Inversion of the leaf area index of rice fields using vegetation isoline patterns considering the fraction of vegetation cover

ABSTRACT

Inversion on the basis of radiative transfer model (RTM) is the prevailing approach for obtaining the leaf area index (LAI) from satellite images. Ill-posed inversions are a problem in RTM-based methods, however, and so here the vegetation isoline pattern in red and near-infrared spectral space was used, with consideration of the fraction of vegetation cover (fCover), to develop a look-up table (LUT) for the LAI inversion of rice fields. In PROSAIL (PROpriétésSPECTrales and Scattering by Arbitrarily Inclined Leaves) model simulations, to avoid some unreasonable parameter combinations, the values of the input parameters were set with some a priori knowledge, and 3580 parameter combinations were generated in the LUT. This represents much fewer combinations than for a conventional LUT. Comparison tests demonstrated that the small-size LUT built with prior knowledge did not decrease the accuracy of the inversed LAIs; rather it improved the accuracy by taking into account fCover. The proposed LUT was applied to the images captured by the wide field of view (WFV) cameras loaded on the Gaofen-1 (GF-1) satellite. Evaluation of the inversed LAIs using in situ data showed that the root-mean-square error (RMSE) was 0.37, and that the relative error (RE) was 14%. Comparison with the error of inversed LAIs produced by the LUT without considering fCover revealed that taking into account the fCover when building a LUT based on the vegetation isoline pattern improved the accuracy of the LAI inversion. This study demonstrates that vegetation isoline-based LUT, with consideration of fCover, is a promising technique for producing LAI maps of crops with high spatial resolution. It will also be helpful for the validation of moderate-resolution LAI products and agricultural monitoring.

Acknowledgements

The authors thank for the helps in field work supported by Ph.D Xu Ma, MSc. Yani Li, Chudi Huang, Jufeng Liu, Xiguang Han, Yue Jiang, and Haixia Lu. The GF-1 WFV images were acquired from China Centre for Resources Satellite Data and Application (http://www.cresda.com/CN/index.shtml).

Disclosure statement

The authors declare that there is no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (grant number 41401372) and the Fundamental Research Funds for the Central Universities (Grant lzujbky-2016-170).