基于Wavelet-ANFIS和MODIS地表温度产品的青藏高原0 cm土壤温度估算方法

doi:10.7522/j.issn.1000-0240.2013.0009

冰川冻土 ›› 2013, Vol. 35 ›› Issue (1): 74-83.doi: 10.7522/j.issn.1000-0240.2013.0009

基于Wavelet-ANFIS和MODIS地表温度产品的青藏高原0 cm土壤温度估算方法

黄培培¹, 南卓铜^1,2

1. 中国科学院寒区旱区环境与工程研究所, 甘肃兰州 730000;
2. 中国科学院寒区旱区环境与工程研究所冻土工程国家重点实验室, 甘肃兰州 730000

收稿日期:2012-08-17 修回日期:2012-12-29 出版日期:2013-02-25 发布日期:2013-07-22
通讯作者: 南卓铜,E-mail:nztong@lzb.ac.cn E-mail:nztong@lzb.ac.cn
作者简介:黄培培(1986-),女,河南永城人,2009年毕业于河南大学,现为硕士研究生,主要从事于GIS、RS的应用研究.E-mail:huangyingbing@yeah.net
基金资助:
冻土工程国家重点实验室开放基金项目(SKLFSE201009); 科技基础性工作专项(2008FY110200)资助

Estimation of 0-cm Soil Temperature over the Tibetan Plateau Based on the Wavelet Analysis and Adaptive Network-fuzzy Inference System

HUANG Pei-pei¹, NAN Zhuo-tong^1,2

1. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou Gansu730000, China;
2. State Key Laboratory of Frozen Soil Engineering, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou Gansu 730000, China

Received:2012-08-17 Revised:2012-12-29 Online:2013-02-25 Published:2013-07-22

摘要/Abstract

摘要：

0 cm土壤温度是冻土模型的上边界条件, 连续的、高质量的青藏高原0 cm土壤温度数据是进行准确冻土模拟的必要条件. 然而受复杂下垫面的影响, 遥感手段无法获取可靠的0 cm土壤温度. 利用自适应网络模糊推理系统(ANFIS)结合青藏高原实测资料建立遥感地表温度产品(LST)与0 cm土壤温度的关系, 以实现通过LST估算青藏高原逐日0 cm土壤温度. 研究了ANFIS的各种参数组合, 发现筛选合适的小波函数、小波窗口、小波层数建立起来的Wavelet-ANFIS模型能较准确实现估算0 cm土壤温度的目的. 验证表明, 估算结果与气象站点实测0 cm土壤温度绝对误差在2 K以下, 相关系数0.98以上. 考虑到原始MODIS LST误差在0~2 K之间, 该方法可以获取较为理想的0 cm土壤温度, 为冻土模型提供准确的上边界输入.

关键词: 小波分析, 自适应网络模糊推理系统, MODIS地表温度产品, 青藏高原, 0 cm土壤温度

Abstract:

0-cm soil temperature is the upper boundary condition of many permafrost models. Continuous, high-quality 0-cm soil temperature data are necessary inputs to simulate permafrost distribution. However, owing to the influence of complex underlying surface, remote sensing approaches cannot provide reliable 0-cm soil temperature. In this study, in order to estimate 0-cm soil temperature, adaptive network-fuzzy inference system (ANFIS) combining with the data measured in the Tibetan Plateau is used to establish the relations between remote sensing land surface temperature (LST) and 0-cm soil temperature. In this paper, different parameter combinations of ANFIS are examined, and a Wavelet-ANFIS model established by optimized wavelet functions, wavelet windows and wavelet layers is found able to estimate the 0-cm soil temperature more accurately. A comparison analysis of the estimated results and the 0-cm soil temperatures measured at the meteorological sites shows that the approach can achieve desirable estimation with an absolute error less than 2 K and a correlation coefficient greater than 0.98. In view of the original MODIS LST error range from 0 to 2 K, the proposed method may provide more accurate 0-m soil temperature inputs to permafrost models.

Key words: wavelet analysis, adaptive network-fuzzy inference system (ANFIS), MODIS land surface temperature product, Tibetan Plateau, 0-cm soil temperature

中图分类号:

P407.8

黄培培, 南卓铜. 基于Wavelet-ANFIS和MODIS地表温度产品的青藏高原0 cm土壤温度估算方法[J]. 冰川冻土, 2013, 35(1): 74-83.

HUANG Pei-pei, NAN Zhuo-tong. Estimation of 0-cm Soil Temperature over the Tibetan Plateau Based on the Wavelet Analysis and Adaptive Network-fuzzy Inference System[J]. JOURNAL OF GLACIOLOGY AND GEOCRYOLOGY, 2013, 35(1): 74-83.

参考文献

[1] Heginbottom J A. Permafrost mapping: a review [J]. Progress in Physical Geography, 2002, 26(4): 623-642.

[2] Wu Qingbai, Zhu Yuanlin, Liu Yongzhi. Application of the permafrost table temperature and thermal offset forecast model in the Tibetan Plateau[J]. Journal of Glaciology and Geocryology, 2002, 24(5): 614-617. [吴青柏, 朱元林, 刘永智. 青藏高原多年冻土顶板温度和温度位移预报模型的应用[J]. 冰川冻土, 2002, 24(5): 614-617.]

[3] Wang Zhixia. Applications of Permafrost Distribution Models on the Qinghai-Tibet Plateau [D]. Master Thesis, Lanzhou: Lanzhou University, 2010. [王之夏. 多年冻土分布模型在青藏高原的应用研究[D]. 硕士论文, 兰州: 兰州大学, 2010.]

[4] Nelson F E, Outcalt S I. A computational method for prediction and regionalization of permafrost[J]. Arctic and Alpine Research, 1987, 19(3): 279-288.

[5] Nan Zhuotong, Li Shuxun, Cheng Guodong, et al. Surface frost number model and its application to the Tibetan Plateau[J]. Journal of Glaciology and Geocryology, 2012, 34(1): 89-95.[南卓铜, 李述训, 程国栋, 等. 地面冻结数模型及其在青藏高原的应用[J]. 冰川冻土, 2012, 34(1): 89-95.]

[6] Zhang Wei, Wang Genxu, Zhou Jian, et al. Simulating the water-heat processes in permafrost regions in the Tibetan Plateau based on CoupModel[J]. Journal of Glaciology and Geocryology, 2012, 34(5): 1099-1109. [张伟, 王根绪, 周剑, 等. 基于CoupModel的青藏高原多年冻土区土壤水热过程模拟[J]. 冰川冻土, 2012, 34(5): 1099-1109.]

[7] Chen Hao, Nan Zhuotong, Wang Shugong, et al. Water-heat simulation on typical sites in upstream mountainous area of the Heihe River Basin[J]. Journal of Glaciology and Geocryology, 2013, 35(1): 126-137. [陈浩, 南卓铜, 王书功, 等. 黑河上游山区典型站点的水热过程模拟研究[J]. 冰川冻土, 2013, 35(1): 126-137.]

[8] Lunardini V J. Heat Transfer in Cold Climates [M]. New York: Van Nostrand Reinhold Co., 1981: 1-731.

[9] Li Shuxun, Wu Tonghua. The relationship between air temperature and ground temperature in the Tibetan Plateau[J]. Journal of Glaciology and Geocryology, 2005, 27(5): 627-632. [李述训, 吴通华. 青藏高原地气温度之间的关系[J]. 冰川冻土, 2005, 27(5): 627-632.]

[10] Cai Fu, Zhou Guangsheng, Li Rongping, et al. Sensitivity of land surfaces model to dynamic land surface parameters [J]. Advances in Earth Science, 2011, 26(3): 300-310.[蔡福, 周广胜, 李荣平, 等. 陆面过程模型对下垫面参数动态变化的敏感性分析[J]. 地球科学进展, 2011, 26(3): 300-310.]

[11] Wang Zhixia, Nan Zhuotong, Zhao Lin. The applicability of MODIS land surface temperature products to simulating the permafrost distribution over the Tibetan Plateau [J]. Journal of Glaciology and Geocryology, 2011, 33(1): 132-143. [王之夏, 南卓铜, 赵林. MODIS地表温度产品在青藏高原冻土模拟中的适用性评价[J]. 冰川冻土, 2011, 33(1): 132-143.]

[12] Ouyang Bin, Che Tao, Dai Liyun, et al. Estimating mean daily surface temperature over the Tibetan Plateau based on MODIS LST products [J]. Journal of Glaciology and Geocryology, 2012, 34(2): 296-303. [欧阳斌, 车涛, 戴礼云, 等. 基于MODIS LST产品估算青藏高原地区的日平均地表温度[J]. 冰川冻土, 2012, 34(2): 296-303.]

[13] Jang J-S R. ANFIS: Adaptive-network-based fuzzy inference system [J]. IEEE Transactions on Systems, Man and Cybernetics, 1993, 23(3): 665-685.

[14] Firat M, Güngör M. Hydrological time-series modeling using an adaptive neuro-fuzzy inference system [J]. Hydrological Processes, 2008, 22(13): 2122-2132.

[15] Lo S-P. The application of an ANFIS and grey system method in turning tool-failure detection [J]. The International Journal of Advanced Manufacturing Technology, 2002, 19(8): 564-572.

[16] Williams R J, Zipser D. A learning algorithm for continually running fully recurrent neural networks [J]. Neural computation, 1989, 1(2): 270-280.

[17] Nelles O. Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models [M]. Berlin Heidelberg: Springer, 2001.

[18] Takagi T, Sugeno M. Fuzzy identification of system and its applications to modeling and control [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1985, 15(1): 116-132.

[19] Sugeno M, Kang G T. Structure identification of fuzzy model [J]. Fuzzy Sets and Systems, 1988, 28(1): 15-33.

[20] Anderberg M R. Cluster Analysis for Applications [M]. New York: Academic Press, 1973: 1-359.

[21] Mallat S G. A theory for multiresolution signal decomposition: The wavelet representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693.

[22] Burrus C S, Gopinath R A, Guo H. Introduction to Wavelets and Wavelet Transforms: A Primer [M]. Upper Saddle River, New Jersey: Prentice Hall, 1998: 1-268.

基于Wavelet-ANFIS和MODIS地表温度产品的青藏高原0 cm土壤温度估算方法

Estimation of 0-cm Soil Temperature over the Tibetan Plateau Based on the Wavelet Analysis and Adaptive Network-fuzzy Inference System

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	王逸凡, 高晶, 胡迈, 姚檀栋, 牛晓伟, 赵爱斌, 申子恒. 青藏高原大气CH₄源汇及其浓度时空变化特征研究进展[J]. 冰川冻土, 2023, 45(1): 1-17.
[2]	张文旭, 王根绪, 胡兆永. 三种蒸散发测算方法的比较 ——以青藏高原风火山地区为例[J]. 冰川冻土, 2023, 45(1): 130-139.
[3]	张愉萱, 王宁练, 武小波, 杨雪雯, 李瑶, 方振祥. 青藏高原东南和西南部冬季积雪化学组成研究[J]. 冰川冻土, 2023, 45(1): 18-30.
[4]	郑艳艳, 郭小芳, 四郎Silang, 德吉. 青藏高原纳木错夏季沿岸水体可培养细菌多样性及其与理化因子的相关性[J]. 冰川冻土, 2023, 45(1): 243-253.
[5]	兰爱玉, 林战举, 范星文, 姚苗苗. 坡向对青藏高原土壤环境及植被生长影响的实验研究[J]. 冰川冻土, 2023, 45(1): 42-53.
[6]	薛伟, 周毓彦, 刘建伟, 鲁帆, 侯保灯, 胡莹莹, 肖伟华. 基于SHAW模型的青藏高原季节冻土区土壤温湿度模拟与评估[J]. 冰川冻土, 2023, 45(1): 54-66.
[7]	文洪, 巫锡勇, 赵思远, 边瑞, 周桂宇, 孟少伟, 孙春卫. 基于机器学习法的青藏高原沙鲁里山系中段雪崩易发性评价研究[J]. 冰川冻土, 2022, 44(6): 1694-1706.
[8]	李诺, 韩其飞, 马英, 黄晓东. 青藏高原MODIS逐日无云积雪范围产品精度验证[J]. 冰川冻土, 2022, 44(6): 1740-1747.
[9]	刘金科, 姚济敏, 谷良雷, 李韧, 吴晓东, 吴通华, 谢昌卫, 邹德富, 乔永平, 胡国杰, 肖瑶, 史健宗. 青藏高原多年冻土区地表能量收支过程及其对活动层影响的初步分析[J]. 冰川冻土, 2022, 44(6): 1773-1783.
[10]	王习敏, 黄荣刚, 徐志达, 焦志平, 江利明. 基于深度学习和高分遥感影像的青藏高原东部融冻泥流阶地提取研究[J]. 冰川冻土, 2022, 44(5): 1419-1428.
[11]	郑锦文, 左志燕, 蔺邹兴, 肖栋. 巴伦支-喀拉海海温和青藏高原冬季地表气温的年代际联系[J]. 冰川冻土, 2022, 44(5): 1513-1522.
[12]	谢梅珍, 赵林, 吴晓东, 周华云, 岳广阳. 青藏高原多年冻土区两种高寒草地生态系统土壤氮季节变化及其与环境因子的关系[J]. 冰川冻土, 2022, 44(5): 1631-1639.
[13]	杨舒然, 杨玮琳, 韩业松, 杨彦敏, 李梦真, 崔之久, 刘耕年. 四川康定折多山末次冰盛期古冰川重建及其气候意义[J]. 冰川冻土, 2022, 44(4): 1119-1129.
[14]	伍永秋, 王立辉, 杜世松, 李静芸, 申玉龙. 青藏高原典型地区沉积物地球化学特征与矿物组成的粒度效应[J]. 冰川冻土, 2022, 44(4): 1140-1149.
[15]	陈军, 刘延昭, 曹立国, 胡建茹, 刘水林. 青藏高原湖泊变化遥感监测及水量平衡定量估算研究进展[J]. 冰川冻土, 2022, 44(4): 1203-1215.