سابِق|تال

Review on the theory， method， and research progress of leaf area index estimation in mountainous areas

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

  • Email：201931051040@mail.bnu.edu.cn
  • Introduction：江海英，1992年生,女,博士研究生, 研究方向为植被定量遥感。E-mail: 201931051040@mail.bnu.edu.cn
JIANG Haiying12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

  • Email：jiakun@bnu.edu.cn
  • Introduction：贾坤，1983年生，男，副教授，研究方向为植被定量遥感、土地覆盖遥感分类和生态遥感。E-mail: jiakun@bnu.edu.cn
JIA Kun12*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

ZHAO Xiang12,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

WEI Xiangqin3,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

WANG Bing12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

YAO Yunjun12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

ZHANG Xiaotong12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

JIANG Bo12

ملخص

Leaf Area Index (LAI) is an important vegetation parameter that represents leaf density and canopy structure characteristics. This parameter plays an important role in climate change, crop growth model, and carbon and water cycle studies. Remote sensing is an important means to estimate LAI on regional and global scales. LAI products are currently mainly obtained by remote sensing retrieval. However, most LAI product algorithms ignore the effect of topographic features, which results in the great uncertainty in the accuracy of retrieved LAI in mountainous areas. The influence of topographic factors on the canopy reflectance needs to be considered to improve the accuracy of mountain LAI retrieval. Generally, there are mainly two methods to eliminate the influence of topography on mountain LAI retrieval. One method is to use the mountain canopy reflectance model to simulate reflectance, and the other method is to perform topographic correction on remote sensing data.In this paper, the research progress of mountain canopy reflectance model and topographic correction method were comprehensively analyzed on the basis of the theories and methods of LAI retrieval in mountainous areas. For mountain LAI retrieval method based on mountain canopy reflectance simulation, some mountain canopy reflectance models simplify the influence of topographic factors on atmospheric scattering and adjacent terrain scattering, resulting in poor model simulation and low LAI retrieval accuracy. Some complex mountain canopy reflectance models, such as geometric-optical hybrid model or computer simulation model, can accurately simulate topographic effect on reflectance, but it is difficult to invert due to complex input parameters. For mountain LAI retrieval method based on image topographic correction, it is difficult to choose suitable topographic correction method, because the generality of the existing models is poor that a single topographic correction model may only be applicable to a certain terrain condition, a certain area, a certain sensor or a certain waveband. In addition to the above two methods, some studies directly add topographic factors into the statistical regression equation of LAI as a control variable, so as to retrieve mountain LAI. However, this method may cause over fitting phenomenon and does not have robustness and portability.Based on the existing problems of mountain canopy reflectance model, topographic correction method and mountain LAI retrieval method, this paper summarizes and discusses the development trend of future research of mountain LAI retrieval. For mountain canopy reflectance model, it is necessary to develop a model that takes into account the non-Lambertian characteristics of the surface, the geotropic growth of trees, and diffuse radiation and other factors to improve the accuracy of model simulation. In addition, the parameter optimization and retrievability of the model should also be considered. For topographic correction method, it can be combined with BRDF correction or atmospheric correction in the future, especially for complex terrain. To accurately and efficiently retrieve mountain LAI, it is necessary to comprehensively consider factors such as the size of the study area, the heterogeneity of the ground surface, and the degree of terrain undulations, and choose an appropriate topographic correction method or mountain canopy reflectance model. Moreover, it is necessary to carry out more in-depth research on the validation of LAI retrieval accuracy in mountainous areas.

مفهوم

remote sensing;optical remote sensing;LAI;topographic correction;mountain canopy reflectance model;DEM

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