Hybrid inversion of radiative transfer models based on topographically corrected Landsat surface reflectance improves leaf area index and aboveground biomass retrievals of grassland on the hilly Loess Plateau

ABSTRACT

Accurate monitoring of the leaf area index (LAI) and aboveground biomass (AGB) using remote sensing at a fine scale is crucial for understanding the spatial heterogeneity of vegetation structure in mountainous ecosystems. Understanding discrepancies in various retrieval strategies considering topographic effects or not is necessary to improve LAI and AGB estimations over mountainous areas. In this study, the performances of the look-up table method (LUT) using radiative transfer model (RTM), machine learning algorithms (MLAs), and hybrid RTM integrating RTM and MLAs based on Landsat surface reflectance (SR) before and after topographic correction were compared and analyzed. The results show that topographic correction improves the accuracies of retrieval methods involving RTM more significantly than the MLAs, meanwhile, it reduces the performance variability of different MLAs. Based on the topographically corrected Landsat SR, the random forest (RF) combined with RTM improves the retrieval accuracy of RTM-based LUT by 7.7% for LAI and 13.8% for AGB, and reduces the simulation error of MLA by 15.1% for LAI and 20.1% for AGB. Compared with available remote sensing products, the hybrid RTM based on Landsat SR with topographic correction has better feasibility to capture LAI and AGB variation at 30 m scale over mountainous areas.

KEYWORDS:

• Leaf area index
• Aboveground biomass
• PROSAIL
• topographic correction
• hybrid RTM

Acknowledgements

The authors thank Yi Zhang, Miaomiao Cheng, Enjun Gong, Xu Li and Haolin Huang for assistance with LAI and $C_m$ measurements.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The code used in this study is available by contacting the corresponding author.

Author contributions

Acquisition of financial support for the project leading to this publication, Z.W.; Application of statistical, mathematical, computational, or other formal techniques to analyze or synthesize study data, S.P. and Z.W.; Preparation, creation, and/or presentation of the published work by those from the original research group, specifically critical review, commentary, or revision, including pre- or post-publication stages, X.L., Z.W. and X.L.

Additional information

Funding

This work was supported by National Key Research and Development Program of China: [Grant Number 2022YFC3205200]; the Joint Funds of the National Natural Science Foundation of China: [Grant Number U2243212]; Science Foundation for Young Elite Talents of YRCC: [Grant Number HQK-202307]; Research on Key Technology of Agricultural Remote Sensing Monitoring: [Grant Number 12210243]; 2016 National Key Research and Development Plan: [Grant Number 2016YFC0803103].