A simple method for the estimation of thermal inertia

Wang, J.; Bras, Rafael L.; Sivandran, G.; Knox, R. G.

doi:10.1029/2009gl041851

Cited by 49 publications

(35 citation statements)

References 12 publications

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“…Based on the NDWI , Wang et al [104] proposed using the normalized multi-band drought index ( NMDI ), which uses the band differences between the sensitivity of 1640 nm and 2130 nm for soil and vegetation, respectively, with enhanced sensitivity to drought monitoring. The successful application in detecting forest fires demonstrated that NMDI can provide a quick response to moisture changes [105,106].

N M D I = \frac{R_{860 n m} - (R_{1640 n m} - R_{2130 n m})}{R_{860 n m} + (R_{1640 n m} - R_{2130 n m})}

where R is the reflectance.…”

Section: Sm Estimation From Optical and Thermal Remote Sensingmentioning

confidence: 99%

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review

Zhang

Zhou

2016

Sensors

204

110

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As an important parameter in recent and numerous environmental studies, soil moisture (SM) influences the exchange of water and energy at the interface between the land surface and atmosphere. Accurate estimate of the spatio-temporal variations of SM is critical for numerous large-scale terrestrial studies. Although microwave remote sensing provides many algorithms to obtain SM at large scale, such as SMOS and SMAP etc., resulting in many data products, they are almost low resolution and not applicable in small catchment or field scale. Estimations of SM from optical and thermal remote sensing have been studied for many years and significant progress has been made. In contrast to previous reviews, this paper presents a new, comprehensive and systematic review of using optical and thermal remote sensing for estimating SM. The physical basis and status of the estimation methods are analyzed and summarized in detail. The most important and latest advances in soil moisture estimation using temporal information have been shown in this paper. SM estimation from optical and thermal remote sensing mainly depends on the relationship between SM and the surface reflectance or vegetation index. The thermal infrared remote sensing methods uses the relationship between SM and the surface temperature or variations of surface temperature/vegetation index. These approaches often have complex derivation processes and many approximations. Therefore, combinations of optical and thermal infrared remotely sensed data can provide more valuable information for SM estimation. Moreover, the advantages and weaknesses of different approaches are compared and applicable conditions as well as key issues in current soil moisture estimation algorithms are discussed. Finally, key problems and suggested solutions are proposed for future research.

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N M D I = \frac{R_{860 n m} - (R_{1640 n m} - R_{2130 n m})}{R_{860 n m} + (R_{1640 n m} - R_{2130 n m})}

where R is the reflectance.…”

Section: Sm Estimation From Optical and Thermal Remote Sensingmentioning

confidence: 99%

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review

Zhang

Zhou

2016

Sensors

204

110

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“…The utility of thermal remote sensing in detecting energy and moisture fluxes at the land surface is well documented [11][12][13][14][15]. For the purpose of monitoring soil moisture content, the common scheme is to decouple the surface thermal properties from ambient temperature (daily temperature cycle) by calculating the thermal inertia (TI), which is a physical property that characterizes the surface resistance to ambient temperature change [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data

et al. 2013

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Thermal remote sensing of soil moisture in vineyards is a challenge. The grass-covered soil, in addition to a standing grape canopy, create complex patterns of heating and cooling and increase the surface temperature variability between vine rows. In this study, we evaluate the strength of relationships between soil moisture, mechanical resistance and thermal inertia calculated from the drop of surface temperature during a clear sky night over a vineyard in the Niagara region. We utilized data from two sensors, an airborne thermal camera (height ≈ 500 m a.g.l.) and a handheld thermal gun (height ≈ 1 m a.g.l.), to explore the effects of different field of views and the high inter-row temperature variability. Spatial patterns of soil moisture correlated more with estimated thermal inertia than with surface temperature recorded at sunrise or sunset. Despite the coarse resolution of airborne thermal inertia images, it performed better than estimates from the handheld thermal gun. Between-row variation was further analyzed using a linear mixed-effects model. Despite the limited spatial variability of soil properties within a single vineyard, the magnitudes of the model coefficients for soil moisture and mechanical resistance are encouraging indicators of the utility of thermal inertia in vineyard management. OPEN ACCESSRemote Sens. 2013, 5 3730

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“…The diurnal variation of near-surface air temperature may be expressed in terms of a weighted time average (i.e., half-order integral) of sensible heat flux analogous to the half-order integral model of soil temperature and ground heat flux (Wang and Bras 1999;Bennett et al 2008):…”

Section: B Sensible Heat Flux Modelmentioning

confidence: 99%

“…In steps one and two, I is set at 3000 thermal inertia units (tiu; 1 tiu 5 1 J m 22 K 21 s 21/2 ) as an initial guess. In step three, the hourly temperature and sensible heat flux obtained from step two are used to estimate I as the regression coefficient between diurnal amplitudes of air temperature DT a and sensible heat flux DH according to the following equation (Wang et al 2010):…”

Section: A Proceduresmentioning

confidence: 99%

“…The retrieval algorithm of hourly meteorological variables from hourly satellite data and in situ daily air temperature is based on three models: 1) surface net radiation R n is estimated using hourly albedo a derived from channel one (visible) of GOES (Bisht and Bras 2010), 2) partition of net radiation into fluxes (sensible, latent, and ground heat fluxes) is estimated using the maximum entropy production (MEP) model (Wang and Bras 2011), and 3) hourly surface air temperature is retrieved from sensible heat flux obtained from the second model using the half-order integral model (Wang and Bras 1999).…”

Section: Algorithm Formulationmentioning

confidence: 99%

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Retrieval of Hourly Records of Surface Hydrometeorological Variables Using Satellite Remote Sensing Data

Moghim

Bowen

Sarachi

et al. 2015

Journal of Hydrometeorology

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A new algorithm is formulated for retrieving hourly time series of surface hydrometeorological variables including net radiation, sensible heat flux, and near-surface air temperature aided by hourly visible images from the Geostationary Operational Environmental Satellite (GOES) and in situ observations of mean daily air temperature. The algorithm is based on two unconventional, recently developed methods: the maximum entropy production model of surface heat fluxes and the half-order derivative-integral model that has been tested previously. The close agreement between the retrieved hourly variables using remotely sensed input and the corresponding field observations indicates that this algorithm is an effective tool in remote sensing of the earth system.

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A simple method for the estimation of thermal inertia

Cited by 49 publications

References 12 publications

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review

Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data

Retrieval of Hourly Records of Surface Hydrometeorological Variables Using Satellite Remote Sensing Data

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