首页|MARMIT-2: An improved version of the MARMIT model to predict soil reflectance as a function of surface water content in the solar domain

MARMIT-2: An improved version of the MARMIT model to predict soil reflectance as a function of surface water content in the solar domain

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  • NSTL
  • Elsevier
This paper presents MARMIT-2, a radiative transfer model that predicts the spectral reflectance of soils in the solar domain (0.4-2.5 mu m) as a function of their surface moisture. This is an improved version of MARMIT (multilayer radiative transfer model of soil reflectance) that represents a wet soil as a dry soil topped with a thin layer of liquid water. The changes brought in this article concern the mixing of the spectral reflectance of the dry and wet soil areas, the transmission of diffuse light in the water layer, and the inclusion of soil particles in the water layer. Wet soil reflectance is now expressed in terms of dry soil reflectance and three free parameters: the thickness of the water layer, the surface fraction of the wet soil, and a new parameter, the volume fraction of soil particles in the water layer. With more accurate physical modeling, MARMIT-2 simulates soil spectral reflectance with better accuracy than MARMIT. In particular, the fit of the soil reflectance spectra is much better for high water contents, both in the visible range (0.4-0.7 mu m) and in the water absorption bands around 1.45 mu m and 1.95 mu m. The average root mean square error between measured and predicted reflectance obtained on a set of 225 soil samples is about 0.8% with MARMIT-2 versus 1.8% with MARMIT.

Hyperspectral remote sensingSoil moisture contentMARMIT modelMineral soil particlesEffective refractive indexDot gain effectCOMPLEX REFRACTIVE-INDEXSAHARAN-MINERAL-DUSTOPTICAL-PROPERTIESSPECTRAL REFLECTANCEMOISTUREDEPENDENCESCATTERINGPARTICLESAEROSOLSEQUATION

Dupiau, A.、Briottet, X.、Fabre, S.、Viallefont-Robinet, F.、Philpot, W.、Di Biagio, C.、Hebert, M.、Formenti, P.、Jacquemoud, S.

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Univ Paris

Univ Fed Toulouse

Cornell Univ

Univ Lyon

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2022

10.1016/j.rse.2022.112951

Remote Sensing of Environment

Remote Sensing of Environment

EISCI
ISSN：0034-4257
年,卷(期)：2022.272
  • 8
  • 67