Estimation of soil moisture status using near infrared reflectance

Whalley, W. R.; Leeds‐Harrison, P. B.; Bowman, G.E.

doi:10.1002/hyp.3360050312

Cited by 38 publications

(12 citation statements)

References 8 publications

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“…As porosity and the refractive index of soil particles vary between soils, a general relationship between overall albedo and gravimetric or volumetric moisture cannot be found. Whalley et al (1991) also found that shrinking of kaolinite clay soils upon drying slowed the increase in reflectance compared with sandy soils. Nevertheless, the feature of increased forward scattering is suggested to relate to the thickness of the water film and thus to moisture tension (Baumgardner et al, 1985).…”

Section: Soil Moisturementioning

confidence: 88%

“…This effect can be largely attributed to a change in real refractive index of the medium surrounding the soil particles from that of air to that of water, which is higher and closer to soil particles. The resulting lower contrast causes more forward scattering and the path length before reemerging is longer, increasing the chances of absorbance (Twomey et al, 1986;Whalley et al, 1991). As porosity and the refractive index of soil particles vary between soils, a general relationship between overall albedo and gravimetric or volumetric moisture cannot be found.…”

Section: Soil Moisturementioning

confidence: 99%

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Visible and Near Infrared Spectroscopy in Soil Science

Stenberg

Rossel

Mouazen

et al. 2010

Advances in Agronomy

1,133

845

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This chapter provides a review on the state of soil visible-near infrared (vis-NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis-NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis-NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis-NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis-NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis-NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic.

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Section: Soil Moisturementioning

confidence: 88%

Section: Soil Moisturementioning

confidence: 99%

Visible and Near Infrared Spectroscopy in Soil Science

Stenberg

Rossel

Mouazen

et al. 2010

Advances in Agronomy

1,133

845

View full text Add to dashboard Cite

show abstract

“…Exposed soil (bare) is expected to have lower soil moisture content while areas with high vegetation density the opposite. The concept of using false color composite images was taken from previously published studies [56,58]. As shown in Figure 8, the soil moisture maps have a direct association with the false color composite maps.…”

Section: Results Extracted From Artificial Neural Network (Anns)mentioning

confidence: 99%

“…The methods were applied by using a 5-fold cross validation method for data generalization. Other water resources related studies have utilized Bowden's approach and concluded that it ensures that the training, testing, and validation sets are representative of the same population [52][53][54][55][56][57].…”

Section: Division Set Up In Ann Model Architecturementioning

confidence: 99%

Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks

et al. 2015

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Many crop production management decisions can be informed using data from high-resolution aerial images that provide information about crop health as influenced by soil fertility and moisture. Surface soil moisture is a key component of soil water balance, which addresses water and energy exchanges at the surface/atmosphere interface; however, high-resolution remotely sensed data is rarely used to acquire soil moisture values. In this study, an artificial neural network (ANN) model was developed to quantify the effectiveness of using spectral images to estimate surface soil moisture. The model produces acceptable estimations of surface soil moisture (root mean square error (RMSE) = 2.0, mean absolute error (MAE) = 1.8, coefficient of correlation (r) = 0.88, coefficient of performance (e) = 0.75 and coefficient of determination (R 2 ) = 0.77) by combining field measurements with inexpensive and readily available remotely sensed inputs. The spatial data (visual spectrum, near infrared, infrared/thermal) are produced by the AggieAir™ platform, which includes an unmanned aerial vehicle (UAV) that enables users to gather aerial imagery at a low price and high spatial and temporal resolutions. This study reports the development of an ANN model that translates AggieAir™ imagery into estimates of surface soil moisture for a large field irrigated by a center pivot sprinkler system.

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“…The WISOIL index was introduced by [58] and named by [24]. It is a ratio-based index (see Equation (10)) between the reflectance at a strong water absorption band (ρλi, with λi = 1.45 µm) and the reflectance at weak water absorption band (ρλj, with λj = 1.300 µm).…”

Section: Local Criteria To Retrieve Smc From Reflectance Spectrummentioning

confidence: 99%

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

et al. 2015

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Abstract:The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 µm) when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON). These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R 2 ) between 0.72 and 0.92. A clay content (CC) dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOLCC and NINSONCC). The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² in all cases) and using in-situ data, where WISOIL, NINSOLCC and NINSONCC criteria stand out among the NSMI and CH.

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Estimation of soil moisture status using near infrared reflectance

Cited by 38 publications

References 8 publications

Visible and Near Infrared Spectroscopy in Soil Science

Visible and Near Infrared Spectroscopy in Soil Science

Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

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