Determination of Soil Organic Matter and Carbon Fractions in Forest Top Soils using Spectral Data Acquired from Visible-Near Infrared Hyperspectral Images

2012 ◽  
Vol 76 (2) ◽  
pp. 586-596 ◽  
Author(s):  
S. M. O'Rourke ◽  
N. M. Holden
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Hyperspectral Images ◽  
Carbon Fractions

Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Macro Scale ◽  
Sentinel 2

<p>Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31<sup>st</sup> and September 5<sup>th </sup>2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSE<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSE<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>

Determination of starch content in single kernel using near-infrared hyperspectral images from two sides of corn seeds

2020 ◽  
Vol 110 ◽  
pp. 103462
Author(s):  
Chen Liu ◽  
Wenqian Huang ◽  
Guiyan Yang ◽  
Qingyan Wang ◽  
Jiangbo Li ◽  
...  
Keyword(s):  
Near Infrared ◽  
Starch Content ◽  
Hyperspectral Images ◽  
Two Sides

scholarly journals Microbiological properties and oxidizable organic carbon fractions of an oxisol under coffee with split phosphorus applications and irrigation regimes

2013 ◽  
Vol 37 (1) ◽  
pp. 55-65 ◽  
Author(s):  
Adriana Rodolfo da Costa ◽  
Juliana Hiromi Sato ◽  
Maria Lucrécia Gerosa Ramos ◽  
Cícero Célio de Figueiredo ◽  
Géssica Pereira de Souza ◽  
...  
Keyword(s):  
Organic Matter ◽  
Acid Phosphatase ◽  
Soil Organic Matter ◽  
Organic Carbon ◽  
Microbial Biomass Carbon ◽  
Block Design ◽  
Phosphorus Fertilization ◽  
Carbon Fractions ◽  
Irrigation Regimes ◽  
Organic Carbon Fractions

Phosphorus fertilization and irrigation increase coffee production, but little is known about the effect of these practices on soil organic matter and soil microbiota in the Cerrado. The objective of this study was to evaluate the microbiological and oxidizable organic carbon fractions of a dystrophic Red Latossol under coffee and split phosphorus (P) applications and different irrigation regimes. The experiment was arranged in a randomized block design in a 3 x 2 factorial design with three split P applications (P1: 300 kg ha-1 P2O5, recommended for the crop year, of which two thirds were applied in September and the third part in December; P2: 600 kg ha-1 P2O5, applied at planting and then every two years, and P3: 1,800 kg ha-1 P2O5, the requirement for six years, applied at once at planting), two irrigation regimes (rainfed and year-round irrigation), with three replications. The layers 0-5 and 5-10 cm were sampled to determine microbial biomass carbon (MBC), basal respiration (BR), enzyme activity of acid phosphatase, the oxidizable organic carbon fractions (F1, F2, F3, and F4), and total organic carbon (TOC). The irrigation regimes increased the levels of MBC, microbial activity and acid phosphatase, TOC and oxidizable fractions of soil organic matter under coffee. In general, the form of dividing P had little influence on the soil microbial properties and OC. Only P3 under irrigation increased the levels of MBC and acid phosphatase activity.

scholarly journals Soil organic matter pools and carbon fractions in soil under different land uses

2013 ◽  
Vol 126 ◽  
pp. 177-182 ◽  
Author(s):  
Daniele Vieira Guimarães ◽  
Maria Isidória Silva Gonzaga ◽  
Tácio Oliveira da Silva ◽  
Thiago Lima da Silva ◽  
Nildo da Silva Dias ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Land Uses ◽  
Carbon Fractions

scholarly journals Deep Learning Application for Predicting Soil Organic Matter Content by VIS-NIR Spectroscopy

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Zhe Xu ◽  
Xiaomin Zhao ◽  
Xi Guo ◽  
Jiaxin Guo
Keyword(s):  
Organic Matter ◽  
Deep Learning ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Near Infrared Spectroscopy ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Spectral Variation ◽  
Full Spectrum ◽  
Reflectance Data

Deep learning is characterized by its strong ability of data feature extraction. This method can provide unique advantages when applying it to visible and near-infrared spectroscopy for predicting soil organic matter (SOM) content in those cases where the SOM content is negatively correlated with the spectral reflectance of soil. This study relied on the SOM content data of 248 red soil samples and their spectral reflectance data of 400–2450 nm in Fengxin County, Jiangxi Province (China) to meet three objectives. First, a multilayer perceptron and two convolutional neural networks (LeNet5 and DenseNet10) were used to predict the SOM content based on spectral variation and variable selection, and the outcomes were compared with that from the traditional back-propagation neural network (BPN). Second, the four methods were applied to full-spectrum modeling to test the difference to selected feature variables. Finally, the potential of direct modeling was evaluated using spectral reflectance data without any spectral variation. The results of prediction accuracy showed that deep learning performed better at predicting the SOM content than did the traditional BPN. Based on full-spectrum data, deep learning was able to obtain more feature information, thus achieving better and more stable results (i.e., similar average accuracy and far lower standard deviation) than those obtained through variable selection. DenseNet achieved the best prediction result, with a coefficient of determination (R2) = 0.892 ± 0.004 and a ratio of performance to deviation (RPD) = 3.053 ± 0.056 in validation. Based on DenseNet, the application of spectral reflectance data (without spectral variation) produced robust results for application-level purposes (validation R2 = 0.853 ± 0.007 and validation RPD = 2.639 ± 0.056). In conclusion, deep learning provides an effective approach to predict the SOM content by visible and near-infrared spectroscopy and DenseNet is a promising method for reducing the amount of data preprocessing.

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