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A laboratory experiment is set up to study both surface and in-depth soil moisture content (SMC). For that purpose, an aquarium is filled successively with two soils, a clay loam and a sand. Reflectance spectra are acquired in the solar domain (400–2400 nm) on the soil surface using an ASD FieldSpec 3 HR spectroradiometer and in-depth through the aquarium glass wall using two hyperspectral cameras. Successive amounts of water ranging from low to heavy rainfall in a temperate region are uniformly poured into the aquarium. The MARMITforSMC method based on the MARMIT (MultilAyer Radiative Transfer Model for soIl reflecTance) model is applied to each reflectance spectrum to determine gravimetric SMC. In particular, vertical profiles of SMC are provided with unprecedented spatial accuracy (~0.287 mm). The results are compared with volumetric SMC measured by two time-domain reflectometry (TDR) sensors. The in-depth SMC image produced on the sand shows lower values within the first 2 cm (5%) than below (17%). In contrast, the SMC image produced on the clay loam shows evenly distributed values whatever the position in the aquarium, even 1 h after moistening. The difference in grain size between the soils explains this result. • A lab experiment is designed to measure surface and in-depth soil reflectance. • The MARMIT model is used to associate surface moisture content to reflectance. • Vertical maps of soil moisture content are generated on clay loam and sandy soils. • The corresponding profiles are calculated for both soils. • Their evolution over time is analyzed. [ABSTRACT FROM AUTHOR]

Abstract Surface soil moisture content (SMC) is known to impact soil reflectance at all wavelengths of the solar spectrum. As a consequence, many semi-empirical methods aim at inferring SMC from soil reflectance, but very few rely on physically-based models. This article presents a multilayer radiative transfer model of soil reflectance called MARMIT (multilayer radiative transfer model of soil reflectance) as a function of SMC given on a mass basis and a method called MARMITforSMC to estimate it from soil reflectance spectra. This model depicts a wet soil as a dry soil covered with a thin film of water. It is used to assess SMC over seven independent laboratory datasets gathered from the literature. A learning phase is required to link the thickness of the water film with the SMC. For that purpose, a sigmoid function, the parameters of which are related to soil physical and chemical properties such as porosity, grain size and mineralogy composition, is fitted. SMC can be inferred with good accuracy (RMSE ≈ 3%) if the learning step is applied soil by soil. The link between SMC and water thickness actually depends on soil texture and chemical composition. If the soils are divided into classes and if the learning phase is applied to a class, the RMSE slightly increases up to 5%. Finally, MARMITforSMC provides lower RMSE than any other existing semi-empirical or physically-based method. Highlights • A multilayer radiative transfer model of soil reflectance as a function of surface water content is developed. • A new method of SMC retrieval is developed. • SMC retrieval combines good accuracy and efficiency after a soil classification. • The new method is compared to other SMC retrieval methods. [ABSTRACT FROM AUTHOR]

This paper presents MARMIT-2, a radiative transfer model that predicts the spectral reflectance of soils in the solar domain (0.4–2.5 μm) 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 μm) and in the water absorption bands around 1.45 μm and 1.95 μm. 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. • MARMIT-2, an improved version of the MARMIT model, is presented. • The spectral reflectance of wet soils is predicted with better accuracy. • The optical properties of a water layer containing soil particles are calculated. • A mixing function is used to calculate an effective complex refractive index. • A database of 225 soil samples measured in the laboratory is used for validation. [ABSTRACT FROM AUTHOR]