Preprints
https://doi.org/10.5194/egusphere-2026-344
https://doi.org/10.5194/egusphere-2026-344
03 Mar 2026
 | 03 Mar 2026
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

A unified Hapke-HSR + MARMIT-2 soil radiative transfer model for reflectance simulation under varying moisture conditions

Anxin Ding, Han Ma, Shunlin Liang, Ziti Jiao, Alexander A. Kokhanovsky, Hanyu Shi, and Rui Xie

Abstract. Soil radiative transfer models (RTMs) provide a physical basis for interpreting surface reflectance and retrieving land-surface parameters. However, most existing soil RTMs represent either the spectral-directional scattering behavior of dry soils or the moisture-induced absorption effects of wet soils, and a physically consistent formulation capable of jointly describing both processes remains limited. In this study, we develop a unified soil RTM by refining the Hapke-based hyperspectral reflectance model (Hapke-HSR) using dry soil reflectance and dynamically coupling it with the improved multilayer RTM of soil reflectance (MARMIT-2). The proposed Hapke- HSR + MARMIT-2 model explicitly represents the interaction between particle scattering and moisture-dependent absorption and refraction processes, enabling joint spectral-directional simulation of soil reflectance under varying soil moisture conditions. The model is systematically evaluated using eight independent soil spectral databases spanning a wide range of textures and moisture levels. Results show that the Hapke-HSR + MARMIT-2 model consistently improves simulation accuracy and stability relative to the individual Hapke-HSR and MARMIT-2 models, with particularly pronounced gains at high soil moisture regimes (SMC ≥ 30 %). Across all datasets, the proposed model achieves higher performance (R2 = 0.993, RMSE = 0.007) than MARMIT-2 (R2 = 0.983, RMSE = 0.012) and Hapke-HSR (R2 = 0.909, RMSE = 0.028). The proposed framework provides a physically interpretable and extensible basis for soil reflectance modeling and offers a robust foundation for future developments in multiangular hyperspectral remote sensing and land-surface parameter inversion.

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Anxin Ding, Han Ma, Shunlin Liang, Ziti Jiao, Alexander A. Kokhanovsky, Hanyu Shi, and Rui Xie

Status: open (until 28 Apr 2026)

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Anxin Ding, Han Ma, Shunlin Liang, Ziti Jiao, Alexander A. Kokhanovsky, Hanyu Shi, and Rui Xie
Anxin Ding, Han Ma, Shunlin Liang, Ziti Jiao, Alexander A. Kokhanovsky, Hanyu Shi, and Rui Xie
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Latest update: 03 Mar 2026
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Short summary
Soil reflectance strongly influences how Earth surface properties are observed from space, especially when soil moisture changes. In this study, we develop a new soil reflectance model by combining two existing physical models to better represent both dry and wet soils. The proposed model improves accuracy and stability across different soil moisture conditions and provides a more reliable basis for interpreting optical remote sensing observations and retrieving land surface parameters.
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