MARMIT: A multilayer radiative transfer model of soil reflectance to estimate surface soil moisture content in the solar domain (400–2500 nm)

Bablet, A.; Vu, Phuong Lan; Jacquemoud, Stéphane; Viallefont-Robinet, Françoise; Fabre, Sophie; Briottet, Xavier; Sadeghi, Morteza; Whiting, Michael L.; Baret, Frédéric; Tian, Jia

doi:10.1016/j.rse.2018.07.031

Cited by 79 publications

(72 citation statements)

References 78 publications

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“…MARMIT is a radiative transfer model that simulates the reflectance spectrum of a wet soil in the solar domain assuming that the reflectance spectrum of the dry soil is known (Bablet et al, 2018). It considers a wet soil as a dry soil covered with a thin film of water characterized by two parameters: the thickness of the film and the efficiency defined as the proportion of the ground covered by water.…”

Section: The Marmit Model and The Marmitforsmc Approachmentioning

confidence: 99%

“…Then, the values of = × are injected into Eqs (4) and (5) to calculate SMC (Figs 11 and 12). The estimation error is about 3% (Bablet et al, 2018). Note that the calibration equations linking SMC and φ do not necessarily pass through the origin (Fig.…”

Section: Smc Maps and Profiles Versus Tdr Measurementsmentioning

confidence: 99%

“…They applied a relationship derived from the Kubelka-Munk theory to study water flux profiles with an unprecedented accuracy. This article presents a laboratory experiment imagined nearly at the same period to assess both surface SMC and in depth SMC profiles thanks to a spectrometer, two hyperspectral cameras, and a radiative transfer model called MARMIT (Bablet et al, 2018). We poured controlled amounts of water on two different soils and measured the evolution of the SMC profiles with high spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

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High-resolution mapping of in-depth soil moisture content through a laboratory experiment coupling a spectroradiometer and two hyperspectral cameras

Bablet

Viallefont-Robinet

Jacquemoud

et al. 2020

Remote Sensing of Environment

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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 2cm (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 1h after moistening. The difference in grain size between the soils explains this result.

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Section: The Marmit Model and The Marmitforsmc Approachmentioning

confidence: 99%

Section: Smc Maps and Profiles Versus Tdr Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-resolution mapping of in-depth soil moisture content through a laboratory experiment coupling a spectroradiometer and two hyperspectral cameras

Bablet

Viallefont-Robinet

Jacquemoud

et al. 2020

Remote Sensing of Environment

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“…Studies have analyzed the relationship between hyper-spectral data and soil moisture, based on physical and statistical models [37,38], and have proposed several soil moisture inversion indices [39][40][41]. Avoiding the difference between complex conditions in the field and spectral data obtained under laboratory conditions, is the main problem for a quantitative monitoring of soil moisture in the future.…”

Section: Spatial Resolution Temporal Resolutionmentioning

confidence: 99%

A New Remote Sensing Dryness Index Based on the Near-Infrared and Red Spectral Space

Zhang

Bao

et al. 2019

Remote Sensing

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Soil moisture, as a crucial indicator of dryness, is an important research topic for dryness monitoring. In this study, we propose a new remote sensing dryness index for measuring soil moisture from spectral space. We first established a spectral space with remote sensing reflectance data at the near-infrared (NIR) and red (R) bands. Considering the distribution regularities of soil moisture in this space, we formulated the Ratio Dryness Monitoring Index (RDMI) as a new dryness monitoring indicator. We compared RDMI values with in situ soil moisture content data measured at 0–10 cm depth. Results showed that there was a strong negative correlation (R = −0.89) between the RDMI values and in situ soil moisture content. We further compared RDMI with existing remote sensing dryness indices, and the results demonstrated the advantages of the RDMI. We applied the RDMI to the Landsat-8 imagery to map dryness distribution around the Fukang area on the Northern slope of the Tianshan Mountains, and to the MODIS imagery to detect the spatial and temporal changes in dryness for the entire Xinjiang in 2013 and 2014. Overall, the RDMI index constructed, based on the NIR–Red spectral space, is simple to calculate, easy to understand, and can be applied to dryness monitoring at different scales.

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“…• Empirical & semi-empirical models relating backscattering coefficient to soil water content (& soil surface roughness) (Attarzadeh et al, 2018;Bao et al, 2018;Bousbih et al, 2018;Dubois et al, 1995;Genis et al, 2013;Hajj et al, 2017;Hosseini et al, 2015;Huang et al, 2019; • RTM-based approaches relating soil dielectric constant to soil moisture (Bablet et al, 2018;Dubois et al, 1995;Hosseini et al, 2015;Jackson, 2002; • Spectral indices e.g. Normalised Multiband Drought Index (NMDI) Wang and Qu, 2009) • Retrieval from thermal data using apparent thermal inertia (ATI) Verstraeten et al, 2006;Wang and Qu, 2009) • Map surface roughness & SM in sparsely vegetated landscapes using a multi-angle (radar-based)…”

Section: Biochemical Traits Inc Chlorophyll (Ch) and Water Content Nimentioning

confidence: 99%

Monitoring the Sustainable Intensification of Arable Agriculture: the Potential Role of Earth Observation

Hunt

Blackburn

Rowland

2019

International Journal of Applied Earth Observation and Geoinfor

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Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increase in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose a development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data.

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MARMIT: A multilayer radiative transfer model of soil reflectance to estimate surface soil moisture content in the solar domain (400–2500 nm)

Cited by 79 publications

References 78 publications

High-resolution mapping of in-depth soil moisture content through a laboratory experiment coupling a spectroradiometer and two hyperspectral cameras

High-resolution mapping of in-depth soil moisture content through a laboratory experiment coupling a spectroradiometer and two hyperspectral cameras

A New Remote Sensing Dryness Index Based on the Near-Infrared and Red Spectral Space

Monitoring the Sustainable Intensification of Arable Agriculture: the Potential Role of Earth Observation

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MARMIT: A multilayer radiative transfer model of soil reflectance to estimate surface soil moisture content in the solar domain (400–2500 nm)

Cited by 79 publications

References 78 publications

High-resolution mapping of in-depth soil moisture content through a laboratory experiment coupling a spectroradiometer and two hyperspectral cameras

High-resolution mapping of in-depth soil moisture content through a laboratory experiment coupling a spectroradiometer and two hyperspectral cameras

A New Remote Sensing Dryness Index Based on the Near-Infrared and Red Spectral Space

Monitoring the Sustainable Intensification of Arable Agriculture: the Potential Role of Earth Observation

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Product

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MARMIT: A multilayer radiative transfer model of soil reflectance to estimate surface soil moisture content in the solar domain (400–2500 nm)