地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (8): 1178-1189.doi: 10.12082/dqxxkx.2018.170620

• 遥感科学与应用技术 • 上一篇    下一篇

3种土壤冻融判别算法在青藏高原的分类精度评价

刘源1,2(), 秦军1,*(), 阳坤1,3,4, 韩孟磊1,4, 拉珠1, 赵龙1,5   

  1. 1. 中国科学院青藏高原研究所,北京 100101
    2. 中国科学院大学,北京 100049
    3. 中国科学院青藏高原地球科学卓越创新中心,北京 100101
    4. 清华大学地球系统科学系,北京 100084
    5. 德克萨斯大学奥斯汀分校地质科学系,美国 78705
  • 收稿日期:2017-12-20 修回日期:2018-05-26 出版日期:2018-08-25 发布日期:2018-08-24
  • 通讯作者: 秦军 E-mail:ryanly@itpcas.ac.cn;shuairenqin@itpcas.ac.cn
  • 作者简介:

    作者简介:刘 源(1993-),男,硕士生,主要从事青藏高原水文气象的相关研究。E-mail: ryanly@itpcas.ac.cn

  • 基金资助:
    国家重点研发计划项目(2017YFA0603101)

Evaluation of Classification Accuracy in Tibetan Plateau of Three Soil Freeze/Thaw Discrimination Algorithms

LIU Yuan1,2(), QIN Jun1,*(), YANG Kun1,3,4, HAN Menglei1,4, LA Zhu1, ZHAO Long1,5   

  1. 1. The Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China
    4. Department of earth system science, Tinghua University,Beijing,100054,China
    5. Department of Geological Sciences, The University of Texas at Austin, Austin, USA
  • Received:2017-12-20 Revised:2018-05-26 Online:2018-08-25 Published:2018-08-24
  • Contact: QIN Jun E-mail:ryanly@itpcas.ac.cn;shuairenqin@itpcas.ac.cn
  • Supported by:
    the National Key Research & Development Program of China, No.2017YFA0603101.

摘要:

本文比较了基于AMSR-E被动微波数据的3种土壤冻融判别算法在青藏高原相关地区的分类精度。3种算法分别是:双指标算法、决策树算法、判别函数算法。本文选取了来自青藏高原那曲、玛曲、阿里3个地区土壤温湿度观测网的地表温度数据,并结合AMSR-E被动亮温数据,对上述算法在以上地区的分类精度分别进行了比较评价。结果表明:不论是白天还是夜间,相较于干旱区微波信号来自深层土壤的难以准确探测,在青藏高原半湿润半干旱区算法可取得相对较好的判别准确率;双指标算法相较于其他2种算法,在观测区具有较高的分类精度,且夜间分类精度高于白天;实测数据存在资料代表性不普遍即网格所包含站点信息量不够的问题,这也是后续工作中提高分类精度值得关注的着手点。

关键词: 青藏高原, 土壤冻融, 土壤温度, 分类精度, AMSR-E

Abstract:

The thesis paper is based on AMSR-E passive microwave brightness temperature data, chosen three soil surface freeze-thaw discrimination algorithms and compares the soil freeze-thaw status classification accuracy of the ground surface on Tibetan plateau area seperatly. Three algorithms are consisted of Dual Index Algorithm, Decision Tree Algorithm and Discriminant Function Algorithm. This thesis collects the soil surface temperature data from three soil temperature and humidity observational networks on Tibetan plateau that includes Naqu, Maqu and Ngari district, and combines with AMSR-E passive microwave brightness temperature data, then separately compares and evaluates the classification accuracy of the mentioned algorithms in the previous three observational areas. The results illustrate that whether during daytime or nighttime, in arid area like Ngari network district the microwave signal comes from deep layer soil, on the one hand, it’s kind of hard to probe in aid area and get an accurate detection as well, on the other hand, for all three discriminat algorithms that leads to the low discriminant accuracy in arid area at the same time. On the contrary each algorithm has relatively high discriminant accuracy in semi-arid area like Naqu network and subhumid area like Maqu network on Tibetan plateau; Dual Index Algorithm, compared with two other algorithm, has the highest classification accuracy among three discriminat algorithms, and has the best entirety classification accuracy in three observational networks on Tibetan plateau as well. The classificition accuracy at nighttime is higher than the dayttime due to the spatial heterogeneity of the air temperature between day and night; Except that the actual measured data also has the problem of incomprehensive representativeness, which means the inadequacy of information from all sites in the networks, and this is what needs to pay attention in the follow-up work to improve the algorithm classification accuracy.

Key words: Tibetan Plateau, soil freeze-thaw, soil temperature, classification accuracy, AMSR-E