资源科学 ›› 2017, Vol. 39 ›› Issue (8): 1592-1604.doi: 10.18402/resci.2017.08.15

所属专题: 气候变化与地表过程

• • 上一篇    下一篇

考虑土壤水分影响的比辐射率方法在地表温度反演中的应用

董雪1,2(), 田静1(), 吴骅3, 刘素华1,2   

  1. 1. 中国科学院地理科学与资源研究所陆地水循环及地表过程重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
  • 收稿日期:2017-01-12 修回日期:2017-06-15 出版日期:2017-08-20 发布日期:2017-08-20
  • 作者简介:

    作者简介:董雪,女,山东德州人,硕士生,主要研究方向为定量遥感。E-mail:dongx.14s@igsnrr.ac.cn

  • 基金资助:
    基金项目:国家自然科学基金面上项目(41271380;41671354)

Application of the emissivity method considering the effects of soil moisture for retrieving land surface temperature

Xue DONG1,2(), Jing TIAN1(), Hua WU3, Suhua LIU1,2   

  1. 1. Key Laboratory of Water Cycle and Related Land Surface Processes,Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
    2. University of Chinese Academy of Sciences,Beijing 100049,China
    3. State Key Laboratory of Resources and Environmental Information System,Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
  • Received:2017-01-12 Revised:2017-06-15 Online:2017-08-20 Published:2017-08-20

摘要:

地表比辐射率是确定地表长波能量平衡的一个关键参数,也是影响地表温度反演的主要因素,因此比辐射率的精确测定具有重要意义。地表比辐射率除了受地表覆盖类型的影响,与土壤水分含量也密切相关。本文针对MODIS通用分裂窗算法和Landsat TM/ETM+单窗算法,根据 Mira等建立的土壤含水量和土壤比辐射率的经验模型,利用SMEX04试验中Arizona研究区的遥感数据和地面观测数据,探讨考虑土壤水分影响后的比辐射率信息在地表温度反演中是否能够提高其反演精度。研究结果表明:利用考虑土壤水分影响后的比辐射率所反演的地表温度平均误差(ME)和均方根误差(RMSE)均低于比辐射率未考虑土壤水分影响反演的地表温度,其中通用分裂窗算法反演的地表温度ME降低了1.0~1.5K,RMSE降低了0.4~0.8K;单窗算法反演的地表温度ME降低了0.7K,RMSE降低了0.9K。因此,基于土壤比辐射率与土壤水分关系模型的比辐射率修正方法能够提高地表温度的反演精度,并且敏感性分析的结果表明目前土壤水分遥感数据0.04cm3/cm3的误差对本文使用的考虑土壤水分获取地表比辐射率进而反演地表温度的方法影响不明显。

关键词: MODIS通用分裂窗算法, Landsat TM/ETM+单窗算法, 比辐射率, SMEX04, 地表温度, 土壤水分

Abstract:

The emissivity of natural surfaces is a major parameter determining land surface temperature(LST). In addition,the surface cover type influences emissivity and soil moisture is closely related to emissivity. Here,methods for obtaining the emissivity of bare soil using the MODIS generalized split-window algorithm and Landsat mono-window algorithm were improved. According to the empirical logarithm linear formula between soil moisture and soil emissivity,based on remote sensing data and ground observation data from the Soil Moisture Experiment 2004 (SMEX04)- Arizona study area,we discuss if the accuracy of land surface temperature retrieval can be improved when surface emissivity acquisition methods consider effects of soil moisture. We found that the accuracy of both improved algorithms considering soil moisture effects were better than algorithms not considering soil moisture effects. The mean error of LST retrieved by the improved MODIS generalized split-window algorithm reduced 1.0~1.5K,and the root mean square error reduced by 0.4~0.8K. Moreover,the mean error of LST retrieved by the improved Landsat mono-window algorithm reduced by 0.7K,and the root mean square error reduced by 0.9K. As a whole,the accuracy of land surface temperature retrieval can be improved when surface emissivity acquisition methods consider the effects of soil moisture,especially areas where vegetation coverage is less. Sensitivity analysis results show that the influence of remote sensing soil moisture data with a 0.04cm3/cm3 error on LST retrieval algorithms considering soil moisture effects is not obvious.

Key words: MODIS generalized split-window algorithm, Landsat TM/ETM+ mono-window algorithm, emissivity, SMEX04, Land surface temperature, soil moisture