Visible and Near-Infrared Reflectance Spectroscopy Analysis of a Coastal Soil Chronosequence

Zheng, Guanghui; Ryu, Dongryeol; Jiao, Caixia; Xie, Xinglong; Cui, Xuefeng; Shang, Gang

doi:10.3390/rs11202336

Cited by 14 publications

(9 citation statements)

References 77 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, temporal changes in soil properties have been considered by evaluating the ability of spectroscopy to characterize soil chrono-sequence. 55 For example, Awiti et al 56 and Zheng et al 57 concluded that spectroscopy can characterize changes in soil properties after analyzing soil types that corresponded to specific steps of pedogenic processes. Moreover, several studies [58][59][60] quantified soil properties and their degree of alterations during different pedogenic processes.…”

Section: Introductionmentioning

confidence: 99%

Detecting the temporal trend of cultivated soil organic carbon content using visible near infrared spectroscopy

Zayani

Fouad

Michot

et al. 2023

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

Monitoring changes in soil properties is essential to ensure ecosystem function and agricultural productivity. This study evaluated the ability of visible near infrared (Vis-NIR) spectroscopy to detect the temporal trend in soil organic carbon (SOC) content after 5 years in a 12 km2 agricultural catchment in western France. Partial least squares regression models were developed using soil samples from a local dataset collected in 2013 at two depths (198 samples at 0–15 and 196 samples at 15–25 cm) to predict SOC content of 111 new samples collected in 2018 at the same locations and at similar depths (0–15 and 15–25 cm). Two approaches, which differed in whether or not they considered the SOC content variability that can result from collecting soil samples at two depths, were applied. For both approaches, the potential benefit of “temporal spiking” was evaluated by adding 10% of 2018 samples to the 2013 dataset. The results showed that removing outliers and stratifying the calibration dataset by depth yielded the highest accuracy, with SOC RMSEP of 4.1 and 2.7 g.kg−1 for 0–15 and 15–25 cm, respectively. Moreover, temporal spiking improved five of eight predictions (stratifying or not the calibration dataset by depth, removing or not poorly predicted outliers), with increases in the ratio of performance to deviation (RPD) of 0.10–0.44. Furthermore, comparing observed and predicted changes in SOC content showed that Vis-NIR spectroscopy estimated its trend over time in most cases.

show abstract

Section: Introductionmentioning

confidence: 99%

Detecting the temporal trend of cultivated soil organic carbon content using visible near infrared spectroscopy

Zayani

Fouad

Michot

et al. 2023

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

show abstract

“…Visible and near infrared reflectance (Vis-NIR) spectroscopy has been widely used to rapidly analyze many properties in various types of soils or sediments [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. As the primary chemical attribute, organic carbon in soils or sediments (SOC) is directly related with Vis-NIR reflectance and has been mainly investigated for 20 years [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Studies also showed that ASD spectrometers are the most commonly used in the Vis-NIR spectral analysis due to their good performance and reputation [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 17 , 18 , 19 ]. However, ASD spectrometers are expensive, being a barrier to studies on Vis-NIR spectroscopy [ 13 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Method to Evaluate Spectral Analysis by Spectroscopy

Liu

Fan

Qiu

et al. 2022

Sensors

View full text Add to dashboard Cite

Visible and near infrared spectroscopy has been widely used to develop a method for rapidly determining organic carbon in soils or sediments (SOC). Most of these studies concentrated on how to establish a good spectral model but ignored how to evaluate the method, such as the use of detection range (max and min), resolution and error for SOC spectral analysis. Here, we proposed a method to evaluate the spectral analysis of SOC. Using 96 sediments sampled in the Yellow Sea and Bohai Sea, China, we established three spectral models of SOC after collecting their spectral reflectance by Agilent Cary 5000, ASD FieldSpec 4 and Ocean Optics QEPro, respectively. For both the calibration set and validation set in each spectrometer, the predicted SOC concentrations followed a distribution curve (function), in which the x-axis was the SOC concentrations. Using these curves, we developed these four technical parameters. The detection ranges were the SOC concentrations where the curve was near to or crossing with the lateral axis, while the detection resolution was the average difference between the two neighboring SOC concentrations. The detection errors were the differences between the predicted SOC and the measured SOC. The results show that these technical parameters were better in the bench-top spectrometer (Cary 5000) than those in the portable spectrometers when analyzing the same samples. For the portable spectrometers, QEPro had a broader detection range and more consistent detection error than FieldSpec 4, suggesting that the low-cost QEPro performed as well as the high-cost FieldSpec 4. This study provides a good example for evaluating spectral analysis by spectroscopy, which can support the development of the spectral method.

show abstract

“…However, when combined with outer product analysis, spectra fusion resulted in improved classification accuracy reaching 68.4%, compared to the SVM data fusion model (61.1%). Another work fused spectral data of eight soil layers collected with a vis-NIR spectrometer for the analysis of a coastal soil chronosequence using 16 soil profiles [16]. A combination of the DUALEM-421S instrument to measure apparent electrical conductivity (ECa) with a traditional soil coring survey and light detection and ranging radar (LiDAR) to measure alleviation and derive terrain attributes was used to measure and map the depth of a peat soil based on a multiple linear regression analysis [17].…”

mentioning

confidence: 99%

“…This is due to the low number of papers submitted, with three of the accepted papers having an indirect link to the topic of the Special Issue. Even when papers linked closely with the project topic, the fusion was sometimes implemented on data collected from the same sensor over time [10,12], space [16], or using different measurement modes [14]. However, the topic holds great potential for application in different domains in soil science, environment, and agriculture.…”

mentioning

confidence: 99%