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Improving the performance of smartphone-derived crop leaf area index

  • role： First author第一作者
  • Affiliation：

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

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：wzx@mail.bnu.edu.cn
  • Introduction：汪梓鑫，研究方向为植被定量遥感。E-mail： wzx@mail.bnu.edu.cn
WANG Zixin12,  
  • role： Corresponding author通信作者
  • Affiliation：

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

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：qyh@bnu.edu.cn
  • Introduction：屈永华，研究方向为定量遥感。E-mail： qyh@bnu.edu.cn
QU Yonghua12*,  
  • Affiliation：

    LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

FANG Hongliang34

resumen

As an important parameter of vegetation canopy structure, the Leaf Area Index (LAI) has become a standard land surface parameter product for many earth observation systems and an important input parameter for several quantitative remote sensing models. Rapid and accurate acquisition of vegetation LAI is of great significance for the verification of remote sensing products and promotion of the development of remote sensing models. With the improvement of smartphone sensor performance and the functions of application software, smartphones have become a new alternative to vegetation LAI measurement instruments. However, due to the limitation of the narrow Field Of View (FOV) angle of the smartphone camera sensor, the existing algorithm relies on the assumption that the leaf inclination belongs to the spherical distribution, which is that the G function (the projection of a unit leaf area on a plane perpendicular to the observed zenith angle) is equal to 0.5. Therefore, the traditional algorithm cannot solve the problem of unknown leaf inclination distribution. In this paper, a G function estimation method based on shape matching was proposed. Based on the finite length method and the gap fraction of multiple images, the vegetation canopy clumping index in the quadrat was calculated, and the effective LAI (LAIeff) and the real LAI (LAItru) were obtained by using the Poisson distribution model. The algorithm was validated by data obtained from destructive measurements (LAIdes) of two crop types (maize and soybean) at Hailun Farm in Heilongjiang Province, China. The measured time covers the main growth stages of the crop. The results showed that the Root Mean Square Error (RMSE) of the estimated LAI using the algorithm before improvement was 0.84 (vertical shooting) and 1.33 (tilted 57° shooting), and the RMSE of LAIeff and LAItru after the improvement was 0.58 and 0.56, respectively. The LAI values retrieved by the new algorithm are more consistent with the growing trend of LAI in the time series. The algorithm in this paper extends the measurement method of crop LAI, which provides the possibility to quickly and accurately extract vegetation LAI from smartphone-captured images. Further research will be considered in two directions: analyzing the influence of external light environment changes on the measurement results and adding validation data of different vegetation types.

palabra clave

remote sensing;smartphone;leaf area index;multi-angle gap fractions;G function;clumping index;effective leaf area index

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