Characterizing surface soil water with field portable diffuse reflectance spectroscopy

Zhu, Yongnan; Weindorf, David C.; Chakraborty, Somsubhra; Haggard, Beatrix; Johnson, Sharon; Bakr, Noura

doi:10.1016/j.jhydrol.2010.07.014

Cited by 46 publications

(31 citation statements)

References 19 publications

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“…This question was critical as we attempted to develop models which could ultimately lead to in-situ measurements. Given that, VisNIR DRS can sense the changes in the matrix material scanned, particularly moisture (as it relates to O-H bonding and color) (Bishop et al, 1994;Zhu et al, 2010), bringing the soil samples to standard water content (field capacity) prior to scanning is critical for obtaining consistent results. However, although under laboratory controlled conditions soil can be scanned under uniform moisture content, maintaining unique water content in the field is not easy.…”

Section: Psr Rmentioning

confidence: 99%

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

Chakraborty

Weindorf

et al. 2015

Science of The Total Environment

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Section: Psr Rmentioning

confidence: 99%

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

Chakraborty

Weindorf

et al. 2015

Science of The Total Environment

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“…Most studies relating reflectance to water content have started with saturated samples and made observations at various stages of drying [2][3] [5][6] [7][8] [12], and reasonable predictive relationships have been derived relying on the assumption that the distribution of water is consistent throughout the sample, and that the surface water is representative of the volume. This assumption is required since the optical penetration depth is a few millimeters, at most, but predictions of water content based on reflectance should be used with the knowledge of this limitation.…”

Section: Discussionmentioning

confidence: 99%

“…Moisture greatly influences the spectral reflectance of soils in both VNIR (400-1100 nm) and SWIR (1100-2500 nm) [2], although the response to soil moisture content is considerably more pronounced in the SWIR region than in VNIR region [3] where water absorption is very strong relative to the visible [4]. Indeed, the response is strongest at the major water absorption bands centered at 970 nm, 1160 nm, 1440 nm, and 1930 nm.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have observed the changes in soil reflectance with changes in water content, and some empirical relationship have been built to relate reflectance to different water content level [5][6] [7], as well as some more physical models [8]. Most studies, however, have not considered the continuous drying process, nor have they examined if water content is the only parameter influencing reflectance of damp soil.…”

Section: Introductionmentioning

confidence: 99%

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Relating water absorption features to soil moisture characteristics

Tian

Philpot

2015

SPIE Proceedings

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The spectral reflectance of a sample of quartz sand was monitored as the sample progressed from air-dry to fully saturated, and then back to air-dry. Wetting was accomplished by spraying small amounts of water on the surface of the sample, and collecting spectra whenever change occurred. Drying was passive, driven by evaporation from the sand surface, with spectra collected every 5 minutes until the sample was air dry. Water content was determined by monitoring the weight of the sample through both wetting and drying. There was a pronounced difference in the pattern of change in reflectance during wetting and drying, with the differences being apparent both in spectral details (i.e., the depth of absorption bands) and in the magnitude of the reflectance for a particular water content. The differences are attributable to the disposition of water in the sample. During wetting, water initially occurred only on the surface, primarily as water adsorbed onto sand particles. With increased wetting the water infiltrated deeper into the sample, gradually covering all particles and filling the pore spaces. During drying, water and air were distributed throughout the sample for most of the drying period. The differences in water distribution are assumed to be the cause of the differences in reflectance and to the differences in the depths of four strong water absorption bands.

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“…The reflectance attribute and its derivatives have been proven to be highly correlated with a number of physicochemical properties [2]. Recent improvement in visible/near infrared (Vis/NIR) spectroscopy have made DRS a convenient, simple, reliable and fast tool in quality evaluation and measurement of agricultural products and food.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis

Liu

Xie

et al. 2012

Sensors

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Effects of the moisture content (MC) of tea on diffuse reflectance spectroscopy were investigated by integrated wavelet transform and multivariate analysis. A total of 738 representative samples, including fresh tea leaves, manufactured tea and partially processed tea were collected for spectral measurement in the 325–1,075 nm range with a field portable spectroradiometer. Then wavelet transform (WT) and multivariate analysis were adopted for quantitative determination of the relationship between MC and spectral data. Three feature extraction methods including WT, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of spectral data. Comparison of those three methods indicated that the variables generated by WT could efficiently discover structural information of spectral data. Calibration involving seeking the relationship between MC and spectral data was executed by using regression analysis, including partial least squares regression, multiple linear regression and least square support vector machine. Results showed that there was a significant correlation between MC and spectral data (r = 0.991, RMSEP = 0.034). Moreover, the effective wavelengths for MC measurement were detected at range of 888–1,007 nm by wavelet transform. The results indicated that the diffuse reflectance spectroscopy of tea is highly correlated with MC.

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Characterizing surface soil water with field portable diffuse reflectance spectroscopy

Cited by 46 publications

References 19 publications

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

Relating water absorption features to soil moisture characteristics

Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis

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