High resolution remote estimation of soil surface water content by a thermal inertia approach

Minacapilli, Mario; Iovino, Massimo; Blanda, F.

doi:10.1016/j.jhydrol.2009.09.055

Cited by 77 publications

(52 citation statements)

References 35 publications

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“…Since the change of soil water content tends to cause the change of thermal conductivity, thermal inertia can be used for soil moisture monitoring [19]. Several parameters, such as bulk density, specific heat capacity and thermal conductivity, are used to calculate thermal inertia.…”

Section: Introductionmentioning

confidence: 99%

A Method for Downscaling FengYun-3B Soil Moisture Based on Apparent Thermal Inertia

Song

2016

Remote Sensing

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FengYun-3B (FY-3B) soil moisture product, retrieved from passive microwave brightness temperature data based on the Qp model, has rarely been applied at the catchment and region scale. One of the reasons for this is its coarse spatial resolution (25-km). The study in this paper presented a new method to obtain a high spatial resolution soil moisture product by downscaling FY-3B soil moisture product from 25-km to 1-km spatial resolution using the theory of Apparent Thermal Inertia (ATI) under bare surface or sparse vegetation covered land surface. The relationship between soil moisture and ATI was first constructed, and the coefficients were obtained directly from 25-km FY-3B soil moisture product and ATI derived from MODIS data, which is different from previous studies often assuming the same set of coefficients applicable at different spatial resolutions. The method was applied to Naqu area on the Tibetan Plateau to obtain the downscaled 1-km resolution soil moisture product, the latter was validated using ground measurements collected from Soil Moisture/Temperature Monitoring Network on the central Tibetan Plateau (TP-STMNS) in 2012. The downscaled soil moisture showed promising results with a coefficient of determination R 2 higher than 0.45 and a root mean-square error (RMSE) less than 0.11 m 3 /m 3 when comparing with the ground measurements at 5 sites out of the 9 selected sites. It was found that the accuracy of downscaled soil moisture was largely influenced by the accuracy of the FY-3B soil moisture product. The proposed method could be applied for both bare soil surface and sparsely vegetated surface.

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Section: Introductionmentioning

confidence: 99%

A Method for Downscaling FengYun-3B Soil Moisture Based on Apparent Thermal Inertia

Song

2016

Remote Sensing

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“…The model proposed provides the water content of the upper soil layer with a standard error of ∼0.01 compared to ∼0.02 and ∼0.04 reported by Minacapilli et al (2009b) applying P RS according to Idso et al (1976) and the models of Johansen (1975) and Campbell (1985) to assess λ(θ d ) on the same kind of airborne images (characterized by identical sensors and spatial resolution).…”

Section: Discussionmentioning

confidence: 87%

“…However, few authors quantitatively compare actual measurements of θ d with estimated values from images at high spatial resolution. Minacapilli et al (2009b) retrieved θ d by inverting equation (3), having first estimated P RS following Idso et al (1976), and using two different models- Johansen (1975) and Campbell (1985)-able to describe the relationship between λ and θ d . The procedure was also applied on ATM images acquired during the same NERC campaign on bare soil on the southern coast of Sicily (Minacapilli et al 2009b).…”

Section: Thermal Inertia and Surface Soil Water Contentmentioning

confidence: 99%

“…Minacapilli et al (2009b) retrieved θ d by inverting equation (3), having first estimated P RS following Idso et al (1976), and using two different models- Johansen (1975) and Campbell (1985)-able to describe the relationship between λ and θ d . The procedure was also applied on ATM images acquired during the same NERC campaign on bare soil on the southern coast of Sicily (Minacapilli et al 2009b). Minacapilli et al report a root mean square error (RMSE) of 0.024 using the Johansen model and 0.036 using the Campbell model with the original spatial resolution data (2 m).…”

Section: Thermal Inertia and Surface Soil Water Contentmentioning

confidence: 99%

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Critical analysis of thermal inertia approaches for surface soil water content retrieval

Maltese

Bates

Capodici

et al. 2013

Hydrological Sciences Journal

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The "thermal inertia" method to retrieve surface soil water content maps on bare or sparsely-vegetated soils is analysed. The study area is a small experimental watershed, where optical and thermal images (in day and night time) and in situ data were simultaneously acquired. The sensitivity of thermal inertia to the phase difference between incoming radiation and soil temperature is demonstrated. Thus, to obtain an accurate value of the phase difference, the temporal distance between thermographs using a three-temperature approach is evaluated. We highlight when a cosine correction of the temperature needs to be applied, depending on whether the thermal inertia formulation includes two generic acquisition times, or not. Finally, the deviation in soil water content retrieval is quantifies for given values of each parameter by performing a sensitivity analysis on the basic parameters of the thermal inertia method that are usually affected by calibration errors.Key words thermal inertia; surface soil water content; remote sensing Analyse critique des approches fondées sur l'inertie thermique pour la cartographie de la teneur en eau de la surface du sol Résumé La méthode de « l'inertie thermique » pour la cartographie de la teneur en eau de la surface des sols nus ou à végétation clairsemée est analysée. La zone d'étude est un petit bassin versant expérimental, où des images optiques et thermiques (de jour et de nuit) et des données in situ ont été acquises simultanément. On a mis en évi-dence la sensibilité de l'inertie thermique à la différence de phase entre le rayonnement incident et la température du sol. Ainsi, pour obtenir une valeur précise de la différence de phase, nous avons évalué la distance temporelle entre thermographes en utilisant une approche à trois températures. Cet article montre la nécessité éventuelle d'une correction cosinusoïdale de la température, selon que la formulation de l'inertie thermique comprend ou non deux temps d'acquisition génériques. Enfin, l'article quantifie la variabilité de la teneur en eau du sol pour des valeurs données de chaque paramètre en effectuant une analyse de sensibilité sur les paramètres de base de la méthode de l'inertie thermique qui sont généralement affectés par des erreurs d'étalonnage.

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“…Recent works by Maltese et al (2010) and Minacapilli et al (2012) emphasize the use of optical sensors of new generation to acquire images of the bare soil in the VIS-NIR and thermal infrared (TIR) through which to evaluate the spatial distribution of the soil water content. In particular, the soil thermal inertia method is widely used to estimate soil moisture from spectral images of bare soil in the VIS-NIR and TIR regions (Lu et al, 2009;Minacapilli et al, 2009Minacapilli et al, , 2012Antonucci et al, 2011), by applying inversion procedures of the thermal inertia or of the apparent thermal inertia derived from the spectral images, for which only the soil porosity is required to be known. Nowadays light and low cost optical and thermal cameras are available that can be mounted on lightweight UAV, thus providing an efficient way of collecting high resolution data.…”

Section: Introductionmentioning

confidence: 99%

Uav Multispectral Survey to Map Soil and Crop for Precision Farming Applications

Sona

Passoni

Pinto

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, ICWG I/VbKEY WORDS: UAV, multispectral images, DEM, orthophoto, NDVI, precision farming, soil and crop characterization ABSTRACT New sensors mounted on UAV and optimal procedures for survey, data acquisition and analysis are continuously developed and tested for applications in precision farming. Procedures to integrate multispectral aerial data about soil and crop and ground-based proximal geophysical data are a recent research topic aimed to delineate homogeneous zones for the management of agricultural inputs (i.e., water, nutrients). Multispectral and multitemporal orthomosaics were produced over a test field (a 100 m x 200 m plot within a maize field), to map vegetation and soil indices, as well as crop heights, with suitable ground resolution. UAV flights were performed in two moments during the crop season, before sowing on bare soil, and just before flowering when maize was nearly at the maximum height. Two cameras, for color (RGB) and false color (NIR-RG) images, were used. The images were processed in Agisoft Photoscan to produce Digital Surface Model (DSM) of bare soil and crop, and multispectral orthophotos. To overcome some difficulties in the automatic searching of matching points for the block adjustment of the crop image, also the scientific software developed by Politecnico of Milan was used to enhance images orientation. Surveys and image processing are described, as well as results about classification of multispectral-multitemporal orthophotos and soil indices.

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High resolution remote estimation of soil surface water content by a thermal inertia approach

Cited by 77 publications

References 35 publications

A Method for Downscaling FengYun-3B Soil Moisture Based on Apparent Thermal Inertia

A Method for Downscaling FengYun-3B Soil Moisture Based on Apparent Thermal Inertia

Critical analysis of thermal inertia approaches for surface soil water content retrieval

Uav Multispectral Survey to Map Soil and Crop for Precision Farming Applications

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