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Article

Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data

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State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
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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
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College of Water Sciences, Beijing Normal University, Beijing 100875, China
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School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China
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State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Academic Editor: Janne Heiskanen
Remote Sens. 2021, 13(23), 4911; https://doi.org/10.3390/rs13234911
Received: 23 August 2021 / Revised: 9 November 2021 / Accepted: 30 November 2021 / Published: 3 December 2021
(This article belongs to the Special Issue Multi-Angular Remote Sensing)
Canopy structure parameters (e.g., leaf area index (LAI)) are key variables of most climate and ecology models. Currently, satellite-observed reflectances at a few viewing angles are often directly used for vegetation structure parameter retrieval; therefore, the information content of multi-angular observations that are sensitive to canopy structure in theory cannot be sufficiently considered. In this study, we proposed a novel method to retrieve LAI based on modelled multi-angular reflectances at sufficient sun-viewing geometries, by linking the PROSAIL model with a kernel-driven Ross-Li bi-directional reflectance function (BRDF) model using the MODIS BRDF parameter product. First, BRDF sensitivity to the PROSAIL input parameters was investigated to reduce the insensitive parameters. Then, MODIS BRDF parameters were used to model sufficient multi-angular reflectances. By comparing these reference MODIS reflectances with simulated PROSAIL reflectances within the range of the sensitive input parameters in the same geometries, the optimal vegetation parameters were determined by searching the minimum discrepancies between them. In addition, a significantly linear relationship between the average leaf angle (ALA) and the coefficient of the volumetric scattering kernel of the Ross-Li model in the near-infrared band was built, which can narrow the search scope of the ALA and accelerate the retrieval. In the validation, the proposed method attains a higher consistency (root mean square error (RMSE) = 1.13, bias = −0.19, and relative RMSE (RRMSE) = 36.8%) with field-measured LAIs and 30-m LAI maps for crops than that obtained with the MODIS LAI product. The results indicate the vegetation inversion potential of sufficient multi-angular data and the ALA relationship, and this method presents promise for large-scale LAI estimation. View Full-Text
Keywords: vegetation estimation; leaf area index (LAI); average leaf angle (ALA); bidirectional reflectance; kernel-driven Ross-Li model vegetation estimation; leaf area index (LAI); average leaf angle (ALA); bidirectional reflectance; kernel-driven Ross-Li model
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MDPI and ACS Style

Zhang, X.; Jiao, Z.; Zhao, C.; Yin, S.; Cui, L.; Dong, Y.; Zhang, H.; Guo, J.; Xie, R.; Li, S.; Zhu, Z.; Tong, Y. Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data. Remote Sens. 2021, 13, 4911. https://doi.org/10.3390/rs13234911

AMA Style

Zhang X, Jiao Z, Zhao C, Yin S, Cui L, Dong Y, Zhang H, Guo J, Xie R, Li S, Zhu Z, Tong Y. Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data. Remote Sensing. 2021; 13(23):4911. https://doi.org/10.3390/rs13234911

Chicago/Turabian Style

Zhang, Xiaoning, Ziti Jiao, Changsen Zhao, Siyang Yin, Lei Cui, Yadong Dong, Hu Zhang, Jing Guo, Rui Xie, Sijie Li, Zidong Zhu, and Yidong Tong. 2021. "Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data" Remote Sensing 13, no. 23: 4911. https://doi.org/10.3390/rs13234911

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