Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties

Summers, David; Lewis, Mervyn K.; Ostendorf, Bertram; Chittleborough, David J.

doi:10.1016/j.ecolind.2009.05.001

Cited by 183 publications

(103 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The 805 nm waveband is related to the red-edge, which is known to be sensitive to biomass [69], and the predictions of P, K, pH and OM were found to be correlated. The 1120 and 1155 nm wavebands may be related to the v S-O stretching bands of sulfate [70], which can be used to predict P and K. The 1225 nm [71] and 1315 nm [72] wavebands were found to be important bands for predicting soil organics; here, several properties are correlated. The 1450 nm waveband in the prediction of OM may be related to the carboxylic acids in organics [25].…”

Section: Feature Importance In Pls-rmentioning

confidence: 90%

Linear Multi-Task Learning for Predicting Soil Properties Using Field Spectroscopy

et al. 2017

View full text Add to dashboard Cite

Abstract:Field spectroscopy has been suggested to be an efficient method for predicting soil properties using quantitative mathematical models in a rapid and non-destructive manner. Traditional multivariate regression algorithms usually regard the modeling of each soil property as a single task, which means only one response variable is considered as the output during modeling. Therefore, these algorithms are less suitable for the prediction of several key soil properties with low concentrations or unobvious spectral absorption signals. In the current study, we investigated the performance of a linear multi-task learning (LMTL) algorithm based on a regularized dirty model for modeling and predicting several key soil properties using field spectroscopy (350-2500 nm) as an integrated approach. We tested seven key soil properties including available nitrogen (N), phosphorus (P) and potassium (K), pH, water content (WC), organic matter (OM), and electrical conductivity (EC) in drylands. The model performances of LMTL models were compared with the commonly used single-task algorithm of the partial least squares regression (PLS-R). Our results show that the LMTL models outperformed the PLS-R models with the advantage of shared features; the ratio of performance to deviation (RPD) values in the validation set improved by 10.24%, 4.93%, 25.77%, 11.76%, 6.74%, 53.13%, and 3.15% for N, P, K, pH, WC, OM, and EC, respectively. The best prediction was obtained for OM with RPD = 2.29, indicating high accuracy (RPD > 2). The prediction results of N, P, WC, and pH were categorized as of moderate accuracy (1.4 < RPD < 2), while K and EC were categorized as of poor accuracy (RPD < 1.4). However, the explanatory power of the LMTL models was moderate due to fewer features being selected by the regularization algorithm of the LMTL approach, which should be further studied in the soil spectral analysis. Our results highlight the use of LMTL in field spectroscopy analysis that can improve the generalization performance of regression models for predicting soil properties.

show abstract

Section: Feature Importance In Pls-rmentioning

confidence: 90%

Linear Multi-Task Learning for Predicting Soil Properties Using Field Spectroscopy

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The reflectance spectra has three concaves at approximately 1370, 1900, and 2200 nm, which are thought to be relevant to the existence of hydroxide (OH) of free water (1400 and 1900) and the Al-OH lattice structure in clay minerals (2200 nm) (Ben-Dor et al, 2002;Viscarra Rossel et al, 2006;Summers et al, 2011). Chang et al (2001), Zornoza et al (2008), and Summers et al (2011) observed that increasing SOM would lower albedo across the whole VNIR spectrum. In the VINR spectral range (350-2500 nm), the average reflectance spectrum of soil set 2 was lower than that of soil set 1 with different mean SOM contents (1.82% vs. 1.59%; Table 1 and Fig.…”

Section: Soil Spectramentioning

confidence: 99%

“…The spectral differences in the 760-2500 nm region were much more obvious than those in the 350-760 nm range, a result also noted in other studies (Morgan et al, 2009;Escribano et al, 2010;Nocita et al, 2011). The noticeable differences in the near and shortwave infrared regions are likely the effect of other factors, such as soil moisture (Morgan et al, 2009), clay (Summers et al, 2011), and SOM contents (Uno et al, 2005).…”

Section: Soil Spectramentioning

confidence: 99%

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Chen

Peng

et al. 2012

Soil and Tillage Research

View full text Add to dashboard Cite

“…Many researchers investigated the capability of the PLSR technique to estimate clay from the spectra of dry soil samples, showing statistics very similar to those obtained in this work (RMSE=3.9%; Table 5). For example, [36] and [19] obtained a RMSE of leave-one-out crossvalidation of 3.1%, whilst [37] reported a RMSE of 3.8%. Although less accurate than PLSR, the results we reached using clay indices are quite good.…”

Section: Clay Estimation From Full Spectramentioning

confidence: 99%

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

et al. 2015

View full text Add to dashboard Cite

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.

show abstract

Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties

Cited by 183 publications

References 39 publications

Linear Multi-Task Learning for Predicting Soil Properties Using Field Spectroscopy

Linear Multi-Task Learning for Predicting Soil Properties Using Field Spectroscopy

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

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

Contact Info

Product

Resources

About