Surface soil moisture quantification models from reflectance data under field conditions

Haubrock, Sören-Nils; Chabrillat, Sabine; Lemmnitz, C.; Kaufmann, Hermann

doi:10.1080/01431160701294695

Cited by 129 publications

(124 citation statements)

References 19 publications

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“…The indices for VW samples use the typical features of all clay minerals (near 2200 nm). Also the PLSR models showed the best estimation statistic for D and VW subsets [18] confirming an improvement of clay estimation accuracy using soil spectra having high soil moisture content as compared to spectra with lower moisture. Thus, both high and low soil moisture data seem to provide the best conditions for the estimation of clay from hyperspectral remote sensing.…”

Section: Clay Estimation From Full Spectramentioning

confidence: 57%

“…We tested these wavelengths for the development of normalized or simple ratio soil moisture indices. Table 2 shows the results for the two best normalized or simple ratio indices (Equations (2) and (3)), named as SMIR_A and SMIR_B, providing the highest r value, alongside results using the NSMI index [18]. The SMIR_A index shows a strong positive correlation (r = 0.89), while SMIR_B shows a strong negative correlation (r = −0.88) with soil moisture.…”

Section: Laboratory Datasetmentioning

confidence: 99%

“…Linear or quadratic regression models were used to describe the relationship between each SMIR and the actual soil moisture values. In addition to these indices, an index proposed in the literature, the Normalized Soil Moisture Index (NSMI) [18] was also tested on the same soil datasets used in this paper (MAC plus SOLREFLIU).…”

Section: Narrow Band Soil Moisture Indices-smirmentioning

confidence: 99%

“…Satellite hyperspectral imagers have been employed to estimate soil moisture, in bare soil conditions, by using narrow band indices [18]. However, the low signal to noise ratio (SNR) in the SWIR band of the hyperspectral satellite sensors currently in operation (e.g., Hyperion) hampers the accuracy of the estimation.…”

Section: Introductionmentioning

confidence: 99%

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Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data

et al. 2015

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Soil moisture hampers the estimation of soil variables such as clay content from remote and proximal sensing data, reducing the strength of the relevant spectral absorption features. In the present study, two different strategies have been evaluated for their ability to minimize the influence of soil moisture on clay estimation by using soil spectra acquired in a laboratory and by simulating satellite hyperspectral data. Simulated satellite data were obtained according to the spectral characteristics of the forthcoming hyperspectral imager on board of the Italian PRISMA satellite mission. The soil datasets were split into four groups according to the water content. For each soil moisture level a prediction model was applied, using either spectral indices or partial least squares regression (PLSR). Prediction models were either specifically developed for the soil moisture level or calibrated using synthetically dry soil spectra, generated from wet soil data. Synthetically dry spectra were obtained using a new technique based on the effects caused by soil moisture on the optical spectrum from 400 to 2400 nm. The estimation of soil clay content, when using different prediction models according to soil moisture, was slightly more accurate as compared to the use of synthetically dry soil spectra, both OPEN ACCESSRemote Sens. 2015, 7 15562 employing clay indices and PLSR models. The results obtained in this study demonstrate that the a priori knowledge of the soil moisture class can reduce the error of clay estimation when using hyperspectral remote sensing data, such as those that will be provided by the PRISMA satellite mission in the near future.

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Section: Clay Estimation From Full Spectramentioning

confidence: 57%

Section: Laboratory Datasetmentioning

confidence: 99%

Section: Narrow Band Soil Moisture Indices-smirmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data

et al. 2015

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“…From these laboratory measurements, models were developed to retrieve the SMC. Most of them are based on calibration models developed to predict soil moisture using Near Infrared (NIR) spectroscopy [36,37]. However, there is a lack of studies applying the laboratory-calibrated models to outdoor datasets.…”

Section: Introductionmentioning

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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Spatial Root Zone Soil Water Content Estimation in Agricultural Lands Using Bayesian-Based Artificial Neural Networks and High- Resolution Visual, NIR, and Thermal Imagery

et al. 2017

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Soil moisture is an important parameter in irrigation scheduling and application. Knowledge of root zone volumetric water content can support decisions for more efficient irrigation management by enabling estimation of required water application rates at appropriate temporal and spatial scales. The study presented here proposes a data mining approach that combines known field conditions with remote sensing observations to provide probabilistic estimates of root zone soil moisture at three different depths in the root zone soil profile. Bayesian data mining algorithms were tested and calibrated to combine the remotely sensed spatial information with field measurements. The remote sensing platform used in this study, called AggieAir™, is an unmanned autonomous aerial system developed by the Utah Water Research Laboratory at Utah State University. Finally, a trapezoidal integration method was used to estimate volumetric water content (VWC) in the root zone using the results of the modelling approach at three different depths in the root zone soil profile. The results show the model could estimate the root zone VWC with good accuracy (RMSE =0.05, MAE = 0.04, r = 0.97, e = 0.92, R2 = 0.94). Copyright © 2017 John Wiley & Sons, Ltd.

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Surface soil moisture quantification models from reflectance data under field conditions

Cited by 129 publications

References 19 publications

Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data

Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data

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

Spatial Root Zone Soil Water Content Estimation in Agricultural Lands Using Bayesian-Based Artificial Neural Networks and High- Resolution Visual, NIR, and Thermal Imagery

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