 0.01). These logarithmic models were further validated by introducing another ground-truth data gathered from 46 meteorological stations in Hebei Province. Good agreement was observed between the measured and estimated soil moisture with RMSE of 0.0374 cm(3)/cm(3) and 0.0503 cm(3)/cm(3) for surface energy balance method at two soil depths (10 cm and 20 cm), with RMSE of 0.0467 cm(3)/cm(3) and 0.0581 cm(3)/cm(3) for maximum temperature method at two soil depths. For vegetated surfaces, the ratio of h(a) and NDVI suggested to be considered. The proposed approach has a great potential for soil moisture and drought evaluation by remote sensing. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:351 / 358
页数:8
相关论文
共 36 条
  • [1] Estimating soil moisture using remote sensing data: A machine learning approach
    Ahmad, Sajjad
    Kalra, Ajay
    Stephen, Haroon
    [J]. ADVANCES IN WATER RESOURCES, 2010, 33 (01) : 69 - 80
  • [2] Allen R. G., 1998, FAO Irrigation and Drainage Paper
  • [3] Allen RG., 1994, ICID Bull, V43, P1, DOI DOI 10.12691/AJWR-5-4-3
  • [4] Soil moisture retrieval from MODIS data in Northern China Plain using thermal inertia model
    Cai, G.
    Xue, Y.
    Hu, Y.
    Wang, Y.
    Guo, J.
    Luo, Y.
    Wu, C.
    Zhong, S.
    Qi, S.
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2007, 28 (16) : 3567 - 3581
  • [5] CHENG Y, 2006, J REMOTE SENSING, V10, P83
  • [6] Modeling and assimilation of root zone soil moisture using remote sensing observations in Walnut Gulch Watershed during SMEX04
    Das, N. N.
    Mohanty, B. P.
    Cosh, M. H.
    Jackson, T. J.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2008, 112 (02) : 415 - 429
  • [7] DeRoo APJ, 1996, HYDROL PROCESS, V10, P1119, DOI 10.1002/(SICI)1099-1085(199608)10:8<1119::AID-HYP416>3.0.CO
  • [8] 2-V
  • [9] Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment
    Fensholt, R
    Sandholt, I
    [J]. REMOTE SENSING OF ENVIRONMENT, 2003, 87 (01) : 111 - 121
  • [10] A re-examination of perpendicular drought indices
    Ghulam, Abduwasit
    Qin, Qiming
    Kusky, Timothy
    Li, Zhao-Liang
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2008, 29 (20) : 6037 - 6044