Predictions of soil surface and topsoil organic carbon content through the use of laboratory and field spectroscopy in the Albany Thicket Biome of Eastern Cape Province of South Africa

Nocita, Marco; Kooistra, Lammert; Bachmann, Martin; Müller, Andreas; Powell, Mike; Weel, S.

doi:10.1016/j.geoderma.2011.09.018

Cited by 56 publications

(37 citation statements)

References 48 publications

(48 reference statements)

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“…It was in agreement with an earlier work dealing with SOC estimation of soil surface (0-5 mm) and topsoil (0-200 mm) in the degraded Albany Thicket Biome, through the combination of soil spectroscopy and partial least square regression (Nocita et al 2011).…”

Section: Cubic Fitting Modelsupporting

confidence: 92%

“…2a). This trend was also observed by Nocita et al (2011), who analyzed spectra originating from soil samples with SOC content of 3.01, 6.03, and 0.25 %, and discovered the highest correlation between SOC and reflectance value around 600 nm. SOC (1.44) showed lower reflectance values than the others, probably due to its more SOC content.…”

Section: Resultssupporting

confidence: 73%

“…Therefore, the prediction model B was established as: SOC ¼ 0:559 þ 537:347 R 444 ð Þ 00 þ 243; 142:9 R 444 ð Þ 00 À Á 2 À 8E þ 007 R 444 ð Þ 00 À Á 3 R 2 ¼ 0:609; P < 0:01 À Á Hyperspectral techniques offer great advantages in predicting SOC because of their high spectral resolution. Many studies have indicated that the spectral differences affected by SOC appear not only in the visible band but also in the infrared band Nocita et al 2011;Viscarra Rossel et al 2006). So, it is expected that the spectral information in the whole waveband must be used to make the model more accurate and practical for predicting SOC.…”

Section: Cubic Fitting Modelmentioning

confidence: 99%

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Predictions of soil organic carbon using laboratory-based hyperspectral data in the northern Tianshan mountains, China

Yang

2012

Environ Monit Assess

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This paper presents a study dealing with soil organic carbon (SOC) estimation of soil through the combination of soil spectroscopy and multivariate stepwise linear regression. Soil samples were collected in the three sub-regions, dominated by brown calcic soil, in the northern Tianshan Mountains, China. Spectral measurements for all soil samples were performed in a controlled laboratory environment by a portable ASD FieldSpec FR spectrometer (350-2,500 nm). Twelve types of transformations were applied to the soil reflectance to remove the noise and to linearize the correlation between reflectance and SOC content. Based on the spectral reflectance and its derivatives, hyperspectral models can be built using correlation analysis and multivariable statistical methods. The results show that the main response range of soil organic carbon is between 400 and 750 nm. Correlation analysis indicated that SOC has stronger correlation with the second derivative than with the original reflectance and other transformations data. The two models developed with laboratory spectra gave good predictions of SOC, with root mean square error (RMSE) <5.0. The use of the full visible near-infrared spectral range gave better SOC predictions than using visible separately. The multivariate stepwise linear regression of second derivate model (model A) is optimal for estimating SOC content, with a determination coefficient of 0.894 and RMSE of 0.322. The results of this research study indicated that, for the grassland regions, combining soil spectroscopy and mathematical statistical methods does favor accurate prediction of SOC.

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Section: Cubic Fitting Modelsupporting

confidence: 92%

Section: Resultssupporting

confidence: 73%

Section: Cubic Fitting Modelmentioning

confidence: 99%

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Predictions of soil organic carbon using laboratory-based hyperspectral data in the northern Tianshan mountains, China

Yang

2012

Environ Monit Assess

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“…After recreating EnMAP channels with the worst SNR values (100:1), Nocita et.al. [195] confirmed the problem of low SNR values when estimating SOC in South Africa. They identified this low SNR as the main reason for the insufficient accuracy.…”

Section: Revisit Timementioning

confidence: 58%

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi-and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors' publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i) brings face to face past and future hyperspectral sensors' applications with Sentinel-2's and (ii) analyzes the applications' requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses.

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“…But the cost and time involved in measurements of TC with standard laboratory methods are impractical. Research has suggested that visible/near-infrared (VNIR) diffuse reflectance spectroscopy can provide robust and accurate estimations for TC and carbon fractions Reeves III, McCarty and Mimmo, 2002;Viscarra Rossel et al, 2006;Vasques, Grunwald and Sickman, 2009;Vasques, Grunwald and Harris, 2010;Nocita et al, 2011;Chaudhary et al, 2012;McDowell et al, 2012b). It is a fast, cheap, and non-destructive approach to characterize soil properties (Shepherd and Walsh, 2002).…”

Section: Introductionmentioning

confidence: 99%

Transferability and scaling of soil total carbon prediction models in Florida

Grunwald

Xiong

2014

Preprint

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The applicability, transfer, and scalability of visible/near-infrared (VNIR)-derived soil models are still poorly understood. The objectives of this study in Florida, U.S. were to: (i) compare three methods to predict soil total carbon (TC) using five fields (local scale) and a pooled (regional scale) VNIR spectral dataset, (ii) assess the model's transferability among fields, and (iii) evaluate the up-and down-scaling behavior of TC prediction models. A total of 560 TC-spectral sets were modeled by Partial Least Square Regression (PLSR), Support Vector Machine (SVM), and Random Forest. The transferability and up-and down-scaling of models were limited by the following factors: (i) the spectral data domain, (ii) soil attribute domain, (iii) methods that describe the internal model structure of VNIR-TC relationships, and (iv) environmental domain space of attributes that control soil carbon dynamics. All soil logTC models showed excellent performance based on all three methods with R 2 > 0.86, bias < 0.01%, root mean square prediction error (RMSE) = 0.09%, residual predication deviation (RPD) > 2.70% , and ratio of prediction error to inter-quartile range (RPIQ) > 4.54. PLSR performed substantially better than SVM to scale and transfer models. Upscaled soil TC models performed somewhat better in terms of model fit (R 2 ), RPD, and RPIQ, whereas downscaled models showed less bias and smaller RMSE based on PLSR. Given the many factors that can impinge on empirically derived soil spectral prediction models, as demonstrated by this study, more focus on the applicability and scaling of them is needed.PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.494v1 | CC-BY 4.0

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Predictions of soil surface and topsoil organic carbon content through the use of laboratory and field spectroscopy in the Albany Thicket Biome of Eastern Cape Province of South Africa

Cited by 56 publications

References 48 publications

Predictions of soil organic carbon using laboratory-based hyperspectral data in the northern Tianshan mountains, China

Predictions of soil organic carbon using laboratory-based hyperspectral data in the northern Tianshan mountains, China

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Transferability and scaling of soil total carbon prediction models in Florida

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