Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.
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ABSTRACT:
SUBMITTER: Shi Y
PROVIDER: S-EPMC8238212 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A variety of biogas residues (BGRs) have been used as organic fertilizer in agriculture. The use of these residues affects the storage of soil organic matter (SOM). In most cases, SOM changes can only be determined in long-term observations. Therefore, predictive modeling can be an efficient alternative, provided that the parameters required by the model are known for the considered BGRs. This study was conducted as a first approach to estimating the organic matter (OM) turnover parameters of BGRs for process modeling. We used carbon mineralization data from six BGRs from an incubation experiment, representing a range of substrate inputs, to calculate a turnover coefficient k controlling the velocity of fresh organic matter (FOM) decay and a synthesis coefficient ? describing the SOM creation from FOM. An SOM turnover model was applied in inverse mode to identify both parameters. In a second step, we related the parameters k and ? to chemical properties of the corresponding BGRs using a linear regression model and applied them to a long-term scenario simulation. According to the results of the incubation experiment, the k values ranged between 0.28 and 0.58 d-1 depending on the chemical composition of the FOM. The estimated ? values ranged between 0.8 and 0.89. The best linear relationship of k was found to occur with pH (R2 = 0.863). Parameter ? is related to the Ct/Norg ratio (R2 = 0.696). Long-term scenario simulations emphasized the necessity of specific k and ? values related to the chemical properties for each BGR. However, further research is needed to validate and improve these preliminary results.
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Project description:Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.
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Project description:Mineral-associated organic matter is associated with a suite of soil minerals that can confer stability, resulting in the potential for long-term storage of carbon (C). Not all interactions impart the same level of protection, however; evidence is suggesting that C in certain mineral pools is dynamic and vulnerable to disturbance in the decades following harvesting. The objective of this research was to describe and characterize organic matter-mineral interactions through depth in horizons of soils of contrasting stand age. Sequential selective dissolutions representing increasingly stable mineral-associated organic matter pools from water soluble minerals (deionized water), organo-metal complexes (Na-pyrophosphate), poorly-crystalline minerals (HCl hydroxylamine), and crystalline secondary minerals (Na-dithionite HCl)) were carried out for Ae, Bf and BC horizons sampled from a Young and Mature forest site (35 and 110 years post-harvest) in Mooseland, Nova Scotia, Canada. Sequential selective dissolution extracts were analyzed for C, ?13C, iron (Fe) and aluminum (Al). Organo-metal complexes (OMC) were the largest mineral-associated OM pool in all horizons. This pool dominated the C distribution in B horizons (~60-70% of Bf bulk C), with a minor contribution from poorly-crystalline (PCrys), crystalline (Crys) minerals and water soluble (WS) associations. C in OMC and PCrys pools explained the variation in bulk C in horizons through depth at both sites. Twice as much C in OMC pools was measured at the Mature site compared to the Young site in the Bf horizons, supported by higher C:(Fe+Al) ratios. Isotopic analysis indicated that this extraction procedure isolated distinct mineral-associated OM pools. ?13C signatures of pyrophosphate-extracted OMC pools ranged from -27‰ to -28‰, similar to ?13C of bulk C and to plant-derived humic acids and associated biomass. The water soluble phase (mean ?13C = -29 ‰) was up to 2 ‰ more depleted, whereas the ?13C of Crys pools were more enriched in 13C (-13‰ to -16 ‰) compared to bulk soil. The results from this study suggest that association with minerals does not necessarily confer stability: organo-metal pools dominate in podzol horizons through depth, and contribute most to C storage, but are potentially susceptible to destabilization following the physical changes resulting from forest harvesting disturbance.
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Project description:Many trees form ectomycorrhizal symbiosis with fungi. During symbiosis, the tree roots supply sugar to the fungi in exchange for nitrogen, and this process is critical for the nitrogen and carbon cycles in forest ecosystems. However, the extents to which ectomycorrhizal fungi can liberate nitrogen and modify the soil organic matter and the mechanisms by which they do so remain unclear since they have lost many enzymes for litter decomposition that were present in their free-living, saprotrophic ancestors. Using time-series spectroscopy and transcriptomics, we examined the ability of two ectomycorrhizal fungi from two independently evolved ectomycorrhizal lineages to mobilize soil organic nitrogen. Both species oxidized the organic matter and accessed the organic nitrogen. The expression of those events was controlled by the availability of glucose and inorganic nitrogen. Despite those similarities, the decomposition mechanisms, including the type of genes involved as well as the patterns of their expression, differed markedly between the two species. Our results suggest that in agreement with their diverse evolutionary origins, ectomycorrhizal fungi use different decomposition mechanisms to access organic nitrogen entrapped in soil organic matter. The timing and magnitude of the expression of the decomposition activity can be controlled by the below-ground nitrogen quality and the above-ground carbon supply.
2018-11-30 | GSE110485 | GEO
Project description:The aim of this study was to estimate bentazone's potential to leach to groundwater in the Arenosols developed from sand, Luvisols developed from loamy sand or sandy loam, and Luvisols or Cambisols developed from loess, and to identify the major factors influencing bentazone's fate in the soils. Potato and maize cultivations were simulated using the FOCUS PELMO 5.5.3 pesticide leaching model. The amount of bentazone reaching groundwater was highly sensitive to degradation parameters, water-holding capacity, evapotranspiration, organic carbon content, and pH. The highest bentazone concentrations in percolate were noted in Arenosols. The risk of bentazone concentration exceeding 0.1 μg/L was low only in Arenosols with high organic carbon content (3.0% for topsoil or higher). In Luvisols developed from loamy sand or sandy loam, the estimated bentazone concentrations in percolate were highly dependent on the climate. In Luvisols or Cambisols developed from loess, concentrations of >0.1 μg/L were the least likely due to the high water-holding capacity and high organic carbon content of these soils. The study also revealed that the FOCUS Hamburg scenario, representing the coarsest soils in the European Union with relatively low organic carbon content, does not reflect the leaching potential of Arenosols and Luvisols.
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Project description:Soil organic matter (SOM) is an important source of nutrients required during crop growth and is an important component of cultivated soil. In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to predict SOM content. Moreover, using Nong'an County of Changchun City as the study area, Sentinel-2A remote sensing images were taken as the data source to construct the dataset by using field sampling and image processing. The LeNet-5 convolutional neural network model was chosen as the deep learning model, which was improved based on the basic model. The evaluation metrics were selected as the root mean square error (RMSE) and the coefficient of determination R2. Through comparison, the R2 of the improved model was found to be higher than that of the linear regression method, Support Vector Machines (SVM) (RMSE = 2.471, R2 = 0.4035), and Random Forest (RF) (RMSE = 2.577, R2 = 0.4913). The result shows that: (1) It is feasible to use the multispectral data extracted from remote sensing images for soil organic matter content inversion based on the deep learning model with a minimum RMSE of 2.979 and with the R2 reaching 0.89. (2) The choice of features has an impact on the prediction of the model to a certain extent. After ranking the importance of features, selecting the appropriate number of features for inversion provides better results than full feature inversion, and the computational speed is improved.
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Project description:Rising temperatures enhance microbial decomposition of soil organic matter (SOM) and thereby increase the soil CO2 efflux. Elevated decomposition rates might differently affect distinct SOM pools, depending on their stability and accessibility. Soil fractions derived from density fractionation have been suggested to represent SOM pools with different turnover times and stability against microbial decomposition. To investigate the effect of soil warming on functionally different soil organic matter pools, we here investigated the chemical and isotopic composition of bulk soil and three density fractions (free particulate organic matter, fPOM; occluded particulate organic matter, oPOM; and mineral associated organic matter, MaOM) of a C-rich soil from a long-term warming experiment in a spruce forest in the Austrian Alps. At the time of sampling, the soil in this experiment had been warmed during the snow-free period for seven consecutive years. During that time no thermal adaptation of the microbial community could be identified and CO2 release from the soil continued to be elevated by the warming treatment. Our results, which included organic carbon content, total nitrogen content, ?13C, ?14C, ?15N and the chemical composition, identified by pyrolysis-GC/MS, showed no significant differences in bulk soil between warming treatment and control. Surprisingly, the differences in the three density fractions were mostly small and the direction of warming induced change was variable with fraction and soil depth. Warming led to reduced N content in topsoil oPOM and subsoil fPOM and to reduced relative abundance of N-bearing compounds in subsoil MaOM. Further, warming increased the ?13C of MaOM at both sampling depths, reduced the relative abundance of carbohydrates while it increased the relative abundance of lignins in subsoil oPOM. As the size of the functionally different SOM pools did not significantly change, we assume that the few and small modifications in SOM chemistry result from an interplay of enhanced microbial decomposition of SOM and increased root litter input in the warmed plots. Overall, stable functional SOM pool sizes indicate that soil warming had similarly affected easily decomposable and stabilized SOM of this C-rich forest soil.
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Project description:The amount of information involved in hyperspectral imaging is large. Hyperspectral band selection is a popular method for reducing dimensionality. Several information based measures such as mutual information have been proposed to reduce information redundancy among spectral bands. Unfortunately, mutual information does not take into account the spatial dependency between adjacent pixels in images thus reducing its robustness as a similarity measure. In this paper, we propose a new band selection method based on spatial mutual information. As validation criteria, a supervised classification method using support vector machine (SVM) is used. Experimental results of the classification of hyperspectral datasets show that the proposed method can achieve more accurate results.
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Project description:Slash-and-burn of Amazon Forest (AF) for pasture establishment has increased the occurrence of AF wildfires. Recent studies emphasize soil organic matter (SOM) molecular composition as a principal driver of post-fire forest regrowth and restoration of AF anti-wildfire ambience. Nevertheless, SOM chemical shifts caused by AF fires and post-fire vegetation are rarely investigated at a molecular level. We employed pyrolysis-gas chromatography-mass spectrometry to reveal molecular changes in SOM (0-10, 40-50 cm depth) of a slash-burn-and-20-month-regrowth AF (BAF) and a 23-year Brachiaria pasture post-AF fire (BRA) site compared to native AF (NAF). In BAF (0-10 cm), increased abundance of unspecific aromatic compounds (UACs), polycyclic aromatic hydrocarbons (PAHs) and lipids (Lip) coupled with a depletion of polysaccharides (Pol) revealed strong lingering effects of fire on SOM. This occurs despite fresh litter deposition on soil, suggesting SOM minimal recovery and toxicity to microorganisms. Accumulation of recalcitrant compounds and slow decomposition of fresh forest material may explain the higher carbon content in BAF (0-5 cm). In BRA, SOM was dominated by Brachiaria contributions. At 40-50 cm, alkyl and hydroaromatic compounds accumulated in BRA, whereas UACs accumulated in BAF. UACs and PAH compounds were abundant in NAF, possibly air-transported from BAF.
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